Clinical Endoscopy

Reviews

Use of artificial intelligence in the management of T1 colorectal cancer: a new tool in the arsenal or is deep learning out of its depth?: James Weiquan Li, Lai Mun Wang, Katsuro Ichimasa, Kenneth Weicong Lin, James Chi-Yong Ngu, Tiing Leong Ang; Clin Endosc 2024;57(1):24-35. Published online September 25, 2023; DOI: https://doi.org/10.5946/ce.2023.036

The field of artificial intelligence is rapidly evolving, and there has been an interest in its use to predict the risk of lymph node metastasis in T1 colorectal cancer. Accurately predicting lymph node invasion may result in fewer patients undergoing unnecessary surgeries; conversely, inadequate assessments will result in suboptimal oncological outcomes. This narrative review aims to summarize the current literature on deep learning for predicting the probability of lymph node metastasis in T1 colorectal cancer, highlighting areas of potential application and barriers that may limit its generalizability and clinical utility.

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Prediction of Lymph Node Metastasis in T1 Colorectal Cancer Using Artificial Intelligence with Hematoxylin and Eosin-Stained Whole-Slide-Images of Endoscopic and Surgical Resection Specimens
Joo Hye Song, Eun Ran Kim, Yiyu Hong, Insuk Sohn, Soomin Ahn, Seok-Hyung Kim, Kee-Taek Jang
Cancers.2024; 16(10): 1900. CrossRef
Approaches and considerations in the endoscopic treatment of T1 colorectal cancer
Yunho Jung
The Korean Journal of Internal Medicine.2024; 39(4): 563. CrossRef
Edge Artificial Intelligence Device in Real-Time Endoscopy for Classification of Gastric Neoplasms: Development and Validation Study
Eun Jeong Gong, Chang Seok Bang, Jae Jun Lee
Biomimetics.2024; 9(12): 783. CrossRef

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Role of artificial intelligence in diagnosing Barrett’s esophagus-related neoplasia: Michael Meinikheim, Helmut Messmann, Alanna Ebigbo; Clin Endosc 2023;56(1):14-22. Published online January 17, 2023; DOI: https://doi.org/10.5946/ce.2022.247

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Barrett’s esophagus is associated with an increased risk of adenocarcinoma. Thorough screening during endoscopic surveillance is crucial to improve patient prognosis. Detecting and characterizing dysplastic or neoplastic Barrett’s esophagus during routine endoscopy are challenging, even for expert endoscopists. Artificial intelligence-based clinical decision support systems have been developed to provide additional assistance to physicians performing diagnostic and therapeutic gastrointestinal endoscopy. In this article, we review the current role of artificial intelligence in the management of Barrett’s esophagus and elaborate on potential artificial intelligence in the future.

Citations

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Artificial intelligence for computer assistance in endoscopic procedures and training
Pablo Achurra, Domingo Mery, Arnoldo Riquelme, Chaya Shwaartz
Global Surgical Education - Journal of the Association for Surgical Education.2025;[Epub] CrossRef
Telemedizin und KI-gestützte Diagnostik im Alltag der Viszeralmedizin
Matthias Grade, Verena Uslar
Die Chirurgie.2025; 96(1): 23. CrossRef
The current state of artificial intelligence in robotic esophageal surgery
Constantine M. Poulos, Ryan Cassidy, Eamon Khatibifar, Erik Holzwanger, Lana Schumacher
Mini-invasive Surgery.2025;[Epub] CrossRef
Artificial Intelligence Applications in Image-Based Diagnosis of Early Esophageal and Gastric Neoplasms
Alanna Ebigbo, Helmut Messmann, Sung Hak Lee
Gastroenterology.2025;[Epub] CrossRef
Endoskopische Therapie von Barrett-Neoplasien und Magenfrühkarzinomen
Florian Berreth, Jan Peveling-Oberhag, Jörg G. Albert
best practice onkologie.2024; 19(1-2): 28. CrossRef
The Role of Screening and Early Detection in Upper Gastrointestinal Cancers
Jin Woo Yoo, Monika Laszkowska, Robin B. Mendelsohn
Hematology/Oncology Clinics of North America.2024; 38(3): 693. CrossRef
Artificial intelligence in gastroenterology: where are we and where are we going?
Laurence B Lovat
Gastrointestinal Nursing.2024; 22(Sup3): S6. CrossRef
As how artificial intelligence is revolutionizing endoscopy
Jean-Francois Rey
Clinical Endoscopy.2024; 57(3): 302. CrossRef
Screening and Diagnostic Advances of Artificial Intelligence in Endoscopy
Muhammed Yaman Swied, Mulham Alom, Obada Daaboul, Abdul Swied
Innovations in Digital Health, Diagnostics, and Biomarkers.2024; 4(2024): 31. CrossRef
Endoscopic Artificial Intelligence for Image Analysis in Gastrointestinal Neoplasms
Ryosuke Kikuchi, Kazuaki Okamoto, Tsuyoshi Ozawa, Junichi Shibata, Soichiro Ishihara, Tomohiro Tada
Digestion.2024; 105(6): 419. CrossRef
Edge Artificial Intelligence Device in Real-Time Endoscopy for Classification of Gastric Neoplasms: Development and Validation Study
Eun Jeong Gong, Chang Seok Bang, Jae Jun Lee
Biomimetics.2024; 9(12): 783. CrossRef
Endoskopische Therapie von Barrett-Neoplasien und Magenfrühkarzinomen
Florian Berreth, Jan Peveling-Oberhag, Jörg G. Albert
Die Gastroenterologie.2023; 18(3): 186. CrossRef

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Preparation of image databases for artificial intelligence algorithm development in gastrointestinal endoscopy: Chang Bong Yang, Sang Hoon Kim, Yun Jeong Lim; Clin Endosc 2022;55(5):594-604. Published online May 31, 2022; DOI: https://doi.org/10.5946/ce.2021.229

Abstract PDF PubReader ePub

Over the past decade, technological advances in deep learning have led to the introduction of artificial intelligence (AI) in medical imaging. The most commonly used structure in image recognition is the convolutional neural network, which mimics the action of the human visual cortex. The applications of AI in gastrointestinal endoscopy are diverse. Computer-aided diagnosis has achieved remarkable outcomes with recent improvements in machine-learning techniques and advances in computer performance. Despite some hurdles, the implementation of AI-assisted clinical practice is expected to aid endoscopists in real-time decision-making. In this summary, we reviewed state-of-the-art AI in the field of gastrointestinal endoscopy and offered a practical guide for building a learning image dataset for algorithm development.

Citations

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Use of artificial intelligence in the management of T1 colorectal cancer: a new tool in the arsenal or is deep learning out of its depth?
James Weiquan Li, Lai Mun Wang, Katsuro Ichimasa, Kenneth Weicong Lin, James Chi-Yong Ngu, Tiing Leong Ang
Clinical Endoscopy.2024; 57(1): 24. CrossRef
Computer‐aided diagnosis in real‐time endoscopy for all stages of gastric carcinogenesis: Development and validation study
Eun Jeong Gong, Chang Seok Bang, Jae Jun Lee
United European Gastroenterology Journal.2024; 12(4): 487. CrossRef
Assessing Endoscopic Response in Locally Advanced Rectal Cancer Treated with Total Neoadjuvant Therapy: Development and Validation of a Highly Accurate Convolutional Neural Network
Hannah Williams, Hannah M. Thompson, Christina Lee, Aneesh Rangnekar, Jorge T. Gomez, Maria Widmar, Iris H. Wei, Emmanouil P. Pappou, Garrett M. Nash, Martin R. Weiser, Philip B. Paty, J. Joshua Smith, Harini Veeraraghavan, Julio Garcia-Aguilar
Annals of Surgical Oncology.2024; 31(10): 6443. CrossRef
As how artificial intelligence is revolutionizing endoscopy
Jean-Francois Rey
Clinical Endoscopy.2024; 57(3): 302. CrossRef
Application of artificial intelligence in gastrointestinal endoscopy in Vietnam: a narrative review
Hang Viet Dao, Binh Phuc Nguyen, Tung Thanh Nguyen, Hoa Ngoc Lam, Trang Thi Huyen Nguyen, Thao Thi Dang, Long Bao Hoang, Hung Quang Le, Long Van Dao
Therapeutic Advances in Gastrointestinal Endoscopy.2024;[Epub] CrossRef
Next-Generation Endoscopy in Inflammatory Bowel Disease
Irene Zammarchi, Giovanni Santacroce, Marietta Iacucci
Diagnostics.2023; 13(15): 2547. CrossRef
Public Imaging Datasets of Gastrointestinal Endoscopy for Artificial Intelligence: a Review
Shiqi Zhu, Jingwen Gao, Lu Liu, Minyue Yin, Jiaxi Lin, Chang Xu, Chunfang Xu, Jinzhou Zhu
Journal of Digital Imaging.2023; 36(6): 2578. CrossRef
AI-powered medical devices for practical clinicians including the diagnosis of colorectal polyps
Donghwan Kim, Eunsun Kim
Journal of the Korean Medical Association.2023; 66(11): 658. CrossRef
Impact of the Volume and Distribution of Training Datasets in the Development of Deep-Learning Models for the Diagnosis of Colorectal Polyps in Endoscopy Images
Eun Jeong Gong, Chang Seok Bang, Jae Jun Lee, Young Joo Yang, Gwang Ho Baik
Journal of Personalized Medicine.2022; 12(9): 1361. CrossRef

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Original Article

Real-time semantic segmentation of gastric intestinal metaplasia using a deep learning approach: Vitchaya Siripoppohn, Rapat Pittayanon, Kasenee Tiankanon, Natee Faknak, Anapat Sanpavat, Naruemon Klaikaew, Peerapon Vateekul, Rungsun Rerknimitr; Clin Endosc 2022;55(3):390-400. Published online May 9, 2022; DOI: https://doi.org/10.5946/ce.2022.005

Abstract PDF Supplementary Material PubReader ePub

Background
/Aims: Previous artificial intelligence (AI) models attempting to segment gastric intestinal metaplasia (GIM) areas have failed to be deployed in real-time endoscopy due to their slow inference speeds. Here, we propose a new GIM segmentation AI model with inference speeds faster than 25 frames per second that maintains a high level of accuracy.
Methods
Investigators from Chulalongkorn University obtained 802 histological-proven GIM images for AI model training. Four strategies were proposed to improve the model accuracy. First, transfer learning was employed to the public colon datasets. Second, an image preprocessing technique contrast-limited adaptive histogram equalization was employed to produce clearer GIM areas. Third, data augmentation was applied for a more robust model. Lastly, the bilateral segmentation network model was applied to segment GIM areas in real time. The results were analyzed using different validity values.
Results
From the internal test, our AI model achieved an inference speed of 31.53 frames per second. GIM detection showed sensitivity, specificity, positive predictive, negative predictive, accuracy, and mean intersection over union in GIM segmentation values of 93%, 80%, 82%, 92%, 87%, and 57%, respectively.
Conclusions
The bilateral segmentation network combined with transfer learning, contrast-limited adaptive histogram equalization, and data augmentation can provide high sensitivity and good accuracy for GIM detection and segmentation.

Citations

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Role of Artificial Intelligence in the Detection and Management of Premalignant and Malignant Lesions of the Esophagus and Stomach
Piyush Nathani, Prateek Sharma
Gastrointestinal Endoscopy Clinics of North America.2025; 35(2): 319. CrossRef
Applications of artificial intelligence in gastroscopy: a narrative review
Hu Chen, Shi-yu Liu, Si-hui Huang, Min Liu, Guang-xia Chen
Journal of International Medical Research.2024;[Epub] CrossRef
Computer‐aided diagnosis in real‐time endoscopy for all stages of gastric carcinogenesis: Development and validation study
Eun Jeong Gong, Chang Seok Bang, Jae Jun Lee
United European Gastroenterology Journal.2024; 12(4): 487. CrossRef
As how artificial intelligence is revolutionizing endoscopy
Jean-Francois Rey
Clinical Endoscopy.2024; 57(3): 302. CrossRef
Accuracy of artificial intelligence-assisted endoscopy in the diagnosis of gastric intestinal metaplasia: A systematic review and meta-analysis
Na Li, Jian Yang, Xiaodong Li, Yanting Shi, Kunhong Wang, Chih-Wei Tseng
PLOS ONE.2024; 19(5): e0303421. CrossRef
Real-time gastric intestinal metaplasia segmentation using a deep neural network designed for multiple imaging modes on high-resolution images
Passin Pornvoraphat, Kasenee Tiankanon, Rapat Pittayanon, Natawut Nupairoj, Peerapon Vateekul, Rungsun Rerknimitr
Knowledge-Based Systems.2024; 300: 112213. CrossRef
A Benchmark Dataset of Endoscopic Images and Novel Deep Learning Method to Detect Intestinal Metaplasia and Gastritis Atrophy
Jie Yang, Yan Ou, Zhiqian Chen, Juan Liao, Wenjian Sun, Yang Luo, Chunbo Luo
IEEE Journal of Biomedical and Health Informatics.2023; 27(1): 7. CrossRef
Real-time gastric intestinal metaplasia diagnosis tailored for bias and noisy-labeled data with multiple endoscopic imaging
Passin Pornvoraphat, Kasenee Tiankanon, Rapat Pittayanon, Phanukorn Sunthornwetchapong, Peerapon Vateekul, Rungsun Rerknimitr
Computers in Biology and Medicine.2023; 154: 106582. CrossRef
Diagnostic value of artificial intelligence-assisted endoscopy for chronic atrophic gastritis: a systematic review and meta-analysis
Yanting Shi, Ning Wei, Kunhong Wang, Tao Tao, Feng Yu, Bing Lv
Frontiers in Medicine.2023;[Epub] CrossRef
Recent Advances in Applying Machine Learning and Deep Learning to Detect Upper Gastrointestinal Tract Lesions
Malinda Vania, Bayu Adhi Tama, Hasan Maulahela, Sunghoon Lim
IEEE Access.2023; 11: 66544. CrossRef
Colon histology slide classification with deep-learning framework using individual and fused features
Venkatesan Rajinikanth, Seifedine Kadry, Ramya Mohan, Arunmozhi Rama, Muhammad Attique Khan, Jungeun Kim
Mathematical Biosciences and Engineering.2023; 20(11): 19454. CrossRef
Clinical Decision Support System for All Stages of Gastric Carcinogenesis in Real-Time Endoscopy: Model Establishment and Validation Study
Eun Jeong Gong, Chang Seok Bang, Jae Jun Lee, Hae Min Jeong, Gwang Ho Baik, Jae Hoon Jeong, Sigmund Dick, Gi Hun Lee
Journal of Medical Internet Research.2023; 25: e50448. CrossRef

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Focused Review Series: Application of Artificial Intelligence in GI Endoscopy

Convolutional Neural Network Technology in Endoscopic Imaging: Artificial Intelligence for Endoscopy: Joonmyeong Choi, Keewon Shin, Jinhoon Jung, Hyun-Jin Bae, Do Hoon Kim, Jeong-Sik Byeon, Namku Kim; Clin Endosc 2020;53(2):117-126. Published online March 30, 2020; DOI: https://doi.org/10.5946/ce.2020.054

Abstract PDF PubReader ePub

Recently, significant improvements have been made in artificial intelligence. The artificial neural network was introduced in the 1950s. However, because of the low computing power and insufficient datasets available at that time, artificial neural networks suffered from overfitting and vanishing gradient problems for training deep networks. This concept has become more promising owing to the enhanced big data processing capability, improvement in computing power with parallel processing units, and new algorithms for deep neural networks, which are becoming increasingly successful and attracting interest in many domains, including computer vision, speech recognition, and natural language processing. Recent studies in this technology augur well for medical and healthcare applications, especially in endoscopic imaging. This paper provides perspectives on the history, development, applications, and challenges of deep-learning technology.

Citations

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Usefulness of an artificial intelligence-based colonoscopy report generation support system
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FastUGI-Net: Enhanced Real-Time Endoscopic Diagnosis with Efficient Multi-task Learning
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Zihan Nie, Muhao Xu, Zhiyong Wang, Xiaoqi Lu, Weiye Song
Journal of Imaging.2024; 10(11): 275. CrossRef
Artificial Intelligence-Based Classification of Anatomical Sites in Esophagogastroduodenoscopy Images
Peng Yuan, Zhong-Hua Ma, Yan Yan, Shi-Jie Li, Jing Wang, Qi Wu
International Journal of General Medicine.2024; Volume 17: 6127. CrossRef
Improving the Computer-Aided Estimation of Ulcerative Colitis Severity According to Mayo Endoscopic Score by Using Regression-Based Deep Learning
Gorkem Polat,, Haluk Tarik Kani, Ilkay Ergenc, Yesim Ozen Alahdab, Alptekin Temizel, Ozlen Atug
Inflammatory Bowel Diseases.2023; 29(9): 1431. CrossRef
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Yu Chang, Zhi Wang, Hai-Bo Sun, Yu-Qin Li, Tong-Yu Tang, James H. Tabibian
Gastroenterology Research and Practice.2023; 2023: 1. CrossRef
Computer-aided demarcation of early gastric cancer: a pilot comparative study with endoscopists
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Reviews

Recent Development of Computer Vision Technology to Improve Capsule Endoscopy: Junseok Park, Youngbae Hwang, Ju-Hong Yoon, Min-Gyu Park, Jungho Kim, Yun Jeong Lim, Hoon Jai Chun; Clin Endosc 2019;52(4):328-333. Published online February 21, 2019; DOI: https://doi.org/10.5946/ce.2018.172

Abstract PDF PubReader ePub

Capsule endoscopy (CE) is a preferred diagnostic method for analyzing small bowel diseases. However, capsule endoscopes capture a sparse number of images because of their mechanical limitations. Post-procedural management using computational methods can enhance image quality. Additional information, including depth, can be obtained by using recently developed computer vision techniques. It is possible to measure the size of lesions and track the trajectory of capsule endoscopes using the computer vision technology, without requiring additional equipment. Moreover, the computational analysis of CE images can help detect lesions more accurately within a shorter time. Newly introduced deep leaning-based methods have shown more remarkable results over traditional computerized approaches. A large-scale standard dataset should be prepared to develop an optimal algorithms for improving the diagnostic yield of CE. The close collaboration between information technology and medical professionals is needed.

Citations

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Multivariate Gaussian Bayes classifier with limited data for segmentation of clean and contaminated regions in the small bowel capsule endoscopy images
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Application of Artificial Intelligence in Capsule Endoscopy: Where Are We Now?: Youngbae Hwang, Junseok Park, Yun Jeong Lim, Hoon Jai Chun; Clin Endosc 2018;51(6):547-551. Published online November 30, 2018; DOI: https://doi.org/10.5946/ce.2018.173

Abstract PDF PubReader ePub

Unlike wired endoscopy, capsule endoscopy requires additional time for a clinical specialist to review the operation and examine the lesions. To reduce the tedious review time and increase the accuracy of medical examinations, various approaches have been reported based on artificial intelligence for computer-aided diagnosis. Recently, deep learning–based approaches have been applied to many possible areas, showing greatly improved performance, especially for image-based recognition and classification. By reviewing recent deep learning–based approaches for clinical applications, we present the current status and future direction of artificial intelligence for capsule endoscopy.

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