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Khan ZF, Ramzan M, Raza M, Khan MA, Iqbal K, Kim T, Cha JH. Deep Convolutional Neural Networks for Accurate Classification of Gastrointestinal Tract Syndromes. COMPUTERS, MATERIALS & CONTINUA 2024; 78:1207-1225. [DOI: 10.32604/cmc.2023.045491] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/25/2024]
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Zhang H, Yang X, Tao Y, Zhang X, Huang X. Diagnostic accuracy of endocytoscopy via artificial intelligence in colorectal lesions: A systematic review and meta‑analysis. PLoS One 2023; 18:e0294930. [PMID: 38113199 PMCID: PMC10729963 DOI: 10.1371/journal.pone.0294930] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2023] [Accepted: 11/10/2023] [Indexed: 12/21/2023] Open
Abstract
BACKGROUND Endocytoscopy (EC) is a nuclei and micro-vessels visualization in real-time and can facilitate "optical biopsy" and "virtual histology" of colorectal lesions. This study aimed to investigate the significance of employing artificial intelligence (AI) in the field of endoscopy, specifically in diagnosing colorectal lesions. The research was conducted under the supervision of experienced professionals and trainees. METHODS EMBASE, PubMed, Cochrane Library, Web of Science, Chinese National Knowledge Infrastructure (CNKI) database, and other potential databases were surveyed for articles related to the EC with AI published before September 2023. RevMan (5.40), Stata (14.0), and R software (4.1.0) were used for statistical assessment. Studies that measured the accuracy of EC using AI for colorectal lesions were included. Two authors independently assessed the selected studies and their extracted data. This included information such as the country, literature, total study population, study design, characteristics of the fundamental study and control groups, sensitivity, number of samples, assay methodology, specificity, true positives or negatives, and false positives or negatives. The diagnostic accuracy of EC by AI was determined by a bivariate random-effects model, avoiding a high heterogeneity effect. The ANOVA model was employed to determine the more effective approach. RESULTS A total of 223 studies were reviewed; 8 articles were selected that included 2984 patients (4241 lesions) for systematic review and meta-analysis. AI assessed 4069 lesions; experts diagnosed 3165 and 5014 by trainees. AI demonstrated high accuracy, sensitivity, and specificity levels in detecting colorectal lesions, with values of 0.93 (95% CI: 0.90, 0.95) and 0.94 (95% CI: 0.73, 0.99). Expert diagnosis was 0.90 (95% CI: 0.85, 0.94), 0.87 (95% CI: 0.78, 0.93), and trainee diagnosis was 0.74 (95% CI: 0.67, 0.79), 0.72 (95% CI: 0.62, 0.80). With the EC by AI, the AUC from SROC was 0.95 (95% CI: 0.93, 0.97), therefore classified as excellent category, expert showed 0.95 (95% CI: 0.93, 0.97), and the trainee had 0.79 (95% CI: 0.75, 0.82). The superior index from the ANOVA model was 4.00 (1.15,5.00), 2.00 (1.15,5.00), and 0.20 (0.20,0.20), respectively. The examiners conducted meta-regression and subgroup analyses to evaluate the presence of heterogeneity. The findings of these investigations suggest that the utilization of NBI technology was correlated with variability in sensitivity and specificity. There was a lack of solid evidence indicating the presence of publishing bias. CONCLUSIONS The present findings indicate that using AI in EC can potentially enhance the efficiency of diagnosing colorectal abnormalities. As a valuable instrument, it can enhance prognostic outcomes in ordinary EC procedures, exhibiting superior diagnostic accuracy compared to trainee-level endoscopists and demonstrating comparability to expert endoscopists. The research is subject to certain constraints, namely a limited number of clinical investigations and variations in the methodologies used for identification. Consequently, it is imperative to conduct comprehensive and extensive research to enhance the precision of diagnostic procedures.
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Affiliation(s)
- Hangbin Zhang
- Department of Gastroenterology, The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China
| | - Xinyu Yang
- Department of Gastroenterology, The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China
| | - Ye Tao
- Department of Gastroenterology, The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China
| | - Xinyi Zhang
- Department of Gastroenterology, The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China
| | - Xuan Huang
- Department of Gastroenterology, The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China
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Sharma A, Kumar R, Yadav G, Garg P. Artificial intelligence in intestinal polyp and colorectal cancer prediction. Cancer Lett 2023; 565:216238. [PMID: 37211068 DOI: 10.1016/j.canlet.2023.216238] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Revised: 05/17/2023] [Accepted: 05/17/2023] [Indexed: 05/23/2023]
Abstract
Artificial intelligence (AI) algorithms and their application to disease detection and decision support for healthcare professions have greatly evolved in the recent decade. AI has been widely applied and explored in gastroenterology for endoscopic analysis to diagnose intestinal cancers, premalignant polyps, gastrointestinal inflammatory lesions, and bleeding. Patients' responses to treatments and prognoses have both been predicted using AI by combining multiple algorithms. In this review, we explored the recent applications of AI algorithms in the identification and characterization of intestinal polyps and colorectal cancer predictions. AI-based prediction models have the potential to help medical practitioners diagnose, establish prognoses, and find accurate conclusions for the treatment of patients. With the understanding that rigorous validation of AI approaches using randomized controlled studies is solicited before widespread clinical use by health authorities, the article also discusses the limitations and challenges associated with deploying AI systems to diagnose intestinal malignancies and premalignant lesions.
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Affiliation(s)
- Anju Sharma
- Department of Pharmacoinformatics, National Institute of Pharmaceutical Education and Research, S.A.S Nagar, 160062, Punjab, India
| | - Rajnish Kumar
- Amity Institute of Biotechnology, Amity University Uttar Pradesh, Lucknow Campus, Uttar Pradesh, 226010, India; Department of Veterinary Medicine and Surgery, College of Veterinary Medicine, University of Missouri, Columbia, MO, USA
| | - Garima Yadav
- Amity Institute of Biotechnology, Amity University Uttar Pradesh, Lucknow Campus, Uttar Pradesh, 226010, India
| | - Prabha Garg
- Department of Pharmacoinformatics, National Institute of Pharmaceutical Education and Research, S.A.S Nagar, 160062, Punjab, India.
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Gimeno-García AZ, Hernández-Pérez A, Nicolás-Pérez D, Hernández-Guerra M. Artificial Intelligence Applied to Colonoscopy: Is It Time to Take a Step Forward? Cancers (Basel) 2023; 15:cancers15082193. [PMID: 37190122 DOI: 10.3390/cancers15082193] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Revised: 04/04/2023] [Accepted: 04/05/2023] [Indexed: 05/17/2023] Open
Abstract
Growing evidence indicates that artificial intelligence (AI) applied to medicine is here to stay. In gastroenterology, AI computer vision applications have been stated as a research priority. The two main AI system categories are computer-aided polyp detection (CADe) and computer-assisted diagnosis (CADx). However, other fields of expansion are those related to colonoscopy quality, such as methods to objectively assess colon cleansing during the colonoscopy, as well as devices to automatically predict and improve bowel cleansing before the examination, predict deep submucosal invasion, obtain a reliable measurement of colorectal polyps and accurately locate colorectal lesions in the colon. Although growing evidence indicates that AI systems could improve some of these quality metrics, there are concerns regarding cost-effectiveness, and large and multicentric randomized studies with strong outcomes, such as post-colonoscopy colorectal cancer incidence and mortality, are lacking. The integration of all these tasks into one quality-improvement device could facilitate the incorporation of AI systems in clinical practice. In this manuscript, the current status of the role of AI in colonoscopy is reviewed, as well as its current applications, drawbacks and areas for improvement.
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Affiliation(s)
- Antonio Z Gimeno-García
- Gastroenterology Department, Hospital Universitario de Canarias, 38200 San Cristóbal de La Laguna, Tenerife, Spain
- Instituto Universitario de Tecnologías Biomédicas (ITB) & Centro de Investigación Biomédica de Canarias (CIBICAN), Internal Medicine Department, Universidad de La Laguna, 38200 San Cristóbal de La Laguna, Tenerife, Spain
| | - Anjara Hernández-Pérez
- Gastroenterology Department, Hospital Universitario de Canarias, 38200 San Cristóbal de La Laguna, Tenerife, Spain
- Instituto Universitario de Tecnologías Biomédicas (ITB) & Centro de Investigación Biomédica de Canarias (CIBICAN), Internal Medicine Department, Universidad de La Laguna, 38200 San Cristóbal de La Laguna, Tenerife, Spain
| | - David Nicolás-Pérez
- Gastroenterology Department, Hospital Universitario de Canarias, 38200 San Cristóbal de La Laguna, Tenerife, Spain
- Instituto Universitario de Tecnologías Biomédicas (ITB) & Centro de Investigación Biomédica de Canarias (CIBICAN), Internal Medicine Department, Universidad de La Laguna, 38200 San Cristóbal de La Laguna, Tenerife, Spain
| | - Manuel Hernández-Guerra
- Gastroenterology Department, Hospital Universitario de Canarias, 38200 San Cristóbal de La Laguna, Tenerife, Spain
- Instituto Universitario de Tecnologías Biomédicas (ITB) & Centro de Investigación Biomédica de Canarias (CIBICAN), Internal Medicine Department, Universidad de La Laguna, 38200 San Cristóbal de La Laguna, Tenerife, Spain
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Gan P, Li P, Xia H, Zhou X, Tang X. The application of artificial intelligence in improving colonoscopic adenoma detection rate: Where are we and where are we going. GASTROENTEROLOGIA Y HEPATOLOGIA 2023; 46:203-213. [PMID: 35489584 DOI: 10.1016/j.gastrohep.2022.03.009] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/01/2021] [Revised: 03/08/2022] [Accepted: 03/18/2022] [Indexed: 02/08/2023]
Abstract
Colorectal cancer (CRC) is one of the common malignant tumors in the world. Colonoscopy is the crucial examination technique in CRC screening programs for the early detection of precursor lesions, and treatment of early colorectal cancer, which can reduce the morbidity and mortality of CRC significantly. However, pooled polyp miss rates during colonoscopic examination are as high as 22%. Artificial intelligence (AI) provides a promising way to improve the colonoscopic adenoma detection rate (ADR). It might assist endoscopists in avoiding missing polyps and offer an accurate optical diagnosis of suspected lesions. Herein, we described some of the milestone studies in using AI for colonoscopy, and the future application directions of AI in improving colonoscopic ADR.
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Affiliation(s)
- Peiling Gan
- Department of Gastroenterology, Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Peiling Li
- Department of Gastroenterology, Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Huifang Xia
- Department of Gastroenterology, Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Xian Zhou
- Department of Gastroenterology, Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Xiaowei Tang
- Department of Gastroenterology, Affiliated Hospital of Southwest Medical University, Luzhou, China; Department of Gastroenterology, The First Medical Center of Chinese PLA General Hospital, Beijing, China.
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Artificial Intelligence-The Rising Star in the Field of Gastroenterology and Hepatology. Diagnostics (Basel) 2023; 13:diagnostics13040662. [PMID: 36832150 PMCID: PMC9955763 DOI: 10.3390/diagnostics13040662] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 01/31/2023] [Accepted: 02/07/2023] [Indexed: 02/12/2023] Open
Abstract
Artificial intelligence (AI) is a term that covers a multitude of techniques that are used in a manner that tries to reproduce human intelligence. AI is helpful in various medical specialties that use imaging for diagnostic purposes, and gastroenterology is no exception. In this field, AI has several applications, such as detecting and classifying polyps, detecting the malignancy in polyps, diagnosing Helicobacter pylori infection, gastritis, inflammatory bowel disease, gastric cancer, esophageal neoplasia, and pancreatic and hepatic lesions. The aim of this mini-review is to analyze the currently available studies regarding AI in the field of gastroenterology and hepatology and to discuss its main applications as well as its main limitations.
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Teramoto A, Hamada S, Ogino B, Yasuda I, Sano Y. Updates in narrow-band imaging for colorectal polyps: Narrow-band imaging generations, detection, diagnosis, and artificial intelligence. Dig Endosc 2022; 35:453-470. [PMID: 36480465 DOI: 10.1111/den.14489] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Accepted: 12/01/2022] [Indexed: 01/20/2023]
Abstract
Narrow-band imaging (NBI) is an optical digital enhancement method that allows the observation of vascular and surface structures of colorectal lesions. Its usefulness in the detection and diagnosis of colorectal polyps has been demonstrated in several clinical trials and the diagnostic algorithms have been simplified after the establishment of endoscopic classifications such as the Japan NBI Expert Team classification. However, there were issues including lack of brightness in the earlier models, poor visibility under insufficient bowel preparation, and the incompatibility of magnifying endoscopes in certain endoscopic platforms, which had impeded NBI from becoming standardized globally. Nonetheless, NBI continued its evolution and the newest endoscopic platform launched in 2020 offers significantly brighter and detailed images. Enhanced visualization is expected to improve the detection of polyps while universal compatibility across all scopes including magnifying endoscopy will promote the global standardization of magnifying diagnosis. Therefore, knowledge related to magnifying colonoscopy will become essential as magnification becomes standardly equipped in future models, although the advent of computer-aided diagnosis and detection may greatly assist endoscopists to ensure quality of practice. Given that most endoscopic departments will be using both old and new models, it is important to understand how each generation of endoscopic platforms differ from each other. We reviewed the advances in the endoscopic platforms, artificial intelligence, and evidence related to NBI essential for the next generation of endoscopic practice.
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Affiliation(s)
- Akira Teramoto
- Third Department of Internal Medicine, Toyama University Hospital, Toyama, Japan
| | - Seiji Hamada
- Gastrointestinal Center, Urasoe General Hospital, Okinawa, Japan
| | - Banri Ogino
- Third Department of Internal Medicine, Toyama University Hospital, Toyama, Japan
| | - Ichiro Yasuda
- Third Department of Internal Medicine, Toyama University Hospital, Toyama, Japan
| | - Yasushi Sano
- Gastrointestinal Center, Sano Hospital, Hyogo, Japan
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García-Rodríguez A, Tudela Y, Córdova H, Carballal S, Ordás I, Moreira L, Vaquero E, Ortiz O, Rivero L, Sánchez FJ, Cuatrecasas M, Pellisé M, Bernal J, Fernández-Esparrach G. In vivo computer-aided diagnosis of colorectal polyps using white light endoscopy. Endosc Int Open 2022; 10:E1201-E1207. [PMID: 36118638 PMCID: PMC9473851 DOI: 10.1055/a-1881-3178] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Accepted: 06/10/2022] [Indexed: 12/24/2022] Open
Abstract
Background and study aims Artificial intelligence is currently able to accurately predict the histology of colorectal polyps. However, systems developed to date use complex optical technologies and have not been tested in vivo. The objective of this study was to evaluate the efficacy of a new deep learning-based optical diagnosis system, ATENEA, in a real clinical setting using only high-definition white light endoscopy (WLE) and to compare its performance with endoscopists. Methods ATENEA was prospectively tested in real life on consecutive polyps detected in colorectal cancer screening colonoscopies at Hospital Clínic. No images were discarded, and only WLE was used. The in vivo ATENEA's prediction (adenoma vs non-adenoma) was compared with the prediction of four staff endoscopists without specific training in optical diagnosis for the study purposes. Endoscopists were blind to the ATENEA output. Histology was the gold standard. Results Ninety polyps (median size: 5 mm, range: 2-25) from 31 patients were included of which 69 (76.7 %) were adenomas. ATENEA correctly predicted the histology in 63 of 69 (91.3 %, 95 % CI: 82 %-97 %) adenomas and 12 of 21 (57.1 %, 95 % CI: 34 %-78 %) non-adenomas while endoscopists made correct predictions in 52 of 69 (75.4 %, 95 % CI: 60 %-85 %) and 20 of 21 (95.2 %, 95 % CI: 76 %-100 %), respectively. The global accuracy was 83.3 % (95 % CI: 74%-90 %) and 80 % (95 % CI: 70 %-88 %) for ATENEA and endoscopists, respectively. Conclusion ATENEA can accurately be used for in vivo characterization of colorectal polyps, enabling the endoscopist to make direct decisions. ATENEA showed a global accuracy similar to that of endoscopists despite an unsatisfactory performance for non-adenomatous lesions.
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Affiliation(s)
- Ana García-Rodríguez
- Endoscopy Unit. Gastroenterology Department. ICMDiM. Hospital Clínic of Barcelona. University of Barcelona, Barcelona, Catalonia, Spain
| | - Yael Tudela
- Computer Science Department. Autonomous University of Barcelona and Computer Vision Center, Barcelona, Catalonia, Spain
| | - Henry Córdova
- Endoscopy Unit. Gastroenterology Department. ICMDiM. Hospital Clínic of Barcelona. University of Barcelona, Barcelona, Catalonia, Spain,IDIBAPS, Barcelona, Catalonia, Spain,CIBEREHD, Spain
| | - Sabela Carballal
- Endoscopy Unit. Gastroenterology Department. ICMDiM. Hospital Clínic of Barcelona. University of Barcelona, Barcelona, Catalonia, Spain,IDIBAPS, Barcelona, Catalonia, Spain,CIBEREHD, Spain
| | - Ingrid Ordás
- Endoscopy Unit. Gastroenterology Department. ICMDiM. Hospital Clínic of Barcelona. University of Barcelona, Barcelona, Catalonia, Spain,IDIBAPS, Barcelona, Catalonia, Spain,CIBEREHD, Spain
| | - Leticia Moreira
- Endoscopy Unit. Gastroenterology Department. ICMDiM. Hospital Clínic of Barcelona. University of Barcelona, Barcelona, Catalonia, Spain,IDIBAPS, Barcelona, Catalonia, Spain,CIBEREHD, Spain
| | - Eva Vaquero
- Endoscopy Unit. Gastroenterology Department. ICMDiM. Hospital Clínic of Barcelona. University of Barcelona, Barcelona, Catalonia, Spain,IDIBAPS, Barcelona, Catalonia, Spain,CIBEREHD, Spain
| | - Oswaldo Ortiz
- Endoscopy Unit. Gastroenterology Department. ICMDiM. Hospital Clínic of Barcelona. University of Barcelona, Barcelona, Catalonia, Spain
| | - Liseth Rivero
- Endoscopy Unit. Gastroenterology Department. ICMDiM. Hospital Clínic of Barcelona. University of Barcelona, Barcelona, Catalonia, Spain,IDIBAPS, Barcelona, Catalonia, Spain,CIBEREHD, Spain
| | - F. Javier Sánchez
- Computer Science Department. Autonomous University of Barcelona and Computer Vision Center, Barcelona, Catalonia, Spain
| | - Miriam Cuatrecasas
- IDIBAPS, Barcelona, Catalonia, Spain,CIBEREHD, Spain,Pathology Department. Hospital Clínic of Barcelona. University of Barcelona, Barcelona, Catalonia, Spain
| | - Maria Pellisé
- Endoscopy Unit. Gastroenterology Department. ICMDiM. Hospital Clínic of Barcelona. University of Barcelona, Barcelona, Catalonia, Spain,IDIBAPS, Barcelona, Catalonia, Spain,CIBEREHD, Spain
| | - Jorge Bernal
- Computer Science Department. Autonomous University of Barcelona and Computer Vision Center, Barcelona, Catalonia, Spain
| | - Glòria Fernández-Esparrach
- Endoscopy Unit. Gastroenterology Department. ICMDiM. Hospital Clínic of Barcelona. University of Barcelona, Barcelona, Catalonia, Spain,IDIBAPS, Barcelona, Catalonia, Spain,CIBEREHD, Spain
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Inoue F, Hirata D, Iwatate M, Hattori S, Fujita M, Sano W, Sugai T, Kawachi H, Ichikawa K, Sano Y. New application of endocytoscope for histopathological diagnosis of colorectal lesions. World J Gastrointest Endosc 2022; 14:495-501. [PMID: 36158633 PMCID: PMC9453310 DOI: 10.4253/wjge.v14.i8.495] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Revised: 04/23/2022] [Accepted: 07/22/2022] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND The endocytoscope with ultra-high magnification (x 520) allows us to observe the cellular structure of the colon epithelium during colonoscopy, known as virtual histopathology. We hypothesized that the endocytoscope could directly observe colorectal histopathological specimens and store them as endocyto-pathological images by the endoscopists without a microscope, potentially saving the burden on histopathologists.
AIM To assess the feasibility of endocyto-pathological images taken by an endoscopist as adequate materials for histopathological diagnosis.
METHODS Three gastrointestinal pathologists were invited and asked to diagnose 40 cases of endocyto-pathological images of colorectal specimens. Each case contained seven endocyto-pathological images taken by an endoscopist, consisting of one loupe image, three low-magnification images, and three ultra-high magnification images. The participants chose hyperplastic polyp or low-grade adenoma for 20 cases of endocyto-pathological images (10 hyperplastic polyps, and 10 Low-grade adenomas in conventional histopathology) in study 1 and high-grade adenoma/ shallow invasive cancer or deep invasive cancer for 20 cases [10 tumor in situ/T1a and 10 T1b] in study 2. We investigated the agreement between the histopathological diagnosis using the endocyto-pathological images and conventional histopathological diagnosis.
RESULTS Agreement between the endocyto-pathological and conventional histopathological diagnosis by the three gastrointestinal pathologists was 100% (95%CI: 94.0%–100%) in studies 1 and 2. The interobserver agreement among the three gastrointestinal pathologists was 100%, and the κ coefficient was 1.00 in both studies.
CONCLUSION Endocyto-pathological images were adequate and reliable materials for histopathological diagnosis.
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Affiliation(s)
- Fumihiro Inoue
- Gastrointestinal Center and Institute of Minimally-invasive Endoscopic Care (i-MEC), Sano Hospital, Kobe 655-0031, Hyogo, Japan
| | - Daizen Hirata
- Gastrointestinal Center and Institute of Minimally-invasive Endoscopic Care (i-MEC), Sano Hospital, Kobe 655-0031, Hyogo, Japan
| | - Mineo Iwatate
- Gastrointestinal Center and Institute of Minimally-invasive Endoscopic Care (i-MEC), Sano Hospital, Kobe 655-0031, Hyogo, Japan
| | - Santa Hattori
- Gastrointestinal Center and Institute of Minimally-invasive Endoscopic Care (i-MEC), Sano Hospital, Kobe 655-0031, Hyogo, Japan
| | - Mikio Fujita
- Gastrointestinal Center and Institute of Minimally-invasive Endoscopic Care (i-MEC), Sano Hospital, Kobe 655-0031, Hyogo, Japan
| | - Wataru Sano
- Gastrointestinal Center and Institute of Minimally-invasive Endoscopic Care (i-MEC), Sano Hospital, Kobe 655-0031, Hyogo, Japan
| | - Tamotsu Sugai
- Molecular Diagnostic Pathology, Iwate Medical University School of Medicine, Shiwa-gun 028-3694, Iwate, Japan
| | - Hiroshi Kawachi
- Department of Pathology, Cancer Institute Hospital of Japanese Foundation for Cancer Research, Koto 135-8550, Tokyo, Japan
| | | | - Yasushi Sano
- Gastrointestinal Center and Institute of Minimally-invasive Endoscopic Care (i-MEC), Sano Hospital, Kobe 655-0031, Hyogo, Japan
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Kavitha MS, Gangadaran P, Jackson A, Venmathi Maran BA, Kurita T, Ahn BC. Deep Neural Network Models for Colon Cancer Screening. Cancers (Basel) 2022; 14:3707. [PMID: 35954370 PMCID: PMC9367621 DOI: 10.3390/cancers14153707] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Revised: 07/26/2022] [Accepted: 07/27/2022] [Indexed: 12/24/2022] Open
Abstract
Early detection of colorectal cancer can significantly facilitate clinicians' decision-making and reduce their workload. This can be achieved using automatic systems with endoscopic and histological images. Recently, the success of deep learning has motivated the development of image- and video-based polyp identification and segmentation. Currently, most diagnostic colonoscopy rooms utilize artificial intelligence methods that are considered to perform well in predicting invasive cancer. Convolutional neural network-based architectures, together with image patches and preprocesses are often widely used. Furthermore, learning transfer and end-to-end learning techniques have been adopted for detection and localization tasks, which improve accuracy and reduce user dependence with limited datasets. However, explainable deep networks that provide transparency, interpretability, reliability, and fairness in clinical diagnostics are preferred. In this review, we summarize the latest advances in such models, with or without transparency, for the prediction of colorectal cancer and also address the knowledge gap in the upcoming technology.
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Affiliation(s)
- Muthu Subash Kavitha
- School of Information and Data Sciences, Nagasaki University, Nagasaki 852-8521, Japan;
| | - Prakash Gangadaran
- BK21 FOUR KNU Convergence Educational Program of Biomedical Sciences for Creative Future Talents, School of Medicine, Kyungpook National University, Daegu 41944, Korea;
- Department of Nuclear Medicine, School of Medicine, Kyungpook National University, Kyungpook National University Hospital, Daegu 41944, Korea
| | - Aurelia Jackson
- Borneo Marine Research Institute, Universiti Malaysia Sabah, Kota Kinabalu 88400, Malaysia; (A.J.); (B.A.V.M.)
| | - Balu Alagar Venmathi Maran
- Borneo Marine Research Institute, Universiti Malaysia Sabah, Kota Kinabalu 88400, Malaysia; (A.J.); (B.A.V.M.)
| | - Takio Kurita
- Graduate School of Advanced Science and Engineering, Hiroshima University, Higashi-Hiroshima 739-8521, Japan;
| | - Byeong-Cheol Ahn
- BK21 FOUR KNU Convergence Educational Program of Biomedical Sciences for Creative Future Talents, School of Medicine, Kyungpook National University, Daegu 41944, Korea;
- Department of Nuclear Medicine, School of Medicine, Kyungpook National University, Kyungpook National University Hospital, Daegu 41944, Korea
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Awidi M, Bagga A. Artificial intelligence and machine learning in colorectal cancer. Artif Intell Gastrointest Endosc 2022; 3:31-43. [DOI: 10.37126/aige.v3.i3.31] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Revised: 03/24/2022] [Accepted: 06/20/2022] [Indexed: 02/06/2023] Open
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Barua I, Wieszczy P, Kudo SE, Misawa M, Holme Ø, Gulati S, Williams S, Mori K, Itoh H, Takishima K, Mochizuki K, Miyata Y, Mochida K, Akimoto Y, Kuroki T, Morita Y, Shiina O, Kato S, Nemoto T, Hayee B, Patel M, Gunasingam N, Kent A, Emmanuel A, Munck C, Nilsen JA, Hvattum SA, Frigstad SO, Tandberg P, Løberg M, Kalager M, Haji A, Bretthauer M, Mori Y. Real-Time Artificial Intelligence-Based Optical Diagnosis of Neoplastic Polyps during Colonoscopy. NEJM EVIDENCE 2022; 1:EVIDoa2200003. [PMID: 38319238 DOI: 10.1056/evidoa2200003] [Citation(s) in RCA: 48] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/07/2024]
Abstract
BACKGROUND: Artificial intelligence using computer-aided diagnosis (CADx) in real time with images acquired during colonoscopy may help colonoscopists distinguish between neoplastic polyps requiring removal and nonneoplastic polyps not requiring removal. In this study, we tested whether CADx analyzed images helped in this decision-making process. METHODS: We performed a multicenter clinical study comparing a novel CADx-system that uses real-time ultra-magnifying polyp visualization during colonoscopy with standard visual inspection of small (≤5 mm in diameter) polyps in the sigmoid colon and the rectum for optical diagnosis of neoplastic histology. After committing to a diagnosis (i.e., neoplastic, uncertain, or nonneoplastic), all imaged polyps were removed. The primary end point was sensitivity for neoplastic polyps by CADx and visual inspection, compared with histopathology. Secondary end points were specificity and colonoscopist confidence level in unaided optical diagnosis. RESULTS: We assessed 1289 individuals for eligibility at colonoscopy centers in Norway, the United Kingdom, and Japan. We detected 892 eligible polyps in 518 patients and included them in analyses: 359 were neoplastic and 533 were nonneoplastic. Sensitivity for the diagnosis of neoplastic polyps with standard visual inspection was 88.4% (95% confidence interval [CI], 84.3 to 91.5) compared with 90.4% (95% CI, 86.8 to 93.1) with CADx (P=0.33). Specificity was 83.1% (95% CI, 79.2 to 86.4) with standard visual inspection and 85.9% (95% CI, 82.3 to 88.8) with CADx. The proportion of polyp assessment with high confidence was 74.2% (95% CI, 70.9 to 77.3) with standard visual inspection versus 92.6% (95% CI, 90.6 to 94.3) with CADx. CONCLUSIONS: Real-time polyp assessment with CADx did not significantly increase the diagnostic sensitivity of neoplastic polyps during a colonoscopy compared with optical evaluation without CADx. (Funded by the Research Council of Norway [Norges Forskningsråd], the Norwegian Cancer Society [Kreftforeningen], and the Japan Society for the Promotion of Science; UMIN number, UMIN000035213.)
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Affiliation(s)
- Ishita Barua
- Clinical Effectiveness Research Group, University of Oslo, Oslo
- Clinical Effectiveness Research Group, Department of Transplantation Medicine, Oslo University Hospital, Oslo
| | - Paulina Wieszczy
- Clinical Effectiveness Research Group, University of Oslo, Oslo
- Clinical Effectiveness Research Group, Department of Transplantation Medicine, Oslo University Hospital, Oslo
- Department of Gastroenterology, Hepatology and Clinical Oncology, Centre of Postgraduate Medical Education, Warsaw, Poland
| | - Shin-Ei Kudo
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
| | - Masashi Misawa
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
| | - Øyvind Holme
- Clinical Effectiveness Research Group, University of Oslo, Oslo
- Clinical Effectiveness Research Group, Department of Transplantation Medicine, Oslo University Hospital, Oslo
- Department of Medicine, Sørlandet Hospital Kristiansand, Kristiansand, Norway
| | - Shraddha Gulati
- King's Institute of Therapeutic Endoscopy, King's College Hospital NHS Foundation Trust, London
| | - Sophie Williams
- King's Institute of Therapeutic Endoscopy, King's College Hospital NHS Foundation Trust, London
| | - Kensaku Mori
- Graduate School of Informatics, Nagoya University, Nagoya, Japan
| | - Hayato Itoh
- Graduate School of Informatics, Nagoya University, Nagoya, Japan
| | - Kazumi Takishima
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
| | - Kenichi Mochizuki
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
| | - Yuki Miyata
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
| | - Kentaro Mochida
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
| | - Yoshika Akimoto
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
| | - Takanori Kuroki
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
| | - Yuriko Morita
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
| | - Osamu Shiina
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
| | - Shun Kato
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
| | - Tetsuo Nemoto
- Department of Diagnostic Pathology, School of Medicine, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Bu Hayee
- King's Institute of Therapeutic Endoscopy, King's College Hospital NHS Foundation Trust, London
| | - Mehul Patel
- King's Institute of Therapeutic Endoscopy, King's College Hospital NHS Foundation Trust, London
| | - Nishmi Gunasingam
- King's Institute of Therapeutic Endoscopy, King's College Hospital NHS Foundation Trust, London
| | - Alexandra Kent
- King's Institute of Therapeutic Endoscopy, King's College Hospital NHS Foundation Trust, London
| | - Andrew Emmanuel
- King's Institute of Therapeutic Endoscopy, King's College Hospital NHS Foundation Trust, London
| | - Carl Munck
- Department of Medicine, Baerum Hospital, Vestre Viken Hospital Trust, Gjettum, Norway
| | - Jens Aksel Nilsen
- Department of Medicine, Baerum Hospital, Vestre Viken Hospital Trust, Gjettum, Norway
| | - Stine Astrup Hvattum
- Department of Medicine, Baerum Hospital, Vestre Viken Hospital Trust, Gjettum, Norway
| | - Svein Oskar Frigstad
- Department of Medicine, Baerum Hospital, Vestre Viken Hospital Trust, Gjettum, Norway
| | - Petter Tandberg
- Department of Medicine, Baerum Hospital, Vestre Viken Hospital Trust, Gjettum, Norway
| | - Magnus Løberg
- Clinical Effectiveness Research Group, University of Oslo, Oslo
- Clinical Effectiveness Research Group, Department of Transplantation Medicine, Oslo University Hospital, Oslo
| | - Mette Kalager
- Clinical Effectiveness Research Group, University of Oslo, Oslo
- Clinical Effectiveness Research Group, Department of Transplantation Medicine, Oslo University Hospital, Oslo
| | - Amyn Haji
- King's Institute of Therapeutic Endoscopy, King's College Hospital NHS Foundation Trust, London
| | - Michael Bretthauer
- Clinical Effectiveness Research Group, University of Oslo, Oslo
- Clinical Effectiveness Research Group, Department of Transplantation Medicine, Oslo University Hospital, Oslo
| | - Yuichi Mori
- Clinical Effectiveness Research Group, University of Oslo, Oslo
- Clinical Effectiveness Research Group, Department of Transplantation Medicine, Oslo University Hospital, Oslo
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
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13
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Current Status and Future Perspectives of Artificial Intelligence in Colonoscopy. J Clin Med 2022; 11:jcm11102923. [PMID: 35629049 PMCID: PMC9143862 DOI: 10.3390/jcm11102923] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Revised: 05/18/2022] [Accepted: 05/20/2022] [Indexed: 12/19/2022] Open
Abstract
The early endoscopic identification, resection, and treatment of precancerous adenoma and early-stage cancer has been shown to reduce not only the prevalence of colorectal cancer but also its mortality rate. Recent advances in endoscopic devices and imaging technology have dramatically improved our ability to detect colorectal lesions and predict their pathological diagnosis. In addition to this, rapid advances in artificial intelligence (AI) technology mean that AI-related research and development is now progressing in the diagnostic imaging field, particularly colonoscopy, and AIs (i.e., devices that mimic cognitive abilities, such as learning and problem-solving) already approved as medical devices are now being introduced into everyday clinical practice. Today, there is an increasing expectation that sophisticated AIs will be able to provide high-level diagnostic performance irrespective of the level of skill of the endoscopist. In this paper, we review colonoscopy-related AI research and the AIs that have already been approved and discuss the future prospects of this technology.
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14
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Shimoda Y, Shimizu Y, Takahashi H, Okahara S, Miyake T, Ichihara S, Tanaka I, Inoue M, Kinowaki S, Ono M, Yamamoto K, Ono S, Sakamoto N. Optical biopsy for esophageal squamous cell neoplasia by using endocytoscopy. BMC Gastroenterol 2022; 22:259. [PMID: 35597920 PMCID: PMC9123668 DOI: 10.1186/s12876-022-02335-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/21/2021] [Accepted: 05/13/2022] [Indexed: 01/10/2023] Open
Abstract
BACKGROUND Endocytoscopy (ECS) enables microscopic observation in vivo for the gastrointestinal mucosa; however, there has been no prospective study in which the diagnostic accuracy of ECS for lesions that have not yet undergone histological diagnosis was evaluated. We conducted a surveillance study for patients in a high-risk group of esophageal squamous cell carcinoma (ESCC) and evaluated the in vivo histological diagnostic accuracy of ECS. METHODS This study was a multicenter prospective study. We enrolled 197 patients in the study between September 1, 2019 and November 30, 2020. The patients first underwent white light imaging and narrow band imaging, and ultra-high magnifying observation was performed if there was a lesion suspected to be an esophageal tumor. Endoscopic submucosal dissection (ESD) was later performed for lesions that were diagnosed to be ESCC by ECS without biopsy. We evaluated the diagnostic accuracy of ECS for esophageal tumorous lesions. RESULTS ESD was performed for 37 patients (41 lesions) who were diagnosed as having ESCC by ECS, and all of them were histopathologically diagnosed as having ESCC. The sensitivity [95% confidence interval (CI)] was 97.6% (87.7-99.7%), specificity (95% CI) was 100% (92.7-100%), diagnostic accuracy (95% CI) was 98.9% (94.0-99.8%), positive predictive value (PPV) (95% CI) was 100% (91.4-100%) and negative predictive value (NPV) (95% CI) was 98.0% (89.5-99.7%). CONCLUSIONS ECS has a high diagnostic accuracy and there were no false positives in cases diagnosed and resected as ESCC. Optical biopsy by using ECS for esophageal lesions that are suspected to be tumorous is considered to be sufficient in clinical practice.
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Affiliation(s)
- Yoshihiko Shimoda
- Department of Gastroenterology and Hepatology, Graduate School of Medicine and Faculty of Medicine, Hokkaido University, Sapporo, Hokkaido, 060-8648, Japan
| | - Yuichi Shimizu
- Division of Endoscopy, Hokkaido University Hospital, Sapporo, Hokkaido, 060-8648, Japan.
- Department of Gastroenterology, National Hospital Organization, Hokkaido Medical Center, 5-7, Yamanote, Nishi-ku, Sapporo, 063-0005, Japan.
| | - Hiroaki Takahashi
- Department of Gastroenterology, Keiyukai Daini Hospital, Sapporo, Hokkaido, 003-0027, Japan
| | - Satoshi Okahara
- Department of Gastroenterology, Keiyukai Daini Hospital, Sapporo, Hokkaido, 003-0027, Japan
| | - Takakazu Miyake
- Department of Gastroenterology, Keiyukai Daini Hospital, Sapporo, Hokkaido, 003-0027, Japan
| | - Shin Ichihara
- Department of Surgical Pathology, Sapporo Kosei General Hospital, Sapporo, Hokkaido, 060-0033, Japan
| | - Ikko Tanaka
- Department of Gastroenterology and Hepatology, Graduate School of Medicine and Faculty of Medicine, Hokkaido University, Sapporo, Hokkaido, 060-8648, Japan
| | - Masaki Inoue
- Department of Gastroenterology and Hepatology, Graduate School of Medicine and Faculty of Medicine, Hokkaido University, Sapporo, Hokkaido, 060-8648, Japan
| | - Sayoko Kinowaki
- Department of Gastroenterology and Hepatology, Graduate School of Medicine and Faculty of Medicine, Hokkaido University, Sapporo, Hokkaido, 060-8648, Japan
| | - Masayoshi Ono
- Division of Endoscopy, Hokkaido University Hospital, Sapporo, Hokkaido, 060-8648, Japan
| | - Keiko Yamamoto
- Division of Endoscopy, Hokkaido University Hospital, Sapporo, Hokkaido, 060-8648, Japan
| | - Shoko Ono
- Department of Gastroenterology, Hokkaido University Hospital, Sapporo, Hokkaido, 060-8648, Japan
| | - Naoya Sakamoto
- Department of Gastroenterology and Hepatology, Graduate School of Medicine and Faculty of Medicine, Hokkaido University, Sapporo, Hokkaido, 060-8648, Japan
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Abstract
Artificial intelligence (AI) is rapidly developing in various medical fields, and there is an increase in research performed in the field of gastrointestinal (GI) endoscopy. In particular, the advent of convolutional neural network, which is a class of deep learning method, has the potential to revolutionize the field of GI endoscopy, including esophagogastroduodenoscopy (EGD), capsule endoscopy (CE), and colonoscopy. A total of 149 original articles pertaining to AI (27 articles in esophagus, 30 articles in stomach, 29 articles in CE, and 63 articles in colon) were identified in this review. The main focuses of AI in EGD are cancer detection, identifying the depth of cancer invasion, prediction of pathological diagnosis, and prediction of Helicobacter pylori infection. In the field of CE, automated detection of bleeding sites, ulcers, tumors, and various small bowel diseases is being investigated. AI in colonoscopy has advanced with several patient-based prospective studies being conducted on the automated detection and classification of colon polyps. Furthermore, research on inflammatory bowel disease has also been recently reported. Most studies of AI in the field of GI endoscopy are still in the preclinical stages because of the retrospective design using still images. Video-based prospective studies are needed to advance the field. However, AI will continue to develop and be used in daily clinical practice in the near future. In this review, we have highlighted the published literature along with providing current status and insights into the future of AI in GI endoscopy.
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Affiliation(s)
- Yutaka Okagawa
- Endoscopy Division, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan.,Department of Gastroenterology, Tonan Hospital, Sapporo, Japan
| | - Seiichiro Abe
- Endoscopy Division, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan.
| | - Masayoshi Yamada
- Endoscopy Division, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan
| | - Ichiro Oda
- Endoscopy Division, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan
| | - Yutaka Saito
- Endoscopy Division, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan
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16
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Ginghina O, Hudita A, Zamfir M, Spanu A, Mardare M, Bondoc I, Buburuzan L, Georgescu SE, Costache M, Negrei C, Nitipir C, Galateanu B. Liquid Biopsy and Artificial Intelligence as Tools to Detect Signatures of Colorectal Malignancies: A Modern Approach in Patient's Stratification. Front Oncol 2022; 12:856575. [PMID: 35356214 PMCID: PMC8959149 DOI: 10.3389/fonc.2022.856575] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Accepted: 02/16/2022] [Indexed: 01/19/2023] Open
Abstract
Colorectal cancer (CRC) is the second most frequently diagnosed type of cancer and a major worldwide public health concern. Despite the global efforts in the development of modern therapeutic strategies, CRC prognosis is strongly correlated with the stage of the disease at diagnosis. Early detection of CRC has a huge impact in decreasing mortality while pre-lesion detection significantly reduces the incidence of the pathology. Even though the management of CRC patients is based on robust diagnostic methods such as serum tumor markers analysis, colonoscopy, histopathological analysis of tumor tissue, and imaging methods (computer tomography or magnetic resonance), these strategies still have many limitations and do not fully satisfy clinical needs due to their lack of sensitivity and/or specificity. Therefore, improvements of the current practice would substantially impact the management of CRC patients. In this view, liquid biopsy is a promising approach that could help clinicians screen for disease, stratify patients to the best treatment, and monitor treatment response and resistance mechanisms in the tumor in a regular and minimally invasive manner. Liquid biopsies allow the detection and analysis of different tumor-derived circulating markers such as cell-free nucleic acids (cfNA), circulating tumor cells (CTCs), and extracellular vesicles (EVs) in the bloodstream. The major advantage of this approach is its ability to trace and monitor the molecular profile of the patient's tumor and to predict personalized treatment in real-time. On the other hand, the prospective use of artificial intelligence (AI) in medicine holds great promise in oncology, for the diagnosis, treatment, and prognosis prediction of disease. AI has two main branches in the medical field: (i) a virtual branch that includes medical imaging, clinical assisted diagnosis, and treatment, as well as drug research, and (ii) a physical branch that includes surgical robots. This review summarizes findings relevant to liquid biopsy and AI in CRC for better management and stratification of CRC patients.
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Affiliation(s)
- Octav Ginghina
- Department II, University of Medicine and Pharmacy “Carol Davila” Bucharest, Bucharest, Romania
- Department of Surgery, “Sf. Ioan” Clinical Emergency Hospital, Bucharest, Romania
| | - Ariana Hudita
- Department of Biochemistry and Molecular Biology, University of Bucharest, Bucharest, Romania
| | - Marius Zamfir
- Department of Surgery, “Sf. Ioan” Clinical Emergency Hospital, Bucharest, Romania
| | - Andrada Spanu
- Department of Surgery, “Sf. Ioan” Clinical Emergency Hospital, Bucharest, Romania
| | - Mara Mardare
- Department of Surgery, “Sf. Ioan” Clinical Emergency Hospital, Bucharest, Romania
| | - Irina Bondoc
- Department of Surgery, “Sf. Ioan” Clinical Emergency Hospital, Bucharest, Romania
| | | | - Sergiu Emil Georgescu
- Department of Biochemistry and Molecular Biology, University of Bucharest, Bucharest, Romania
| | - Marieta Costache
- Department of Biochemistry and Molecular Biology, University of Bucharest, Bucharest, Romania
| | - Carolina Negrei
- Department of Toxicology, University of Medicine and Pharmacy “Carol Davila” Bucharest, Bucharest, Romania
| | - Cornelia Nitipir
- Department II, University of Medicine and Pharmacy “Carol Davila” Bucharest, Bucharest, Romania
- Department of Oncology, Elias University Emergency Hospital, Bucharest, Romania
| | - Bianca Galateanu
- Department of Biochemistry and Molecular Biology, University of Bucharest, Bucharest, Romania
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17
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Rodriguez-Diaz E, Jepeal LI, Baffy G, Lo WK, MashimoMD H, A'amar O, Bigio IJ, Singh SK. Artificial Intelligence-Based Assessment of Colorectal Polyp Histology by Elastic-Scattering Spectroscopy. Dig Dis Sci 2022; 67:613-621. [PMID: 33761089 DOI: 10.1007/s10620-021-06901-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Accepted: 02/09/2021] [Indexed: 12/09/2022]
Abstract
BACKGROUND Colonoscopic screening and surveillance for colorectal cancer could be made safer and more efficient if endoscopists could predict histology without the need to biopsy and perform histopathology on every polyp. Elastic-scattering spectroscopy (ESS), using fiberoptic probes integrated into standard biopsy tools, can assess, both in vivo and in real time, the scattering and absorption properties of tissue related to its underlying pathology. AIMS The objective of this study was to evaluate prospectively the potential of ESS to predict polyp pathology accurately. METHODS We obtained ESS measurements from patients undergoing screening/surveillance colonoscopy using an ESS fiberoptic probe integrated into biopsy forceps. The integrated forceps were used for tissue acquisition, following current standards of care, and optical measurement. All measurements were correlated to the index pathology. A machine learning model was then applied to measurements from 367 polyps in 169 patients to prospectively evaluate its predictive performance. RESULTS The model achieved sensitivity of 0.92, specificity of 0.87, negative predictive value (NPV) of 0.87, and high-confidence rate (HCR) of 0.84 for distinguishing 220 neoplastic polyps from 147 non-neoplastic polyps of all sizes. Among 138 neoplastic and 131 non-neoplastic polyps ≤ 5 mm, the model achieved sensitivity of 0.91, specificity of 0.88, NPV of 0.89, and HCR of 0.83. CONCLUSIONS Results show that ESS is a viable endoscopic platform for real-time polyp histology, particularly for polyps ≤ 5 mm. ESS is a simple, low-cost, clinically friendly, optical biopsy modality that, when interfaced with minimally obtrusive endoscopic tools, offers an attractive platform for in situ polyp assessment.
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Affiliation(s)
- Eladio Rodriguez-Diaz
- Research Service, VA Boston Healthcare System, 150 South Huntington Ave., Boston, MA, 02130, USA.,Department of Biomedical Engineering, College of Engineering, Boston University, 44 Cummington Mall, Boston, MA, 02215, USA
| | - Lisa I Jepeal
- Research Service, VA Boston Healthcare System, 150 South Huntington Ave., Boston, MA, 02130, USA
| | - György Baffy
- Department of Medicine, Section of Gastroenterology, VA Boston Healthcare System, 150 South Huntington Ave., Boston, MA, 02130, USA.,Department of Medicine, Brigham and Women's Hospital Boston and Harvard Medical School, 25 Shattuck St., Boston, MA, 02115, USA
| | - Wai-Kit Lo
- Department of Medicine, Section of Gastroenterology, VA Boston Healthcare System, 150 South Huntington Ave., Boston, MA, 02130, USA.,Department of Medicine, Brigham and Women's Hospital Boston and Harvard Medical School, 25 Shattuck St., Boston, MA, 02115, USA
| | - Hiroshi MashimoMD
- Department of Medicine, Section of Gastroenterology, VA Boston Healthcare System, 150 South Huntington Ave., Boston, MA, 02130, USA.,Department of Medicine, Brigham and Women's Hospital Boston and Harvard Medical School, 25 Shattuck St., Boston, MA, 02115, USA
| | - Ousama A'amar
- Department of Biomedical Engineering, College of Engineering, Boston University, 44 Cummington Mall, Boston, MA, 02215, USA
| | - Irving J Bigio
- Department of Biomedical Engineering, College of Engineering, Boston University, 44 Cummington Mall, Boston, MA, 02215, USA.,Department of Medicine, Boston University School of Medicine, 72 E. Concord St., Boston, MA, 02118, USA
| | - Satish K Singh
- Research Service, VA Boston Healthcare System, 150 South Huntington Ave., Boston, MA, 02130, USA. .,Department of Biomedical Engineering, College of Engineering, Boston University, 44 Cummington Mall, Boston, MA, 02215, USA. .,Department of Medicine, Section of Gastroenterology, VA Boston Healthcare System, 150 South Huntington Ave., Boston, MA, 02130, USA. .,Department of Medicine, Brigham and Women's Hospital Boston and Harvard Medical School, 25 Shattuck St., Boston, MA, 02115, USA. .,Department of Medicine, Boston University School of Medicine, 72 E. Concord St., Boston, MA, 02118, USA.
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18
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Kudo SE, Mori Y, Abdel-Aal UM, Misawa M, Itoh H, Oda M, Mori K. Artificial intelligence and computer-aided diagnosis for colonoscopy: where do we stand now? Transl Gastroenterol Hepatol 2021; 6:64. [PMID: 34805586 DOI: 10.21037/tgh.2019.12.14] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/10/2019] [Accepted: 11/12/2019] [Indexed: 12/22/2022] Open
Abstract
Computer-aided diagnosis (CAD) for colonoscopy with use of artificial intelligence (AI) is catching increased attention of endoscopists. CAD allows automated detection and pathological prediction, namely optical biopsy, of colorectal polyps during real-time endoscopy, which help endoscopists avoid missing and/or misdiagnosing colorectal lesions. With the increased number of publications in this field and emergence of the AI medical device that have already secured regulatory approval, CAD in colonoscopy is now being implemented into clinical practice. On the other side, drawbacks and weak points of CAD in colonoscopy have not been thoroughly discussed. In this review, we provide an overview of CAD for optical biopsy of colorectal lesions with a particular focus on its clinical applications and limitations.
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Affiliation(s)
- Shin-Ei Kudo
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
| | - Yuichi Mori
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
| | - Usama M Abdel-Aal
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan.,Internal Medicine, Faculty of Medicine, Sohag University, Sohag, Egypt
| | - Masashi Misawa
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
| | - Hayato Itoh
- Graduate School of Informatics, Nagoya University, Nagoya, Japan
| | - Masahiro Oda
- Graduate School of Informatics, Nagoya University, Nagoya, Japan
| | - Kensaku Mori
- Graduate School of Informatics, Nagoya University, Nagoya, Japan
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19
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Kröner PT, Engels MML, Glicksberg BS, Johnson KW, Mzaik O, van Hooft JE, Wallace MB, El-Serag HB, Krittanawong C. Artificial intelligence in gastroenterology: A state-of-the-art review. World J Gastroenterol 2021; 27:6794-6824. [PMID: 34790008 PMCID: PMC8567482 DOI: 10.3748/wjg.v27.i40.6794] [Citation(s) in RCA: 74] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Revised: 06/15/2021] [Accepted: 09/16/2021] [Indexed: 02/06/2023] Open
Abstract
The development of artificial intelligence (AI) has increased dramatically in the last 20 years, with clinical applications progressively being explored for most of the medical specialties. The field of gastroenterology and hepatology, substantially reliant on vast amounts of imaging studies, is not an exception. The clinical applications of AI systems in this field include the identification of premalignant or malignant lesions (e.g., identification of dysplasia or esophageal adenocarcinoma in Barrett’s esophagus, pancreatic malignancies), detection of lesions (e.g., polyp identification and classification, small-bowel bleeding lesion on capsule endoscopy, pancreatic cystic lesions), development of objective scoring systems for risk stratification, predicting disease prognosis or treatment response [e.g., determining survival in patients post-resection of hepatocellular carcinoma), determining which patients with inflammatory bowel disease (IBD) will benefit from biologic therapy], or evaluation of metrics such as bowel preparation score or quality of endoscopic examination. The objective of this comprehensive review is to analyze the available AI-related studies pertaining to the entirety of the gastrointestinal tract, including the upper, middle and lower tracts; IBD; the hepatobiliary system; and the pancreas, discussing the findings and clinical applications, as well as outlining the current limitations and future directions in this field.
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Affiliation(s)
- Paul T Kröner
- Division of Gastroenterology and Hepatology, Mayo Clinic, Jacksonville, FL 32224, United States
| | - Megan ML Engels
- Division of Gastroenterology and Hepatology, Mayo Clinic, Jacksonville, FL 32224, United States
- Cancer Center Amsterdam, Department of Gastroenterology and Hepatology, Amsterdam UMC, Location AMC, Amsterdam 1105, The Netherlands
| | - Benjamin S Glicksberg
- The Hasso Plattner Institute for Digital Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States
| | - Kipp W Johnson
- The Hasso Plattner Institute for Digital Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States
| | - Obaie Mzaik
- Division of Gastroenterology and Hepatology, Mayo Clinic, Jacksonville, FL 32224, United States
| | - Jeanin E van Hooft
- Department of Gastroenterology and Hepatology, Leiden University Medical Center, Amsterdam 2300, The Netherlands
| | - Michael B Wallace
- Division of Gastroenterology and Hepatology, Mayo Clinic, Jacksonville, FL 32224, United States
- Division of Gastroenterology and Hepatology, Sheikh Shakhbout Medical City, Abu Dhabi 11001, United Arab Emirates
| | - Hashem B El-Serag
- Section of Gastroenterology and Hepatology, Michael E. DeBakey VA Medical Center and Baylor College of Medicine, Houston, TX 77030, United States
- Section of Health Services Research, Michael E. DeBakey VA Medical Center and Baylor College of Medicine, Houston, TX 77030, United States
| | - Chayakrit Krittanawong
- Section of Health Services Research, Michael E. DeBakey VA Medical Center and Baylor College of Medicine, Houston, TX 77030, United States
- Section of Cardiology, Michael E. DeBakey VA Medical Center, Houston, TX 77030, United States
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Hann A, Troya J, Fitting D. Current status and limitations of artificial intelligence in colonoscopy. United European Gastroenterol J 2021; 9:527-533. [PMID: 34617420 PMCID: PMC8259277 DOI: 10.1002/ueg2.12108] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/28/2020] [Accepted: 02/28/2021] [Indexed: 12/11/2022] Open
Abstract
BACKGROUND Artificial intelligence (AI) using deep learning methods for polyp detection (CADe) and characterization (CADx) is on the verge of clinical application. CADe already implied its potential use in randomized controlled trials. Further efforts are needed to take CADx to the next level of development. AIM This work aims to give an overview of the current status of AI in colonoscopy, without going into too much technical detail. METHODS A literature search to identify important studies exploring the use of AI in colonoscopy was performed. RESULTS This review focuses on AI performance in screening colonoscopy summarizing the first prospective trials for CADe, the state of research in CADx as well as current limitations of those systems and legal issues.
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Affiliation(s)
- Alexander Hann
- Department of Internal Medicine II, Interventional and Experimental Endoscopy (InExEn), University Hospital Wuerzburg, Würzburg, Germany
| | - Joel Troya
- Department of Internal Medicine II, Interventional and Experimental Endoscopy (InExEn), University Hospital Wuerzburg, Würzburg, Germany
| | - Daniel Fitting
- Department of Internal Medicine II, Interventional and Experimental Endoscopy (InExEn), University Hospital Wuerzburg, Würzburg, Germany
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21
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Schreuder RM, van der Zander QE, Fonollà R, Gilissen LP, Stronkhorst A, Klerkx B, de With PH, Masclee AM, van der Sommen F, Schoon EJ. Algorithm combining virtual chromoendoscopy features for colorectal polyp classification. Endosc Int Open 2021; 9:E1497-E1503. [PMID: 34540541 PMCID: PMC8445691 DOI: 10.1055/a-1512-5175] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/26/2020] [Accepted: 05/11/2021] [Indexed: 11/06/2022] Open
Abstract
Background and study aims Colonoscopy is considered the gold standard for decreasing colorectal cancer incidence and mortality. Optical diagnosis of colorectal polyps (CRPs) is an ongoing challenge in clinical colonoscopy and its accuracy among endoscopists varies widely. Computer-aided diagnosis (CAD) for CRP characterization may help to improve this accuracy. In this study, we investigated the diagnostic accuracy of a novel algorithm for polyp malignancy classification by exploiting the complementary information revealed by three specific modalities. Methods We developed a CAD algorithm for CRP characterization based on high-definition, non-magnified white light (HDWL), Blue light imaging (BLI) and linked color imaging (LCI) still images from routine exams. All CRPs were collected prospectively and classified into benign or premalignant using histopathology as gold standard. Images and data were used to train the CAD algorithm using triplet network architecture. Our training dataset was validated using a threefold cross validation. Results In total 609 colonoscopy images of 203 CRPs of 154 consecutive patients were collected. A total of 174 CRPs were found to be premalignant and 29 were benign. Combining the triplet network features with all three image enhancement modalities resulted in an accuracy of 90.6 %, 89.7 % sensitivity, 96.6 % specificity, a positive predictive value of 99.4 %, and a negative predictive value of 60.9 % for CRP malignancy classification. The classification time for our CAD algorithm was approximately 90 ms per image. Conclusions Our novel approach and algorithm for CRP classification differentiates accurately between benign and premalignant polyps in non-magnified endoscopic images. This is the first algorithm combining three optical modalities (HDWL/BLI/LCI) exploiting the triplet network approach.
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Affiliation(s)
- Ramon-Michel Schreuder
- Department of Gastroenterology and Hepatology, Catharina Cancer Institute, Catharina Hospital Eindhoven, The Netherlands
| | - Qurine E.W. van der Zander
- Department of Gastroenterology and Hepatology, Maastricht University Medical Center, Maastricht, The Netherlands,GROW School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands
| | - Roger Fonollà
- Department of Electrical Engineering, Eindhoven University of Technology, The Netherlands
| | - Lennard P.L. Gilissen
- Department of Gastroenterology and Hepatology, Catharina Cancer Institute, Catharina Hospital Eindhoven, The Netherlands
| | - Arnold Stronkhorst
- Department of Gastroenterology and Hepatology, Catharina Cancer Institute, Catharina Hospital Eindhoven, The Netherlands
| | - Birgitt Klerkx
- Department of Gastroenterology and Hepatology, Catharina Cancer Institute, Catharina Hospital Eindhoven, The Netherlands
| | - Peter H.N. de With
- Department of Electrical Engineering, Eindhoven University of Technology, The Netherlands
| | - Ad M. Masclee
- Department of Gastroenterology and Hepatology, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Fons van der Sommen
- Department of Electrical Engineering, Eindhoven University of Technology, The Netherlands
| | - Erik J. Schoon
- Department of Gastroenterology and Hepatology, Catharina Cancer Institute, Catharina Hospital Eindhoven, The Netherlands
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22
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Bang CS, Lee JJ, Baik GH. Computer-Aided Diagnosis of Diminutive Colorectal Polyps in Endoscopic Images: Systematic Review and Meta-analysis of Diagnostic Test Accuracy. J Med Internet Res 2021; 23:e29682. [PMID: 34432643 PMCID: PMC8427459 DOI: 10.2196/29682] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Revised: 07/04/2021] [Accepted: 07/27/2021] [Indexed: 12/19/2022] Open
Abstract
Background Most colorectal polyps are diminutive and benign, especially those in the rectosigmoid colon, and the resection of these polyps is not cost-effective. Advancements in image-enhanced endoscopy have improved the optical prediction of colorectal polyp histology. However, subjective interpretability and inter- and intraobserver variability prohibits widespread implementation. The number of studies on computer-aided diagnosis (CAD) is increasing; however, their small sample sizes limit statistical significance. Objective This review aims to evaluate the diagnostic test accuracy of CAD models in predicting the histology of diminutive colorectal polyps by using endoscopic images. Methods Core databases were searched for studies that were based on endoscopic imaging, used CAD models for the histologic diagnosis of diminutive colorectal polyps, and presented data on diagnostic performance. A systematic review and diagnostic test accuracy meta-analysis were performed. Results Overall, 13 studies were included. The pooled area under the curve, sensitivity, specificity, and diagnostic odds ratio of CAD models for the diagnosis of diminutive colorectal polyps (adenomatous or neoplastic vs nonadenomatous or nonneoplastic) were 0.96 (95% CI 0.93-0.97), 0.93 (95% CI 0.91-0.95), 0.87 (95% CI 0.76-0.93), and 87 (95% CI 38-201), respectively. The meta-regression analysis showed no heterogeneity, and no publication bias was detected. Subgroup analyses showed robust results. The negative predictive value of CAD models for the diagnosis of adenomatous polyps in the rectosigmoid colon was 0.96 (95% CI 0.95-0.97), and this value exceeded the threshold of the diagnosis and leave strategy. Conclusions CAD models show potential for the optical histological diagnosis of diminutive colorectal polyps via the use of endoscopic images. Trial Registration PROSPERO CRD42021232189; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=232189
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Affiliation(s)
- Chang Seok Bang
- Department of Internal Medicine, Hallym University College of Medicine, Chuncheon, Republic of Korea.,Institute for Liver and Digestive Diseases, Hallym University College of Medicine, Chuncheon, Republic of Korea.,Institute of New Frontier Research, Hallym University College of Medicine, Chuncheon, Republic of Korea.,Division of Big Data and Artificial Intelligence, Chuncheon Sacred Heart Hospital, Chuncheon, Republic of Korea
| | - Jae Jun Lee
- Institute of New Frontier Research, Hallym University College of Medicine, Chuncheon, Republic of Korea.,Division of Big Data and Artificial Intelligence, Chuncheon Sacred Heart Hospital, Chuncheon, Republic of Korea.,Department of Anesthesiology and Pain Medicine, Hallym University College of Medicine, Chuncheon, Republic of Korea
| | - Gwang Ho Baik
- Department of Internal Medicine, Hallym University College of Medicine, Chuncheon, Republic of Korea.,Institute for Liver and Digestive Diseases, Hallym University College of Medicine, Chuncheon, Republic of Korea
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23
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Nazarian S, Glover B, Ashrafian H, Darzi A, Teare J. Diagnostic Accuracy of Artificial Intelligence and Computer-Aided Diagnosis for the Detection and Characterization of Colorectal Polyps: Systematic Review and Meta-analysis. J Med Internet Res 2021; 23:e27370. [PMID: 34259645 PMCID: PMC8319784 DOI: 10.2196/27370] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Revised: 03/09/2021] [Accepted: 05/06/2021] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Colonoscopy reduces the incidence of colorectal cancer (CRC) by allowing detection and resection of neoplastic polyps. Evidence shows that many small polyps are missed on a single colonoscopy. There has been a successful adoption of artificial intelligence (AI) technologies to tackle the issues around missed polyps and as tools to increase the adenoma detection rate (ADR). OBJECTIVE The aim of this review was to examine the diagnostic accuracy of AI-based technologies in assessing colorectal polyps. METHODS A comprehensive literature search was undertaken using the databases of Embase, MEDLINE, and the Cochrane Library. PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines were followed. Studies reporting the use of computer-aided diagnosis for polyp detection or characterization during colonoscopy were included. Independent proportions and their differences were calculated and pooled through DerSimonian and Laird random-effects modeling. RESULTS A total of 48 studies were included. The meta-analysis showed a significant increase in pooled polyp detection rate in patients with the use of AI for polyp detection during colonoscopy compared with patients who had standard colonoscopy (odds ratio [OR] 1.75, 95% CI 1.56-1.96; P<.001). When comparing patients undergoing colonoscopy with the use of AI to those without, there was also a significant increase in ADR (OR 1.53, 95% CI 1.32-1.77; P<.001). CONCLUSIONS With the aid of machine learning, there is potential to improve ADR and, consequently, reduce the incidence of CRC. The current generation of AI-based systems demonstrate impressive accuracy for the detection and characterization of colorectal polyps. However, this is an evolving field and before its adoption into a clinical setting, AI systems must prove worthy to patients and clinicians. TRIAL REGISTRATION PROSPERO International Prospective Register of Systematic Reviews CRD42020169786; https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42020169786.
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Affiliation(s)
- Scarlet Nazarian
- Department of Surgery and Cancer, Imperial College London, London, United Kingdom
| | - Ben Glover
- Department of Surgery and Cancer, Imperial College London, London, United Kingdom
| | - Hutan Ashrafian
- Department of Surgery and Cancer, Imperial College London, London, United Kingdom
| | - Ara Darzi
- Department of Surgery and Cancer, Imperial College London, London, United Kingdom
| | - Julian Teare
- Department of Surgery and Cancer, Imperial College London, London, United Kingdom
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24
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Sano Y, Iwatate M. Endocystoscopy for colonic polyps: Is there a future for this diagnostic modality in routine practice? Endosc Int Open 2021; 9:E1012-E1013. [PMID: 34222623 PMCID: PMC8211480 DOI: 10.1055/a-1468-4414] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
Affiliation(s)
- Yasushi Sano
- Gastrointestinal Center and Institute of Minimally Invasive Endoscopic Care (iMEC), Sano Hospital, Hyogo, Japan
| | - Mineo Iwatate
- Gastrointestinal Center and Institute of Minimally Invasive Endoscopic Care (iMEC), Sano Hospital, Hyogo, Japan
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25
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Tziortziotis I, Laskaratos FM, Coda S. Role of Artificial Intelligence in Video Capsule Endoscopy. Diagnostics (Basel) 2021; 11:1192. [PMID: 34209029 PMCID: PMC8303156 DOI: 10.3390/diagnostics11071192] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2021] [Accepted: 06/28/2021] [Indexed: 02/06/2023] Open
Abstract
Capsule endoscopy (CE) has been increasingly utilised in recent years as a minimally invasive tool to investigate the whole gastrointestinal (GI) tract and a range of capsules are currently available for evaluation of upper GI, small bowel, and lower GI pathology. Although CE is undoubtedly an invaluable test for the investigation of small bowel pathology, it presents considerable challenges and limitations, such as long and laborious reading times, risk of missing lesions, lack of bowel cleansing score and lack of locomotion. Artificial intelligence (AI) seems to be a promising tool that may help improve the performance metrics of CE, and consequently translate to better patient care. In the last decade, significant progress has been made to apply AI in the field of endoscopy, including CE. Although it is certain that AI will find soon its place in day-to-day endoscopy clinical practice, there are still some open questions and barriers limiting its widespread application. In this review, we provide some general information about AI, and outline recent advances in AI and CE, issues around implementation of AI in medical practice and potential future applications of AI-aided CE.
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Affiliation(s)
- Ioannis Tziortziotis
- Endoscopy Unit, Digestive Diseases Centre, Queen’s Hospital, Barking Havering and Redbridge University Hospitals NHS Trust, Rom Valley Way, Romford, London RM7 0AG, UK; (I.T.); (S.C.)
| | - Faidon-Marios Laskaratos
- Endoscopy Unit, Digestive Diseases Centre, Queen’s Hospital, Barking Havering and Redbridge University Hospitals NHS Trust, Rom Valley Way, Romford, London RM7 0AG, UK; (I.T.); (S.C.)
| | - Sergio Coda
- Endoscopy Unit, Digestive Diseases Centre, Queen’s Hospital, Barking Havering and Redbridge University Hospitals NHS Trust, Rom Valley Way, Romford, London RM7 0AG, UK; (I.T.); (S.C.)
- Photonics Group-Department of Physics, Imperial College London, Exhibition Rd, South Kensington, London SW7 2BX, UK
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26
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Shah N, Jyala A, Patel H, Makker J. Utility of artificial intelligence in colonoscopy. Artif Intell Gastrointest Endosc 2021; 2:79-88. [DOI: 10.37126/aige.v2.i3.79] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Revised: 06/20/2021] [Accepted: 06/28/2021] [Indexed: 02/06/2023] Open
Abstract
Colorectal cancer is one of the major causes of death worldwide. Colonoscopy is the most important tool that can identify neoplastic lesion in early stages and resect it in a timely manner which helps in reducing mortality related to colorectal cancer. However, the quality of colonoscopy findings depends on the expertise of the endoscopist and thus the rate of missed adenoma or polyp cannot be controlled. It is desirable to standardize the quality of colonoscopy by reducing the number of missed adenoma/polyps. Introduction of artificial intelligence (AI) in the field of medicine has become popular among physicians nowadays. The application of AI in colonoscopy can help in reducing miss rate and increasing colorectal cancer detection rate as per recent studies. Moreover, AI assistance during colonoscopy has also been utilized in patients with inflammatory bowel disease to improve diagnostic accuracy, assessing disease severity and predicting clinical outcomes. We conducted a literature review on the available evidence on use of AI in colonoscopy. In this review article, we discuss about the principles, application, limitations, and future aspects of AI in colonoscopy.
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Affiliation(s)
- Niel Shah
- Department of Internal Medicine, BronxCare Hospital Center, Bronx, NY 10457, United States
| | - Abhilasha Jyala
- Department of Internal Medicine, BronxCare Hospital Center, Bronx, NY 10457, United States
| | - Harish Patel
- Department of Internal Medicine, Gastroenterology, BronxCare Hospital Center, Bronx, NY 10457, United States
| | - Jasbir Makker
- Department of Internal Medicine, Gastroenterology, BronxCare Hospital Center, Bronx, NY 10457, United States
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27
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Shah N, Jyala A, Patel H, Makker J. Utility of artificial intelligence in colonoscopy. Artif Intell Gastrointest Endosc 2021. [DOI: 10.37126/aige.v2.i3.78] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
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28
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Komeda Y, Handa H, Matsui R, Hatori S, Yamamoto R, Sakurai T, Takenaka M, Hagiwara S, Nishida N, Kashida H, Watanabe T, Kudo M. Artificial intelligence-based endoscopic diagnosis of colorectal polyps using residual networks. PLoS One 2021; 16:e0253585. [PMID: 34157030 PMCID: PMC8219125 DOI: 10.1371/journal.pone.0253585] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Accepted: 06/09/2021] [Indexed: 01/03/2023] Open
Abstract
Convolutional neural networks (CNNs) are widely used for artificial intelligence (AI)-based image classification. Residual network (ResNet) is a new technology that facilitates the accuracy of image classification by CNN-based AI. In this study, we developed a novel AI model combined with ResNet to diagnose colorectal polyps. In total, 127,610 images consisting of 62,510 images with adenomatous polyps, 30,443 with non-adenomatous hyperplastic polyps, and 34,657 with healthy colorectal normal mucosa were subjected to deep learning after annotation. Each validation process was performed using 12,761 stored images of colorectal polyps by a 10-fold cross validation. The efficacy of the ResNet system was evaluated by sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and diagnostic accuracy. The sensitivity, specificity, PPV, NPV, and diagnostic accuracy for adenomatous polyps at WLIs were 98.8%, 94.3%, 90.5%, 87.4%, and 92.8%, respectively. Similar results were obtained for adenomatous polyps at narrow-band imagings (NBIs) and chromoendoscopy images (CEIs) (NBIs vs. CEIs: sensitivity, 94.9% vs. 98.2%; specificity, 93.9% vs. 85.8%; PPV, 92.5% vs. 81.7%; NPV, 93.5% vs. 99.9%; and overall accuracy, 91.5% vs. 90.1%). The ResNet model is a powerful tool that can be used for AI-based accurate diagnosis of colorectal polyps.
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Affiliation(s)
- Yoriaki Komeda
- Department of Gastroenterology and Hepatology, Kindai University Faculty of Medicine, Osaka, Japan
| | - Hisashi Handa
- Faculty of Science and Engineering, Kindai University, Osaka, Japan
- Research Institute for Science and Technology, Kindai University, Osaka, Japan
- Cyber Informatics Research Institute, Kindai University, Osaka, Japan
| | - Ryoma Matsui
- Faculty of Science and Engineering, Kindai University, Osaka, Japan
| | - Shohei Hatori
- Faculty of Science and Engineering, Kindai University, Osaka, Japan
| | - Riku Yamamoto
- Faculty of Science and Engineering, Kindai University, Osaka, Japan
| | - Toshiharu Sakurai
- Department of Gastroenterology and Hepatology, Kindai University Faculty of Medicine, Osaka, Japan
| | - Mamoru Takenaka
- Department of Gastroenterology and Hepatology, Kindai University Faculty of Medicine, Osaka, Japan
| | - Satoru Hagiwara
- Department of Gastroenterology and Hepatology, Kindai University Faculty of Medicine, Osaka, Japan
| | - Naoshi Nishida
- Department of Gastroenterology and Hepatology, Kindai University Faculty of Medicine, Osaka, Japan
| | - Hiroshi Kashida
- Department of Gastroenterology and Hepatology, Kindai University Faculty of Medicine, Osaka, Japan
| | - Tomohiro Watanabe
- Department of Gastroenterology and Hepatology, Kindai University Faculty of Medicine, Osaka, Japan
| | - Masatoshi Kudo
- Department of Gastroenterology and Hepatology, Kindai University Faculty of Medicine, Osaka, Japan
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Li JW, Ang TL. Colonoscopy and artificial intelligence: Bridging the gap or a gap needing to be bridged? Artif Intell Gastrointest Endosc 2021; 2:36-49. [DOI: 10.37126/aige.v2.i2.36] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Revised: 03/27/2021] [Accepted: 04/20/2021] [Indexed: 02/06/2023] Open
Abstract
Research in artificial intelligence (AI) in gastroenterology has increased over the last decade. Colonoscopy represents the most widely published field with regards to its use in gastroenterology. Most studies to date center on polyp detection and characterization, as well as real-time evaluation of adequacy of mucosal exposure for inspection. This review article discusses how advances in AI has bridged certain gaps in colonoscopy. In addition, the gaps formed with the development of AI that currently prevent its routine use in colonoscopy will be explored.
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Affiliation(s)
- James Weiquan Li
- Department of Gastroenterology and Hepatology, Changi General Hospital, Singapore 529889, Singapore
| | - Tiing Leong Ang
- Department of Gastroenterology and Hepatology, Changi General Hospital, Singapore 529889, Singapore
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30
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Mitsala A, Tsalikidis C, Pitiakoudis M, Simopoulos C, Tsaroucha AK. Artificial Intelligence in Colorectal Cancer Screening, Diagnosis and Treatment. A New Era. ACTA ACUST UNITED AC 2021; 28:1581-1607. [PMID: 33922402 PMCID: PMC8161764 DOI: 10.3390/curroncol28030149] [Citation(s) in RCA: 122] [Impact Index Per Article: 30.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Revised: 04/09/2021] [Accepted: 04/20/2021] [Indexed: 12/24/2022]
Abstract
The development of artificial intelligence (AI) algorithms has permeated the medical field with great success. The widespread use of AI technology in diagnosing and treating several types of cancer, especially colorectal cancer (CRC), is now attracting substantial attention. CRC, which represents the third most commonly diagnosed malignancy in both men and women, is considered a leading cause of cancer-related deaths globally. Our review herein aims to provide in-depth knowledge and analysis of the AI applications in CRC screening, diagnosis, and treatment based on current literature. We also explore the role of recent advances in AI systems regarding medical diagnosis and therapy, with several promising results. CRC is a highly preventable disease, and AI-assisted techniques in routine screening represent a pivotal step in declining incidence rates of this malignancy. So far, computer-aided detection and characterization systems have been developed to increase the detection rate of adenomas. Furthermore, CRC treatment enters a new era with robotic surgery and novel computer-assisted drug delivery techniques. At the same time, healthcare is rapidly moving toward precision or personalized medicine. Machine learning models have the potential to contribute to individual-based cancer care and transform the future of medicine.
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Affiliation(s)
- Athanasia Mitsala
- Second Department of Surgery, University General Hospital of Alexandroupolis, Democritus University of Thrace Medical School, Dragana, 68100 Alexandroupolis, Greece; (C.T.); (M.P.); (C.S.)
- Correspondence: ; Tel.: +30-6986423707
| | - Christos Tsalikidis
- Second Department of Surgery, University General Hospital of Alexandroupolis, Democritus University of Thrace Medical School, Dragana, 68100 Alexandroupolis, Greece; (C.T.); (M.P.); (C.S.)
| | - Michail Pitiakoudis
- Second Department of Surgery, University General Hospital of Alexandroupolis, Democritus University of Thrace Medical School, Dragana, 68100 Alexandroupolis, Greece; (C.T.); (M.P.); (C.S.)
| | - Constantinos Simopoulos
- Second Department of Surgery, University General Hospital of Alexandroupolis, Democritus University of Thrace Medical School, Dragana, 68100 Alexandroupolis, Greece; (C.T.); (M.P.); (C.S.)
| | - Alexandra K. Tsaroucha
- Laboratory of Experimental Surgery & Surgical Research, Democritus University of Thrace Medical School, Dragana, 68100 Alexandroupolis, Greece;
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Ashat M, Klair JS, Singh D, Murali AR, Krishnamoorthi R. Impact of real-time use of artificial intelligence in improving adenoma detection during colonoscopy: A systematic review and meta-analysis. Endosc Int Open 2021; 9:E513-E521. [PMID: 33816771 PMCID: PMC7969136 DOI: 10.1055/a-1341-0457] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/24/2020] [Accepted: 11/23/2020] [Indexed: 02/06/2023] Open
Abstract
Background and study aims With the advent of deep neural networks (DNN) learning, the field of artificial intelligence (AI) is rapidly evolving. Recent randomized controlled trials (RCT) have investigated the influence of integrating AI in colonoscopy and its impact on adenoma detection rates (ADRs) and polyp detection rates (PDRs). We performed a systematic review and meta-analysis to reliably assess if the impact is statistically significant enough to warrant the adoption of AI -assisted colonoscopy (AIAC) in clinical practice. Methods We conducted a comprehensive search of multiple electronic databases and conference proceedings to identify RCTs that compared outcomes between AIAC and conventional colonoscopy (CC). The primary outcome was ADR. The secondary outcomes were PDR and total withdrawal time (WT). Results Six RCTs (comparing AIAC vs CC) with 5058 individuals undergoing average-risk screening colonoscopy were included in the meta-analysis. ADR was significantly higher with AIAC compared to CC (33.7 % versus 22.9 %; odds ratio (OR) 1.76, 95 % confidence interval (CI) 1.55-2.00; I 2 = 28 %). Similarly, PDR was significantly higher with AIAC (45.6 % versus 30.6 %; OR 1.90, 95 %CI, 1.68-2.15, I 2 = 0 %). The overall WT was higher for AIAC compared to CC (mean difference [MD] 0.46 (0.00-0.92) minutes, I 2 = 94 %). Conclusions There is an increase in adenoma and polyp detection with the utilization of AIAC.
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Affiliation(s)
- Munish Ashat
- Department of Gastroenterology and Hepatology, University of Iowa Hospitals and Clinics, Iowa City, Iowa, United States
| | - Jagpal Singh Klair
- Digestive Disease Institute, Virginia Mason Medical Center, Seattle, Washington, United States
| | - Dhruv Singh
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, New York, United States
| | - Arvind Rangarajan Murali
- Department of Gastroenterology and Hepatology, University of Iowa Hospitals and Clinics, Iowa City, Iowa, United States
| | - Rajesh Krishnamoorthi
- Digestive Disease Institute, Virginia Mason Medical Center, Seattle, Washington, United States
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Rodriguez-Diaz E, Baffy G, Lo WK, Mashimo H, Vidyarthi G, Mohapatra SS, Singh SK. Real-time artificial intelligence-based histologic classification of colorectal polyps with augmented visualization. Gastrointest Endosc 2021; 93:662-670. [PMID: 32949567 DOI: 10.1016/j.gie.2020.09.018] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Accepted: 09/10/2020] [Indexed: 02/08/2023]
Abstract
BACKGROUND AND AIMS Artificial intelligence (AI)-based computer-aided diagnostic (CADx) algorithms are a promising approach for real-time histology (RTH) of colonic polyps. Our aim is to present a novel in situ CADx approach that seeks to increase transparency and interpretability of results by generating an intuitive augmented visualization of the model's predicted histology over the polyp surface. METHODS We developed a deep learning model using semantic segmentation to delineate polyp boundaries and a deep learning model to classify subregions within the segmented polyp. These subregions were classified independently and were subsequently aggregated to generate a histology map of the polyp's surface. We used 740 high-magnification narrow-band images from 607 polyps in 286 patients and over 65,000 subregions to train and validate the model. RESULTS The model achieved a sensitivity of .96, specificity of .84, negative predictive value (NPV) of .91, and high-confidence rate (HCR) of .88, distinguishing 171 neoplastic polyps from 83 non-neoplastic polyps of all sizes. Among 93 neoplastic and 75 non-neoplastic polyps ≤5 mm, the model achieved a sensitivity of .95, specificity of .84, NPV of .91, and HCR of .86. CONCLUSIONS The CADx model is capable of accurately distinguishing neoplastic from non-neoplastic polyps and provides a histology map of the spatial distribution of localized histologic predictions along the delineated polyp surface. This capability may improve interpretability and transparency of AI-based RTH and offer intuitive, accurate, and user-friendly guidance in real time for the clinical management and documentation of optical histology results.
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Affiliation(s)
- Eladio Rodriguez-Diaz
- Research Service, VA Boston Healthcare System, Boston, MA; Department of Biomedical Engineering, Boston University College of Engineering, Boston, MA
| | - György Baffy
- Department of Medicine, Section of Gastroenterology, VA Boston Healthcare System, Boston, MA; Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | - Wai-Kit Lo
- Department of Medicine, Section of Gastroenterology, VA Boston Healthcare System, Boston, MA; Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | - Hiroshi Mashimo
- Department of Medicine, Section of Gastroenterology, VA Boston Healthcare System, Boston, MA; Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | - Gitanjali Vidyarthi
- Section of Gastroenterology, James A. Haley Veterans Hospital, Tampa, FL; Department of Medicine, Morsani College of Medicine, University of South Florida, Tampa, FL
| | - Shyam S Mohapatra
- Research Service, James A. Haley Veterans Hospital, Tampa, FL; Department of Medicine, Morsani College of Medicine, University of South Florida, Tampa, FL
| | - Satish K Singh
- Research Service, VA Boston Healthcare System, Boston, MA; Department of Biomedical Engineering, Boston University College of Engineering, Boston, MA; Department of Medicine, Section of Gastroenterology, VA Boston Healthcare System, Boston, MA; Department of Medicine, Boston University School of Medicine, Boston, MA; Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA
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Tokunaga M, Matsumura T, Nankinzan R, Suzuki T, Oura H, Kaneko T, Fujie M, Hirai S, Saiki R, Akizue N, Okimoto K, Arai M, Kato J, Kato N. Computer-aided diagnosis system using only white-light endoscopy for the prediction of invasion depth in colorectal cancer. Gastrointest Endosc 2021; 93:647-653. [PMID: 32735946 DOI: 10.1016/j.gie.2020.07.053] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/23/2020] [Accepted: 07/24/2020] [Indexed: 02/06/2023]
Abstract
BACKGROUND AND AIMS Endoscopic treatment is recommended for low-grade dysplasia (LGD), high-grade dysplasia (HGD), and colorectal cancer (CRC) with submucosal (SM) invasion <1000 μm. However, diagnosis of invasion depth requires experience and is often difficult. This study developed and evaluated a novel computer-aided diagnosis (CAD) system to determine whether endoscopic treatment is appropriate for colorectal lesions using only white-light endoscopy (WLE). METHODS We extracted 3442 images from 1035 consecutive colorectal lesions (105 LGDs, 377 HGDs, 107 CRCs with SM <1000 μm, 146 CRCs with SM ≥1000 μm, and 300 advanced CRCs). All images were WLE, nonmagnified, and nonstained. We developed a novel CAD system using 2751 images; the remaining 691 images were evaluated by the CAD system as a test set. The capability of the CAD system to distinguish endoscopically treatable lesions and untreatable lesions was assessed and compared with the results from 2 trainees and 2 experts. RESULTS The CAD system distinguished endoscopically treatable from untreatable lesions with 96.7% sensitivity, 75.0% specificity, and 90.3% accuracy. These values were significantly higher than those from trainees (92.1%, 67.6%, and 84.9%; P < .01, <.01, and <.01, respectively) and were comparable with those from experts (96.5%, 72.5%, and 89.4%, respectively). Trainees assisted by the CAD system demonstrated a diagnostic capability comparable with that of experts. CONCLUSIONS The CAD system had good diagnostic capability for making treatment decisions for colorectal lesions. This system may enable a more convenient and accurate diagnosis using only WLE.
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Affiliation(s)
- Mamoru Tokunaga
- Department of Gastroenterology, Graduate School of Medicine, Chiba University, Chiba, Japan
| | - Tomoaki Matsumura
- Department of Gastroenterology, Graduate School of Medicine, Chiba University, Chiba, Japan
| | | | - Takuto Suzuki
- Endoscopy Division, Chiba Cancer Center, Chiba, Japan
| | - Hirotaka Oura
- Department of Gastroenterology, Graduate School of Medicine, Chiba University, Chiba, Japan
| | - Tatsuya Kaneko
- Department of Gastroenterology, Graduate School of Medicine, Chiba University, Chiba, Japan
| | - Mai Fujie
- Department of Clinical Engineering Center, Chiba University Hospital, Chiba, Japan
| | | | | | - Naoki Akizue
- Department of Gastroenterology, Graduate School of Medicine, Chiba University, Chiba, Japan
| | - Kenichiro Okimoto
- Department of Gastroenterology, Graduate School of Medicine, Chiba University, Chiba, Japan
| | - Makoto Arai
- Department of Gastroenterology, Graduate School of Medicine, Chiba University, Chiba, Japan
| | - Jun Kato
- Department of Gastroenterology, Graduate School of Medicine, Chiba University, Chiba, Japan
| | - Naoya Kato
- Department of Gastroenterology, Graduate School of Medicine, Chiba University, Chiba, Japan
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Application of Artificial Intelligence in Gastrointestinal Endoscopy. J Clin Gastroenterol 2021; 55:110-120. [PMID: 32925304 DOI: 10.1097/mcg.0000000000001423] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/10/2020] [Accepted: 08/07/2020] [Indexed: 12/24/2022]
Abstract
Artificial intelligence (AI), also known as computer-aided diagnosis, is a technology that enables machines to process information and functions at or above human level and has great potential in gastrointestinal endoscopy applications. At present, the research on medical image recognition usually adopts the deep-learning algorithm based on the convolutional neural network. AI has been used in gastrointestinal endoscopy including esophagogastroduodenoscopy, capsule endoscopy, colonoscopy, etc. AI can help endoscopic physicians improve the diagnosis rate of various lesions, reduce the rate of missed diagnosis, improve the quality of endoscopy, assess the severity of the disease, and improve the efficiency of endoscopy. The diversity, susceptibility, and imaging specificity of gastrointestinal endoscopic images are all difficulties and challenges on the road to intelligence. We need more large-scale, high-quality, multicenter prospective studies to explore the clinical applicability of AI, and ethical issues need to be taken into account.
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Misawa M, Kudo SE, Mori Y, Maeda Y, Ogawa Y, Ichimasa K, Kudo T, Wakamura K, Hayashi T, Miyachi H, Baba T, Ishida F, Itoh H, Oda M, Mori K. Current status and future perspective on artificial intelligence for lower endoscopy. Dig Endosc 2021; 33:273-284. [PMID: 32969051 DOI: 10.1111/den.13847] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/11/2020] [Revised: 09/03/2020] [Accepted: 09/16/2020] [Indexed: 12/23/2022]
Abstract
The global incidence and mortality rate of colorectal cancer remains high. Colonoscopy is regarded as the gold standard examination for detecting and eradicating neoplastic lesions. However, there are some uncertainties in colonoscopy practice that are related to limitations in human performance. First, approximately one-fourth of colorectal neoplasms are missed on a single colonoscopy. Second, it is still difficult for non-experts to perform adequately regarding optical biopsy. Third, recording of some quality indicators (e.g. cecal intubation, bowel preparation, and withdrawal speed) which are related to adenoma detection rate, is sometimes incomplete. With recent improvements in machine learning techniques and advances in computer performance, artificial intelligence-assisted computer-aided diagnosis is being increasingly utilized by endoscopists. In particular, the emergence of deep-learning, data-driven machine learning techniques have made the development of computer-aided systems easier than that of conventional machine learning techniques, the former currently being considered the standard artificial intelligence engine of computer-aided diagnosis by colonoscopy. To date, computer-aided detection systems seem to have improved the rate of detection of neoplasms. Additionally, computer-aided characterization systems may have the potential to improve diagnostic accuracy in real-time clinical practice. Furthermore, some artificial intelligence-assisted systems that aim to improve the quality of colonoscopy have been reported. The implementation of computer-aided system clinical practice may provide additional benefits such as helping in educational poorly performing endoscopists and supporting real-time clinical decision-making. In this review, we have focused on computer-aided diagnosis during colonoscopy reported by gastroenterologists and discussed its status, limitations, and future prospects.
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Affiliation(s)
- Masashi Misawa
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Shin-Ei Kudo
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Yuichi Mori
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
- Clinical Effectiveness Research Group, Institute of Heath and Society, University of Oslo, Oslo, Norway
| | - Yasuharu Maeda
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Yushi Ogawa
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Katsuro Ichimasa
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Toyoki Kudo
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Kunihiko Wakamura
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Takemasa Hayashi
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Hideyuki Miyachi
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Toshiyuki Baba
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Fumio Ishida
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Hayato Itoh
- Graduate School of Informatics, Nagoya University, Aichi, Japan
| | - Masahiro Oda
- Graduate School of Informatics, Nagoya University, Aichi, Japan
| | - Kensaku Mori
- Graduate School of Informatics, Nagoya University, Aichi, Japan
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Iwagami H, Ishihara R, Aoyama K, Fukuda H, Shimamoto Y, Kono M, Nakahira H, Matsuura N, Shichijo S, Kanesaka T, Kanzaki H, Ishii T, Nakatani Y, Tada T. Artificial intelligence for the detection of esophageal and esophagogastric junctional adenocarcinoma. J Gastroenterol Hepatol 2021; 36:131-136. [PMID: 32511793 DOI: 10.1111/jgh.15136] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/24/2020] [Revised: 05/03/2020] [Accepted: 06/05/2020] [Indexed: 12/12/2022]
Abstract
BACKGROUND AND AIM Conventional endoscopy for the early detection of esophageal and esophagogastric junctional adenocarcinoma (E/J cancer) is limited because early lesions are asymptomatic, and the associated changes in the mucosa are subtle. There are no reports on artificial intelligence (AI) diagnosis for E/J cancer from Asian countries. Therefore, we aimed to develop a computerized image analysis system using deep learning for the detection of E/J cancers. METHODS A total of 1172 images from 166 pathologically proven superficial E/J cancer cases and 2271 images of normal mucosa in esophagogastric junctional from 219 cases were used as the training image data. A total of 232 images from 36 cancer cases and 43 non-cancerous cases were used as the validation test data. The same validation test data were diagnosed by 15 board-certified specialists (experts). RESULTS The sensitivity, specificity, and accuracy of the AI system were 94%, 42%, and 66%, respectively, and that of the experts were 88%, 43%, and 63%, respectively. The sensitivity of the AI system was favorable, while its specificity for non-cancerous lesions was similar to that of the experts. Interobserver agreement among the experts for detecting superficial E/J was fair (Fleiss' kappa = 0.26, z = 20.4, P < 0.001). CONCLUSIONS Our AI system achieved high sensitivity and acceptable specificity for the detection of E/J cancers and may be a good supporting tool for the screening of E/J cancers.
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Affiliation(s)
- Hiroyoshi Iwagami
- Department of Gastrointestinal Oncology, Osaka International Cancer Institute, Osaka, Japan
| | - Ryu Ishihara
- Department of Gastrointestinal Oncology, Osaka International Cancer Institute, Osaka, Japan
| | | | - Hiromu Fukuda
- Department of Gastrointestinal Oncology, Osaka International Cancer Institute, Osaka, Japan
| | - Yusaku Shimamoto
- Department of Gastrointestinal Oncology, Osaka International Cancer Institute, Osaka, Japan
| | - Mitsuhiro Kono
- Department of Gastrointestinal Oncology, Osaka International Cancer Institute, Osaka, Japan
| | - Hiroko Nakahira
- Department of Gastrointestinal Oncology, Osaka International Cancer Institute, Osaka, Japan
| | - Noriko Matsuura
- Department of Gastrointestinal Oncology, Osaka International Cancer Institute, Osaka, Japan
| | - Satoki Shichijo
- Department of Gastrointestinal Oncology, Osaka International Cancer Institute, Osaka, Japan
| | - Takashi Kanesaka
- Department of Gastrointestinal Oncology, Osaka International Cancer Institute, Osaka, Japan
| | - Hiromitsu Kanzaki
- Department of Gastroenterology and Hepatology, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama, Japan
| | - Tatsuya Ishii
- Center for Gastroenterology, Teine Keijinkai Hospital, Sapporo, Japan
| | - Yasuki Nakatani
- Department of Gastroenterology and Hepatology, Japanese Red Cross Society Wakayama Medical Center, Wakayama, Japan
| | - Tomohiro Tada
- Engineering, AI Medical Service Inc., Tokyo, Japan
- Department of Gastroenterology, Tada Tomohiro Institute of Gastroenterology and Proctology, Saitama, Japan
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Peshevska-Sekulovska M, Velikova TV, Peruhova M. Artificial intelligence assisted endocytoscopy: A novel eye in endoscopy. Artif Intell Gastrointest Endosc 2020; 1:44-52. [DOI: 10.37126/aige.v1.i3.44] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/01/2020] [Revised: 11/29/2020] [Accepted: 12/06/2020] [Indexed: 02/06/2023] Open
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Pannala R, Krishnan K, Melson J, Parsi MA, Schulman AR, Sullivan S, Trikudanathan G, Trindade AJ, Watson RR, Maple JT, Lichtenstein DR. Artificial intelligence in gastrointestinal endoscopy. VIDEOGIE : AN OFFICIAL VIDEO JOURNAL OF THE AMERICAN SOCIETY FOR GASTROINTESTINAL ENDOSCOPY 2020; 5:598-613. [PMID: 33319126 PMCID: PMC7732722 DOI: 10.1016/j.vgie.2020.08.013] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
BACKGROUND AND AIMS Artificial intelligence (AI)-based applications have transformed several industries and are widely used in various consumer products and services. In medicine, AI is primarily being used for image classification and natural language processing and has great potential to affect image-based specialties such as radiology, pathology, and gastroenterology (GE). This document reviews the reported applications of AI in GE, focusing on endoscopic image analysis. METHODS The MEDLINE database was searched through May 2020 for relevant articles by using key words such as machine learning, deep learning, artificial intelligence, computer-aided diagnosis, convolutional neural networks, GI endoscopy, and endoscopic image analysis. References and citations of the retrieved articles were also evaluated to identify pertinent studies. The manuscript was drafted by 2 authors and reviewed in person by members of the American Society for Gastrointestinal Endoscopy Technology Committee and subsequently by the American Society for Gastrointestinal Endoscopy Governing Board. RESULTS Deep learning techniques such as convolutional neural networks have been used in several areas of GI endoscopy, including colorectal polyp detection and classification, analysis of endoscopic images for diagnosis of Helicobacter pylori infection, detection and depth assessment of early gastric cancer, dysplasia in Barrett's esophagus, and detection of various abnormalities in wireless capsule endoscopy images. CONCLUSIONS The implementation of AI technologies across multiple GI endoscopic applications has the potential to transform clinical practice favorably and improve the efficiency and accuracy of current diagnostic methods.
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Key Words
- ADR, adenoma detection rate
- AI, artificial intelligence
- AMR, adenoma miss rate
- ANN, artificial neural network
- BE, Barrett’s esophagus
- CAD, computer-aided diagnosis
- CADe, CAD studies for colon polyp detection
- CADx, CAD studies for colon polyp classification
- CI, confidence interval
- CNN, convolutional neural network
- CRC, colorectal cancer
- DL, deep learning
- GI, gastroenterology
- HD-WLE, high-definition white light endoscopy
- HDWL, high-definition white light
- ML, machine learning
- NBI, narrow-band imaging
- NPV, negative predictive value
- PIVI, preservation and Incorporation of Valuable Endoscopic Innovations
- SVM, support vector machine
- VLE, volumetric laser endomicroscopy
- WCE, wireless capsule endoscopy
- WL, white light
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Affiliation(s)
- Rahul Pannala
- Department of Gastroenterology and Hepatology, Mayo Clinic, Scottsdale, Arizona
| | - Kumar Krishnan
- Division of Gastroenterology, Department of Internal Medicine, Harvard Medical School and Massachusetts General Hospital, Boston, Massachusetts
| | - Joshua Melson
- Division of Digestive Diseases, Department of Internal Medicine, Rush University Medical Center, Chicago, Illinois
| | - Mansour A Parsi
- Section for Gastroenterology and Hepatology, Tulane University Health Sciences Center, New Orleans, Louisiana
| | - Allison R Schulman
- Department of Gastroenterology, Michigan Medicine, University of Michigan, Ann Arbor, Michigan
| | - Shelby Sullivan
- Division of Gastroenterology and Hepatology, University of Colorado School of Medicine, Aurora, Colorado
| | - Guru Trikudanathan
- Department of Gastroenterology, Hepatology and Nutrition, University of Minnesota, Minneapolis, Minnesota
| | - Arvind J Trindade
- Department of Gastroenterology, Zucker School of Medicine at Hofstra/Northwell, Long Island Jewish Medical Center, New Hyde Park, New York
| | - Rabindra R Watson
- Department of Gastroenterology, Interventional Endoscopy Services, California Pacific Medical Center, San Francisco, California
| | - John T Maple
- Division of Digestive Diseases and Nutrition, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma
| | - David R Lichtenstein
- Division of Gastroenterology, Boston Medical Center, Boston University School of Medicine, Boston, Massachusetts
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Abstract
Artificial intelligence (AI) is now a trendy subject in clinical medicine and especially in gastrointestinal (GI) endoscopy. AI has the potential to improve the quality of GI endoscopy at all levels. It will compensate for humans' errors and limited capabilities by bringing more accuracy, consistency, and higher speed, making endoscopic procedures more efficient and of higher quality. AI showed great results in diagnostic and therapeutic endoscopy in all parts of the GI tract. More studies are still needed before the introduction of this new technology in our daily practice and clinical guidelines. Furthermore, ethical clearance and new legislations might be needed. In conclusion, the introduction of AI will be a big breakthrough in the field of GI endoscopy in the upcoming years. It has the potential to bring major improvements to GI endoscopy at all levels.
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Affiliation(s)
- Ahmad El Hajjar
- Department of Gastroenterology and Digestive Endoscopy, Arnault Tzanck Institute, Saint-Laurent du Var 06700, France
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Sinagra E, Badalamenti M, Maida M, Spadaccini M, Maselli R, Rossi F, Conoscenti G, Raimondo D, Pallio S, Repici A, Anderloni A. Use of artificial intelligence in improving adenoma detection rate during colonoscopy: Might both endoscopists and pathologists be further helped. World J Gastroenterol 2020; 26:5911-5918. [PMID: 33132644 PMCID: PMC7584058 DOI: 10.3748/wjg.v26.i39.5911] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/27/2020] [Revised: 08/18/2020] [Accepted: 09/23/2020] [Indexed: 02/06/2023] Open
Abstract
Colonoscopy remains the standard strategy for screening for colorectal cancer around the world due to its efficacy in both detecting adenomatous or pre-cancerous lesions and the capacity to remove them intra-procedurally. Computer-aided detection and diagnosis (CAD), thanks to the brand new developed innovations of artificial intelligence, and especially deep-learning techniques, leads to a promising solution to human biases in performance by guarantying decision support during colonoscopy. The application of CAD on real-time colonoscopy helps increasing the adenoma detection rate, and therefore contributes to reduce the incidence of interval cancers improving the effectiveness of colonoscopy screening on critical outcome such as colorectal cancer related mortality. Furthermore, a significant reduction in costs is also expected. In addition, the assistance of the machine will lead to a reduction of the examination time and therefore an optimization of the endoscopic schedule. The aim of this opinion review is to analyze the clinical applications of CAD and artificial intelligence in colonoscopy, as it is reported in literature, addressing evidence, limitations, and future prospects.
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Affiliation(s)
- Emanuele Sinagra
- Gastroenterology and Endoscopy Unit, Fondazione Istituto San Raffaele Giglio, Cefalù 90015, Italy
| | - Matteo Badalamenti
- Digestive Endoscopy Unit, Division of Gastroenterology, Humanitas Clinical and Research Center IRCCS, Rozzano 20089, Italy
| | - Marcello Maida
- Gastroenterology and Endoscopy Unit, S. Elia-Raimondi Hospital, Caltanissetta 93100, Italy
| | - Marco Spadaccini
- Digestive Endoscopy Unit, Division of Gastroenterology, Humanitas Clinical and Research Center IRCCS, Rozzano 20089, Italy
| | - Roberta Maselli
- Digestive Endoscopy Unit, Division of Gastroenterology, Humanitas Clinical and Research Center IRCCS, Rozzano 20089, Italy
| | - Francesca Rossi
- Gastroenterology and Endoscopy Unit, Fondazione Istituto San Raffaele Giglio, Cefalù 90015, Italy
| | - Giuseppe Conoscenti
- Gastroenterology and Endoscopy Unit, Fondazione Istituto San Raffaele Giglio, Cefalù 90015, Italy
| | - Dario Raimondo
- Gastroenterology and Endoscopy Unit, Fondazione Istituto San Raffaele Giglio, Cefalù 90015, Italy
| | - Socrate Pallio
- Endoscopy Unit, AOUP Policlinico G. Martino, Messina 98125, Italy
| | - Alessandro Repici
- Digestive Endoscopy Unit, Division of Gastroenterology, Humanitas Clinical and Research Center IRCCS, Rozzano 20089, Italy
| | - Andrea Anderloni
- Digestive Endoscopy Unit, Division of Gastroenterology, Humanitas Clinical and Research Center IRCCS, Rozzano 20089, Italy
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Tonozuka R, Itoi T, Nagata N, Kojima H, Sofuni A, Tsuchiya T, Ishii K, Tanaka R, Nagakawa Y, Mukai S. Deep learning analysis for the detection of pancreatic cancer on endosonographic images: a pilot study. JOURNAL OF HEPATO-BILIARY-PANCREATIC SCIENCES 2020; 28:95-104. [PMID: 32910528 DOI: 10.1002/jhbp.825] [Citation(s) in RCA: 72] [Impact Index Per Article: 14.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/22/2020] [Revised: 07/29/2020] [Accepted: 08/31/2020] [Indexed: 12/14/2022]
Abstract
BACKGROUND/PURPOSE The application of artificial intelligence to clinical diagnostics using deep learning has been developed in recent years. In this study, we developed an original computer-assisted diagnosis (CAD) system using deep learning analysis of EUS images (EUS-CAD), and assessed its ability to detect pancreatic ductal carcinoma (PDAC), using control images from patients with chronic pancreatitis (CP) and those with a normal pancreas (NP). METHODS A total of 920 endosonographic images were used for the training and 10-fold cross-validation, and another 470 images were independently tested. The detection abilities in both the validation and test setting were assessed, and independent factors associated with misdetection were identified among participants' characteristics and endosonographic image features. RESULTS Regarding the detection ability of EUS-CAD, the areas under the receiver operating characteristic curve were found to be 0.924 and 0.940 in the validation and test setting, respectively. In the analysis of misdetection, no factors were identified on univariate analysis in PDAC cases. On multivariate analysis of non-PDAC cases, only mass formation was associated with overdiagnosis of tumors. CONCLUSIONS Our pilot study demonstrated the efficacy of EUS-CAD for the detection of PDAC.
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Affiliation(s)
- Ryosuke Tonozuka
- Department of Gastroenterology and Hepatology, Tokyo Medical University, Tokyo, Japan
| | - Takao Itoi
- Department of Gastroenterology and Hepatology, Tokyo Medical University, Tokyo, Japan
| | | | - Hiroyuki Kojima
- Department of Gastroenterology and Hepatology, Tokyo Medical University, Tokyo, Japan
| | - Atsushi Sofuni
- Department of Gastroenterology and Hepatology, Tokyo Medical University, Tokyo, Japan
| | - Takayoshi Tsuchiya
- Department of Gastroenterology and Hepatology, Tokyo Medical University, Tokyo, Japan
| | - Kentaro Ishii
- Department of Gastroenterology and Hepatology, Tokyo Medical University, Tokyo, Japan
| | - Reina Tanaka
- Department of Gastroenterology and Hepatology, Tokyo Medical University, Tokyo, Japan
| | - Yuichi Nagakawa
- Department of Gastrointestinal and Pediatric Surgery, Tokyo Medical University, Tokyo, Japan
| | - Shuntaro Mukai
- Department of Gastroenterology and Hepatology, Tokyo Medical University, Tokyo, Japan
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Goyal H, Mann R, Gandhi Z, Perisetti A, Ali A, Aman Ali K, Sharma N, Saligram S, Tharian B, Inamdar S. Scope of Artificial Intelligence in Screening and Diagnosis of Colorectal Cancer. J Clin Med 2020; 9:3313. [PMID: 33076511 PMCID: PMC7602532 DOI: 10.3390/jcm9103313] [Citation(s) in RCA: 40] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2020] [Revised: 10/09/2020] [Accepted: 10/12/2020] [Indexed: 12/15/2022] Open
Abstract
Globally, colorectal cancer is the third most diagnosed malignancy. It causes significant mortality and morbidity, which can be reduced by early diagnosis with an effective screening test. Integrating artificial intelligence (AI) and computer-aided detection (CAD) with screening methods has shown promising colorectal cancer screening results. AI could provide a "second look" for endoscopists to decrease the rate of missed polyps during a colonoscopy. It can also improve detection and characterization of polyps by integration with colonoscopy and various advanced endoscopic modalities such as magnifying narrow-band imaging, endocytoscopy, confocal endomicroscopy, laser-induced fluorescence spectroscopy, and magnifying chromoendoscopy. This descriptive review discusses various AI and CAD applications in colorectal cancer screening, polyp detection, and characterization.
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Affiliation(s)
- Hemant Goyal
- Department of Internal Medicine, The Wright Center for Graduate Medical Education, Scranton, PA 18505, USA
| | | | - Zainab Gandhi
- Department of Medicine, Geisinger Community Medical Center, Scranton, PA 18510, USA;
| | - Abhilash Perisetti
- Department of Gastroenterology and Hepatology, The University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA;
| | - Aman Ali
- Division of Gastroenterology, The Commonwealth Medical College, Wilkes Barre General Hospital, Wilkes-Barre, PA 18764, USA;
- Digestive Care Associates, Kingston, PA 18704, USA;
| | | | - Neil Sharma
- Division of Interventional Oncology & Surgical Endoscopy (IOSE), Parkview Cancer Institute, Fort Wayne, IN 46845, USA;
- Division of Interventional Oncology & Surgical Endoscopy, Indiana University School of Medicine, Fort Wayne, IN 46805, USA
| | - Shreyas Saligram
- Department of Medicine, University of Texas Health San Antonio, San Antonio, TX 78229, USA;
| | - Benjamin Tharian
- General and Advanced Endoscopy, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA;
| | - Sumant Inamdar
- Advanced Endoscopy Fellowship, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA;
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Mori Y, Kudo SE, East JE, Rastogi A, Bretthauer M, Misawa M, Sekiguchi M, Matsuda T, Saito Y, Ikematsu H, Hotta K, Ohtsuka K, Kudo T, Mori K. Cost savings in colonoscopy with artificial intelligence-aided polyp diagnosis: an add-on analysis of a clinical trial (with video). Gastrointest Endosc 2020; 92:905-911.e1. [PMID: 32240683 DOI: 10.1016/j.gie.2020.03.3759] [Citation(s) in RCA: 88] [Impact Index Per Article: 17.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/16/2020] [Accepted: 03/16/2020] [Indexed: 02/06/2023]
Abstract
BACKGROUND AND AIMS Artificial intelligence (AI) is being implemented in colonoscopy practice, but no study has investigated whether AI is cost saving. We aimed to quantify the cost reduction using AI as an aid in the optical diagnosis of colorectal polyps. METHODS This study is an add-on analysis of a clinical trial that investigated the performance of AI for differentiating colorectal polyps (ie, neoplastic versus non-neoplastic). We included all patients with diminutive (≤5 mm) rectosigmoid polyps in the analyses. The average colonoscopy cost was compared for 2 scenarios: (1) a diagnose-and-leave strategy supported by the AI prediction (ie, diminutive rectosigmoid polyps were not removed when predicted as non-neoplastic), and (2) a resect-all-polyps strategy. Gross annual costs for colonoscopies were also calculated based on the number and reimbursement of colonoscopies conducted under public health insurances in 4 countries. RESULTS Overall, 207 patients with 250 diminutive rectosigmoid polyps (104 neoplastic, 144 non-neoplastic, and 2 indeterminate) were included. AI correctly differentiated neoplastic polyps with 93.3% sensitivity, 95.2% specificity, and 95.2% negative predictive value. Thus, 105 polyps were removed and 145 were left under the diagnose-and-leave strategy, which was estimated to reduce the average colonoscopy cost and the gross annual reimbursement for colonoscopies by 18.9% and US$149.2 million in Japan, 6.9% and US$12.3 million in England, 7.6% and US$1.1 million in Norway, and 10.9% and US$85.2 million in the United States, respectively, compared with the resect-all-polyps strategy. CONCLUSIONS The use of AI to enable the diagnose-and-leave strategy results in substantial cost reductions for colonoscopy.
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Affiliation(s)
- Yuichi Mori
- Department of Health Management and Health Economics, Institute of Health and Society, University of Oslo, Oslo, Norway
| | - Shin-Ei Kudo
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
| | - James E East
- Translational Gastroenterology Unit, John Radcliffe Hospital, Oxford, United Kingdom; Oxford NIHR Biomedical Research Centre, University of Oxford, Oxford, United Kingdom
| | - Amit Rastogi
- Division of Gastroenterology, University of Kansas Medical Center, Kansas City, Kansas, USA
| | - Michael Bretthauer
- Department of Health Management and Health Economics, Institute of Health and Society, University of Oslo, Oslo, Norway; Department of Transplantation Medicine, Oslo University Hospital, Oslo, Norway
| | - Masashi Misawa
- Department of Health Management and Health Economics, Institute of Health and Society, University of Oslo, Oslo, Norway
| | - Masau Sekiguchi
- Cancer Screening Center, National Cancer Center Hospital, Tokyo, Japan; Division of Screening Technology, Center for Public Health Sciences, National Cancer Center, Tokyo, Japan; Endoscopy Division, National Cancer Center Hospital, Tokyo, Japan
| | - Takahisa Matsuda
- Cancer Screening Center, National Cancer Center Hospital, Tokyo, Japan; Division of Screening Technology, Center for Public Health Sciences, National Cancer Center, Tokyo, Japan; Endoscopy Division, National Cancer Center Hospital, Tokyo, Japan
| | - Yutaka Saito
- Endoscopy Division, National Cancer Center Hospital, Tokyo, Japan
| | - Hiroaki Ikematsu
- Department of Gastroenterology and Endoscopy, National Cancer Center Hospital East, Kashiwa, Japan
| | - Kinichi Hotta
- Division of Endoscopy, Shizuoka Cancer Center, Shizuoka, Japan
| | - Kazuo Ohtsuka
- Department of Endoscopy, Tokyo Medical and Dental University, Tokyo, Japan
| | - Toyoki Kudo
- Department of Health Management and Health Economics, Institute of Health and Society, University of Oslo, Oslo, Norway
| | - Kensaku Mori
- Graduate School of Informatics, Nagoya University, Nagoya, Japan
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Wang KW, Dong M. Potential applications of artificial intelligence in colorectal polyps and cancer: Recent advances and prospects. World J Gastroenterol 2020; 26:5090-5100. [PMID: 32982111 PMCID: PMC7495038 DOI: 10.3748/wjg.v26.i34.5090] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/22/2020] [Revised: 07/01/2020] [Accepted: 08/12/2020] [Indexed: 02/06/2023] Open
Abstract
Since the advent of artificial intelligence (AI) technology, it has been constantly studied and has achieved rapid development. The AI assistant system is expected to improve the quality of automatic polyp detection and classification. It could also help prevent endoscopists from missing polyps and make an accurate optical diagnosis. These functions provided by AI could result in a higher adenoma detection rate and decrease the cost of polypectomy for hyperplastic polyps. In addition, AI has good performance in the staging, diagnosis, and segmentation of colorectal cancer. This article provides an overview of recent research focusing on the application of AI in colorectal polyps and cancer and highlights the advances achieved.
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Affiliation(s)
- Ke-Wei Wang
- Department of Gastrointestinal Surgery, the First Affiliated Hospital of China Medical University, Shenyang 110001, Liaoning Province, China
| | - Ming Dong
- Department of Gastrointestinal Surgery, the First Affiliated Hospital of China Medical University, Shenyang 110001, Liaoning Province, China
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Takamaru H, Wu SYS, Saito Y. Endocytoscopy: technology and clinical application in the lower GI tract. Transl Gastroenterol Hepatol 2020; 5:40. [PMID: 32632391 DOI: 10.21037/tgh.2019.12.04] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/08/2019] [Accepted: 11/13/2019] [Indexed: 12/13/2022] Open
Abstract
Endocytoscopy (EC) is now one of the valuable technologies in diagnosing colorectal tumors. Providing ultra-high-resolution white light images (520×), endocytoscopy attains the so called virtual histology or optical biopsy, making it a promising tool to diagnose colorectal lesions. Recent studies about artificial intelligence (AI) or computer aided diagnosis (CAD) are also increasingly reported. We investigate the current application of endocytoscopy, as well as the benefit of AI and CAD. Furthermore, we performed a meta-analysis comparing the diagnostic performance of endocytoscopy and magnified chromoendoscopy. In conclusion, this systematic review and meta-analysis supports the recent finding indicating the higher diagnostic performance of endocytoscope in the depth assessment of colorectal neoplasms.
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Affiliation(s)
| | | | - Yutaka Saito
- Endoscopy Division, National Cancer Center Hospital, Tokyo, Japan
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Lui TKL, Guo CG, Leung WK. Accuracy of artificial intelligence on histology prediction and detection of colorectal polyps: a systematic review and meta-analysis. Gastrointest Endosc 2020; 92:11-22.e6. [PMID: 32119938 DOI: 10.1016/j.gie.2020.02.033] [Citation(s) in RCA: 63] [Impact Index Per Article: 12.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/15/2020] [Accepted: 02/17/2020] [Indexed: 02/08/2023]
Abstract
BACKGROUND AND AIMS We performed a meta-analysis of all published studies to determine the diagnostic accuracy of artificial intelligence (AI) on histology prediction and detection of colorectal polyps. METHOD We searched Embase, PubMed, Medline, Web of Science, and Cochrane library databases to identify studies using AI for colorectal polyp histology prediction and detection. The quality of included studies was measured by the Quality Assessment of Diagnostic Accuracy Studies tool. We used a bivariate meta-analysis following a random-effects model to summarize the data and plotted hierarchical summary receiver operating characteristic curves. The area under the hierarchical summary receiver operating characteristic curve (AUC) served as an indicator of the diagnostic accuracy and during head-to-head comparisons. RESULTS A total of 7680 images of colorectal polyps from 18 studies were included in the analysis of histology prediction. The accuracy of the AI (AUC) was .96 (95% confidence interval [CI], .95-.98), with a corresponding pooled sensitivity of 92.3% (95% CI, 88.8%-94.9%) and specificity of 89.8% (95% CI, 85.3%-93.0%). The AUC of AI using narrow-band imaging (NBI) was significantly higher than the AUC using non-NBI (.98 vs .84, P < .01). The performance of AI was superior to nonexpert endoscopists (.97 vs .90, P < .01). For characterization of diminutive polyps using a deep learning model with nonmagnifying NBI, the pooled negative predictive value was 95.1% (95% CI, 87.7%-98.1%). For polyp detection, the pooled AUC was .90 (95% CI, .67-1.00) with a sensitivity of 95.0% (95% CI, 91.0%-97.0%) and a specificity of 88.0% (95% CI, 58.0%-99.0%). CONCLUSIONS AI was accurate in histology prediction and detection of colorectal polyps, including diminutive polyps. The performance of AI was better under NBI and was superior to nonexpert endoscopists. Despite the difference in AI models and study designs, AI performances are rather consistent, which could serve as a reference for future AI studies.
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Affiliation(s)
- Thomas K L Lui
- Department of Medicine, Queen Mary Hospital, University of Hong Kong, Hong Kong
| | - Chuan-Guo Guo
- Department of Medicine, Queen Mary Hospital, University of Hong Kong, Hong Kong
| | - Wai K Leung
- Department of Medicine, Queen Mary Hospital, University of Hong Kong, Hong Kong.
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Zhou D, Tian F, Tian X, Sun L, Huang X, Zhao F, Zhou N, Chen Z, Zhang Q, Yang M, Yang Y, Guo X, Li Z, Liu J, Wang J, Wang J, Wang B, Zhang G, Sun B, Zhang W, Kong D, Chen K, Li X. Diagnostic evaluation of a deep learning model for optical diagnosis of colorectal cancer. Nat Commun 2020; 11:2961. [PMID: 32528084 PMCID: PMC7289893 DOI: 10.1038/s41467-020-16777-6] [Citation(s) in RCA: 53] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2020] [Accepted: 05/19/2020] [Indexed: 12/24/2022] Open
Abstract
Colonoscopy is commonly used to screen for colorectal cancer (CRC). We develop a deep learning model called CRCNet for optical diagnosis of CRC by training on 464,105 images from 12,179 patients and test its performance on 2263 patients from three independent datasets. At the patient-level, CRCNet achieves an area under the precision-recall curve (AUPRC) of 0.882 (95% CI: 0.828-0.931), 0.874 (0.820-0.926) and 0.867 (0.795-0.923). CRCNet exceeds average endoscopists performance on recall rate across two test sets (91.3% versus 83.8%; two-sided t-test, p < 0.001 and 96.5% versus 90.3%; p = 0.006) and precision for one test set (93.7% versus 83.8%; p = 0.02), while obtains comparable recall rate on one test set and precision on the other two. At the image-level, CRCNet achieves an AUPRC of 0.990 (0.987-0.993), 0.991 (0.987-0.995), and 0.997 (0.995-0.999). Our study warrants further investigation of CRCNet by prospective clinical trials.
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Affiliation(s)
- Dejun Zhou
- Department of Endoscopic Diagnosis and Therapy, National Clinical Research Center for Cancer, Key Laboratory of Molecular Cancer Epidemiology of Tianjin, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin, China
| | - Fei Tian
- Department of Colorectal Cancer, National Clinical Research Center for Cancer, Key Laboratory of Molecular Cancer Epidemiology of Tianjin, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin, China
| | - Xiangdong Tian
- Department of Endoscopic Diagnosis and Therapy, National Clinical Research Center for Cancer, Key Laboratory of Molecular Cancer Epidemiology of Tianjin, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin, China
| | - Lin Sun
- Department of Pathology, National Clinical Research Center for Cancer, Key Laboratory of Molecular Cancer Epidemiology of Tianjin, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin, China
| | - Xianghui Huang
- General Surgery Department, Ningbo First Hospital, Zhejiang University Ningbo Hospital, Ningbo, China
| | - Feng Zhao
- Anorectal Department, Nanyang Hospital of Traditional Chinese Medicine, Nanyang, China
| | - Nan Zhou
- Anorectal Department, Nanyang Hospital of Traditional Chinese Medicine, Nanyang, China
| | - Zuoyu Chen
- General Surgery Department, General Hospital of Tianjin Medical University, Tianjin Medical University, Tianjin, China
| | - Qiang Zhang
- Department of Maxillofacial and Otorhinolaryngology Oncology, National Clinical Research Center for Cancer, Key Laboratory of Molecular Cancer Epidemiology of Tianjin, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin, China
| | - Meng Yang
- Tianjin Cancer Institute, National Clinical Research Center for Cancer, Key Laboratory of Molecular Cancer Epidemiology of Tianjin, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin, China
| | - Yichen Yang
- Tianjin Cancer Institute, National Clinical Research Center for Cancer, Key Laboratory of Molecular Cancer Epidemiology of Tianjin, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin, China
| | - Xuexi Guo
- Department of Pathology, Tianjin First Central Hospital, Tianjin, China
| | - Zhibin Li
- Department of Gastroenterology and Hepatobiliary Surgery, People's First Hospital of Shangqiu, Shangqiu, China
| | - Jia Liu
- Department of Colorectal Cancer, National Clinical Research Center for Cancer, Key Laboratory of Molecular Cancer Epidemiology of Tianjin, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin, China
| | - Jiefu Wang
- Department of Colorectal Cancer, National Clinical Research Center for Cancer, Key Laboratory of Molecular Cancer Epidemiology of Tianjin, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin, China
| | - Junfeng Wang
- Department of Colorectal Cancer, National Clinical Research Center for Cancer, Key Laboratory of Molecular Cancer Epidemiology of Tianjin, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin, China
| | - Bangmao Wang
- Department of Gastroenterology, General Hospital of Tianjin Medical University, Tianjin Medical University, Tianjin, China
| | - Guoliang Zhang
- Department of Digestion and Endoscopy, Tianjin First Central Hospital, Tianjin, China
| | - Baocun Sun
- Department of Pathology, National Clinical Research Center for Cancer, Key Laboratory of Molecular Cancer Epidemiology of Tianjin, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin, China
| | - Wei Zhang
- Wake Forest Baptist Comprehensive Cancer Center, Wake Forest Baptist Medical Center, Winston-Salem, NC, USA
- Department of Cancer Biology, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Dalu Kong
- Department of Colorectal Cancer, National Clinical Research Center for Cancer, Key Laboratory of Molecular Cancer Epidemiology of Tianjin, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin, China
| | - Kexin Chen
- Department of Epidemiology and Biostatistics, National Clinical Research Center for Cancer, Key Laboratory of Molecular Cancer Epidemiology of Tianjin, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University, Tianjin, China.
| | - Xiangchun Li
- Tianjin Cancer Institute, National Clinical Research Center for Cancer, Key Laboratory of Molecular Cancer Epidemiology of Tianjin, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin, China.
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Jin EH, Lee D, Bae JH, Kang HY, Kwak MS, Seo JY, Yang JI, Yang SY, Lim SH, Yim JY, Lim JH, Chung GE, Chung SJ, Choi JM, Han YM, Kang SJ, Lee J, Chan Kim H, Kim JS. Improved Accuracy in Optical Diagnosis of Colorectal Polyps Using Convolutional Neural Networks with Visual Explanations. Gastroenterology 2020; 158:2169-2179.e8. [PMID: 32119927 DOI: 10.1053/j.gastro.2020.02.036] [Citation(s) in RCA: 87] [Impact Index Per Article: 17.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/19/2019] [Revised: 01/10/2020] [Accepted: 02/20/2020] [Indexed: 02/08/2023]
Abstract
BACKGROUND & AIMS Narrow-band imaging (NBI) can be used to determine whether colorectal polyps are adenomatous or hyperplastic. We investigated whether an artificial intelligence (AI) system can increase the accuracy of characterizations of polyps by endoscopists of different skill levels. METHODS We developed convolutional neural networks (CNNs) for evaluation of diminutive colorectal polyps, based on efficient neural architecture searches via parameter sharing with augmentation using NBIs of diminutive (≤5 mm) polyps, collected from October 2015 through October 2017 at the Seoul National University Hospital, Healthcare System Gangnam Center (training set). We trained the CNN using images from 1100 adenomatous polyps and 1050 hyperplastic polyps from 1379 patients. We then tested the system using 300 images of 180 adenomatous polyps and 120 hyperplastic polyps, obtained from January 2018 to May 2019. We compared the accuracy of 22 endoscopists of different skill levels (7 novices, 4 experts, and 11 NBI-trained experts) vs the CNN in evaluation of images (adenomatous vs hyperplastic) from 180 adenomatous and 120 hyperplastic polyps. The endoscopists then evaluated the polyp images with knowledge of the CNN-processed results. We conducted mixed-effect logistic and linear regression analyses to determine the effects of AI assistance on the accuracy of analysis of diminutive colorectal polyps by endoscopists (primary outcome). RESULTS The CNN distinguished adenomatous vs hyperplastic diminutive polyps with 86.7% accuracy, based on histologic analysis as the reference standard. Endoscopists distinguished adenomatous vs hyperplastic diminutive polyps with 82.5% overall accuracy (novices, 73.8% accuracy; experts, 83.8% accuracy; and NBI-trained experts, 87.6% accuracy). With knowledge of the CNN-processed results, the overall accuracy of the endoscopists increased to 88.5% (P < .05). With knowledge of the CNN-processed results, the accuracy of novice endoscopists increased to 85.6% (P < .05). The CNN-processed results significantly reduced endoscopist time of diagnosis (from 3.92 to 3.37 seconds per polyp, P = .042). CONCLUSIONS We developed a CNN that significantly increases the accuracy of evaluation of diminutive colorectal polyps (as adenomatous vs hyperplastic) and reduces the time of diagnosis by endoscopists. This AI assistance system significantly increased the accuracy of analysis by novice endoscopists, who achieved near-expert levels of accuracy without extra training. The CNN assistance system can reduce the skill-level dependence of endoscopists and costs.
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Affiliation(s)
- Eun Hyo Jin
- Department of Internal Medicine, Healthcare Research Institute, Seoul National University Hospital Healthcare System Gangnam Center, Seoul, Korea
| | - Dongheon Lee
- Interdisciplinary Program in Bioengineering, Graduate School, Seoul National University, Seoul, Korea
| | - Jung Ho Bae
- Department of Internal Medicine, Healthcare Research Institute, Seoul National University Hospital Healthcare System Gangnam Center, Seoul, Korea
| | - Hae Yeon Kang
- Department of Internal Medicine, Healthcare Research Institute, Seoul National University Hospital Healthcare System Gangnam Center, Seoul, Korea
| | - Min-Sun Kwak
- Department of Internal Medicine, Healthcare Research Institute, Seoul National University Hospital Healthcare System Gangnam Center, Seoul, Korea
| | - Ji Yeon Seo
- Department of Internal Medicine, Healthcare Research Institute, Seoul National University Hospital Healthcare System Gangnam Center, Seoul, Korea
| | - Jong In Yang
- Department of Internal Medicine, Healthcare Research Institute, Seoul National University Hospital Healthcare System Gangnam Center, Seoul, Korea
| | - Sun Young Yang
- Department of Internal Medicine, Healthcare Research Institute, Seoul National University Hospital Healthcare System Gangnam Center, Seoul, Korea
| | - Seon Hee Lim
- Department of Internal Medicine, Healthcare Research Institute, Seoul National University Hospital Healthcare System Gangnam Center, Seoul, Korea
| | - Jeong Yoon Yim
- Department of Internal Medicine, Healthcare Research Institute, Seoul National University Hospital Healthcare System Gangnam Center, Seoul, Korea
| | - Joo Hyun Lim
- Department of Internal Medicine, Healthcare Research Institute, Seoul National University Hospital Healthcare System Gangnam Center, Seoul, Korea
| | - Goh Eun Chung
- Department of Internal Medicine, Healthcare Research Institute, Seoul National University Hospital Healthcare System Gangnam Center, Seoul, Korea
| | - Su Jin Chung
- Department of Internal Medicine, Healthcare Research Institute, Seoul National University Hospital Healthcare System Gangnam Center, Seoul, Korea
| | - Ji Min Choi
- Department of Internal Medicine, Healthcare Research Institute, Seoul National University Hospital Healthcare System Gangnam Center, Seoul, Korea
| | - Yoo Min Han
- Department of Internal Medicine, Healthcare Research Institute, Seoul National University Hospital Healthcare System Gangnam Center, Seoul, Korea
| | - Seung Joo Kang
- Department of Internal Medicine, Healthcare Research Institute, Seoul National University Hospital Healthcare System Gangnam Center, Seoul, Korea
| | - Jooyoung Lee
- Department of Internal Medicine, Liver Research Institute, Seoul National University College of Medicine, Seoul, Korea
| | - Hee Chan Kim
- Interdisciplinary Program in Bioengineering, Graduate School, Seoul National University, Seoul, Korea; Department of Biomedical Engineering College of Medicine, Seoul National University, Seoul, Korea; Institute of Medical & Biological Engineering, Medical Research Center, Seoul National University, Seoul, Korea.
| | - Joo Sung Kim
- Department of Internal Medicine, Healthcare Research Institute, Seoul National University Hospital Healthcare System Gangnam Center, Seoul, Korea; Department of Internal Medicine, Liver Research Institute, Seoul National University College of Medicine, Seoul, Korea.
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Mori Y, Kudo SE, Misawa M, Takeda K, Kudo T, Itoh H, Oda M, Mori K. How Far Will Clinical Application of AI Applications Advance for Colorectal Cancer Diagnosis? JOURNAL OF THE ANUS RECTUM AND COLON 2020; 4:47-50. [PMID: 32346642 PMCID: PMC7186008 DOI: 10.23922/jarc.2019-045] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 12/30/2019] [Accepted: 01/22/2020] [Indexed: 12/16/2022]
Abstract
Integrating artificial intelligence (AI) applications into colonoscopy practice is being accelerated as deep learning technologies emerge. In this field, most of the preceding research has focused on polyp detection and characterization, which can mitigate inherent human errors accompanying colonoscopy procedures. On the other hand, more challenging research areas are currently capturing attention: the automated prediction of invasive cancers. Colorectal cancers (CRCs) harbor potential lymph node metastasis when they invade deeply into submucosal layers, which should be resected surgically rather than endoscopically. However, pretreatment discrimination of deeply invasive submucosal CRCs is considered difficult, according to previous prospective studies (e.g., <70% sensitivity), leading to an increased number of unnecessary surgeries for large adenomas or slightly invasive submucosal CRCs. AI is now expected to overcome this challenging hurdle because it is considered to provide better performance in predicting invasive cancer than non-expert endoscopists. In this review, we introduce five relevant publications in this area. Unfortunately, progress in this research area is in a very preliminary phase, compared to that of automated polyp detection and characterization, because of the lack of number of invasive CRCs used for machine learning. However, this issue will be overcome with more target images and cases. The research field of AI for invasive CRCs is just starting but could be a game changer of patient care in the near future, given rapidly growing technologies, and research will gradually increase.
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Affiliation(s)
- Yuichi Mori
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
| | - Shin-Ei Kudo
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
| | - Masashi Misawa
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
| | - Kenichi Takeda
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
| | - Toyoki Kudo
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
| | - Hayato Itoh
- Graduate School of Informatics, Nagoya University, Nagoya, Japan
| | - Masahiro Oda
- Graduate School of Informatics, Nagoya University, Nagoya, Japan
| | - Kensaku Mori
- Graduate School of Informatics, Nagoya University, Nagoya, Japan
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50
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Rex DK. Can we do resect and discard with artificial intelligence-assisted colon polyp “optical biopsy?”. ACTA ACUST UNITED AC 2020. [DOI: 10.1016/j.tgie.2019.150638] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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