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Lei C, Sun W, Wang K, Weng R, Kan X, Li R. Artificial intelligence-assisted diagnosis of early gastric cancer: present practice and future prospects. Ann Med 2025; 57:2461679. [PMID: 39928093 PMCID: PMC11812113 DOI: 10.1080/07853890.2025.2461679] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/08/2024] [Revised: 12/09/2024] [Accepted: 01/23/2025] [Indexed: 02/11/2025] Open
Abstract
Gastric cancer (GC) occupies the first few places in the world among tumors in terms of incidence and mortality, causing serious harm to human health, and at the same time, its treatment greatly consumes the health care resources of all countries in the world. The diagnosis of GC is usually based on histopathologic examination, and it is very important to be able to detect and identify cancerous lesions at an early stage, but some endoscopists' lack of diagnostic experience and fatigue at work lead to a certain rate of under diagnosis. The rapid and striking development of Artificial intelligence (AI) has helped to enhance the ability to extract abnormal information from endoscopic images to some extent, and more and more researchers are applying AI technology to the diagnosis of GC. This initiative has not only improved the detection rate of early gastric cancer (EGC), but also significantly improved the survival rate of patients after treatment. This article reviews the results of various AI-assisted diagnoses of EGC in recent years, including the identification of EGC, the determination of differentiation type and invasion depth, and the identification of borders. Although AI has a better application prospect in the early diagnosis of ECG, there are still major challenges, and the prospects and limitations of AI application need to be further discussed.
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Affiliation(s)
- Changda Lei
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou, China
- Suzhou Medical College, Soochow University, Suzhou, China
| | - Wenqiang Sun
- Suzhou Medical College, Soochow University, Suzhou, China
- Department of Neonatology, Children’s Hospital of Soochow University, Suzhou, China
| | - Kun Wang
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou, China
- Suzhou Medical College, Soochow University, Suzhou, China
| | - Ruixia Weng
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou, China
- Suzhou Medical College, Soochow University, Suzhou, China
| | - Xiuji Kan
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou, China
- Suzhou Medical College, Soochow University, Suzhou, China
| | - Rui Li
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou, China
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Santos-Antunes J. Non-Curative Endoscopic Submucosal Dissection: Current Concepts, Pitfalls and Future Perspectives. J Clin Med 2025; 14:2488. [PMID: 40217937 PMCID: PMC11990033 DOI: 10.3390/jcm14072488] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2025] [Revised: 03/31/2025] [Accepted: 04/04/2025] [Indexed: 04/14/2025] Open
Abstract
Endoscopic submucosal dissection (ESD) is very effective for the treatment of digestive tract neoplasia. However, it is very demanding, with a long learning curve, and, therefore, a significant rate of non-curative resections is expected, considering lesion characteristics, location, and endoscopist experience. The management of patients after a non-curative ESD is not definitely established. It must consider patients' comorbidities and expected survival, as well as the morbidity and mortality of complementary treatments such as surgery, chemotherapy, or radiotherapy. On the other hand, there is a window of opportunity to offer those additional treatments to complete neoplastic treatment and give patients an oncological cure. This decision is sometimes difficult, since the diverse histological criteria that define a non-curative ESD do not have the same weight regarding residual risk and oncological progression. The prediction of residual lesion would be paramount to decide whether to refer patients to surgery; nowadays, this prediction is far from perfect, since most of the patients that undergo surgery due to a non-curative ESD do not have residual neoplasia in the surgical specimen. In this review, ESD curativeness and the management of non-curative ESDs performed for esophageal, gastric, and colorectal lesions will be addressed.
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Affiliation(s)
- João Santos-Antunes
- Gastroenterology Department, Porto WGO Training Center, Centro Hospitalar S. João, 4200-319 Porto, Portugal;
- Department of Medicine, Faculty of Medicine, University of Porto, 4200-319 Porto, Portugal
- IPATIMUP—Institute of Molecular Pathology and Immunology, University of Porto, 4200-135 Porto, Portugal
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3
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Zhou S, Xie Y, Feng X, Li Y, Shen L, Chen Y. Artificial intelligence in gastrointestinal cancer research: Image learning advances and applications. Cancer Lett 2025; 614:217555. [PMID: 39952597 DOI: 10.1016/j.canlet.2025.217555] [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] [Received: 12/04/2024] [Revised: 01/31/2025] [Accepted: 02/11/2025] [Indexed: 02/17/2025]
Abstract
With the rapid advancement of artificial intelligence (AI) technologies, including deep learning, large language models, and neural networks, these methodologies are increasingly being developed and integrated into cancer research. Gastrointestinal tumors are characterized by complexity and heterogeneity, posing significant challenges for early detection, diagnostic accuracy, and the development of personalized treatment strategies. The application of AI in digestive oncology has demonstrated its transformative potential. AI not only alleviates the diagnostic burden on clinicians, but it improves tumor screening sensitivity, specificity, and accuracy. Additionally, AI aids the detection of biomarkers such as microsatellite instability and mismatch repair, supports intraoperative assessments of tumor invasion depth, predicts treatment responses, and facilitates the design of personalized treatment plans to potentially significantly enhance patient outcomes. Moreover, the integration of AI with multiomics analyses and imaging technologies has led to substantial advancements in foundational research on the tumor microenvironment. This review highlights the progress of AI in gastrointestinal oncology over the past 5 years with focus on early tumor screening, diagnosis, molecular marker identification, treatment planning, and prognosis predictions. We also explored the potential of AI to enhance medical imaging analyses to aid tumor detection and characterization as well as its role in automating and refining histopathological assessments.
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Affiliation(s)
- Shengyuan Zhou
- Department of Gastrointestinal Oncology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education, Beijing), Peking University Cancer Hospital and Institute, Beijing, China
| | - Yi Xie
- Department of Gastrointestinal Oncology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education, Beijing), Peking University Cancer Hospital and Institute, Beijing, China
| | - Xujiao Feng
- Department of Gastrointestinal Oncology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education, Beijing), Peking University Cancer Hospital and Institute, Beijing, China
| | - Yanyan Li
- Department of Gastrointestinal Oncology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education, Beijing), Peking University Cancer Hospital and Institute, Beijing, China
| | - Lin Shen
- Department of Gastrointestinal Oncology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education, Beijing), Peking University Cancer Hospital and Institute, Beijing, China
| | - Yang Chen
- Department of Gastrointestinal Oncology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education, Beijing), Peking University Cancer Hospital and Institute, Beijing, China; Department of Gastrointestinal Cancer, Beijing GoBroad Hospital, Beijing, 102200, China.
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4
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Li R, Cao R, Zhao Q, Zhao Z. Utilizing a Novel Convolutional Neural Network for Diagnosis and Lesion Delineation in Colorectal Cancer Screening. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2025:10.1007/s10278-025-01396-8. [PMID: 39821781 DOI: 10.1007/s10278-025-01396-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/03/2024] [Revised: 12/10/2024] [Accepted: 12/24/2024] [Indexed: 01/19/2025]
Abstract
Early detection of colorectal cancer is vital for enhancing cure rates and alleviating treatment burdens. Nevertheless, the high demand for screenings coupled with a limited number of endoscopists underscores the necessity for advanced deep learning techniques to improve screening efficiency and accuracy. This study presents an innovative convolutional neural network (CNN) model, trained on 8260 images from screenings conducted at four medical institutions. The model incorporates parallel global and local feature extraction branches and a distinctive classification head, facilitating both cancer classification and the creation of heatmaps that outline cancerous lesion regions. Performance evaluations of the CNN model, measured against five leading models using accuracy, precision, recall, and F1 score, revealed its superior efficacy across these metrics. Furthermore, the heatmaps proved effective in aiding the automatic identification of lesion locations. In summary, this CNN model represents a promising advancement in early colorectal cancer screening, delivering precise, swift diagnostic results and robust interpretability through its automatic lesion highlighting capabilities.
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Affiliation(s)
- Renbo Li
- School of Control Science and Engineering, Shandong University, Jinan, 250012, Shandong, China
| | - Ruofan Cao
- Department of Gastroenterology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, 250021, Shandong, China
| | - Qi Zhao
- Department of Gastroenterology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, 250021, Shandong, China
| | - Zijian Zhao
- School of Control Science and Engineering, Shandong University, Jinan, 250012, Shandong, China.
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Nie Z, Xu M, Wang Z, Lu X, Song W. A Review of Application of Deep Learning in Endoscopic Image Processing. J Imaging 2024; 10:275. [PMID: 39590739 PMCID: PMC11595772 DOI: 10.3390/jimaging10110275] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2024] [Revised: 10/24/2024] [Accepted: 10/29/2024] [Indexed: 11/28/2024] Open
Abstract
Deep learning, particularly convolutional neural networks (CNNs), has revolutionized endoscopic image processing, significantly enhancing the efficiency and accuracy of disease diagnosis through its exceptional ability to extract features and classify complex patterns. This technology automates medical image analysis, alleviating the workload of physicians and enabling a more focused and personalized approach to patient care. However, despite these remarkable achievements, there are still opportunities to further optimize deep learning models for endoscopic image analysis, including addressing limitations such as the requirement for large annotated datasets and the challenge of achieving higher diagnostic precision, particularly for rare or subtle pathologies. This review comprehensively examines the profound impact of deep learning on endoscopic image processing, highlighting its current strengths and limitations. It also explores potential future directions for research and development, outlining strategies to overcome existing challenges and facilitate the integration of deep learning into clinical practice. Ultimately, the goal is to contribute to the ongoing advancement of medical imaging technologies, leading to more accurate, personalized, and optimized medical care for patients.
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Affiliation(s)
- Zihan Nie
- School of Mechanical Engineering, Shandong University, Jinan 250061, China; (Z.N.); (M.X.); (Z.W.); (X.L.)
- Key Laboratory of High Efficiency and Clean Mechanical Manufacture of Ministry of Education, Shandong University, Jinan 250061, China
| | - Muhao Xu
- School of Mechanical Engineering, Shandong University, Jinan 250061, China; (Z.N.); (M.X.); (Z.W.); (X.L.)
- Key Laboratory of High Efficiency and Clean Mechanical Manufacture of Ministry of Education, Shandong University, Jinan 250061, China
| | - Zhiyong Wang
- School of Mechanical Engineering, Shandong University, Jinan 250061, China; (Z.N.); (M.X.); (Z.W.); (X.L.)
- Key Laboratory of High Efficiency and Clean Mechanical Manufacture of Ministry of Education, Shandong University, Jinan 250061, China
| | - Xiaoqi Lu
- School of Mechanical Engineering, Shandong University, Jinan 250061, China; (Z.N.); (M.X.); (Z.W.); (X.L.)
- Key Laboratory of High Efficiency and Clean Mechanical Manufacture of Ministry of Education, Shandong University, Jinan 250061, China
| | - Weiye Song
- School of Mechanical Engineering, Shandong University, Jinan 250061, China; (Z.N.); (M.X.); (Z.W.); (X.L.)
- Key Laboratory of High Efficiency and Clean Mechanical Manufacture of Ministry of Education, Shandong University, Jinan 250061, China
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Zubair M, Owais M, Mahmood T, Iqbal S, Usman SM, Hussain I. Enhanced gastric cancer classification and quantification interpretable framework using digital histopathology images. Sci Rep 2024; 14:22533. [PMID: 39342030 PMCID: PMC11439054 DOI: 10.1038/s41598-024-73823-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2024] [Accepted: 09/20/2024] [Indexed: 10/01/2024] Open
Abstract
Recent developments have highlighted the critical role that computer-aided diagnosis (CAD) systems play in analyzing whole-slide digital histopathology images for detecting gastric cancer (GC). We present a novel framework for gastric histology classification and segmentation (GHCS) that offers modest yet meaningful improvements over existing CAD models for GC classification and segmentation. Our methodology achieves marginal improvements over conventional deep learning (DL) and machine learning (ML) models by adaptively focusing on pertinent characteristics of images. This contributes significantly to our study, highlighting that the proposed model, which performs well on normalized images, is robust in certain respects, particularly in handling variability and generalizing to different datasets. We anticipate that this robustness will lead to better results across various datasets. An expectation-maximizing Naïve Bayes classifier that uses an updated Gaussian Mixture Model is at the heart of the suggested GHCS framework. The effectiveness of our classifier is demonstrated by experimental validation on two publicly available datasets, which produced exceptional classification accuracies of 98.87% and 97.28% on validation sets and 98.47% and 97.31% on test sets. Our framework shows a slight but consistent improvement over previously existing techniques in gastric histopathology image classification tasks, as demonstrated by comparative analysis. This may be attributed to its ability to capture critical features of gastric histopathology images better. Furthermore, using an improved Fuzzy c-means method, our study produces good results in GC histopathology picture segmentation, outperforming state-of-the-art segmentation models with a Dice coefficient of 65.21% and a Jaccard index of 60.24%. The model's interpretability is complemented by Grad-CAM visualizations, which help understand the decision-making process and increase the model's trustworthiness for end-users, especially clinicians.
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Affiliation(s)
- Muhammad Zubair
- Faculty of Information Technology & Computer Science, University of Central Punjab, Lahore, Punjab, Pakistan
| | - Muhammad Owais
- Khalifa University Center for Autonomous Robotic Systems (KUCARS) and Department of Mechanical & Nuclear Engineering, Khalifa University, Abu Dhabi, United Arab Emirates.
| | - Tahir Mahmood
- Division of Electronics and Electrical Engineering, Dongguk University, Seoul, Korea
| | - Saeed Iqbal
- Faculty of Information Technology & Computer Science, University of Central Punjab, Lahore, Punjab, Pakistan
| | - Syed Muhammad Usman
- Department of Computer Science, School of Engineering and Applied Sciences, Bahria University, Islamabad, Pakistan
| | - Irfan Hussain
- Khalifa University Center for Autonomous Robotic Systems (KUCARS) and Department of Mechanical & Nuclear Engineering, Khalifa University, Abu Dhabi, United Arab Emirates
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Uema R, Hayashi Y, Kizu T, Igura T, Ogiyama H, Yamada T, Takeda R, Nagai K, Inoue T, Yamamoto M, Yamaguchi S, Kanesaka T, Yoshihara T, Kato M, Yoshii S, Tsujii Y, Shinzaki S, Takehara T. A novel artificial intelligence-based endoscopic ultrasonography diagnostic system for diagnosing the invasion depth of early gastric cancer. J Gastroenterol 2024; 59:543-555. [PMID: 38713263 PMCID: PMC11217111 DOI: 10.1007/s00535-024-02102-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Accepted: 03/30/2024] [Indexed: 05/08/2024]
Abstract
BACKGROUND We developed an artificial intelligence (AI)-based endoscopic ultrasonography (EUS) system for diagnosing the invasion depth of early gastric cancer (EGC), and we evaluated the performance of this system. METHODS A total of 8280 EUS images from 559 EGC cases were collected from 11 institutions. Within this dataset, 3451 images (285 cases) from one institution were used as a development dataset. The AI model consisted of segmentation and classification steps, followed by the CycleGAN method to bridge differences in EUS images captured by different equipment. AI model performance was evaluated using an internal validation dataset collected from the same institution as the development dataset (1726 images, 135 cases). External validation was conducted using images collected from the other 10 institutions (3103 images, 139 cases). RESULTS The area under the curve (AUC) of the AI model in the internal validation dataset was 0.870 (95% CI: 0.796-0.944). Regarding diagnostic performance, the accuracy/sensitivity/specificity values of the AI model, experts (n = 6), and nonexperts (n = 8) were 82.2/63.4/90.4%, 81.9/66.3/88.7%, and 68.3/60.9/71.5%, respectively. The AUC of the AI model in the external validation dataset was 0.815 (95% CI: 0.743-0.886). The accuracy/sensitivity/specificity values of the AI model (74.1/73.1/75.0%) and the real-time diagnoses of experts (75.5/79.1/72.2%) in the external validation dataset were comparable. CONCLUSIONS Our AI model demonstrated a diagnostic performance equivalent to that of experts.
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Affiliation(s)
- Ryotaro Uema
- Department of Gastroenterology and Hepatology, Osaka University Graduate School of Medicine, 2-2, Yamadaoka, Suita, Osaka, 565-0871, Japan
| | - Yoshito Hayashi
- Department of Gastroenterology and Hepatology, Osaka University Graduate School of Medicine, 2-2, Yamadaoka, Suita, Osaka, 565-0871, Japan
| | - Takashi Kizu
- Department of Gastroenterology, Yao Municipal Hospital, Yao, 581-0069, Japan
| | - Takumi Igura
- Department of Gastroenterology, Sumitomo Hospital, Osaka, 530-0005, Japan
| | - Hideharu Ogiyama
- Department of Gastroenterology, Ikeda Municipal Hospital, Ikeda, 563-0025, Japan
| | - Takuya Yamada
- Department of Gastroenterology, Osaka Rosai Hospital, Sakai, 591-8025, Japan
| | - Risato Takeda
- Department of Gastroenterology, Itami City Hospital, Itami, 664-0015, Japan
| | - Kengo Nagai
- Department of Gastroenterology, Suita Municipal Hospital, Suita, 564-0018, Japan
| | - Takuya Inoue
- Department of Gastroenterology, Osaka General Medical Center, Osaka, 558-8558, Japan
| | - Masashi Yamamoto
- Department of Gastroenterology, Toyonaka Municipal Hospital, Toyonaka, 560-8565, Japan
| | - Shinjiro Yamaguchi
- Department of Gastroenterology, Kansai Rosai Hospital, Amagasaki, 660-0064, Japan
| | - Takashi Kanesaka
- Department of Gastrointestinal Oncology, Osaka International Cancer Institute, Osaka, 540-0008, Japan
| | - Takeo Yoshihara
- Department of Gastroenterology and Hepatology, Osaka University Graduate School of Medicine, 2-2, Yamadaoka, Suita, Osaka, 565-0871, Japan
| | - Minoru Kato
- Department of Gastroenterology and Hepatology, Osaka University Graduate School of Medicine, 2-2, Yamadaoka, Suita, Osaka, 565-0871, Japan
- Department of Gastrointestinal Oncology, Osaka International Cancer Institute, Osaka, 540-0008, Japan
| | - Shunsuke Yoshii
- Department of Gastroenterology and Hepatology, Osaka University Graduate School of Medicine, 2-2, Yamadaoka, Suita, Osaka, 565-0871, Japan
- Department of Gastrointestinal Oncology, Osaka International Cancer Institute, Osaka, 540-0008, Japan
| | - Yoshiki Tsujii
- Department of Gastroenterology and Hepatology, Osaka University Graduate School of Medicine, 2-2, Yamadaoka, Suita, Osaka, 565-0871, Japan
| | - Shinichiro Shinzaki
- Department of Gastroenterology and Hepatology, Osaka University Graduate School of Medicine, 2-2, Yamadaoka, Suita, Osaka, 565-0871, Japan
- Department of Gastroenterology, Faculty of Medicine, Hyogo Medical University, Nishinomiya, 663-8501, Japan
| | - Tetsuo Takehara
- Department of Gastroenterology and Hepatology, Osaka University Graduate School of Medicine, 2-2, Yamadaoka, Suita, Osaka, 565-0871, Japan.
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Chen C, Tang F, Herth FJF, Zuo Y, Ren J, Zhang S, Jian W, Tang C, Li S. Building and validating an artificial intelligence model to identify tracheobronchopathia osteochondroplastica by using bronchoscopic images. Ther Adv Respir Dis 2024; 18:17534666241253694. [PMID: 38803144 PMCID: PMC11131396 DOI: 10.1177/17534666241253694] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Accepted: 04/22/2024] [Indexed: 05/29/2024] Open
Abstract
BACKGROUND Given the rarity of tracheobronchopathia osteochondroplastica (TO), many young doctors in primary hospitals are unable to identify TO based on bronchoscopy findings. OBJECTIVES To build an artificial intelligence (AI) model for differentiating TO from other multinodular airway diseases by using bronchoscopic images. DESIGN We designed the study by comparing the imaging data of patients undergoing bronchoscopy from January 2010 to October 2022 by using EfficientNet. Bronchoscopic images of 21 patients with TO at Anhui Chest Hospital from October 2019 to October 2022 were collected for external validation. METHODS Bronchoscopic images of patients with multinodular airway lesions (including TO, amyloidosis, tumors, and inflammation) and without airway lesions in the First Affiliated Hospital of Guangzhou Medical University were collected. The images were randomized (4:1) into training and validation groups based on different diseases and utilized for deep learning by convolutional neural networks (CNNs). RESULTS We enrolled 201 patients with multinodular airway disease (38, 15, 75, and 73 patients with TO, amyloidosis, tumors, and inflammation, respectively) and 213 without any airway lesions. To find multinodular lesion images for deep learning, we utilized 2183 bronchoscopic images of multinodular lesions (including TO, amyloidosis, tumor, and inflammation) and compared them with images without any airway lesions (1733). The accuracy of multinodular lesion identification was 98.9%. Further, the accuracy of TO detection based on the bronchoscopic images of multinodular lesions was 89.2%. Regarding external validation (using images from 21 patients with TO), all patients could be diagnosed with TO; the accuracy was 89.8%. CONCLUSION We built an AI model that could differentiate TO from other multinodular airway diseases (mainly amyloidosis, tumors, and inflammation) by using bronchoscopic images. The model could help young physicians identify this rare airway disease.
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Affiliation(s)
- Chongxiang Chen
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong Province, China
| | - Fei Tang
- Department of Interventional Pulmonary and Endoscopic Diagnosis and Treatment Center, Anhui Chest Hospital, Hefei, Anhui Province, China
| | - Felix J. F. Herth
- Department of Pneumology and Critical Care Medicine and Translational Research Unit, Thoraxklinik, University Hospital Heidelberg, Heidelberg, Germany
| | - Yingnan Zuo
- Guangzhou Tianpeng Computer Technology Co., Ltd. Guangzhou, Guangdong, China
| | - Jiangtao Ren
- School of Computer Science and Engineering, Guangdong Province Key Lab of Computational Science, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Shuaiqi Zhang
- School of Public Health, Sun Yat-sen University, Guangzhou, China
| | - Wenhua Jian
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong Province, China
| | - Chunli Tang
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong Province 510000, China
| | - Shiyue Li
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong Province 510000, China
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9
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Klang E, Sourosh A, Nadkarni GN, Sharif K, Lahat A. Deep Learning and Gastric Cancer: Systematic Review of AI-Assisted Endoscopy. Diagnostics (Basel) 2023; 13:3613. [PMID: 38132197 PMCID: PMC10742887 DOI: 10.3390/diagnostics13243613] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2023] [Revised: 11/23/2023] [Accepted: 12/02/2023] [Indexed: 12/23/2023] Open
Abstract
BACKGROUND Gastric cancer (GC), a significant health burden worldwide, is typically diagnosed in the advanced stages due to its non-specific symptoms and complex morphological features. Deep learning (DL) has shown potential for improving and standardizing early GC detection. This systematic review aims to evaluate the current status of DL in pre-malignant, early-stage, and gastric neoplasia analysis. METHODS A comprehensive literature search was conducted in PubMed/MEDLINE for original studies implementing DL algorithms for gastric neoplasia detection using endoscopic images. We adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The focus was on studies providing quantitative diagnostic performance measures and those comparing AI performance with human endoscopists. RESULTS Our review encompasses 42 studies that utilize a variety of DL techniques. The findings demonstrate the utility of DL in GC classification, detection, tumor invasion depth assessment, cancer margin delineation, lesion segmentation, and detection of early-stage and pre-malignant lesions. Notably, DL models frequently matched or outperformed human endoscopists in diagnostic accuracy. However, heterogeneity in DL algorithms, imaging techniques, and study designs precluded a definitive conclusion about the best algorithmic approach. CONCLUSIONS The promise of artificial intelligence in improving and standardizing gastric neoplasia detection, diagnosis, and segmentation is significant. This review is limited by predominantly single-center studies and undisclosed datasets used in AI training, impacting generalizability and demographic representation. Further, retrospective algorithm training may not reflect actual clinical performance, and a lack of model details hinders replication efforts. More research is needed to substantiate these findings, including larger-scale multi-center studies, prospective clinical trials, and comprehensive technical reporting of DL algorithms and datasets, particularly regarding the heterogeneity in DL algorithms and study designs.
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Affiliation(s)
- Eyal Klang
- Division of Data-Driven and Digital Medicine (D3M), Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA (A.S.); (G.N.N.)
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- ARC Innovation Center, Sheba Medical Center, Affiliated with Tel Aviv University Medical School, Tel Hashomer, Ramat Gan 52621, Tel Aviv, Israel
| | - Ali Sourosh
- Division of Data-Driven and Digital Medicine (D3M), Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA (A.S.); (G.N.N.)
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Girish N. Nadkarni
- Division of Data-Driven and Digital Medicine (D3M), Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA (A.S.); (G.N.N.)
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Kassem Sharif
- Department of Gastroenterology, Sheba Medical Center, Affiliated with Tel Aviv University Medical School, Tel Hashomer, Ramat Gan 52621, Tel Aviv, Israel;
| | - Adi Lahat
- Department of Gastroenterology, Sheba Medical Center, Affiliated with Tel Aviv University Medical School, Tel Hashomer, Ramat Gan 52621, Tel Aviv, Israel;
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10
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Chen C, Herth FJF, Zuo Y, Li H, Liang X, Chen Y, Ren J, Jian W, Zhong C, Li S. Distinguishing bronchoscopically observed anatomical positions of airway under by convolutional neural network. Ther Adv Chronic Dis 2023; 14:20406223231181495. [PMID: 37637372 PMCID: PMC10457519 DOI: 10.1177/20406223231181495] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2023] [Accepted: 05/24/2023] [Indexed: 08/29/2023] Open
Abstract
Background Artificial intelligence (AI) technology has been used for finding lesions via gastrointestinal endoscopy. However, there were few AI-associated studies that discuss bronchoscopy. Objectives To use convolutional neural network (CNN) to recognize the observed anatomical positions of the airway under bronchoscopy. Design We designed the study by comparing the imaging data of patients undergoing bronchoscopy from March 2022 to October 2022 by using EfficientNet (one of the CNNs) and U-Net. Methods Based on the inclusion and exclusion criteria, 1527 clear images of normal anatomical positions of the airways from 200 patients were used for training, and 475 clear images from 72 patients were utilized for validation. Further, 20 bronchoscopic videos of examination procedures in another 20 patients with normal airway structures were used to extract the bronchoscopic images of normal anatomical positions to evaluate the accuracy for the model. Finally, 21 respiratory doctors were enrolled for the test of recognizing corrected anatomical positions using the validating datasets. Results In all, 1527 bronchoscopic images of 200 patients with nine anatomical positions of the airway, including carina, right main bronchus, right upper lobe bronchus, right intermediate bronchus, right middle lobe bronchus, right lower lobe bronchus, left main bronchus, left upper lobe bronchus, and left lower lobe bronchus, were used for supervised machine learning and training, and 475 clear bronchoscopic images of 72 patients were used for validation. The mean accuracy of recognizing these 9 positions was 91% (carina: 98%, right main bronchus: 98%, right intermediate bronchus: 90%, right upper lobe bronchus: 91%, right middle lobe bronchus 92%, right lower lobe bronchus: 83%, left main bronchus: 89%, left upper bronchus: 91%, left lower bronchus: 76%). The area under the curves for these nine positions were >0.98. In addition, the accuracy of extracting the images via the video by the trained model was 94.7%. We also conducted a deep learning study to segment 10 segment bronchi in right lung, and 8 segment bronchi in Left lung. Because of the problem of radial depth, only segment bronchi distributions below right upper bronchus and right middle bronchus could be correctly recognized. The accuracy of recognizing was 84.33 ± 7.52% by doctors receiving interventional pulmonology education in our hospital over 6 months. Conclusion Our study proved that AI technology can be used to distinguish the normal anatomical positions of the airway, and the model we trained could extract the corrected images via the video to help standardize data collection and control quality.
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Affiliation(s)
- Chongxiang Chen
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Felix JF Herth
- Department of Pneumology and Critical Care Medicine and Translational Research Unit, Thoraxklinik, University Hospital Heidelberg, Heidelberg, Germany
| | - Yingnan Zuo
- Guangzhou Tianpeng Computer Technology Co., Ltd. Guangzhou, China
| | - Hongjia Li
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Xinyuan Liang
- School of Public Health, Sun Yat-sen University, Guangzhou, China
| | - Yaqing Chen
- School of Public Health, Sun Yat-sen University, Guangzhou, China
| | - Jiangtao Ren
- School of Computer Science and Engineering, Guangdong Province Key Lab of Computational Science, Sun Yat-sen University, Guangzhou, China
| | - Wenhua Jian
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Changhao Zhong
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong 510000, China
| | - Shiyue Li
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong 510000, China
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Takemoto S, Hori K, Yoshimasa S, Nishimura M, Nakajo K, Inaba A, Sasabe M, Aoyama N, Watanabe T, Minakata N, Ikematsu H, Yokota H, Yano T. Computer-aided demarcation of early gastric cancer: a pilot comparative study with endoscopists. J Gastroenterol 2023; 58:741-750. [PMID: 37256409 DOI: 10.1007/s00535-023-02001-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Accepted: 05/04/2023] [Indexed: 06/01/2023]
Abstract
BACKGROUND Precise area diagnosis of early gastric cancer (EGC) is critical for reliable endoscopic resection. Computer-aided diagnosis (CAD) shows strong potential for detecting EGC and reducing cancer-care disparities caused by differences in endoscopists' skills. To be used in clinical practice, CAD should enable both the detection and the demarcation of lesions. This study proposes a scheme for the detection and delineation of EGC under white-light endoscopy and validates its performance using 1-year consecutive cases. METHODS Only 300 endoscopic images randomly selected from 68 consecutive cases were used for training a convolutional neural network. All cases were treated with endoscopic submucosal dissection, enabling the accumulation of a training dataset in which the extent of lesions was precisely determined. For validation, 462 cancer images and 396 normal images from 137 consecutive cases were used. From the validation results, 38 randomly selected images were compared with those delineated by six endoscopists. RESULTS Successful detections of EGC in 387 cancer images (83.8%) and the absence of lesions in 307 normal images (77.5%) were achieved. Positive and negative predictive values were 81.3% and 80.4%, respectively. Successful detection was achieved in 130 cases (94.9%). We achieved precise demarcation of EGC with a mean intersection over union of 66.5%, showing the extent of lesions with a smooth boundary; the results were comparable to those achieved by specialists. CONCLUSIONS Our scheme, validated using 1-year consecutive cases, shows potential for demarcating EGC. Its performance matched that of specialists; it might therefore be suitable for clinical use in the future.
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Affiliation(s)
- Satoko Takemoto
- Image Processing Research Team, Center for Advanced Photonics, RIKEN, 2-1 Hirosawa, Wako-shi, Saitama, 351-0198, Japan
| | - Keisuke Hori
- Department of Gastroenterology and Endoscopy, National Cancer Center Hospital East, Kashiwa, Japan
- Department of Internal Medicine, Tsuyama Chuo Hospital, Tsuyama, Japan
| | - Sakai Yoshimasa
- Image Processing Research Team, Center for Advanced Photonics, RIKEN, 2-1 Hirosawa, Wako-shi, Saitama, 351-0198, Japan
| | - Masaomi Nishimura
- Image Processing Research Team, Center for Advanced Photonics, RIKEN, 2-1 Hirosawa, Wako-shi, Saitama, 351-0198, Japan
| | - Keiichiro Nakajo
- Department of Gastroenterology and Endoscopy, National Cancer Center Hospital East, Kashiwa, Japan
- Division of Science and Technology for Endoscopy, Exploratory Oncology Research and Clinical Trial Center, National Cancer Center, Kashiwa, Japan
| | - Atsushi Inaba
- Department of Gastroenterology and Endoscopy, National Cancer Center Hospital East, Kashiwa, Japan
| | - Maasa Sasabe
- Department of Gastroenterology and Endoscopy, National Cancer Center Hospital East, Kashiwa, Japan
| | - Naoki Aoyama
- Department of Gastroenterology and Endoscopy, National Cancer Center Hospital East, Kashiwa, Japan
| | - Takashi Watanabe
- Department of Gastroenterology and Endoscopy, National Cancer Center Hospital East, Kashiwa, Japan
| | - Nobuhisa Minakata
- Department of Gastroenterology and Endoscopy, National Cancer Center Hospital East, Kashiwa, Japan
| | - Hiroaki Ikematsu
- Department of Gastroenterology and Endoscopy, National Cancer Center Hospital East, Kashiwa, Japan
- Division of Science and Technology for Endoscopy, Exploratory Oncology Research and Clinical Trial Center, National Cancer Center, Kashiwa, Japan
| | - Hideo Yokota
- Image Processing Research Team, Center for Advanced Photonics, RIKEN, 2-1 Hirosawa, Wako-shi, Saitama, 351-0198, Japan.
| | - Tomonori Yano
- Department of Gastroenterology and Endoscopy, National Cancer Center Hospital East, Kashiwa, Japan
- Division of Science and Technology for Endoscopy, Exploratory Oncology Research and Clinical Trial Center, National Cancer Center, Kashiwa, Japan
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12
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Ning B, Zhao C, Zhao X, Linghu E. The application of artificial intelligence in the digestive system. GASTROENTEROLOGY & ENDOSCOPY 2023; 1:150-151. [DOI: 10.1016/j.gande.2023.07.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/04/2025]
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13
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Lee J, Lee H, Chung JW. The Role of Artificial Intelligence in Gastric Cancer: Surgical and Therapeutic Perspectives: A Comprehensive Review. J Gastric Cancer 2023; 23:375-387. [PMID: 37553126 PMCID: PMC10412973 DOI: 10.5230/jgc.2023.23.e31] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Revised: 07/31/2023] [Accepted: 07/31/2023] [Indexed: 08/10/2023] Open
Abstract
Stomach cancer has a high annual mortality rate worldwide necessitating early detection and accurate treatment. Even experienced specialists can make erroneous judgments based on several factors. Artificial intelligence (AI) technologies are being developed rapidly to assist in this field. Here, we aimed to determine how AI technology is used in gastric cancer diagnosis and analyze how it helps patients and surgeons. Early detection and correct treatment of early gastric cancer (EGC) can greatly increase survival rates. To determine this, it is important to accurately determine the diagnosis and depth of the lesion and the presence or absence of metastasis to the lymph nodes, and suggest an appropriate treatment method. The deep learning algorithm, which has learned gastric lesion endoscopyimages, morphological characteristics, and patient clinical information, detects gastric lesions with high accuracy, sensitivity, and specificity, and predicts morphological characteristics. Through this, AI assists the judgment of specialists to help select the correct treatment method among endoscopic procedures and radical resections and helps to predict the resection margins of lesions. Additionally, AI technology has increased the diagnostic rate of both relatively inexperienced and skilled endoscopic diagnosticians. However, there were limitations in the data used for learning, such as the amount of quantitatively insufficient data, retrospective study design, single-center design, and cases of non-various lesions. Nevertheless, this assisted endoscopic diagnosis technology that incorporates deep learning technology is sufficiently practical and future-oriented and can play an important role in suggesting accurate treatment plans to surgeons for resection of lesions in the treatment of EGC.
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Affiliation(s)
- JunHo Lee
- Division of Gastroenterology, Department of Internal Medicine, Gachon University Gil Medical Center, Incheon, Korea
- Corp. CAIMI, Incheon, Korea
| | - Hanna Lee
- Division of Gastroenterology, Department of Internal Medicine, Gachon University Gil Medical Center, Incheon, Korea
| | - Jun-Won Chung
- Division of Gastroenterology, Department of Internal Medicine, Gachon University Gil Medical Center, Incheon, Korea
- Corp. CAIMI, Incheon, Korea.
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14
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Furube T, Takeuchi M, Kawakubo H, Maeda Y, Matsuda S, Fukuda K, Nakamura R, Kitagawa Y. The relationship between the esophageal endoscopic submucosal dissection technical difficulty and its intraoperative process. Esophagus 2023; 20:264-271. [PMID: 36508068 DOI: 10.1007/s10388-022-00974-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Accepted: 11/28/2022] [Indexed: 02/03/2023]
Abstract
BACKGROUND Estimating the esophageal endoscopic submucosal dissection (ESD) technical difficulty is important to reduce complications. Endoscopic duration is one of the related factors to a technical difficulty. The relationship between the esophageal ESD technical difficulty and its intraoperative process was analyzed as a first step toward automatic technical difficulty recognition using artificial intelligence. METHODS This study enrolled 75 patients with superficial esophageal cancer who underwent esophageal ESD. The technical difficulty score was established, which consisted of three factors, including total procedure duration, en bloc resection, and complications. Additionally, technical difficulty-related factors, which were perioperative factors that included the intraoperative process, were investigated. RESULTS Eight (11%) patients were allocated to high difficulty, whereas 67 patients (89%) were allocated to low difficulty. The intraoperative process, which was shown as the extension of each endoscopic phase, was significantly related to a technical difficulty. The area under the curve (AUC) values were higher at all the phase duration than at the clinical characteristics. Submucosal dissection phase (AUC 0.902; 95% confidence intervals (CI) 0.752-1.000), marking phase (AUC 0.827; 95% CI 0.703-0.951), and early phase which was defined as the duration from the start of marking to the end of submucosal injection (AUC 0.847; 95% CI 0.701-0.992) were significantly related to technical difficulty. CONCLUSIONS The intraoperative process, particularly early phase, was strongly associated with esophageal ESD technical difficulty. This study demonstrated the potential for automatic evaluation of esophageal ESD technical difficulty when combined with an AI-based automatic phase evaluation system.
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Affiliation(s)
- Tasuku Furube
- Department of Surgery, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-Ku, Tokyo, 160-8582, Japan
| | - Masashi Takeuchi
- Department of Surgery, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-Ku, Tokyo, 160-8582, Japan
| | - Hirofumi Kawakubo
- Department of Surgery, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-Ku, Tokyo, 160-8582, Japan.
| | - Yusuke Maeda
- Department of Surgery, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-Ku, Tokyo, 160-8582, Japan
| | - Satoru Matsuda
- Department of Surgery, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-Ku, Tokyo, 160-8582, Japan
| | - Kazumasa Fukuda
- Department of Surgery, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-Ku, Tokyo, 160-8582, Japan
| | - Rieko Nakamura
- Department of Surgery, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-Ku, Tokyo, 160-8582, Japan
| | - Yuko Kitagawa
- Department of Surgery, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-Ku, Tokyo, 160-8582, Japan
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15
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Yin M, Liu L, Gao J, Lin J, Qu S, Xu W, Liu X, Xu C, Zhu J. Deep learning for pancreatic diseases based on endoscopic ultrasound: A systematic review. Int J Med Inform 2023; 174:105044. [PMID: 36948061 DOI: 10.1016/j.ijmedinf.2023.105044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Revised: 03/06/2023] [Accepted: 03/10/2023] [Indexed: 03/19/2023]
Abstract
BACKGROUND AND AIMS Endoscopic ultrasonography (EUS) is one of the main examinations in pancreatic diseases. A series of the studies reported the application of deep learning (DL)-assisted EUS in the diagnosis of pancreatic diseases. This systematic review is to evaluate the role of DL algorithms in assisting EUS diagnosis of pancreatic diseases. METHODS Literature search were conducted in PubMed and Semantic Scholar databases. Studies that developed DL models for pancreatic diseases based on EUS were eligible for inclusion. This review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines and quality assessment of the included studies was performed according to the IJMEDI checklist. RESULTS A total of 23 studies were enrolled into this systematic review, which could be categorized into three groups according to computer vision tasks: classification, detection and segmentation. Seventeen studies focused on the classification task, among which five studies developed simple neural network (NN) models while twelve studies constructed convolutional NN (CNN) models. Three studies were concerned the detection task and five studies were the segmentation task, all based on CNN architectures. All models presented in the studies performed well based on EUS images, videos or voice. According to the IJMEDI checklist, six studies were recognized as high-grade quality, with scores beyond 35 points. CONCLUSIONS DL algorithms show great potential in EUS images/videos/voice for pancreatic diseases. However, there is room for improvement such as sample sizes, multi-center cooperation, data preprocessing, model interpretability, and code sharing.
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Affiliation(s)
- Minyue Yin
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou 215000, China; Suzhou Clinical Center of Digestive Diseases, Suzhou 215000, China
| | - Lu Liu
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou 215000, China; Suzhou Clinical Center of Digestive Diseases, Suzhou 215000, China
| | - Jingwen Gao
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou 215000, China; Suzhou Clinical Center of Digestive Diseases, Suzhou 215000, China
| | - Jiaxi Lin
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou 215000, China; Suzhou Clinical Center of Digestive Diseases, Suzhou 215000, China
| | - Shuting Qu
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou 215000, China; Suzhou Clinical Center of Digestive Diseases, Suzhou 215000, China
| | - Wei Xu
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou 215000, China; Suzhou Clinical Center of Digestive Diseases, Suzhou 215000, China
| | - Xiaolin Liu
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou 215000, China; Suzhou Clinical Center of Digestive Diseases, Suzhou 215000, China
| | - Chunfang Xu
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou 215000, China; Suzhou Clinical Center of Digestive Diseases, Suzhou 215000, China.
| | - Jinzhou Zhu
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou 215000, China; Suzhou Clinical Center of Digestive Diseases, Suzhou 215000, China.
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A deep-learning based system using multi-modal data for diagnosing gastric neoplasms in real-time (with video). Gastric Cancer 2023; 26:275-285. [PMID: 36520317 DOI: 10.1007/s10120-022-01358-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Accepted: 11/25/2022] [Indexed: 12/23/2022]
Abstract
BACKGROUND White light (WL) and weak-magnifying (WM) endoscopy are both important methods for diagnosing gastric neoplasms. This study constructed a deep-learning system named ENDOANGEL-MM (multi-modal) aimed at real-time diagnosing gastric neoplasms using WL and WM data. METHODS WL and WM images of a same lesion were combined into image-pairs. A total of 4201 images, 7436 image-pairs, and 162 videos were used for model construction and validation. Models 1-5 including two single-modal models (WL, WM) and three multi-modal models (data fusion on task-level, feature-level, and input-level) were constructed. The models were tested on three levels including images, videos, and prospective patients. The best model was selected for constructing ENDOANGEL-MM. We compared the performance between the models and endoscopists and conducted a diagnostic study to explore the ENDOANGEL-MM's assistance ability. RESULTS Model 4 (ENDOANGEL-MM) showed the best performance among five models. Model 2 performed better in single-modal models. The accuracy of ENDOANGEL-MM was higher than that of Model 2 in still images, real-time videos, and prospective patients. (86.54 vs 78.85%, P = 0.134; 90.00 vs 85.00%, P = 0.179; 93.55 vs 70.97%, P < 0.001). Model 2 and ENDOANGEL-MM outperformed endoscopists on WM data (85.00 vs 71.67%, P = 0.002) and multi-modal data (90.00 vs 76.17%, P = 0.002), significantly. With the assistance of ENDOANGEL-MM, the accuracy of non-experts improved significantly (85.75 vs 70.75%, P = 0.020), and performed no significant difference from experts (85.75 vs 89.00%, P = 0.159). CONCLUSIONS The multi-modal model constructed by feature-level fusion showed the best performance. ENDOANGEL-MM identified gastric neoplasms with good accuracy and has a potential role in real-clinic.
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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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18
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Yuan XL, Zhou Y, Liu W, Luo Q, Zeng XH, Yi Z, Hu B. Artificial intelligence for diagnosing gastric lesions under white-light endoscopy. Surg Endosc 2022; 36:9444-9453. [PMID: 35879572 DOI: 10.1007/s00464-022-09420-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Accepted: 06/24/2022] [Indexed: 02/05/2023]
Abstract
BACKGROUND The ability of endoscopists to identify gastric lesions is uneven. Even experienced endoscopists may miss or misdiagnose lesions due to heavy workload or fatigue or subtle changes in lesions under white-light endoscopy (WLE). This study aimed to develop an artificial intelligence (AI) system that could diagnose six common gastric lesions under WLE and to explore its role in assisting endoscopists in diagnosis. METHODS Images of early gastric cancer, advanced gastric cancer, submucosal tumor, polyp, peptic ulcer, erosion, and lesion-free gastric mucosa were retrospectively collected to train and test the system. The performance of the system was compared with that of 12 endoscopists. The performance of endoscopists with or without referring to the system was also evaluated. RESULTS A total of 29,809 images from 8947 patients and 1579 images from 496 patients were used to train and test the system, respectively. For per-lesion analysis, the overall accuracy of the system was 85.7%, which was comparable to that of senior endoscopists (85.1%, P = 0.729) and significantly higher than that of junior endoscopists (78.8%, P < 0.001). With system assistance, the overall accuracies of senior and junior endoscopists increased to 89.3% (4.2%, P < 0.001) and 86.2% (7.4%, P < 0.001), respectively. Senior and junior endoscopists achieved varying degrees of improvement in the diagnostic performance of other types of lesions except for polyp. The diagnostic times of senior (3.8 vs 3.2 s per image, P = 0.500) and junior endoscopists (6.2 vs 4.6 s per image, P = 0.144) assisted by the system were both slightly shortened, despite no significant differences. CONCLUSIONS The proposed AI system could be applied as an auxiliary tool to reduce the workload of endoscopists and improve the diagnostic accuracy of gastric lesions.
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Affiliation(s)
- Xiang-Lei Yuan
- Department of Gastroenterology, West China Hospital, Sichuan University, No. 37 Guo Xue Alley, Wu Hou District, Chengdu, 610041, Sichuan, China
| | - Yao Zhou
- Center of Intelligent Medicine, College of Computer Science, Sichuan University, No.24 South Section 1, Yihuan Road Chengdu, Chengdu, 610065, Sichuan, China
| | - Wei Liu
- Department of Gastroenterology, West China Hospital, Sichuan University, No. 37 Guo Xue Alley, Wu Hou District, Chengdu, 610041, Sichuan, China
| | - Qi Luo
- Department of Gastroenterology, West China Hospital, Sichuan University, No. 37 Guo Xue Alley, Wu Hou District, Chengdu, 610041, Sichuan, China
| | - Xian-Hui Zeng
- Department of Gastroenterology, West China Hospital, Sichuan University, No. 37 Guo Xue Alley, Wu Hou District, Chengdu, 610041, Sichuan, China
| | - Zhang Yi
- Center of Intelligent Medicine, College of Computer Science, Sichuan University, No.24 South Section 1, Yihuan Road Chengdu, Chengdu, 610065, Sichuan, China.
| | - Bing Hu
- Department of Gastroenterology, West China Hospital, Sichuan University, No. 37 Guo Xue Alley, Wu Hou District, Chengdu, 610041, Sichuan, China.
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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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Alrowais F, S. Alotaibi S, Marzouk R, S. Salama A, Rizwanullah M, Zamani AS, Atta Abdelmageed A, I. Eldesouki M. Manta Ray Foraging Optimization Transfer Learning-Based Gastric Cancer Diagnosis and Classification on Endoscopic Images. Cancers (Basel) 2022; 14:5661. [PMID: 36428752 PMCID: PMC9688577 DOI: 10.3390/cancers14225661] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Revised: 11/10/2022] [Accepted: 11/14/2022] [Indexed: 11/19/2022] Open
Abstract
Gastric cancer (GC) diagnoses using endoscopic images have gained significant attention in the healthcare sector. The recent advancements of computer vision (CV) and deep learning (DL) technologies pave the way for the design of automated GC diagnosis models. Therefore, this study develops a new Manta Ray Foraging Optimization Transfer Learning technique that is based on Gastric Cancer Diagnosis and Classification (MRFOTL-GCDC) using endoscopic images. For enhancing the quality of the endoscopic images, the presented MRFOTL-GCDC technique executes the Wiener filter (WF) to perform a noise removal process. In the presented MRFOTL-GCDC technique, MRFO with SqueezeNet model is used to derive the feature vectors. Since the trial-and-error hyperparameter tuning is a tedious process, the MRFO algorithm-based hyperparameter tuning results in enhanced classification results. Finally, the Elman Neural Network (ENN) model is utilized for the GC classification. To depict the enhanced performance of the presented MRFOTL-GCDC technique, a widespread simulation analysis is executed. The comparison study reported the improvement of the MRFOTL-GCDC technique for endoscopic image classification purposes with an improved accuracy of 99.25%.
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Affiliation(s)
- Fadwa Alrowais
- Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
| | - Saud S. Alotaibi
- Department of Information Systems, College of Computing and Information System, Umm Al-Qura University, Saudi Arabia
| | - Radwa Marzouk
- Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
| | - Ahmed S. Salama
- Department of Electrical Engineering, Faculty of Engineering & Technology, Future University in Egypt, New Cairo 11845, Egypt
| | - Mohammed Rizwanullah
- Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, AlKharj, Saudi Arabia
| | - Abu Sarwar Zamani
- Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, AlKharj, Saudi Arabia
| | - Amgad Atta Abdelmageed
- Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, AlKharj, Saudi Arabia
| | - Mohamed I. Eldesouki
- Department of Information System, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, AlKharj, Saudi Arabia
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21
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Lee SW. Need for careful endoscopic evaluation of large gastric neoplasms before endoscopic submucosal dissection. Clin Endosc 2022; 55:753-754. [PMID: 36464822 PMCID: PMC9726438 DOI: 10.5946/ce.2022.266] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Revised: 11/05/2022] [Accepted: 11/12/2022] [Indexed: 11/30/2022] Open
Affiliation(s)
- Seung Woo Lee
- Division of Gastroenterology, Department of Internal Medicine, College of Medicine, The Catholic University of Korea, Seoul, Korea
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22
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Jin J, Zhang Q, Dong B, Ma T, Mei X, Wang X, Song S, Peng J, Wu A, Dong L, Kong D. Automatic detection of early gastric cancer in endoscopy based on Mask region-based convolutional neural networks (Mask R-CNN)(with video). Front Oncol 2022; 12:927868. [PMID: 36338757 PMCID: PMC9630732 DOI: 10.3389/fonc.2022.927868] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Accepted: 09/05/2022] [Indexed: 12/04/2022] Open
Abstract
The artificial intelligence (AI)-assisted endoscopic detection of early gastric cancer (EGC) has been preliminarily developed. The currently used algorithms still exhibit limitations of large calculation and low-precision expression. The present study aimed to develop an endoscopic automatic detection system in EGC based on a mask region-based convolutional neural network (Mask R-CNN) and to evaluate the performance in controlled trials. For this purpose, a total of 4,471 white light images (WLIs) and 2,662 narrow band images (NBIs) of EGC were obtained for training and testing. In total, 10 of the WLIs (videos) were obtained prospectively to examine the performance of the RCNN system. Furthermore, 400 WLIs were randomly selected for comparison between the Mask R-CNN system and doctors. The evaluation criteria included accuracy, sensitivity, specificity, positive predictive value and negative predictive value. The results revealed that there were no significant differences between the pathological diagnosis with the Mask R-CNN system in the WLI test (χ2 = 0.189, P=0.664; accuracy, 90.25%; sensitivity, 91.06%; specificity, 89.01%) and in the NBI test (χ2 = 0.063, P=0.802; accuracy, 95.12%; sensitivity, 97.59%). Among 10 WLI real-time videos, the speed of the test videos was up to 35 frames/sec, with an accuracy of 90.27%. In a controlled experiment of 400 WLIs, the sensitivity of the Mask R-CNN system was significantly higher than that of experts (χ2 = 7.059, P=0.000; 93.00% VS 80.20%), and the specificity was higher than that of the juniors (χ2 = 9.955, P=0.000, 82.67% VS 71.87%), and the overall accuracy rate was higher than that of the seniors (χ2 = 7.009, P=0.000, 85.25% VS 78.00%). On the whole, the present study demonstrates that the Mask R-CNN system exhibited an excellent performance status for the detection of EGC, particularly for the real-time analysis of WLIs. It may thus be effectively applied to clinical settings.
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Affiliation(s)
- Jing Jin
- Key Laboratory of Digestive Diseases of Anhui Province, Department of Gastroenterology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Qianqian Zhang
- Key Laboratory of Digestive Diseases of Anhui Province, Department of Gastroenterology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Bill Dong
- School of Computer Science and Technology, University of Science and Technology of China, Hefei, China
| | - Tao Ma
- School of Computer Science and Technology, University of Science and Technology of China, Hefei, China
| | - Xuecan Mei
- Key Laboratory of Digestive Diseases of Anhui Province, Department of Gastroenterology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Xi Wang
- Key Laboratory of Digestive Diseases of Anhui Province, Department of Gastroenterology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Shaofang Song
- Research and Development Department, Hefei Zhongna Medical Instrument Co. LTD, Hefei, China
| | - Jie Peng
- Research and Development Department, Hefei Zhongna Medical Instrument Co. LTD, Hefei, China
| | - Aijiu Wu
- Research and Development Department, Hefei Zhongna Medical Instrument Co. LTD, Hefei, China
| | - Lanfang Dong
- School of Computer Science and Technology, University of Science and Technology of China, Hefei, China
| | - Derun Kong
- Key Laboratory of Digestive Diseases of Anhui Province, Department of Gastroenterology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
- *Correspondence: Derun Kong,
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Abstract
Artificial intelligence (AI) is rapidly developing in various medical fields, and there is an increase in research performed in the field of gastrointestinal (GI) endoscopy. In particular, the advent of convolutional neural network, which is a class of deep learning method, has the potential to revolutionize the field of GI endoscopy, including esophagogastroduodenoscopy (EGD), capsule endoscopy (CE), and colonoscopy. A total of 149 original articles pertaining to AI (27 articles in esophagus, 30 articles in stomach, 29 articles in CE, and 63 articles in colon) were identified in this review. The main focuses of AI in EGD are cancer detection, identifying the depth of cancer invasion, prediction of pathological diagnosis, and prediction of Helicobacter pylori infection. In the field of CE, automated detection of bleeding sites, ulcers, tumors, and various small bowel diseases is being investigated. AI in colonoscopy has advanced with several patient-based prospective studies being conducted on the automated detection and classification of colon polyps. Furthermore, research on inflammatory bowel disease has also been recently reported. Most studies of AI in the field of GI endoscopy are still in the preclinical stages because of the retrospective design using still images. Video-based prospective studies are needed to advance the field. However, AI will continue to develop and be used in daily clinical practice in the near future. In this review, we have highlighted the published literature along with providing current status and insights into the future of AI in GI endoscopy.
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Affiliation(s)
- Yutaka Okagawa
- Endoscopy Division, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan.,Department of Gastroenterology, Tonan Hospital, Sapporo, Japan
| | - Seiichiro Abe
- Endoscopy Division, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan.
| | - Masayoshi Yamada
- Endoscopy Division, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan
| | - Ichiro Oda
- Endoscopy Division, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan
| | - Yutaka Saito
- Endoscopy Division, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan
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24
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Panarese A. Usefulness of artificial intelligence in early gastric cancer. Artif Intell Cancer 2022; 3:17-26. [DOI: 10.35713/aic.v3.i2.17] [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: 12/31/2021] [Revised: 03/27/2022] [Accepted: 04/19/2022] [Indexed: 02/06/2023] Open
Abstract
Gastric cancer (GC) is a major cancer worldwide, with high mortality and morbidity. Endoscopy, important for the early detection of GC, requires trained skills, high-quality technologies, surveillance and screening programs. Early diagnosis allows a better prognosis, through surgical or curative endoscopic therapy. Magnified endoscopy with virtual chromoendoscopy remarkably improve the detection of early gastric cancer (EGC) when endoscopy is performed by expert endoscopists. Artificial intelligence (AI) has also been introduced to GC diagnostics to increase diagnostic efficiency. AI improves the early detection of gastric lesions because it supports the non-expert and experienced endoscopist in defining the margins of the tumor and the depth of infiltration. AI increases the detection rate of EGC, reduces the rate of missing tumors, and characterizes EGCs, allowing clinicians to make the best therapeutic decision, that is, one that ensures curability. AI has had a remarkable evolution in medicine in recent years, moving from the research phase to clinical practice. In addition, the diagnosis of GC has markedly progressed. We predict that AI will allow great evolution in the diagnosis and treatment of EGC by overcoming the variability in performance that is currently a limitation of chromoendoscopy.
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Affiliation(s)
- Alba Panarese
- Department of Gastroenterology and Endoscopy, Central Hospital, Taranto 74123, Italy
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25
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Mizuno S, Okabayashi K, Ikebata A, Matsui S, Seishima R, Shigeta K, Kitagawa Y. Prediction of pouchitis after ileal pouch-anal anastomosis in patients with ulcerative colitis using artificial intelligence and deep learning. Tech Coloproctol 2022; 26:471-478. [PMID: 35233723 DOI: 10.1007/s10151-022-02602-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Accepted: 02/16/2022] [Indexed: 02/07/2023]
Abstract
BACKGROUND Pouchitis is one of the major postoperative complications of ulcerative colitis (UC), and it is still difficult to predict the development of pouchitis after ileal pouch-anal anastomosis (IPAA) in UC patients. In this study, we examined whether a deep learning (DL) model could predict the development of pouchitis. METHODS UC patients who underwent two-stage restorative proctocolectomy with IPAA at Keio University Hospital were included in this retrospective analysis. The modified pouchitis disease activity index (mPDAI) was evaluated by the clinical and endoscopic findings. Pouchitis was defined as an mPDAI ≥ 5.860; endoscopic pouch images before ileostomy closure were collected. A convolutional neural network was used as the DL model, and the prediction rates of pouchitis after ileostomy closure were evaluated by fivefold cross-validation. RESULTS A total of 43 patients were included (24 males and 19 females, mean age 39.2 ± 13.2 years). Pouchitis occurred in 14 (33%) patients after ileostomy closure. In less than half of the patients, mPDAI scores matched before and after ileostomy closure. Most of patients whose mPDAI scores did not match before and after ileostomy closure had worse mPDAI scores after than before. The prediction rate of pouchitis calculated by the area under the curve using the DL model was 84%. Conversely, the prediction rate of pouchitis using mPDAI before ileostomy closure was 62%. CONCLUSION The prediction rate of pouchitis using the DL model was more than 20% higher than that using mPDAI, suggesting the utility of the DL model as a prediction model for the development of pouchitis. It could also be used to determine early interventions for pouchitis.
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Affiliation(s)
- S Mizuno
- Department of Surgery, Keio University School of Medicine, 35 Shinano-machi, Shinjuku-ku, Tokyo, 160-8582, Japan
| | - K Okabayashi
- Department of Surgery, Keio University School of Medicine, 35 Shinano-machi, Shinjuku-ku, Tokyo, 160-8582, Japan.
| | - A Ikebata
- Department of Surgery, Saitama Medical Center, Saitama, Japan
| | - S Matsui
- Department of Surgery, Keio University School of Medicine, 35 Shinano-machi, Shinjuku-ku, Tokyo, 160-8582, Japan
| | - R Seishima
- Department of Surgery, Keio University School of Medicine, 35 Shinano-machi, Shinjuku-ku, Tokyo, 160-8582, Japan
| | - K Shigeta
- Department of Surgery, Keio University School of Medicine, 35 Shinano-machi, Shinjuku-ku, Tokyo, 160-8582, Japan
| | - Y Kitagawa
- Department of Surgery, Keio University School of Medicine, 35 Shinano-machi, Shinjuku-ku, Tokyo, 160-8582, Japan
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26
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Zhao Y, Hu B, Wang Y, Yin X, Jiang Y, Zhu X. Identification of gastric cancer with convolutional neural networks: a systematic review. MULTIMEDIA TOOLS AND APPLICATIONS 2022; 81:11717-11736. [PMID: 35221775 PMCID: PMC8856868 DOI: 10.1007/s11042-022-12258-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Revised: 06/20/2021] [Accepted: 01/14/2022] [Indexed: 06/14/2023]
Abstract
The identification of diseases is inseparable from artificial intelligence. As an important branch of artificial intelligence, convolutional neural networks play an important role in the identification of gastric cancer. We conducted a systematic review to summarize the current applications of convolutional neural networks in the gastric cancer identification. The original articles published in Embase, Cochrane Library, PubMed and Web of Science database were systematically retrieved according to relevant keywords. Data were extracted from published papers. A total of 27 articles were retrieved for the identification of gastric cancer using medical images. Among them, 19 articles were applied in endoscopic images and 8 articles were applied in pathological images. 16 studies explored the performance of gastric cancer detection, 7 studies explored the performance of gastric cancer classification, 2 studies reported the performance of gastric cancer segmentation and 2 studies analyzed the performance of gastric cancer delineating margins. The convolutional neural network structures involved in the research included AlexNet, ResNet, VGG, Inception, DenseNet and Deeplab, etc. The accuracy of studies was 77.3 - 98.7%. Good performances of the systems based on convolutional neural networks have been showed in the identification of gastric cancer. Artificial intelligence is expected to provide more accurate information and efficient judgments for doctors to diagnose diseases in clinical work.
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Affiliation(s)
- Yuxue Zhao
- School of Nursing, Department of Medicine, Qingdao University, No. 15, Ningde Road, Shinan District, Qingdao, 266073 China
| | - Bo Hu
- Department of Thoracic Surgery, Qingdao Municipal Hospital, Qingdao, China
| | - Ying Wang
- School of Nursing, Department of Medicine, Qingdao University, No. 15, Ningde Road, Shinan District, Qingdao, 266073 China
| | - Xiaomeng Yin
- Pediatrics Intensive Care Unit, Qingdao Municipal Hospital, Qingdao, China
| | - Yuanyuan Jiang
- International Medical Services, Qilu Hospital of Shandong University, Jinan, China
| | - Xiuli Zhu
- School of Nursing, Department of Medicine, Qingdao University, No. 15, Ningde Road, Shinan District, Qingdao, 266073 China
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27
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Jin Z, Gan T, Wang P, Fu Z, Zhang C, Yan Q, Zheng X, Liang X, Ye X. Deep learning for gastroscopic images: computer-aided techniques for clinicians. Biomed Eng Online 2022; 21:12. [PMID: 35148764 PMCID: PMC8832738 DOI: 10.1186/s12938-022-00979-8] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Accepted: 01/21/2022] [Indexed: 12/13/2022] Open
Abstract
Gastric disease is a major health problem worldwide. Gastroscopy is the main method and the gold standard used to screen and diagnose many gastric diseases. However, several factors, such as the experience and fatigue of endoscopists, limit its performance. With recent advancements in deep learning, an increasing number of studies have used this technology to provide on-site assistance during real-time gastroscopy. This review summarizes the latest publications on deep learning applications in overcoming disease-related and nondisease-related gastroscopy challenges. The former aims to help endoscopists find lesions and characterize them when they appear in the view shed of the gastroscope. The purpose of the latter is to avoid missing lesions due to poor-quality frames, incomplete inspection coverage of gastroscopy, etc., thus improving the quality of gastroscopy. This study aims to provide technical guidance and a comprehensive perspective for physicians to understand deep learning technology in gastroscopy. Some key issues to be handled before the clinical application of deep learning technology and the future direction of disease-related and nondisease-related applications of deep learning to gastroscopy are discussed herein.
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Affiliation(s)
- Ziyi Jin
- Biosensor National Special Laboratory, Key Laboratory of Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, 310027, People's Republic of China
| | - Tianyuan Gan
- Biosensor National Special Laboratory, Key Laboratory of Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, 310027, People's Republic of China
| | - Peng Wang
- Biosensor National Special Laboratory, Key Laboratory of Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, 310027, People's Republic of China
| | - Zuoming Fu
- Biosensor National Special Laboratory, Key Laboratory of Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, 310027, People's Republic of China
| | - Chongan Zhang
- Biosensor National Special Laboratory, Key Laboratory of Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, 310027, People's Republic of China
| | - Qinglai Yan
- Hangzhou Center for Medical Device Quality Supervision and Testing, CFDA, Hangzhou, 310000, People's Republic of China
| | - Xueyong Zheng
- Department of General Surgery, Sir Run-Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, 310016, People's Republic of China
| | - Xiao Liang
- Department of General Surgery, Sir Run-Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, 310016, People's Republic of China
| | - Xuesong Ye
- Biosensor National Special Laboratory, Key Laboratory of Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, 310027, People's Republic of China.
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28
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Wu L, Xu M, Jiang X, He X, Zhang H, Ai Y, Tong Q, Lv P, Lu B, Guo M, Huang M, Ye L, Shen L, Yu H. Real-time artificial intelligence for detecting focal lesions and diagnosing neoplasms of the stomach by white-light endoscopy (with videos). Gastrointest Endosc 2022; 95:269-280.e6. [PMID: 34547254 DOI: 10.1016/j.gie.2021.09.017] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/23/2021] [Accepted: 09/04/2021] [Indexed: 12/24/2022]
Abstract
BACKGROUND AND AIMS White-light endoscopy (WLE) is the most pivotal tool to detect gastric cancer in an early stage. However, the skill among endoscopists varies greatly. Here, we aim to develop a deep learning-based system named ENDOANGEL-LD (lesion detection) to assist in detecting all focal gastric lesions and predicting neoplasms by WLE. METHODS Endoscopic images were retrospectively obtained from Renmin Hospital of Wuhan University (RHWU) for the development, validation, and internal test of the system. Additional external tests were conducted in 5 other hospitals to evaluate the robustness. Stored videos from RHWU were used for assessing and comparing the performance of ENDOANGEL-LD with that of experts. Prospective consecutive patients undergoing upper endoscopy were enrolled from May 6, 2021 to August 2, 2021 in RHWU to assess clinical practice applicability. RESULTS Over 10,000 patients undergoing upper endoscopy were enrolled in this study. The sensitivities were 96.9% and 95.6% for detecting gastric lesions and 92.9% and 91.7% for diagnosing neoplasms in internal and external patients, respectively. In 100 videos, ENDOANGEL-LD achieved superior sensitivity and negative predictive value for detecting gastric neoplasms from that of experts (100% vs 85.5% ± 3.4% [P = .003] and 100% vs 86.4% ± 2.8% [P = .002], respectively). In 2010 prospective consecutive patients, ENDOANGEL-LD achieved a sensitivity of 92.8% for detecting gastric lesions with 3.04 ± 3.04 false positives per gastroscopy and a sensitivity of 91.8% and specificity of 92.4% for diagnosing neoplasms. CONCLUSIONS Our results show that ENDOANGEL-LD has great potential for assisting endoscopists in screening gastric lesions and suspicious neoplasms in clinical work. (Clinical trial registration number: ChiCTR2100045963.).
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Affiliation(s)
- Lianlian Wu
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China; Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China; Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Ming Xu
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China; Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China; Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Xiaoda Jiang
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China; Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China; Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Xinqi He
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China; Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China; Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Heng Zhang
- Department of Gastroenterology, Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yaowei Ai
- Department of Gastroenterology, The People's Hospital of China Three Gorges University, The First People's Hospital of Yichang, Yichang, China
| | - Qiaoyun Tong
- Department of Gastroenterology, Yichang Central People's Hospital & Institute of Digestive Diseases, China Three Gorges University, Yichang, China
| | - Peihua Lv
- Spleen and Stomach Department, Jingmen Petrochemical Hospital, Jingmen, China
| | - Bin Lu
- Department of Gastroenterology, Xiaogan Central Hospital, Xiaogan, China
| | - Mingwen Guo
- Department of Gastroenterology, The People's Hospital of China Three Gorges University, The First People's Hospital of Yichang, Yichang, China
| | - Manling Huang
- Department of Gastroenterology, Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Liping Ye
- Department of Gastroenterology, Taizhou Hospital of Zhejiang Province affiliated to Wenzhou Medical University, Linhai, China
| | - Lei Shen
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China; Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China; Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Honggang Yu
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China; Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China; Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
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29
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Huang L, Liu J, Wu L, Xu M, Yao L, Zhang L, Shang R, Zhang M, Xiong Q, Wang D, Dong Z, Xu Y, Li J, Zhu Y, Gong D, Wu H, Yu H. Impact of Computer-Assisted System on the Learning Curve and Quality in Esophagogastroduodenoscopy: Randomized Controlled Trial. Front Med (Lausanne) 2021; 8:781256. [PMID: 34970565 PMCID: PMC8713729 DOI: 10.3389/fmed.2021.781256] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Accepted: 11/03/2021] [Indexed: 11/13/2022] Open
Abstract
Background and Aims: To investigate the impact of the computer-assisted system on esophagogastroduodenoscopy (EGD) training for novice trainees in a prospective randomized controlled trial. Methods: We have constructed a computer-aided system (CAD) using retrospective images based on deep learning which could automatically monitor the 26 anatomical landmarks of the upper digestive tract and document standard photos. Six novice trainees were allocated and grouped into the CAD group and control group. Each of them took the training course, pre and post-test, and EGD examination scored by two experts. The CAD group was trained with the assistance of the CAD system and the control group without. Results: Both groups achieved great improvements in EGD skills. The CAD group received a higher examination grading score in the EGD examination (72.83 ± 16.12 vs. 67.26 ± 15.64, p = 0.039), especially in the mucosa observation (26.40 ± 6.13 vs. 24.11 ± 6.21, p = 0.020) and quality of collected images (7.29 ± 1.09 vs. 6.70 ± 1.05). The CAD showed a lower blind spot rate (2.19 ± 2.28 vs. 3.92 ± 3.30, p = 0.008) compared with the control group. Conclusion: The artificial intelligence assistant system displayed assistant capacity on standard EGD training, and assisted trainees in achieving a learning curve with high operation quality, which has great potential for application. Clinical Trial Registration: This trial is registered at https:/clinicaltrials.gov/, number NCT04682821.
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Affiliation(s)
- Li Huang
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Jun Liu
- Nursing Department of Renmin Hospital of Wuhan University, Wuhan, China
| | - Lianlian Wu
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Ming Xu
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Liwen Yao
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Lihui Zhang
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Renduo Shang
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Mengjiao Zhang
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Qiutang Xiong
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Dawei Wang
- Department of Gastroenterology, Xiaogan Chinese Medical Hospital, Xiaogan, China
| | - Zehua Dong
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Youming Xu
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Jia Li
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Yijie Zhu
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Dexin Gong
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Huiling Wu
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Honggang Yu
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
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Advances in the Aetiology & Endoscopic Detection and Management of Early Gastric Cancer. Cancers (Basel) 2021; 13:cancers13246242. [PMID: 34944861 PMCID: PMC8699285 DOI: 10.3390/cancers13246242] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Revised: 12/06/2021] [Accepted: 12/10/2021] [Indexed: 12/24/2022] Open
Abstract
Simple Summary Gastric adenocarcinoma has remained a highly lethal disease. Awareness and recognition of preneoplastic conditions (including gastric atrophy and intestinal metaplasia) using high-resolution white-light endoscopy as well as chromoendoscopy is therefore essential. Helicobacter pylori, a class I carcinogen, remains the main contributor to the development of sporadic distal gastric neoplasia. Management of early gastric neoplasia with endoscopic resections should be in line with standard indications. A multidisciplinary approach to any case of an early gastric neoplasia is imperative. Hereditary forms of gastric cancer require a tailored approach and individua-lized surveillance. Abstract The mortality rates of gastric carcinoma remain high, despite the progress in research and development in disease mechanisms and treatment. Therefore, recognition of gastric precancerous lesions and early neoplasia is crucial. Two subtypes of sporadic gastric cancer have been recognized: cardia subtype and non-cardia (distal) subtype, the latter being more frequent and largely associated with infection of Helicobacter pylori, a class I carcinogen. Helicobacter pylori initiates the widely accepted Correa cascade, describing a stepwise progression through precursor lesions from chronic inflammation to gastric atrophy, gastric intestinal metaplasia and neoplasia. Our knowledge on He-licobacter pylori is still limited, and multiple questions in the context of its contribution to the pathogenesis of gastric neoplasia are yet to be answered. Awareness and recognition of gastric atrophy and intestinal metaplasia on high-definition white-light endoscopy, image-enhanced endoscopy and magnification endoscopy, in combination with histology from the biopsies taken accurately according to the protocol, are crucial to guiding the management. Standard indications for endoscopic resections (endoscopic mucosal resection and endoscopic submucosal dissection) of gastric dysplasia and intestinal type of gastric carcinoma have been recommended by multiple societies. Endoscopic evaluation and surveillance should be offered to individuals with an inherited predisposition to gastric carcinoma.
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Wu L, He X, Liu M, Xie H, An P, Zhang J, Zhang H, Ai Y, Tong Q, Guo M, Huang M, Ge C, Yang Z, Yuan J, Liu J, Zhou W, Jiang X, Huang X, Mu G, Wan X, Li Y, Wang H, Wang Y, Zhang H, Chen D, Gong D, Wang J, Huang L, Li J, Yao L, Zhu Y, Yu H. Evaluation of the effects of an artificial intelligence system on endoscopy quality and preliminary testing of its performance in detecting early gastric cancer: a randomized controlled trial. Endoscopy 2021; 53:1199-1207. [PMID: 33429441 DOI: 10.1055/a-1350-5583] [Citation(s) in RCA: 93] [Impact Index Per Article: 23.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
BACKGROUND Esophagogastroduodenoscopy (EGD) is a prerequisite for detecting upper gastrointestinal lesions especially early gastric cancer (EGC). An artificial intelligence system has been shown to monitor blind spots during EGD. In this study, we updated the system (ENDOANGEL), verified its effectiveness in improving endoscopy quality, and pretested its performance in detecting EGC in a multicenter randomized controlled trial. METHODS ENDOANGEL was developed using deep convolutional neural networks and deep reinforcement learning. Patients undergoing EGD in five hospitals were randomly assigned to the ENDOANGEL-assisted group or to a control group without use of ENDOANGEL. The primary outcome was the number of blind spots. Secondary outcomes included performance of ENDOANGEL in predicting EGC in a clinical setting. RESULTS 1050 patients were randomized, and 498 and 504 patients in the ENDOANGEL and control groups, respectively, were analyzed. Compared with the control group, the ENDOANGEL group had fewer blind spots (mean 5.38 [standard deviation (SD) 4.32] vs. 9.82 [SD 4.98]; P < 0.001) and longer inspection time (5.40 [SD 3.82] vs. 4.38 [SD 3.91] minutes; P < 0.001). In the ENDOANGEL group, 196 gastric lesions with pathological results were identified. ENDOANGEL correctly predicted all three EGCs (one mucosal carcinoma and two high grade neoplasias) and two advanced gastric cancers, with a per-lesion accuracy of 84.7 %, sensitivity of 100 %, and specificity of 84.3 % for detecting gastric cancer. CONCLUSIONS In this multicenter study, ENDOANGEL was an effective and robust system to improve the quality of EGD and has the potential to detect EGC in real time.
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Affiliation(s)
- Lianlian Wu
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Xinqi He
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Mei Liu
- Department of Gastroenterology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Huaping Xie
- Department of Gastroenterology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Ping An
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Jun Zhang
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Heng Zhang
- Department of Gastroenterology, Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yaowei Ai
- Department of Gastroenterology, The People's Hospital of China Three Gorges University/The First People's Hospital of Yichang, Yichang, China
| | - Qiaoyun Tong
- Department of Gastroenterology, Yichang Central People's Hospital, China Three Gorges University, Yichang, China
| | - Mingwen Guo
- Department of Gastroenterology, The People's Hospital of China Three Gorges University/The First People's Hospital of Yichang, Yichang, China
| | - Manling Huang
- Department of Gastroenterology, Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Cunjin Ge
- Department of Gastroenterology, Yichang Central People's Hospital, China Three Gorges University, Yichang, China
| | - Zhi Yang
- Department of Gastroenterology, Yichang Central People's Hospital, China Three Gorges University, Yichang, China
| | - Jingping Yuan
- Department of Pathology, Renmin Hospital of Wuhan University, Wuhan, China
| | - Jun Liu
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Wei Zhou
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Xiaoda Jiang
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Xu Huang
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Ganggang Mu
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Xinyue Wan
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Yanxia Li
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Hongguang Wang
- Department of Gastroenterology, Jilin People's Hospital, Jilin, China
| | - Yonggui Wang
- School of Geography and Information Engineering, China University of Geosciences, Wuhan, China
| | - Hongfeng Zhang
- Department of Pathology, Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Di Chen
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Dexin Gong
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Jing Wang
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Li Huang
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Jia Li
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Liwen Yao
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Yijie Zhu
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Honggang Yu
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
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Yang R, Yan C, Lu S, Li J, Ji J, Yan R, Yuan F, Zhu Z, Yu Y. Tracking cancer lesions on surgical samples of gastric cancer by artificial intelligent algorithms. J Cancer 2021; 12:6473-6483. [PMID: 34659538 PMCID: PMC8489126 DOI: 10.7150/jca.63879] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Accepted: 08/29/2021] [Indexed: 01/10/2023] Open
Abstract
To quickly locate cancer lesions, especially suspected metastatic lesions after gastrectomy, AI algorithms of object detection and semantic segmentation were established. A total of 509 macroscopic images from 381 patients were collected. The RFB-SSD object detection algorithm and ResNet50-PSPNet semantic segmentation algorithm were used. Another 57 macroscopic images from 48 patients were collected for prospective verification. We used mAP as the metrics of object detection. The best mAP was 95.90% with an average of 89.89% in the test set. The mAP reached 92.60% in validation set. We used mIoU for evaluation of semantic segmentation. The best mIoU was 80.97% with an average of 79.26% in the test set. In addition, 81 out of 92 (88.04%) gastric specimens were accurately predicted for the cancer lesion located at the serosa by ResNet50-PSPNet semantic segmentation model. The positive rate and accuracy of AI prediction were different based on cancer invasive depth. The metastatic lymph nodes were predicted in 24 cases by semantic segmentation model. Among them, 18 cases were confirmed by pathology. The predictive accuracy was 75.00%. Our well-trained AI algorithms effectively identified the subtle features of gastric cancer in resected specimens that may be missed by naked eyes. Taken together, AI algorithms could assist clinical doctors quickly locating cancer lesions and improve their work efficiency.
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Affiliation(s)
- Ruixin Yang
- Department of General Surgery of Ruijin Hospital, Shanghai Institute of Digestive Surgery, and Shanghai Key Laboratory for Gastric Neoplasms, Shanghai Jiao Tong University School of Medicine, 200025, Shanghai, China
| | - Chao Yan
- Department of General Surgery of Ruijin Hospital, Shanghai Institute of Digestive Surgery, and Shanghai Key Laboratory for Gastric Neoplasms, Shanghai Jiao Tong University School of Medicine, 200025, Shanghai, China
| | - Sheng Lu
- Department of General Surgery of Ruijin Hospital, Shanghai Institute of Digestive Surgery, and Shanghai Key Laboratory for Gastric Neoplasms, Shanghai Jiao Tong University School of Medicine, 200025, Shanghai, China
| | - Jun Li
- Department of General Surgery of Ruijin Hospital, Shanghai Institute of Digestive Surgery, and Shanghai Key Laboratory for Gastric Neoplasms, Shanghai Jiao Tong University School of Medicine, 200025, Shanghai, China
| | - Jun Ji
- Department of General Surgery of Ruijin Hospital, Shanghai Institute of Digestive Surgery, and Shanghai Key Laboratory for Gastric Neoplasms, Shanghai Jiao Tong University School of Medicine, 200025, Shanghai, China
| | - Ranlin Yan
- Department of General Surgery of Ruijin Hospital, Shanghai Institute of Digestive Surgery, and Shanghai Key Laboratory for Gastric Neoplasms, Shanghai Jiao Tong University School of Medicine, 200025, Shanghai, China
| | - Fei Yuan
- Department of Pathology of Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, 200025, Shanghai, China
| | - Zhenggang Zhu
- Department of General Surgery of Ruijin Hospital, Shanghai Institute of Digestive Surgery, and Shanghai Key Laboratory for Gastric Neoplasms, Shanghai Jiao Tong University School of Medicine, 200025, Shanghai, China
| | - Yingyan Yu
- Department of General Surgery of Ruijin Hospital, Shanghai Institute of Digestive Surgery, and Shanghai Key Laboratory for Gastric Neoplasms, Shanghai Jiao Tong University School of Medicine, 200025, Shanghai, China
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Yan T, Wong PK, Qin YY. Deep learning for diagnosis of precancerous lesions in upper gastrointestinal endoscopy: A review. World J Gastroenterol 2021; 27:2531-2544. [PMID: 34092974 PMCID: PMC8160615 DOI: 10.3748/wjg.v27.i20.2531] [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: 01/24/2021] [Revised: 03/27/2021] [Accepted: 04/09/2021] [Indexed: 02/06/2023] Open
Abstract
Upper gastrointestinal (GI) cancers are the leading cause of cancer-related deaths worldwide. Early identification of precancerous lesions has been shown to minimize the incidence of GI cancers and substantiate the vital role of screening endoscopy. However, unlike GI cancers, precancerous lesions in the upper GI tract can be subtle and difficult to detect. Artificial intelligence techniques, especially deep learning algorithms with convolutional neural networks, might help endoscopists identify the precancerous lesions and reduce interobserver variability. In this review, a systematic literature search was undertaken of the Web of Science, PubMed, Cochrane Library and Embase, with an emphasis on the deep learning-based diagnosis of precancerous lesions in the upper GI tract. The status of deep learning algorithms in upper GI precancerous lesions has been systematically summarized. The challenges and recommendations targeting this field are comprehensively analyzed for future research.
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Affiliation(s)
- Tao Yan
- School of Mechanical Engineering, Hubei University of Arts and Science, Xiangyang 441053, Hubei Province, China
- Department of Electromechanical Engineering, University of Macau, Taipa 999078, Macau, China
| | - Pak Kin Wong
- Department of Electromechanical Engineering, University of Macau, Taipa 999078, Macau, China
| | - Ye-Ying Qin
- Department of Electromechanical Engineering, University of Macau, Taipa 999078, Macau, China
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Yang R, Yu Y. Artificial Convolutional Neural Network in Object Detection and Semantic Segmentation for Medical Imaging Analysis. Front Oncol 2021; 11:638182. [PMID: 33768000 PMCID: PMC7986719 DOI: 10.3389/fonc.2021.638182] [Citation(s) in RCA: 66] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2020] [Accepted: 02/11/2021] [Indexed: 12/18/2022] Open
Abstract
In the era of digital medicine, a vast number of medical images are produced every day. There is a great demand for intelligent equipment for adjuvant diagnosis to assist medical doctors with different disciplines. With the development of artificial intelligence, the algorithms of convolutional neural network (CNN) progressed rapidly. CNN and its extension algorithms play important roles on medical imaging classification, object detection, and semantic segmentation. While medical imaging classification has been widely reported, the object detection and semantic segmentation of imaging are rarely described. In this review article, we introduce the progression of object detection and semantic segmentation in medical imaging study. We also discuss how to accurately define the location and boundary of diseases.
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Affiliation(s)
| | - Yingyan Yu
- Department of General Surgery of Ruijin Hospital, Shanghai Institute of Digestive Surgery and Shanghai Key Laboratory for Gastric Neoplasms, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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