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1
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Nathani P, Sharma P. Role of Artificial Intelligence in the Detection and Management of Premalignant and Malignant Lesions of the Esophagus and Stomach. Gastrointest Endosc Clin N Am 2025; 35:319-353. [PMID: 40021232 DOI: 10.1016/j.giec.2024.10.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/03/2025]
Abstract
The advent of artificial intelligence (AI) and deep learning algorithms, particularly convolutional neural networks, promises to address pitfalls, bridging the care for patients at high risk with improved detection (computer-aided detection [CADe]) and characterization (computer-aided diagnosis [CADx]) of lesions. This review describes the available artificial intelligence (AI) technology and the current data on AI tools for screening esophageal squamous cell cancer, Barret's esophagus-related neoplasia, and gastric cancer. These tools outperformed endoscopists in many situations. Recent randomized controlled trials have demonstrated the successful application of AI tools in clinical practice with improved outcomes.
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Affiliation(s)
- Piyush Nathani
- Department of Gastroenterology, University of Kansas School of Medicine, Kansas City, KS, USA.
| | - Prateek Sharma
- Department of Gastroenterology, University of Kansas School of Medicine, Kansas City, KS, USA; Kansas City Veteran Affairs Medical Center, Kansas City, MO, USA
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Jiang Y, Yan H, Cui J, Yang K, An Y. Artificial Intelligence in Endoscopy for Predicting Helicobacter pylori Infection: A Systematic Review and Meta-Analysis. Helicobacter 2025; 30:e70026. [PMID: 40116054 DOI: 10.1111/hel.70026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/08/2025] [Revised: 03/07/2025] [Accepted: 03/14/2025] [Indexed: 03/23/2025]
Abstract
PURPOSE This meta-analysis aimed to assess the diagnostic performance of artificial intelligence (AI) based on endoscopy for detecting Helicobacter pylori (H. pylori) infection. METHODS A comprehensive literature search was conducted across PubMed, Embase, and Web of Science to identify relevant studies published up to January 10, 2025. The selected studies focused on the diagnostic accuracy of AI in detecting H. pylori. A bivariate random-effects model was employed to calculate pooled sensitivity and specificity, both presented with 95% confidence intervals (CIs). Study heterogeneity was assessed using the I2 statistic. RESULTS Of 604 studies identified, 16 studies (25,002 images or patients) were included. For the internal validation set, the pooled sensitivity, specificity, and area under the curve (AUC) for detecting H. pylori were 0.91 (95% CI: 0.84-0.95), 0.91 (95% CI: 0.86-0.94), and 0.96 (95% CI: 0.94-0.97), respectively. For the external validation set, the pooled sensitivity, specificity, and AUC were 0.91 (95% CI: 0.86-0.95), 0.94 (95% CI: 0.90-0.97), and 0.98 (95% CI: 0.96-0.99). For junior clinicians, the pooled sensitivity, specificity, and AUC were 0.76 (95% CI: 0.66-0.83), 0.75 (95% CI: 0.70-0.80), and 0.81 (95% CI: 0.77-0.84). For senior clinicians, the pooled sensitivity, specificity, and AUC were 0.81 (95% CI: 0.74-0.86), 0.89 (95% CI: 0.86-0.91), and 0.92 (95% CI: 0.90-0.94). CONCLUSIONS Endoscopy-based AI demonstrates higher diagnostic performance compared to both junior and senior endoscopists. However, the high heterogeneity among studies limits the strength of these findings, and further research with external validation datasets is necessary to confirm the results.
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Affiliation(s)
- Yiwen Jiang
- The First Clinical College, China Medical University, Shenyang, China
| | - Hengxu Yan
- The Second Clinical College, China Medical University, Shenyang, China
| | - Jiatong Cui
- The First Clinical College, China Medical University, Shenyang, China
| | - Kaiqiang Yang
- The First Clinical College, China Medical University, Shenyang, China
| | - Yue An
- Department of Gastroenterology, The First Hospital of China Medical University, Shenyang, Liaoning, China
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Zou PY, Zhu JR, Zhao Z, Mei H, Zhao JT, Sun WJ, Wang GH, Chen DF, Fan LL, Lan CH. Development and application of an artificial intelligence-assisted endoscopy system for diagnosis of Helicobacter pylori infection: a multicenter randomized controlled study. BMC Gastroenterol 2024; 24:335. [PMID: 39350033 PMCID: PMC11440712 DOI: 10.1186/s12876-024-03389-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Accepted: 08/27/2024] [Indexed: 10/04/2024] Open
Abstract
BACKGROUND The early diagnosis and treatment of Heliobacter pylori (H.pylori) gastrointestinal infection provide significant benefits to patients. We constructed a convolutional neural network (CNN) model based on an endoscopic system to diagnose H. pylori infection, and then examined the potential benefit of this model to endoscopists in their diagnosis of H. pylori infection. MATERIALS AND METHODS A CNN neural network system for endoscopic diagnosis of H.pylori infection was established by collecting 7377 endoscopic images from 639 patients. The accuracy, sensitivity, and specificity were determined. Then, a randomized controlled study was used to compare the accuracy of diagnosis of H. pylori infection by endoscopists who were assisted or unassisted by this CNN model. RESULTS The deep CNN model for diagnosis of H. pylori infection had an accuracy of 89.6%, a sensitivity of 90.9%, and a specificity of 88.9%. Relative to the group of endoscopists unassisted by AI, the AI-assisted group had better accuracy (92.8% [194/209; 95%CI: 89.3%, 96.4%] vs. 75.6% [158/209; 95%CI: 69.7%, 81.5%]), sensitivity (91.8% [67/73; 95%CI: 85.3%, 98.2%] vs. 78.6% [44/56; 95%CI: 67.5%, 89.7%]), and specificity (93.4% [127/136; 95%CI: 89.2%, 97.6%] vs. 74.5% [114/153; 95%CI: 67.5%, 81.5%]). All of these differences were statistically significant (P < 0.05). CONCLUSION Our AI-assisted system for diagnosis of H. pylori infection has significant ability for diagnostic, and can improve the accuracy of endoscopists in gastroscopic diagnosis. TRIAL REGISTRATION This study was approved by the Ethics Committee of Daping Hospital (10/07/2020) (No.89,2020) and was registered with the Chinese Clinical Trial Registration Center (02/09/2020) ( www.chictr.org.cn ; registration number: ChiCTR2000037801).
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Affiliation(s)
- Pei-Ying Zou
- Department of Gastroenterology, Chongqing Key Laboratory of Digestive, Malignancies, Daping Hospital, Army Medical University, Third Military Medical University), 10 Changjiang Branch Road, Chongqing, 400000, China
| | - Jian-Ru Zhu
- Department of Gastroenterology, Chongqing Key Laboratory of Digestive, Malignancies, Daping Hospital, Army Medical University, Third Military Medical University), 10 Changjiang Branch Road, Chongqing, 400000, China
| | - Zhe Zhao
- Department of Gastroenterology, Chongqing Key Laboratory of Digestive, Malignancies, Daping Hospital, Army Medical University, Third Military Medical University), 10 Changjiang Branch Road, Chongqing, 400000, China
| | - Hao Mei
- Department of Gastroenterology, Chongqing Key Laboratory of Digestive, Malignancies, Daping Hospital, Army Medical University, Third Military Medical University), 10 Changjiang Branch Road, Chongqing, 400000, China
| | - Jing-Tao Zhao
- Department of Gastroenterology, Chongqing Key Laboratory of Digestive, Malignancies, Daping Hospital, Army Medical University, Third Military Medical University), 10 Changjiang Branch Road, Chongqing, 400000, China
| | - Wen-Jing Sun
- Chongqing 13, People's Hospital, Chongqing, China
| | | | - Dong-Feng Chen
- Department of Gastroenterology, Chongqing Key Laboratory of Digestive, Malignancies, Daping Hospital, Army Medical University, Third Military Medical University), 10 Changjiang Branch Road, Chongqing, 400000, China
| | - Li-Lin Fan
- Chongqing Jiulongpo District Second People's Hospital, Chongqing, China
| | - Chun-Hui Lan
- Department of Gastroenterology, Chongqing Key Laboratory of Digestive, Malignancies, Daping Hospital, Army Medical University, Third Military Medical University), 10 Changjiang Branch Road, Chongqing, 400000, China.
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Hao W, Huang L, Li X, Jia H. Novel endoscopic techniques for the diagnosis of gastric Helicobacter pylori infection: a systematic review and network meta-analysis. Front Microbiol 2024; 15:1377541. [PMID: 39286347 PMCID: PMC11404567 DOI: 10.3389/fmicb.2024.1377541] [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: 01/27/2024] [Accepted: 08/02/2024] [Indexed: 09/19/2024] Open
Abstract
Objective This study aimed to conduct a network meta-analysis to compare the diagnostic efficacy of diverse novel endoscopic techniques for detecting gastric Helicobacter pylori infection. Methods From inception to August 2023, literature was systematically searched across Pubmed, Embase, and Web of Science databases. Cochrane's risk of bias tool assessed the methodological quality of the included studies. Data analysis was conducted using the R software, employing a ranking chart to determine the most effective diagnostic method comprehensively. Convergence analysis was performed to assess the stability of the results. Results The study encompassed 36 articles comprising 54 observational studies, investigating 14 novel endoscopic techniques and involving 7,230 patients diagnosed with gastric H. pylori infection. Compared with the gold standard, the comprehensive network meta-analysis revealed the superior diagnostic performance of two new endoscopic techniques, Magnifying blue laser imaging endoscopy (M-BLI) and high-definition magnifying endoscopy with i-scan (M-I-SCAN). Specifically, M-BLI demonstrated the highest ranking in both sensitivity (SE) and positive predictive value (PPV), ranking second in negative predictive value (NPV) and fourth in specificity (SP). M-I-SCAN secured the top position in NPV, third in SE and SP, and fifth in PPV. Conclusion After thoroughly analyzing the ranking chart, we conclude that M-BLI and M-I-SCAN stand out as the most suitable new endoscopic techniques for diagnosing gastric H. pylori infection. Systematic review registration https://inplasy.com/inplasy-2023-11-0051/, identifier INPLASY2023110051.
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Affiliation(s)
- Wenzhe Hao
- The Graduated School, Anhui University of Chinese Medicine, Hefei, China
| | - Lin Huang
- The Graduated School, Anhui University of Chinese Medicine, Hefei, China
| | - Xuejun Li
- Department of Gastroenterology, The Second Affiliated Hospital of Anhui University of Chinese Medicine, Hefei, China
| | - Hongyu Jia
- School of Public Health, Anhui Medical University, Hefei, China
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Li N, Yang J, Li X, Shi Y, Wang K. Accuracy of artificial intelligence-assisted endoscopy in the diagnosis of gastric intestinal metaplasia: A systematic review and meta-analysis. PLoS One 2024; 19:e0303421. [PMID: 38743709 PMCID: PMC11093381 DOI: 10.1371/journal.pone.0303421] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Accepted: 04/25/2024] [Indexed: 05/16/2024] Open
Abstract
BACKGROUND AND AIMS Gastric intestinal metaplasia is a precancerous disease, and a timely diagnosis is essential to delay or halt cancer progression. Artificial intelligence (AI) has found widespread application in the field of disease diagnosis. This study aimed to conduct a comprehensive evaluation of AI's diagnostic accuracy in detecting gastric intestinal metaplasia in endoscopy, compare it to endoscopists' ability, and explore the main factors affecting AI's performance. METHODS The study followed the PRISMA-DTA guidelines, and the PubMed, Embase, Web of Science, Cochrane, and IEEE Xplore databases were searched to include relevant studies published by October 2023. We extracted the key features and experimental data of each study and combined the sensitivity and specificity metrics by meta-analysis. We then compared the diagnostic ability of the AI versus the endoscopists using the same test data. RESULTS Twelve studies with 11,173 patients were included, demonstrating AI models' efficacy in diagnosing gastric intestinal metaplasia. The meta-analysis yielded a pooled sensitivity of 94% (95% confidence interval: 0.92-0.96) and specificity of 93% (95% confidence interval: 0.89-0.95). The combined area under the receiver operating characteristics curve was 0.97. The results of meta-regression and subgroup analysis showed that factors such as study design, endoscopy type, number of training images, and algorithm had a significant effect on the diagnostic performance of AI. The AI exhibited a higher diagnostic capacity than endoscopists (sensitivity: 95% vs. 79%). CONCLUSIONS AI-aided diagnosis of gastric intestinal metaplasia using endoscopy showed high performance and clinical diagnostic value. However, further prospective studies are required to validate these findings.
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Affiliation(s)
- Na Li
- Department of Gastroenterology, Zibo Central Hospital, Zibo, Shandong, China
| | - Jian Yang
- Department of Gastroenterology, Zibo Central Hospital, Zibo, Shandong, China
| | - Xiaodong Li
- Department of Gastroenterology, Zibo Central Hospital, Zibo, Shandong, China
| | - Yanting Shi
- Department of Gastroenterology, Zibo Central Hospital, Zibo, Shandong, China
| | - Kunhong Wang
- Department of Gastroenterology, Zibo Central Hospital, Zibo, Shandong, China
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Sridhar GR, Siva Prasad AV, Lakshmi G. Scope and caveats: Artificial intelligence in gastroenterology. Artif Intell Gastroenterol 2024; 5:91607. [DOI: 10.35712/aig.v5.i1.91607] [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: 01/23/2024] [Revised: 02/18/2024] [Accepted: 03/29/2024] [Indexed: 04/29/2024] Open
Abstract
The use of Artificial intelligence (AI) has evolved from its mid-20th century origins to playing a pivotal tool in modern medicine. It leverages digital data and computational hardware for diverse applications, including diagnosis, prognosis, and treatment responses in gastrointestinal and hepatic conditions. AI has had an impact in diagnostic techniques, particularly endoscopy, ultrasound, and histopathology. AI encompasses machine learning, natural language processing, and robotics, with machine learning being central. This involves sophisticated algorithms capable of managing complex datasets, far surpassing traditional statistical methods. These algorithms, both supervised and unsupervised, are integral for interpreting large datasets. In liver diseases, AI's non-invasive diagnostic applications, particularly in non-alcoholic fatty liver disease, and its role in characterizing hepatic lesions is promising. AI aids in distinguishing between normal and cirrhotic livers and improves the accuracy of lesion characterization and prognostication of hepatocellular carcinoma. AI enhances lesion identification during endoscopy, showing potential in the diagnosis and management of early-stage esophageal carcinoma. In peptic ulcer disease, AI technologies influence patient management strategies. AI is useful in colonoscopy, particularly in detecting smaller colonic polyps. However, its applicability in non-academic settings requires further validation. Addressing these issues is vital for harnessing the potential of AI. In conclusion, while AI offers transformative possibilities in gastroenterology, careful integration and balancing of technical possibilities with ethical and practical application, is essential for optimal use.
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Affiliation(s)
| | - Atmakuri V Siva Prasad
- Department of Gastroenterology, Institute of Gastroenterology, Visakhapatnam 530003, India
| | - Gumpeny Lakshmi
- Department of Internal Medicine, Gayatri Vidya Parishad Institute of Healthcare & Medical Technology, Visakhapatnam 530048, India
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Xia J, Jiang B, Pan J, Liao Z. Imaging of gastrointestinal endoscopy. TRANSPATHOLOGY 2024:171-183. [DOI: 10.1016/b978-0-323-95223-1.00026-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/03/2025]
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Jhang JY, Tsai YC, Hsu TC, Huang CR, Cheng HC, Sheu BS. Gastric Section Correlation Network for Gastric Precancerous Lesion Diagnosis. IEEE OPEN JOURNAL OF ENGINEERING IN MEDICINE AND BIOLOGY 2023; 5:434-442. [PMID: 38899022 PMCID: PMC11186652 DOI: 10.1109/ojemb.2023.3277219] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Revised: 03/24/2023] [Accepted: 05/10/2023] [Indexed: 06/21/2024] Open
Abstract
Goal: Diagnosing the corpus-predominant gastritis index (CGI) which is an early precancerous lesion in the stomach has been shown its effectiveness in identifying high gastric cancer risk patients for preventive healthcare. However, invasive biopsies and time-consuming pathological analysis are required for the CGI diagnosis. Methods: We propose a novel gastric section correlation network (GSCNet) for the CGI diagnosis from endoscopic images of three dominant gastric sections, the antrum, body and cardia. The proposed network consists of two dominant modules including the scaling feature fusion module and section correlation module. The front one aims to extract scaling fusion features which can effectively represent the mucosa under variant viewing angles and scale changes for each gastric section. The latter one aims to apply the medical prior knowledge with three section correlation losses to model the correlations of different gastric sections for the CGI diagnosis. Results: The proposed method outperforms competing deep learning methods and achieves high testing accuracy, sensitivity, and specificity of 0.957, 0.938 and 0.962, respectively. Conclusions: The proposed method is the first method to identify high gastric cancer risk patients with CGI from endoscopic images without invasive biopsies and time-consuming pathological analysis.
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Affiliation(s)
- Jyun-Yao Jhang
- Department of Computer Science and EngineeringNational Chung Hsing UniversityTaichung402Taiwan
| | - Yu-Ching Tsai
- Department of Internal MedicineTainan Hospital, Ministry of Health and WelfareTainan701Taiwan
- Department of Internal Medicine, National Cheng Kung University Hospital, College of MedicineNational Cheng Kung UniversityTainan701Taiwan
| | - Tzu-Chun Hsu
- Department of Computer Science and EngineeringNational Chung Hsing UniversityTaichung402Taiwan
| | - Chun-Rong Huang
- Cross College Elite Program, and Academy of Innovative Semiconductor and Sustainable ManufacturingNational Cheng Kung UniversityTainan701Taiwan
- Department of Computer Science and EngineeringNational Chung Hsing UniversityTaichung402Taiwan
| | - Hsiu-Chi Cheng
- Department of Internal Medicine, Institute of Clinical Medicine and Molecular MedicineNational Cheng Kung UniversityTainan701Taiwan
- Department of Internal MedicineTainan Hospital, Ministry of Health and WelfareTainan701Taiwan
| | - Bor-Shyang Sheu
- Institute of Clinical Medicine and Department of Internal Medicine, National Cheng Kung University HospitalNational Cheng Kung UniversityTainan701Taiwan
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Halvorsen N, Mori Y. Artificial intelligence and upper gastrointestinal endoscopy: what is the optimal study design? Minerva Surg 2023; 78:81-85. [PMID: 36843555 DOI: 10.23736/s2724-5691.22.09810-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/28/2023]
Abstract
Upper gastrointestinal cancers (i.e., esophageal and gastric cancers) are common cancers worldwide with high mortality and morbidity. Although there is no randomized controlled trial-based evidence, early detection with endoscopy is expected to positively affect prognosis and morbidity. However, endoscopic procedures are always accompanied by human-induced errors such as overlooking of neoplasia and cancers. Recently, the use of artificial intelligence (AI) during upper gastrointestinal endoscopy is catching attention because it is expected to reduce human-induced variability of the examination. This review article introduces the overview of the expectation and current status of the AI tools for upper gastrointestinal endoscopy and shares possible challenges and corresponding solutions with readers.
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Affiliation(s)
- Natalie Halvorsen
- Clinical Effectiveness Research Group, University Hospital of Oslo, University of Oslo, Oslo, Norway
| | - Yuichi Mori
- Clinical Effectiveness Research Group, University Hospital of Oslo, University of Oslo, Oslo, Norway - .,Department of Transplantation Medicine, University Hospital of Oslo, Oslo, Norway.,Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
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Luo Q, Yang H, Hu B. Application of artificial intelligence in the endoscopic diagnosis of early gastric cancer, atrophic gastritis, and Helicobacter pylori infection. J Dig Dis 2022; 23:666-674. [PMID: 36661411 DOI: 10.1111/1751-2980.13154] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Revised: 01/04/2023] [Accepted: 01/17/2023] [Indexed: 01/21/2023]
Abstract
Gastric cancer (GC) is one of the most serious health problems worldwide. Chronic atrophic gastritis (CAG) is most commonly caused by Helicobacter pylori (H. pylori) infection. Currently, endoscopic detection of early gastric cancer (EGC) and CAG remains challenging for endoscopists, and the diagnostic accuracy of H. pylori infection by endoscopy is approximately 70%. Artificial intelligence (AI) can assist endoscopic diagnosis including detection, prediction of depth of invasion, boundary delineation, and anatomical location of EGC, and has achievable diagnostic ability even comparable to experienced endoscopists. In this review we summarized various AI-assisted systems in the diagnosis of EGC, CAG, and H. pylori infection.
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Affiliation(s)
- Qi Luo
- Department of Gastroenterology, West China Hospital, Sichuan University, Chengdu, Sichuan Province, China
| | - Hang Yang
- Department of Gastroenterology, West China Hospital, Sichuan University, Chengdu, Sichuan Province, China
| | - Bing Hu
- Department of Gastroenterology, West China Hospital, Sichuan University, Chengdu, Sichuan Province, China
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Dilaghi E, Lahner E, Annibale B, Esposito G. Systematic review and meta-analysis: Artificial intelligence for the diagnosis of gastric precancerous lesions and Helicobacter pylori infection. Dig Liver Dis 2022; 54:1630-1638. [PMID: 35382973 DOI: 10.1016/j.dld.2022.03.007] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/29/2022] [Revised: 03/12/2022] [Accepted: 03/16/2022] [Indexed: 02/08/2023]
Abstract
BACKGROUND The endoscopic diagnosis of Helicobacter-pylori(H.pylori) infection and gastric precancerous lesions(GPL), namely atrophic-gastritis and intestinal-metaplasia, still remains challenging. Artificial intelligence(AI) may represent a powerful resource for the endoscopic recognition of these conditions. AIMS To explore the diagnostic performance(DP) of AI in the diagnosis of GPL and H.pylori infection. METHODS A systematic-review was performed by two independent authors up to September 2021. Inclusion criteria were studies focusing on the DP of AI-system in the diagnosis of GPL and H.pylori infection. The pooled accuracy of studies included was reported. RESULTS Overall, 128 studies were found (PubMed-Embase-Cochrane Library) and four and nine studies were finally included regarding GPL and H.pylori infection, respectively. The pooled-accuracy(random effects model) was 90.3%(95%CI 84.3-94.9) and 79.6%(95%CI 66.7-90.0) with a significant heterogeneity[I2=90.4%(95%CI 78.5-95.7);I2=97.9%(97.2-98.6)] for GPL and H.pylori infection, respectively. The Begg's-test showed a significant publication-bias(p = 0.0371) only among studies regarding H.pylori infection. The pooled-accuracy(random-effects-model) was similar considering only studies using CNN-model for the diagnosis of H.pylori infection: 74.1%[(95%CI 51.6-91.3);I2=98.9%(95%CI 98.5-99.3)], Begg's-test(p = 0.1416) did not show publication-bias. CONCLUSION AI-system seems to be a good resource for an easier diagnosis of GPL and H.pylori infection, showing a pooled-diagnostic-accuracy of 90% and 80%, respectively. However, considering the high heterogeneity, these promising data need an external validation by randomized control trials and prospective real-time studies.
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Affiliation(s)
- E Dilaghi
- Department of Medical-Surgical Sciences and Translational Medicine, Sant'Andrea Hospital, Sapienza University of Rome, Via di Grottarossa, Roma 1035 - 00189, Italy
| | - E Lahner
- Department of Medical-Surgical Sciences and Translational Medicine, Sant'Andrea Hospital, Sapienza University of Rome, Via di Grottarossa, Roma 1035 - 00189, Italy
| | - B Annibale
- Department of Medical-Surgical Sciences and Translational Medicine, Sant'Andrea Hospital, Sapienza University of Rome, Via di Grottarossa, Roma 1035 - 00189, Italy
| | - G Esposito
- Department of Medical-Surgical Sciences and Translational Medicine, Sant'Andrea Hospital, Sapienza University of Rome, Via di Grottarossa, Roma 1035 - 00189, Italy.
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Cao R, Tang L, Fang M, Zhong L, Wang S, Gong L, Li J, Dong D, Tian J. Artificial intelligence in gastric cancer: applications and challenges. Gastroenterol Rep (Oxf) 2022; 10:goac064. [PMID: 36457374 PMCID: PMC9707405 DOI: 10.1093/gastro/goac064] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Revised: 09/27/2022] [Accepted: 10/18/2022] [Indexed: 08/10/2023] Open
Abstract
Gastric cancer (GC) is one of the most common malignant tumors with high mortality. Accurate diagnosis and treatment decisions for GC rely heavily on human experts' careful judgments on medical images. However, the improvement of the accuracy is hindered by imaging conditions, limited experience, objective criteria, and inter-observer discrepancies. Recently, the developments of machine learning, especially deep-learning algorithms, have been facilitating computers to extract more information from data automatically. Researchers are exploring the far-reaching applications of artificial intelligence (AI) in various clinical practices, including GC. Herein, we aim to provide a broad framework to summarize current research on AI in GC. In the screening of GC, AI can identify precancerous diseases and assist in early cancer detection with endoscopic examination and pathological confirmation. In the diagnosis of GC, AI can support tumor-node-metastasis (TNM) staging and subtype classification. For treatment decisions, AI can help with surgical margin determination and prognosis prediction. Meanwhile, current approaches are challenged by data scarcity and poor interpretability. To tackle these problems, more regulated data, unified processing procedures, and advanced algorithms are urgently needed to build more accurate and robust AI models for GC.
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Affiliation(s)
| | | | - Mengjie Fang
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, P. R. China
- CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, the State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, P. R. China
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Engineering Medicine, Beihang University, Beijing, P. R. China
| | - Lianzhen Zhong
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, P. R. China
- CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, the State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, P. R. China
| | - Siwen Wang
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, P. R. China
- CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, the State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, P. R. China
| | - Lixin Gong
- College of Medicine and Biological Information Engineering School, Northeastern University, Shenyang, Liaoning, P. R. China
| | - Jiazheng Li
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Radiology Department, Peking University Cancer Hospital & Institute, Beijing, P. R. China
| | - Di Dong
- Corresponding authors. Di Dong, CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, P. R. China. Tel: +86-13811833760; ; Jie Tian, Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Engineering Medicine, Beihang University, Beijing, P. R. China. Tel: +86-10-82618465;
| | - Jie Tian
- Corresponding authors. Di Dong, CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, P. R. China. Tel: +86-13811833760; ; Jie Tian, Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Engineering Medicine, Beihang University, Beijing, P. R. China. Tel: +86-10-82618465;
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Chen HY, Ge P, Liu JY, Qu JL, Bao F, Xu CM, Chen HL, Shang D, Zhang GX. Artificial intelligence: Emerging player in the diagnosis and treatment of digestive disease. World J Gastroenterol 2022; 28:2152-2162. [PMID: 35721881 PMCID: PMC9157617 DOI: 10.3748/wjg.v28.i20.2152] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Revised: 11/24/2021] [Accepted: 04/24/2022] [Indexed: 02/06/2023] Open
Abstract
Given the breakthroughs in key technologies, such as image recognition, deep learning and neural networks, artificial intelligence (AI) continues to be increasingly developed, leading to closer and deeper integration with an increasingly data-, knowledge- and brain labor-intensive medical industry. As society continues to advance and individuals become more aware of their health needs, the problems associated with the aging of the population are receiving increasing attention, and there is an urgent demand for improving medical technology, prolonging human life and enhancing health. Digestive system diseases are the most common clinical diseases and are characterized by complex clinical manifestations and a general lack of obvious symptoms in the early stage. Such diseases are very difficult to diagnose and treat. In recent years, the incidence of diseases of the digestive system has increased. As AI applications in the field of health care continue to be developed, AI has begun playing an important role in the diagnosis and treatment of diseases of the digestive system. In this paper, the application of AI in assisted diagnosis and the application and prospects of AI in malignant and benign digestive system diseases are reviewed.
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Affiliation(s)
- Hai-Yang Chen
- Laboratory of Integrative Medicine, The First Affiliated Hospital of Dalian Medical University, Dalian 116011, Liaoning Province, China
- Department of General Surgery, Pancreatic-Biliary Center, The First Affiliated Hospital of Dalian Medical University, Dalian 116011, Liaoning Province, China
| | - Peng Ge
- Laboratory of Integrative Medicine, The First Affiliated Hospital of Dalian Medical University, Dalian 116011, Liaoning Province, China
- Department of General Surgery, Pancreatic-Biliary Center, The First Affiliated Hospital of Dalian Medical University, Dalian 116011, Liaoning Province, China
| | - Jia-Yue Liu
- Laboratory of Integrative Medicine, The First Affiliated Hospital of Dalian Medical University, Dalian 116011, Liaoning Province, China
- Department of General Surgery, Pancreatic-Biliary Center, The First Affiliated Hospital of Dalian Medical University, Dalian 116011, Liaoning Province, China
| | - Jia-Lin Qu
- Laboratory of Integrative Medicine, The First Affiliated Hospital of Dalian Medical University, Dalian 116011, Liaoning Province, China
- Institute (College) of Integrative Medicine, Dalian Medical University, Dalian 116044, Liaoning Province, China
| | - Fang Bao
- Laboratory of Integrative Medicine, The First Affiliated Hospital of Dalian Medical University, Dalian 116011, Liaoning Province, China
- Department of General Surgery, Pancreatic-Biliary Center, The First Affiliated Hospital of Dalian Medical University, Dalian 116011, Liaoning Province, China
| | - Cai-Ming Xu
- Laboratory of Integrative Medicine, The First Affiliated Hospital of Dalian Medical University, Dalian 116011, Liaoning Province, China
- Department of General Surgery, Pancreatic-Biliary Center, The First Affiliated Hospital of Dalian Medical University, Dalian 116011, Liaoning Province, China
- Institute (College) of Integrative Medicine, Dalian Medical University, Dalian 116044, Liaoning Province, China
| | - Hai-Long Chen
- Laboratory of Integrative Medicine, The First Affiliated Hospital of Dalian Medical University, Dalian 116011, Liaoning Province, China
- Department of General Surgery, Pancreatic-Biliary Center, The First Affiliated Hospital of Dalian Medical University, Dalian 116011, Liaoning Province, China
- Institute (College) of Integrative Medicine, Dalian Medical University, Dalian 116044, Liaoning Province, China
| | - Dong Shang
- Laboratory of Integrative Medicine, The First Affiliated Hospital of Dalian Medical University, Dalian 116011, Liaoning Province, China
- Department of General Surgery, Pancreatic-Biliary Center, The First Affiliated Hospital of Dalian Medical University, Dalian 116011, Liaoning Province, China
- Institute (College) of Integrative Medicine, Dalian Medical University, Dalian 116044, Liaoning Province, China
| | - Gui-Xin Zhang
- Laboratory of Integrative Medicine, The First Affiliated Hospital of Dalian Medical University, Dalian 116011, Liaoning Province, China
- Department of General Surgery, Pancreatic-Biliary Center, The First Affiliated Hospital of Dalian Medical University, Dalian 116011, Liaoning Province, China
- Institute (College) of Integrative Medicine, Dalian Medical University, Dalian 116044, Liaoning Province, China
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Penrice DD, Rattan P, Simonetto DA. Artificial Intelligence and the Future of Gastroenterology and Hepatology. GASTRO HEP ADVANCES 2022; 1:581-595. [PMID: 39132066 PMCID: PMC11307848 DOI: 10.1016/j.gastha.2022.02.025] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Accepted: 02/22/2022] [Indexed: 08/13/2024]
Abstract
The integration of artificial intelligence (AI) into gastroenterology and hepatology (GI) will inevitably transform the practice of GI in the coming decade. While the application of AI in health care is not new, advancements are occurring rapidly, and the future landscape of AI is beginning to come into focus. From endoscopic assistance via computer vision technology to the predictive capabilities of the vast information contained in the electronic health records, AI promises to optimize and expedite clinical and procedural practice and research in GI. The extensive body of literature already available on AI applications in gastroenterology may seem daunting at first; however, this review aims to provide a breakdown of the key studies conducted thus far and demonstrate the many potential ways this technology may impact the field. This review will also take a look into the future and imagine how GI can be transformed over the coming years, as well as potential limitations and pitfalls that must be overcome to realize this future.
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Affiliation(s)
- Daniel D. Penrice
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, Minnesota
| | - Puru Rattan
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, Minnesota
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15
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Nagao S, Tani Y, Shibata J, Tsuji Y, Tada T, Ishihara R, Fujishiro M. Implementation of artificial intelligence in upper gastrointestinal endoscopy. DEN OPEN 2022; 2:e72. [PMID: 35873509 PMCID: PMC9302271 DOI: 10.1002/deo2.72] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 10/11/2021] [Accepted: 10/16/2021] [Indexed: 12/24/2022]
Abstract
The application of artificial intelligence (AI) using deep learning has significantly expanded in the field of esophagogastric endoscopy. Recent studies have shown promising results in detecting and differentiating early gastric cancer using AI tools built using white light, magnified, or image-enhanced endoscopic images. Some studies have reported the use of AI tools to predict the depth of early gastric cancer based on endoscopic images. Similarly, studies based on using AI for detecting early esophageal cancer have also been reported, with an accuracy comparable to that of endoscopy specialists. Moreover, an AI system, developed to diagnose pharyngeal cancer, has shown promising performance with high sensitivity. These reports suggest that, if introduced for regular use in clinical settings, AI systems can significantly reduce the burden on physicians. This review summarizes the current status of AI applications in the upper gastrointestinal tract and presents directions for clinical practice implementation and future research.
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Affiliation(s)
- Sayaka Nagao
- Department of GastroenterologyGraduate School of Medicinethe University of TokyoTokyoJapan
- Department of Endoscopy and Endoscopic SurgeryGraduate School of Medicinethe University of TokyoTokyoJapan
| | - Yasuhiro Tani
- Department of Gastrointestinal OncologyOsaka International Cancer InstituteOsakaJapan
| | - Junichi Shibata
- Tada Tomohiro Institute of Gastroenterology and ProctologySaitamaJapan
| | - Yosuke Tsuji
- Department of GastroenterologyGraduate School of Medicinethe University of TokyoTokyoJapan
| | - Tomohiro Tada
- Tada Tomohiro Institute of Gastroenterology and ProctologySaitamaJapan
- AI Medical Service Inc.TokyoJapan
- Department of Surgical OncologyGraduate School of Medicinethe University of TokyoTokyoJapan
| | - Ryu Ishihara
- Department of Gastrointestinal OncologyOsaka International Cancer InstituteOsakaJapan
| | - Mitsuhiro Fujishiro
- Department of GastroenterologyGraduate School of Medicinethe University of TokyoTokyoJapan
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16
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Zhao PY, Han K, Yao RQ, Ren C, Du XH. Application Status and Prospects of Artificial Intelligence in Peptic Ulcers. Front Surg 2022; 9:894775. [PMID: 35784921 PMCID: PMC9244632 DOI: 10.3389/fsurg.2022.894775] [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: 03/12/2022] [Accepted: 05/31/2022] [Indexed: 02/05/2023] Open
Abstract
Peptic ulcer (PU) is a common and frequently occurring disease. Although PU seriously threatens the lives and health of global residents, the applications of artificial intelligence (AI) have strongly promoted diversification and modernization in the diagnosis and treatment of PU. This minireview elaborates on the research progress of AI in the field of PU, from PU's pathogenic factor Helicobacter pylori (Hp) infection, diagnosis and differential diagnosis, to its management and complications (bleeding, obstruction, perforation and canceration). Finally, the challenges and prospects of AI application in PU are prospected and expounded. With the in-depth understanding of modern medical technology, AI remains a promising option in the management of PU patients and plays a more indispensable role. How to realize the robustness, versatility and diversity of multifunctional AI systems in PU and conduct multicenter prospective clinical research as soon as possible are the top priorities in the future.
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Affiliation(s)
- Peng-yue Zhao
- Department of General Surgery, First Medical Center of the Chinese PLA General Hospital, Beijing, China
| | - Ke Han
- Department of Gastroenterology, First Medical Center of the Chinese PLA General Hospital, Beijing, China
| | - Ren-qi Yao
- Translational Medicine Research Center, Medical Innovation Research Division and Fourth Medical Center of the Chinese PLA General Hospital, Beijing, China
- Correspondence: Xiao-hui Du Chao Ren Ren-qi Yao
| | - Chao Ren
- Department of Pulmonary and Critical Care Medicine, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China
- Correspondence: Xiao-hui Du Chao Ren Ren-qi Yao
| | - Xiao-hui Du
- Department of General Surgery, First Medical Center of the Chinese PLA General Hospital, Beijing, China
- Correspondence: Xiao-hui Du Chao Ren Ren-qi Yao
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17
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Nien CM, Yang EH, Chang WL, Cheng HC, Huang CR. Criss-Cross Attention Based Multi-level Fusion Network for Gastric Intestinal Metaplasia Segmentation. LECTURE NOTES IN COMPUTER SCIENCE 2022:13-23. [DOI: 10.1007/978-3-031-21083-9_2] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/06/2025]
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18
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Visaggi P, de Bortoli N, Barberio B, Savarino V, Oleas R, Rosi EM, Marchi S, Ribolsi M, Savarino E. Artificial Intelligence in the Diagnosis of Upper Gastrointestinal Diseases. J Clin Gastroenterol 2022; 56:23-35. [PMID: 34739406 PMCID: PMC9988236 DOI: 10.1097/mcg.0000000000001629] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Artificial intelligence (AI) has enormous potential to support clinical routine workflows and therefore is gaining increasing popularity among medical professionals. In the field of gastroenterology, investigations on AI and computer-aided diagnosis (CAD) systems have mainly focused on the lower gastrointestinal (GI) tract. However, numerous CAD tools have been tested also in upper GI disorders showing encouraging results. The main application of AI in the upper GI tract is endoscopy; however, the need to analyze increasing loads of numerical and categorical data in short times has pushed researchers to investigate applications of AI systems in other upper GI settings, including gastroesophageal reflux disease, eosinophilic esophagitis, and motility disorders. AI and CAD systems will be increasingly incorporated into daily clinical practice in the coming years, thus at least basic notions will be soon required among physicians. For noninsiders, the working principles and potential of AI may be as fascinating as obscure. Accordingly, we reviewed systematic reviews, meta-analyses, randomized controlled trials, and original research articles regarding the performance of AI in the diagnosis of both malignant and benign esophageal and gastric diseases, also discussing essential characteristics of AI.
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Affiliation(s)
- Pierfrancesco Visaggi
- Gastroenterology Unit, Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa
| | - Nicola de Bortoli
- Gastroenterology Unit, Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa
| | - Brigida Barberio
- Department of Surgery, Oncology, and Gastroenterology, Division of Gastroenterology, University of Padua, Padua
| | - Vincenzo Savarino
- Gastroenterology Unit, Department of Internal Medicine, University of Genoa, Genoa
| | - Roberto Oleas
- Ecuadorean Institute of Digestive Diseases, Guayaquil, Ecuador
| | - Emma M. Rosi
- Gastroenterology Unit, Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa
| | - Santino Marchi
- Gastroenterology Unit, Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa
| | - Mentore Ribolsi
- Department of Digestive Diseases, Campus Bio Medico University of Rome, Roma, Italy
| | - Edoardo Savarino
- Department of Surgery, Oncology, and Gastroenterology, Division of Gastroenterology, University of Padua, Padua
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19
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Bang CS. Artificial Intelligence in the Analysis of Upper Gastrointestinal Disorders. THE KOREAN JOURNAL OF HELICOBACTER AND UPPER GASTROINTESTINAL RESEARCH 2021. [DOI: 10.7704/kjhugr.2021.0030] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
In the past, conventional machine learning was applied to analyze tabulated medical data while deep learning was applied to study afflictions such as gastrointestinal disorders. Neural networks were used to detect, classify, and delineate various images of lesions because the local feature selection and optimization of the deep learning model enabled accurate image analysis. With the accumulation of medical records, the evolution of computational power and graphics processing units, and the widespread use of open-source libraries in large-scale machine learning processes, medical artificial intelligence (AI) is overcoming its limitations. While early studies prioritized the automatic diagnosis of cancer or pre-cancerous lesions, the current expanded scope of AI includes benign lesions, quality control, and machine learning analysis of big data. However, the limited commercialization of medical AI and the need to justify its application in each field of research are restricting factors. Modeling assumes that observations follow certain statistical rules, and external validation checks whether assumption is correct or generalizable. Therefore, unused data are essential in the training or internal testing process to validate the performance of the established AI models. This article summarizes the studies on the application of AI models in upper gastrointestinal disorders. The current limitations and the perspectives on future development have also been discussed.
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20
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Goyal H, Sherazi SAA, Mann R, Gandhi Z, Perisetti A, Aziz M, Chandan S, Kopel J, Tharian B, Sharma N, Thosani N. Scope of Artificial Intelligence in Gastrointestinal Oncology. Cancers (Basel) 2021; 13:5494. [PMID: 34771658 PMCID: PMC8582733 DOI: 10.3390/cancers13215494] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Accepted: 10/27/2021] [Indexed: 12/12/2022] Open
Abstract
Gastrointestinal cancers are among the leading causes of death worldwide, with over 2.8 million deaths annually. Over the last few decades, advancements in artificial intelligence technologies have led to their application in medicine. The use of artificial intelligence in endoscopic procedures is a significant breakthrough in modern medicine. Currently, the diagnosis of various gastrointestinal cancer relies on the manual interpretation of radiographic images by radiologists and various endoscopic images by endoscopists. This can lead to diagnostic variabilities as it requires concentration and clinical experience in the field. Artificial intelligence using machine or deep learning algorithms can provide automatic and accurate image analysis and thus assist in diagnosis. In the field of gastroenterology, the application of artificial intelligence can be vast from diagnosis, predicting tumor histology, polyp characterization, metastatic potential, prognosis, and treatment response. It can also provide accurate prediction models to determine the need for intervention with computer-aided diagnosis. The number of research studies on artificial intelligence in gastrointestinal cancer has been increasing rapidly over the last decade due to immense interest in the field. This review aims to review the impact, limitations, and future potentials of artificial intelligence in screening, diagnosis, tumor staging, treatment modalities, and prediction models for the prognosis of various gastrointestinal cancers.
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Affiliation(s)
- Hemant Goyal
- Department of Internal Medicine, The Wright Center for Graduate Medical Education, 501 S. Washington Avenue, Scranton, PA 18505, USA
| | - Syed A. A. Sherazi
- Department of Medicine, John H Stroger Jr Hospital of Cook County, 1950 W Polk St, Chicago, IL 60612, USA;
| | - Rupinder Mann
- Department of Medicine, Saint Agnes Medical Center, 1303 E. Herndon Ave, Fresno, CA 93720, USA;
| | - Zainab Gandhi
- Department of Medicine, Geisinger Wyoming Valley Medical Center, 1000 E Mountain Dr, Wilkes-Barre, PA 18711, USA;
| | - Abhilash Perisetti
- Division of Interventional Oncology & Surgical Endoscopy (IOSE), Parkview Cancer Institute, 11050 Parkview Circle, Fort Wayne, IN 46845, USA; (A.P.); (N.S.)
| | - Muhammad Aziz
- Department of Gastroenterology and Hepatology, University of Toledo Medical Center, 3000 Arlington Avenue, Toledo, OH 43614, USA;
| | - Saurabh Chandan
- Division of Gastroenterology and Hepatology, CHI Health Creighton University Medical Center, 7500 Mercy Rd, Omaha, NE 68124, USA;
| | - Jonathan Kopel
- Department of Medicine, Texas Tech University Health Sciences Center, 3601 4th St, Lubbock, TX 79430, USA;
| | - Benjamin Tharian
- Department of Gastroenterology and Hepatology, The University of Arkansas for Medical Sciences, 4301 W Markham St, Little Rock, AR 72205, USA;
| | - Neil Sharma
- Division of Interventional Oncology & Surgical Endoscopy (IOSE), Parkview Cancer Institute, 11050 Parkview Circle, Fort Wayne, IN 46845, USA; (A.P.); (N.S.)
| | - Nirav Thosani
- Division of Gastroenterology, Hepatology & Nutrition, McGovern Medical School, UTHealth, 6410 Fannin, St #1014, Houston, TX 77030, USA;
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21
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Pecere S, Milluzzo SM, Esposito G, Dilaghi E, Telese A, Eusebi LH. Applications of Artificial Intelligence for the Diagnosis of Gastrointestinal Diseases. Diagnostics (Basel) 2021; 11:diagnostics11091575. [PMID: 34573917 PMCID: PMC8469485 DOI: 10.3390/diagnostics11091575] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Revised: 08/20/2021] [Accepted: 08/23/2021] [Indexed: 12/16/2022] Open
Abstract
The development of convolutional neural networks has achieved impressive advances of machine learning in recent years, leading to an increasing use of artificial intelligence (AI) in the field of gastrointestinal (GI) diseases. AI networks have been trained to differentiate benign from malignant lesions, analyze endoscopic and radiological GI images, and assess histological diagnoses, obtaining excellent results and high overall diagnostic accuracy. Nevertheless, there data are lacking on side effects of AI in the gastroenterology field, and high-quality studies comparing the performance of AI networks to health care professionals are still limited. Thus, large, controlled trials in real-time clinical settings are warranted to assess the role of AI in daily clinical practice. This narrative review gives an overview of some of the most relevant potential applications of AI for gastrointestinal diseases, highlighting advantages and main limitations and providing considerations for future development.
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Affiliation(s)
- Silvia Pecere
- Digestive Endoscopy Unit, Fondazione Policlinico Universitario A. Gemelli IRCCS, Università Cattolica del Sacro Cuore, 00135 Rome, Italy;
- Center for Endoscopic Research Therapeutics and Training (CERTT), Catholic University, 00168 Rome, Italy
- Correspondence: (S.P.); (L.H.E.)
| | - Sebastian Manuel Milluzzo
- Digestive Endoscopy Unit, Fondazione Policlinico Universitario A. Gemelli IRCCS, Università Cattolica del Sacro Cuore, 00135 Rome, Italy;
- Fondazione Poliambulanza Istituto Ospedaliero, 25121 Brescia, Italy
| | - Gianluca Esposito
- Department of Medical-Surgical Sciences and Translational Medicine, Sant’Andrea Hospital, Sapienza University of Rome, 00168 Rome, Italy; (G.E.); (E.D.)
| | - Emanuele Dilaghi
- Department of Medical-Surgical Sciences and Translational Medicine, Sant’Andrea Hospital, Sapienza University of Rome, 00168 Rome, Italy; (G.E.); (E.D.)
| | - Andrea Telese
- Department of Gastroenterology, University College London Hospital (UCLH), London NW1 2AF, UK;
| | - Leonardo Henry Eusebi
- Division of Gastroenterology and Endoscopy, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40121 Bologna, Italy
- Department of Medical and Surgical Sciences, University of Bologna, 40121 Bologna, Italy
- Correspondence: (S.P.); (L.H.E.)
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22
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Tokat M, van Tilburg L, Koch AD, Spaander MCW. Artificial Intelligence in Upper Gastrointestinal Endoscopy. Dig Dis 2021; 40:395-408. [PMID: 34348267 DOI: 10.1159/000518232] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Accepted: 06/23/2021] [Indexed: 02/02/2023]
Abstract
BACKGROUND Over the past decade, several artificial intelligence (AI) systems are developed to assist in endoscopic assessment of (pre-)cancerous lesions of the gastrointestinal (GI) tract. In this review, we aimed to provide an overview of the possible indications of AI technology in upper GI endoscopy and hypothesize about potential challenges for its use in clinical practice. SUMMARY Application of AI in upper GI endoscopy has been investigated for several indications: (1) detection, characterization, and delineation of esophageal and gastric cancer (GC) and their premalignant conditions; (2) prediction of tumor invasion; and (3) detection of Helicobacter pylori. AI systems show promising results with an accuracy of up to 99% for the detection of superficial and advanced upper GI cancers. AI outperformed trainee and experienced endoscopists for the detection of esophageal lesions and atrophic gastritis. For GC, AI outperformed mid-level and trainee endoscopists but not expert endoscopists. KEY MESSAGES Application of artificial intelligence (AI) in upper gastrointestinal endoscopy may improve early diagnosis of esophageal and gastric cancer and may enable endoscopists to better identify patients eligible for endoscopic resection. The benefit of AI on the quality of upper endoscopy still needs to be demonstrated, while prospective trials are needed to confirm accuracy and feasibility during real-time daily endoscopy.
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Affiliation(s)
- Meltem Tokat
- Department of Gastroenterology and Hepatology, Erasmus MC Cancer Institute, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Laurelle van Tilburg
- Department of Gastroenterology and Hepatology, Erasmus MC Cancer Institute, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Arjun D Koch
- Department of Gastroenterology and Hepatology, Erasmus MC Cancer Institute, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Manon C W Spaander
- Department of Gastroenterology and Hepatology, Erasmus MC Cancer Institute, University Medical Center Rotterdam, Rotterdam, The Netherlands
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Lu YF, Lyu B. Current situation and prospect of artificial intelligence application in endoscopic diagnosis of Helicobacter pylori infection. Artif Intell Gastrointest Endosc 2021; 2:50-62. [DOI: 10.37126/aige.v2.i3.50] [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: 05/02/2021] [Revised: 06/01/2021] [Accepted: 06/18/2021] [Indexed: 02/06/2023] Open
Abstract
With the appearance and prevalence of deep learning, artificial intelligence (AI) has been broadly studied and made great progress in various fields of medicine, including gastroenterology. Helicobacter pylori (H. pylori), closely associated with various digestive and extradigestive diseases, has a high infection rate worldwide. Endoscopic surveillance can evaluate H. pylori infection situations and predict the risk of gastric cancer, but there is no objective diagnostic criteria to eliminate the differences between operators. The computer-aided diagnosis system based on AI technology has demonstrated excellent performance for the diagnosis of H. pylori infection, which is superior to novice endoscopists and similar to skilled. Compared with the visual diagnosis of H. pylori infection by endoscopists, AI possesses voluminous advantages: High accuracy, high efficiency, high quality control, high objectivity, and high-effect teaching. This review summarizes the previous and recent studies on AI-assisted diagnosis of H. pylori infection, points out the limitations, and puts forward prospect for future research.
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Affiliation(s)
- Yi-Fan Lu
- Department of Gastroenterology, The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou 310006, Zhejiang Province, China
| | - Bin Lyu
- Department of Gastroenterology, The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou 310006, Zhejiang Province, China
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Hsiao YJ, Wen YC, Lai WY, Lin YY, Yang YP, Chien Y, Yarmishyn AA, Hwang DK, Lin TC, Chang YC, Lin TY, Chang KJ, Chiou SH, Jheng YC. Application of artificial intelligence-driven endoscopic screening and diagnosis of gastric cancer. World J Gastroenterol 2021; 27:2979-2993. [PMID: 34168402 PMCID: PMC8192292 DOI: 10.3748/wjg.v27.i22.2979] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/03/2021] [Revised: 03/10/2021] [Accepted: 04/22/2021] [Indexed: 02/06/2023] Open
Abstract
The landscape of gastrointestinal endoscopy continues to evolve as new technologies and techniques become available. The advent of image-enhanced and magnifying endoscopies has highlighted the step toward perfecting endoscopic screening and diagnosis of gastric lesions. Simultaneously, with the development of convolutional neural network, artificial intelligence (AI) has made unprecedented breakthroughs in medical imaging, including the ongoing trials of computer-aided detection of colorectal polyps and gastrointestinal bleeding. In the past demi-decade, applications of AI systems in gastric cancer have also emerged. With AI's efficient computational power and learning capacities, endoscopists can improve their diagnostic accuracies and avoid the missing or mischaracterization of gastric neoplastic changes. So far, several AI systems that incorporated both traditional and novel endoscopy technologies have been developed for various purposes, with most systems achieving an accuracy of more than 80%. However, their feasibility, effectiveness, and safety in clinical practice remain to be seen as there have been no clinical trials yet. Nonetheless, AI-assisted endoscopies shed light on more accurate and sensitive ways for early detection, treatment guidance and prognosis prediction of gastric lesions. This review summarizes the current status of various AI applications in gastric cancer and pinpoints directions for future research and clinical practice implementation from a clinical perspective.
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Affiliation(s)
- Yu-Jer Hsiao
- Department of Medical Research, Taipei Veterans General Hospital, Taipei 112201, Taiwan
- School of Medicine, National Yang-Ming Chiao Tung University, Taipei 112304, Taiwan
| | - Yuan-Chih Wen
- School of Medicine, National Yang-Ming Chiao Tung University, Taipei 112304, Taiwan
- Department of Medical Education, Taipei Veterans General Hospital, Taipei 112201, Taiwan
| | - Wei-Yi Lai
- Department of Medical Research, Taipei Veterans General Hospital, Taipei 112201, Taiwan
- School of Medicine, National Yang-Ming Chiao Tung University, Taipei 112304, Taiwan
- Institute of Pharmacology, National Yang-Ming Chiao Tung University, Taipei 112304, Taiwan
| | - Yi-Ying Lin
- Department of Medical Research, Taipei Veterans General Hospital, Taipei 112201, Taiwan
- School of Medicine, National Yang-Ming Chiao Tung University, Taipei 112304, Taiwan
- Institute of Pharmacology, National Yang-Ming Chiao Tung University, Taipei 112304, Taiwan
| | - Yi-Ping Yang
- Department of Medical Research, Taipei Veterans General Hospital, Taipei 112201, Taiwan
- School of Medicine, National Yang-Ming Chiao Tung University, Taipei 112304, Taiwan
- Department of Internal Medicine, Taipei Veterans General Hospital, Taipei 112201, Taiwan
- Critical Center, Taipei Veterans General Hospital, Taipei 112201, Taiwan
| | - Yueh Chien
- Department of Medical Research, Taipei Veterans General Hospital, Taipei 112201, Taiwan
| | | | - De-Kuang Hwang
- Department of Medical Research, Taipei Veterans General Hospital, Taipei 112201, Taiwan
- School of Medicine, National Yang-Ming Chiao Tung University, Taipei 112304, Taiwan
- Department of Ophthalmology, Taipei Veterans General Hospital, Taipei 112201, Taiwan
- Institute of Clinical Medicine, National Yang-Ming Chiao Tung University, Taipei 112201, Taiwan
| | - Tai-Chi Lin
- Department of Medical Research, Taipei Veterans General Hospital, Taipei 112201, Taiwan
- School of Medicine, National Yang-Ming Chiao Tung University, Taipei 112304, Taiwan
- Department of Ophthalmology, Taipei Veterans General Hospital, Taipei 112201, Taiwan
- Institute of Clinical Medicine, National Yang-Ming Chiao Tung University, Taipei 112201, Taiwan
| | - Yun-Chia Chang
- Department of Medical Research, Taipei Veterans General Hospital, Taipei 112201, Taiwan
- Department of Ophthalmology, Taipei Veterans General Hospital, Taipei 112201, Taiwan
| | - Ting-Yi Lin
- Department of Medical Research, Taipei Veterans General Hospital, Taipei 112201, Taiwan
- Department of Medicine, Kaohsiung Medical University, Kaohsiung 80708, Taiwan
| | - Kao-Jung Chang
- Department of Medical Research, Taipei Veterans General Hospital, Taipei 112201, Taiwan
- School of Medicine, National Yang-Ming Chiao Tung University, Taipei 112304, Taiwan
- Institute of Clinical Medicine, National Yang-Ming Chiao Tung University, Taipei 112304, Taiwan
| | - Shih-Hwa Chiou
- Department of Medical Research, Taipei Veterans General Hospital, Taipei 112201, Taiwan
- Institute of Pharmacology, National Yang-Ming Chiao Tung University, Taipei 112304, Taiwan
- Institute of Clinical Medicine, National Yang-Ming Chiao Tung University, Taipei 112304, Taiwan
| | - Ying-Chun Jheng
- Department of Medical Research, Taipei Veterans General Hospital, Taipei 112201, Taiwan
- Big Data Center, Taipei Veterans General Hospital, Taipei 112201, Taiwan
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25
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Bang CS. [Deep Learning in Upper Gastrointestinal Disorders: Status and Future Perspectives]. THE KOREAN JOURNAL OF GASTROENTEROLOGY 2021; 75:120-131. [PMID: 32209800 DOI: 10.4166/kjg.2020.75.3.120] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/13/2020] [Revised: 03/01/2020] [Accepted: 03/02/2020] [Indexed: 12/18/2022]
Abstract
Artificial intelligence using deep learning has been applied to gastrointestinal disorders for the detection, classification, and delineation of various lesion images. With the accumulation of enormous medical records, the evolution of computation power with graphic processing units, and the widespread use of open-source libraries in large-scale machine learning processes, medical artificial intelligence is overcoming its traditional limitations. This paper explains the basic concepts of deep learning model establishment and summarizes previous studies on upper gastrointestinal disorders. The limitations and perspectives on future development are also discussed.
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Affiliation(s)
- Chang Seok Bang
- Department of Internal Medicine, Hallym University College of Medicine, Chuncheon, Korea
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26
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Lazăr DC, Avram MF, Faur AC, Romoşan I, Goldiş A. The role of computer-assisted systems for upper-endoscopy quality monitoring and assessment of gastric lesions. Gastroenterol Rep (Oxf) 2021; 9:185-204. [PMID: 34316369 PMCID: PMC8309682 DOI: 10.1093/gastro/goab008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/26/2020] [Revised: 12/05/2020] [Accepted: 12/20/2020] [Indexed: 12/24/2022] Open
Abstract
This article analyses the literature regarding the value of computer-assisted systems in esogastroduodenoscopy-quality monitoring and the assessment of gastric lesions. Current data show promising results in upper-endoscopy quality control and a satisfactory detection accuracy of gastric premalignant and malignant lesions, similar or even exceeding that of experienced endoscopists. Moreover, artificial systems enable the decision for the best treatment strategies in gastric-cancer patient care, namely endoscopic vs surgical resection according to tumor depth. In so doing, unnecessary surgical interventions would be avoided whilst providing a better quality of life and prognosis for these patients. All these performance data have been revealed by numerous studies using different artificial intelligence (AI) algorithms in addition to white-light endoscopy or novel endoscopic techniques that are available in expert endoscopy centers. It is expected that ongoing clinical trials involving AI and the embedding of computer-assisted diagnosis systems into endoscopic devices will enable real-life implementation of AI endoscopic systems in the near future and at the same time will help to overcome the current limits of the computer-assisted systems leading to an improvement in performance. These benefits should lead to better diagnostic and treatment strategies for gastric-cancer patients. Furthermore, the incorporation of AI algorithms in endoscopic tools along with the development of large electronic databases containing endoscopic images might help in upper-endoscopy assistance and could be used for telemedicine purposes and second opinion for difficult cases.
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Affiliation(s)
- Daniela Cornelia Lazăr
- Department V of Internal Medicine I, Discipline of Internal Medicine IV, “Victor Babeș” University of Medicine and Pharmacy Timișoara, Romania,Timișoara, Romania
| | - Mihaela Flavia Avram
- Department of Surgery X, 1st Surgery Discipline, “Victor Babeș” University of Medicine and Pharmacy Timișoara, Romania, Timișoara, Romania
| | - Alexandra Corina Faur
- Department I, Discipline of Anatomy and Embriology, “Victor Babeș” University of Medicine and Pharmacy Timișoara, Romania, Timișoara, Romania
| | - Ioan Romoşan
- Department V of Internal Medicine I, Discipline of Internal Medicine IV, “Victor Babeș” University of Medicine and Pharmacy Timișoara, Romania,Timișoara, Romania
| | - Adrian Goldiş
- Department VII of Internal Medicine II, Discipline of Gastroenterology and Hepatology, “Victor Babeș” University of Medicine and Pharmacy Timișoara, Romania, Timișoara, Romania
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27
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Lin TH, Jhang JY, Huang CR, Tsai YC, Cheng HC, Sheu BS. Deep Ensemble Feature Network for Gastric Section Classification. IEEE J Biomed Health Inform 2021; 25:77-87. [PMID: 32750926 DOI: 10.1109/jbhi.2020.2999731] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
In this paper, we propose a novel deep ensemble feature (DEF) network to classify gastric sections from endoscopic images. Different from recent deep ensemble learning methods, which need to train deep features and classifiers individually to obtain fused classification results, the proposed method can simultaneously learn the deep ensemble feature from arbitrary number of convolutional neural networks (CNNs) and the decision classifier in an end-to-end trainable manner. It comprises two sub networks, the ensemble feature network and the decision network. The former sub network learns the deep ensemble feature from multiple CNNs to represent endoscopic images. The latter sub network learns to obtain the classification labels by using the deep ensemble feature. Both sub networks are optimized based on the proposed ensemble feature loss and the decision loss which guide the learning of deep features and decisions. As shown in the experimental results, the proposed method outperforms the state-of-the-art deep learning, ensemble learning, and deep ensemble learning methods.
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28
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Sinonquel P, Eelbode T, Bossuyt P, Maes F, Bisschops R. Artificial intelligence and its impact on quality improvement in upper and lower gastrointestinal endoscopy. Dig Endosc 2021; 33:242-253. [PMID: 33145847 DOI: 10.1111/den.13888] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/28/2020] [Revised: 10/14/2020] [Accepted: 11/01/2020] [Indexed: 12/24/2022]
Abstract
Artificial intelligence (AI) and its application in medicine has grown large interest. Within gastrointestinal (GI) endoscopy, the field of colonoscopy and polyp detection is the most investigated, however, upper GI follows the lead. Since endoscopy is performed by humans, it is inherently an imperfect procedure. Computer-aided diagnosis may improve its quality by helping prevent missing lesions and supporting optical diagnosis for those detected. An entire evolution in AI systems has been established in the last decades, resulting in optimization of the diagnostic performance with lower variability and matching or even outperformance of expert endoscopists. This shows a great potential for future quality improvement of endoscopy, given the outstanding diagnostic features of AI. With this narrative review, we highlight the potential benefit of AI to improve overall quality in daily endoscopy and describe the most recent developments for characterization and diagnosis as well as the recent conditions for regulatory approval.
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Affiliation(s)
- Pieter Sinonquel
- Department of Gastroenterology and Hepatology, University Hospitals Leuven, Leuven, Belgium.,Departments of, Department of, Translational Research in Gastrointestinal Diseases (TARGID), KU Leuven, Leuven, Belgium
| | - Tom Eelbode
- Medical Imaging Research Center (MIRC), University Hospitals Leuven, Leuven, Belgium.,Department of Electrical Engineering (ESAT/PSI), KU Leuven, Leuven, Belgium
| | - Peter Bossuyt
- Department of Gastroenterology and Hepatology, University Hospitals Leuven, Leuven, Belgium.,Department of Gastroenterology and Hepatology, Imelda Hospital, Bonheiden, Belgium
| | - Frederik Maes
- Medical Imaging Research Center (MIRC), University Hospitals Leuven, Leuven, Belgium.,Department of Electrical Engineering (ESAT/PSI), KU Leuven, Leuven, Belgium
| | - Raf Bisschops
- Department of Gastroenterology and Hepatology, University Hospitals Leuven, Leuven, Belgium.,Departments of, Department of, Translational Research in Gastrointestinal Diseases (TARGID), KU Leuven, Leuven, Belgium
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29
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Sinonquel P, Bisschops R. Striving for quality improvement: can artificial intelligence help? Best Pract Res Clin Gastroenterol 2020; 52-53:101722. [PMID: 34172249 DOI: 10.1016/j.bpg.2020.101722] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/10/2020] [Accepted: 12/22/2020] [Indexed: 02/06/2023]
Abstract
Artificial intelligence (AI) is of keen interest for global health development as potential support for current human shortcomings. Gastrointestinal (GI) endoscopy is an excellent substrate for AI, since it holds the genuine potential to improve quality in GI endoscopy and overall patient care by improving detection and diagnosis guiding the endoscopists in performing endoscopy to the highest quality standards. The possibility of large data acquisitioning to refine algorithms makes implementation of AI into daily practice a potential reality. With the start of a new era adopting deep learning, large amounts of data can easily be processed, resulting in better diagnostic performances. In the upper gastrointestinal tract, research currently focusses on the detection and characterization of neoplasia, including Barrett's, squamous cell and gastric carcinoma, with an increasing amount of AI studies demonstrating the potential and benefit of AI-augmented endoscopy. Deep learning applied to small bowel video capsule endoscopy also appears to enhance pathology detection and reduce capsule reading time. In the colon, multiple prospective trials including five randomized trials, showed a consistent improvement in polyp and adenoma detection rates, one of the main quality indicators in endoscopy. There are however potential additional roles for AI to assist in quality improvement of endoscopic procedures, training and therapeutic decision making. Further large-scale, multicenter validation trials are required before AI-augmented diagnostic gastrointestinal endoscopy can be integrated into our routine clinical practice.
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Affiliation(s)
- P Sinonquel
- Department of Gastroenterology and Hepatology, University Hospitals Leuven, Herestraat 49, 3000, Leuven, Belgium; Department of Translational Research in Gastrointestinal Diseases (TARGID), Catholic University Leuven, Herestraat 49, 3000, Leuven, Belgium.
| | - R Bisschops
- Department of Gastroenterology and Hepatology, University Hospitals Leuven, Herestraat 49, 3000, Leuven, Belgium; Department of Translational Research in Gastrointestinal Diseases (TARGID), Catholic University Leuven, Herestraat 49, 3000, Leuven, Belgium.
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30
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Parasher G, Wong M, Rawat M. Evolving role of artificial intelligence in gastrointestinal endoscopy. World J Gastroenterol 2020; 26:7287-7298. [PMID: 33362384 PMCID: PMC7739161 DOI: 10.3748/wjg.v26.i46.7287] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/01/2020] [Revised: 11/02/2020] [Accepted: 11/29/2020] [Indexed: 02/06/2023] Open
Abstract
Artificial intelligence (AI) is a combination of different technologies that enable machines to sense, comprehend, and learn with human-like levels of intelligence. AI technology will eventually enhance human capability, provide machines genuine autonomy, and reduce errors, and increase productivity and efficiency. AI seems promising, and the field is full of invention, novel applications; however, the limitation of machine learning suggests a cautious optimism as the right strategy. AI is also becoming incorporated into medicine to improve patient care by speeding up processes and achieving greater accuracy for optimal patient care. AI using deep learning technology has been used to identify, differentiate catalog images in several medical fields including gastrointestinal endoscopy. The gastrointestinal endoscopy field involves endoscopic diagnoses and prognostication of various digestive diseases using image analysis with the help of various gastrointestinal endoscopic device systems. AI-based endoscopic systems can reliably detect and provide crucial information on gastrointestinal pathology based on their training and validation. These systems can make gastroenterology practice easier, faster, more reliable, and reduce inter-observer variability in the coming years. However, the thought that these systems will replace human decision making replace gastrointestinal endoscopists does not seem plausible in the near future. In this review, we discuss AI and associated various technological terminologies, evolving role in gastrointestinal endoscopy, and future possibilities.
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Affiliation(s)
- Gulshan Parasher
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, NM 87131, United States
| | - Morgan Wong
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, NM 87131, United States
| | - Manmeet Rawat
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, NM 87131, United States
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31
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Klein S, Gildenblat J, Ihle MA, Merkelbach-Bruse S, Noh KW, Peifer M, Quaas A, Büttner R. Deep learning for sensitive detection of Helicobacter Pylori in gastric biopsies. BMC Gastroenterol 2020; 20:417. [PMID: 33308189 PMCID: PMC7731757 DOI: 10.1186/s12876-020-01494-7] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/02/2019] [Accepted: 10/08/2020] [Indexed: 11/26/2022] Open
Abstract
Background Helicobacter pylori, a 2 × 1 μm spiral-shaped bacterium, is the most common risk factor for gastric cancer worldwide. Clinically, patients presenting with symptoms of gastritis, routinely undergo gastric biopsies. The following histo-morphological evaluation dictates therapeutic decisions, where antibiotics are used for H. pylori eradication. There is a strong rational to accelerate the detection process of H. pylori on histological specimens, using novel technologies, such as deep learning.
Methods We designed a deep-learning-based decision support algorithm that can be applied on regular whole slide images of gastric biopsies. In detail, we can detect H. pylori both on Giemsa- and regular H&E stained whole slide images. Results With the help of our decision support algorithm, we show an increased sensitivity in a subset of 87 cases that underwent additional PCR- and immunohistochemical testing to define a sensitive ground truth of HP presence. For Giemsa stained sections, the decision support algorithm achieved a sensitivity of 100% compared to 68.4% (microscopic diagnosis), with a tolerable specificity of 66.2% for the decision support algorithm compared to 92.6 (microscopic diagnosis). Conclusion Together, we provide the first evidence of a decision support algorithm proving as a sensitive screening option for H. pylori that can potentially aid pathologists to accurately diagnose H. pylori presence on gastric biopsies.
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Affiliation(s)
- Sebastian Klein
- Else-Kröner-Forschungskolleg, Clonal Evolution in Cancer, University Hospital Cologne, Cologne, Germany. .,Institute for Pathology, University Hospital Cologne, Cologne, Germany.
| | | | | | | | - Ka-Won Noh
- Institute for Pathology, University Hospital Cologne, Cologne, Germany
| | - Martin Peifer
- Department of Translational Genomics, Center of Integrated Oncology Cologne-Bonn, Medical Faculty, University of Cologne, 50931, Cologne, Germany
| | - Alexander Quaas
- Institute for Pathology, University Hospital Cologne, Cologne, Germany
| | - Reinhard Büttner
- Institute for Pathology, University Hospital Cologne, Cologne, Germany.
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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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33
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Bang CS, Lee JJ, Baik GH. Artificial Intelligence for the Prediction of Helicobacter Pylori Infection in Endoscopic Images: Systematic Review and Meta-Analysis Of Diagnostic Test Accuracy. J Med Internet Res 2020; 22:e21983. [PMID: 32936088 PMCID: PMC7527948 DOI: 10.2196/21983] [Citation(s) in RCA: 61] [Impact Index Per Article: 12.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Revised: 08/02/2020] [Accepted: 08/03/2020] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND Helicobacter pylori plays a central role in the development of gastric cancer, and prediction of H pylori infection by visual inspection of the gastric mucosa is an important function of endoscopy. However, there are currently no established methods of optical diagnosis of H pylori infection using endoscopic images. Definitive diagnosis requires endoscopic biopsy. Artificial intelligence (AI) has been increasingly adopted in clinical practice, especially for image recognition and classification. OBJECTIVE This study aimed to evaluate the diagnostic test accuracy of AI for the prediction of H pylori infection using endoscopic images. METHODS Two independent evaluators searched core databases. The inclusion criteria included studies with endoscopic images of H pylori infection and with application of AI for the prediction of H pylori infection presenting diagnostic performance. Systematic review and diagnostic test accuracy meta-analysis were performed. RESULTS Ultimately, 8 studies were identified. Pooled sensitivity, specificity, diagnostic odds ratio, and area under the curve of AI for the prediction of H pylori infection were 0.87 (95% CI 0.72-0.94), 0.86 (95% CI 0.77-0.92), 40 (95% CI 15-112), and 0.92 (95% CI 0.90-0.94), respectively, in the 1719 patients (385 patients with H pylori infection vs 1334 controls). Meta-regression showed methodological quality and included the number of patients in each study for the purpose of heterogeneity. There was no evidence of publication bias. The accuracy of the AI algorithm reached 82% for discrimination between noninfected images and posteradication images. CONCLUSIONS An AI algorithm is a reliable tool for endoscopic diagnosis of H pylori infection. The limitations of lacking external validation performance and being conducted only in Asia should be overcome. TRIAL REGISTRATION PROSPERO CRD42020175957; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=175957.
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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, 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, Chuncheon, Republic of Korea
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34
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Jin P, Ji X, Kang W, Li Y, Liu H, Ma F, Ma S, Hu H, Li W, Tian Y. Artificial intelligence in gastric cancer: a systematic review. J Cancer Res Clin Oncol 2020; 146:2339-2350. [PMID: 32613386 DOI: 10.1007/s00432-020-03304-9] [Citation(s) in RCA: 55] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2020] [Accepted: 06/26/2020] [Indexed: 02/08/2023]
Abstract
OBJECTIVE This study aims to systematically review the application of artificial intelligence (AI) techniques in gastric cancer and to discuss the potential limitations and future directions of AI in gastric cancer. METHODS A systematic review was performed that follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Pubmed, EMBASE, the Web of Science, and the Cochrane Library were used to search for gastric cancer publications with an emphasis on AI that were published up to June 2020. The terms "artificial intelligence" and "gastric cancer" were used to search for the publications. RESULTS A total of 64 articles were included in this review. In gastric cancer, AI is mainly used for molecular bio-information analysis, endoscopic detection for Helicobacter pylori infection, chronic atrophic gastritis, early gastric cancer, invasion depth, and pathology recognition. AI may also be used to establish predictive models for evaluating lymph node metastasis, response to drug treatments, and prognosis. In addition, AI can be used for surgical training, skill assessment, and surgery guidance. CONCLUSIONS In the foreseeable future, AI applications can play an important role in gastric cancer management in the era of precision medicine.
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Affiliation(s)
- Peng Jin
- Department of Pancreatic and Gastric Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17, Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China
| | - Xiaoyan Ji
- Department of Emergency Ward, First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, Tianjin, 300193, China
| | - Wenzhe Kang
- Department of Pancreatic and Gastric Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17, Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China
| | - Yang Li
- Department of Pancreatic and Gastric Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17, Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China
| | - Hao Liu
- Department of Pancreatic and Gastric Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17, Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China
| | - Fuhai Ma
- Department of Pancreatic and Gastric Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17, Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China
| | - Shuai Ma
- Department of Pancreatic and Gastric Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17, Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China
| | - Haitao Hu
- Department of Pancreatic and Gastric Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17, Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China
| | - Weikun Li
- Department of Pancreatic and Gastric Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17, Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China
| | - Yantao Tian
- Department of Pancreatic and Gastric Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17, Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China.
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Morreale GC, Sinagra E, Vitello A, Shahini E, Shahini E, Maida M. Emerging artificia intelligence applications in gastroenterology: A review of the literature. Artif Intell Gastrointest Endosc 2020; 1:6-18. [DOI: 10.37126/aige.v1.i1.6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/23/2020] [Revised: 07/07/2020] [Accepted: 07/16/2020] [Indexed: 02/06/2023] Open
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36
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Morreale GC, Sinagra E, Vitello A, Shahini E, Shahini E, Maida M. Emerging artificia intelligence applications in gastroenterology: A review of the literature. Artif Intell Gastrointest Endosc 2020. [DOI: 10.37126/wjem.v1.i1.19] [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: 02/06/2023] Open
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Gulati S, Patel M, Emmanuel A, Haji A, Hayee B, Neumann H. The future of endoscopy: Advances in endoscopic image innovations. Dig Endosc 2020; 32:512-522. [PMID: 31286574 DOI: 10.1111/den.13481] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/14/2019] [Accepted: 07/01/2019] [Indexed: 02/08/2023]
Abstract
The latest state of the art technological innovations have led to a palpable progression in endoscopic imaging and may facilitate standardisation of practice. One of the most rapidly evolving modalities is artificial intelligence with recent studies providing real-time diagnoses and encouraging results in the first randomised trials to conventional endoscopic imaging. Advances in functional hypoxia imaging offer novel opportunities to be used to detect neoplasia and the assessment of colitis. Three-dimensional volumetric imaging provides spatial information and has shown promise in the increased detection of small polyps. Studies to date of self-propelling colonoscopes demonstrate an increased caecal intubation rate and possibly offer patients a more comfortable procedure. Further development in robotic technology has introduced ex vivo automated locomotor upper gastrointestinal and small bowel capsule devices. Eye-tracking has the potential to revolutionise endoscopic training through the identification of differences in experts and non-expert endoscopist as trainable parameters. In this review, we discuss the latest innovations of all these technologies and provide perspective into the exciting future of diagnostic luminal endoscopy.
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Affiliation(s)
- Shraddha Gulati
- King's Institute of Therapeutic Endoscopy, King's College Hospital NHS Foundation Trust, London, UK
| | - Mehul Patel
- King's Institute of Therapeutic Endoscopy, King's College Hospital NHS Foundation Trust, London, UK
| | - Andrew Emmanuel
- King's Institute of Therapeutic Endoscopy, King's College Hospital NHS Foundation Trust, London, UK
| | - Amyn Haji
- King's Institute of Therapeutic Endoscopy, King's College Hospital NHS Foundation Trust, London, UK
| | - Bu'Hussain Hayee
- King's Institute of Therapeutic Endoscopy, King's College Hospital NHS Foundation Trust, London, UK
| | - Helmut Neumann
- Department of Medicine, University Hospital Mainz, Mainz, Germany
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38
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Gulati S, Emmanuel A, Patel M, Williams S, Haji A, Hayee B, Neumann H. Artificial intelligence in luminal endoscopy. Ther Adv Gastrointest Endosc 2020; 13:2631774520935220. [PMID: 32637935 PMCID: PMC7315657 DOI: 10.1177/2631774520935220] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/16/2020] [Accepted: 05/22/2020] [Indexed: 12/15/2022] Open
Abstract
Artificial intelligence is a strong focus of interest for global health development. Diagnostic endoscopy is an attractive substrate for artificial intelligence with a real potential to improve patient care through standardisation of endoscopic diagnosis and to serve as an adjunct to enhanced imaging diagnosis. The possibility to amass large data to refine algorithms makes adoption of artificial intelligence into global practice a potential reality. Initial studies in luminal endoscopy involve machine learning and are retrospective. Improvement in diagnostic performance is appreciable through the adoption of deep learning. Research foci in the upper gastrointestinal tract include the diagnosis of neoplasia, including Barrett's, squamous cell and gastric where prospective and real-time artificial intelligence studies have been completed demonstrating a benefit of artificial intelligence-augmented endoscopy. Deep learning applied to small bowel capsule endoscopy also appears to enhance pathology detection and reduce capsule reading time. Prospective evaluation including the first randomised trial has been performed in the colon, demonstrating improved polyp and adenoma detection rates; however, these appear to be relevant to small polyps. There are potential additional roles of artificial intelligence relevant to improving the quality of endoscopic examinations, training and triaging of referrals. Further large-scale, multicentre and cross-platform validation studies are required for the robust incorporation of artificial intelligence-augmented diagnostic luminal endoscopy into our routine clinical practice.
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Affiliation(s)
- Shraddha Gulati
- King’s Institute of Therapeutic Endoscopy, King’s College Hospital NHS Foundation Trust, London, UK
| | - Andrew Emmanuel
- King’s Institute of Therapeutic Endoscopy, King’s College Hospital NHS Foundation Trust, London, UK
| | - Mehul Patel
- King’s Institute of Therapeutic Endoscopy, King’s College Hospital NHS Foundation Trust, London, UK
| | - Sophie Williams
- King’s Institute of Therapeutic Endoscopy, King’s College Hospital NHS Foundation Trust, London, UK
| | - Amyn Haji
- King’s Institute of Therapeutic Endoscopy, King’s College Hospital NHS Foundation Trust, London, UK
| | - Bu’Hussain Hayee
- King’s Institute of Therapeutic Endoscopy, King’s College Hospital NHS Foundation Trust, London, UK
| | - Helmut Neumann
- Department of Interdisciplinary Endoscopy, University Hospital Mainz, 55131 Mainz, Germany
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Mori Y, Kudo SE, Mohmed HEN, Misawa M, Ogata N, Itoh H, Oda M, Mori K. Artificial intelligence and upper gastrointestinal endoscopy: Current status and future perspective. Dig Endosc 2019; 31:378-388. [PMID: 30549317 DOI: 10.1111/den.13317] [Citation(s) in RCA: 77] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/19/2018] [Accepted: 12/07/2018] [Indexed: 02/08/2023]
Abstract
With recent breakthroughs in artificial intelligence, computer-aided diagnosis (CAD) for upper gastrointestinal endoscopy is gaining increasing attention. Main research focuses in this field include automated identification of dysplasia in Barrett's esophagus and detection of early gastric cancers. By helping endoscopists avoid missing and mischaracterizing neoplastic change in both the esophagus and the stomach, these technologies potentially contribute to solving current limitations of gastroscopy. Currently, optical diagnosis of early-stage dysplasia related to Barrett's esophagus can be precisely achieved only by endoscopists proficient in advanced endoscopic imaging, and the false-negative rate for detecting gastric cancer is approximately 10%. Ideally, these novel technologies should work during real-time gastroscopy to provide on-site decision support for endoscopists regardless of their skill; however, previous studies of these topics remain ex vivo and experimental in design. Therefore, the feasibility, effectiveness, and safety of CAD for upper gastrointestinal endoscopy in clinical practice remain unknown, although a considerable number of pilot studies have been conducted by both engineers and medical doctors with excellent results. This review summarizes current publications relating to CAD for upper gastrointestinal endoscopy from the perspective of endoscopists and aims to indicate what is required for future research and implementation in clinical practice.
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Affiliation(s)
- Yuichi Mori
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Shin-Ei Kudo
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Hussein E N Mohmed
- Showa University International Center for Endoscopy, Showa University Northern Yokohama Hospital, Kanagawa, Japan.,Department of Gastroenterology/Tropical Medicine, Ain Shams University, Cairo, Egypt
| | - Masashi Misawa
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Noriyuki Ogata
- 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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Yang YJ, Bang CS. Application of artificial intelligence in gastroenterology. World J Gastroenterol 2019; 25:1666-1683. [PMID: 31011253 PMCID: PMC6465941 DOI: 10.3748/wjg.v25.i14.1666] [Citation(s) in RCA: 156] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/09/2019] [Revised: 03/04/2019] [Accepted: 03/16/2019] [Indexed: 02/06/2023] Open
Abstract
Artificial intelligence (AI) using deep-learning (DL) has emerged as a breakthrough computer technology. By the era of big data, the accumulation of an enormous number of digital images and medical records drove the need for the utilization of AI to efficiently deal with these data, which have become fundamental resources for a machine to learn by itself. Among several DL models, the convolutional neural network showed outstanding performance in image analysis. In the field of gastroenterology, physicians handle large amounts of clinical data and various kinds of image devices such as endoscopy and ultrasound. AI has been applied in gastroenterology in terms of diagnosis, prognosis, and image analysis. However, potential inherent selection bias cannot be excluded in the form of retrospective study. Because overfitting and spectrum bias (class imbalance) have the possibility of overestimating the accuracy, external validation using unused datasets for model development, collected in a way that minimizes the spectrum bias, is mandatory. For robust verification, prospective studies with adequate inclusion/exclusion criteria, which represent the target populations, are needed. DL has its own lack of interpretability. Because interpretability is important in that it can provide safety measures, help to detect bias, and create social acceptance, further investigations should be performed.
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Affiliation(s)
- Young Joo Yang
- Department of Internal Medicine, Hallym University College of Medicine, Chuncheon, Gangwon-do 24253, South Korea
| | - Chang Seok Bang
- Department of Internal Medicine, Hallym University College of Medicine, Chuncheon, Gangwon-do 24253, South Korea
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Wang S, Cong Y, Fan H, Liu L, Li X, Yang Y, Tang Y, Zhao H, Yu H. Computer-Aided Endoscopic Diagnosis Without Human-Specific Labeling. IEEE Trans Biomed Eng 2016; 63:2347-2358. [DOI: 10.1109/tbme.2016.2530141] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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Huang CR, Chen YT, Chen WY, Cheng HC, Sheu BS. Gastroesophageal Reflux Disease Diagnosis Using Hierarchical Heterogeneous Descriptor Fusion Support Vector Machine. IEEE Trans Biomed Eng 2015; 63:588-99. [PMID: 26276981 DOI: 10.1109/tbme.2015.2466460] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
A new computer-aided diagnosis method is proposed to diagnose the gastroesophageal reflux disease (GERD) from endoscopic images of the esophageal-gastric junction. To avoid the interferences of different endoscope devices and automatic camera white balance adjustment, heterogeneous descriptors computed from heterogeneous color models are used to represent endoscopic images. Instead of concatenating these descriptors to a super vector, a hierarchical heterogeneous descriptor fusion support vector machine (HHDF-SVM) framework is proposed to simultaneously apply heterogeneous descriptors for GERD diagnosis and overcome the curse of dimensionality problem. During validation, heterogeneous descriptors are extracted from test endoscopic images at first. The classification result is obtained by using HHDF-SVM with heterogeneous descriptors. As shown in the experiments, our method can automatically diagnose GERD without any manual selection of region of interest and achieve better accuracy compared to states-of-the-art methods.
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Yang HB, Sheu BS, Wang ST, Cheng HC, Chang WL, Chen WY. H. pylori eradication prevents the progression of gastric intestinal metaplasia in reflux esophagitis patients using long-term esomeprazole. Am J Gastroenterol 2009; 104:1642-9. [PMID: 19436275 DOI: 10.1038/ajg.2009.172] [Citation(s) in RCA: 34] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
OBJECTIVES This study aimed to determine whether Helicobacter pylori eradication limits the progression of precancerous changes, manifested as intestinal metaplasia (IM), in patients with reflux esophagitis using long-term esomeprazole. METHODS Three hundred twenty-five reflux esophagitis patients were enrolled and randomly assigned to (i) the H. pylori-positive eradication group receiving 1-week triple therapy (n=105); (ii) H. pylori-positive non-eradication controls (n=105); and (iii) H. pylori-negative controls (n=115). All the patients received continuous esomeprazole until sustained symptomatic response, and when possible, shifted to on-demand therapy (ODT) thereafter. Serial gastroscopy was scheduled on enrollment and at the end of the first and second years to assess the prevalence and progression or regression of gastric atrophy (AT) and IM. RESULTS There were 93 patients in the H. pylori-eradication group, 83 in the non-eradication controls, and 100 in the negative controls to complete the study. The negative controls had no progression of AT and IM during follow-up. For the H. pylori-positive eradication group, there was significant regression of AT and IM during follow-up (P<0.05). In the H. pylori-positive non-treated controls, the prevalence rates of AT and IM were significantly greater on the second year than on enrollment (P<0.05). During the second-year follow-up, the patients in the eradication group achieved more regression and less development of AT and IM than did the non-eradication controls (P<0.001). CONCLUSIONS In patients using long-term esomeprazole for reflux esophagitis, screening for and eradicating H. pylori infection are necessary in order to limit the progression or cause the regression of gastric precancerous changes.
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Affiliation(s)
- Hsiao-Bai Yang
- Department of Pathology, National Cheng Kung University Medical Center, Tainan, Taiwan, Republic of China
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Huang CR, Chung PC, Sheu BS, Kuo HJ, Popper M. Helicobacter pylori-related gastric histology classification using support-vector-machine-based feature selection. ACTA ACUST UNITED AC 2008; 12:523-31. [PMID: 18632332 DOI: 10.1109/titb.2007.913128] [Citation(s) in RCA: 27] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
This study presents a computer-aided diagnosis system using sequential forward floating selection (SFFS) with support vector machine (SVM) to diagnose gastric histology of Helicobacter pylori (H. pylori) from endoscopic images. To achieve this goal, candidate image features associated with clinical symptoms are extracted from endoscopic images. With these candidate features, the SFFS method is applied to select feature subsets, which perform the best classification results under SVM with respect to different histological features. By using the classifiers obtained from the feature subsets, a new diagnosis system is implemented to provide physicians with H. pylori -related histological results from endoscopic images.
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Affiliation(s)
- Chun-Rong Huang
- Institute of Information Science, Academia Sinica, Taipei 11523, Taiwan, ROC.
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Savarino V, Dulbecco P. Can artificial neural networks be beneficial in diagnosing gastro-oesophageal reflux disease? Eur J Gastroenterol Hepatol 2005; 17:599-601. [PMID: 15879719 DOI: 10.1097/00042737-200506000-00001] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
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