Clinical Endoscopy

Original Article

Effectiveness of a novel ex vivo training model for gastric endoscopic submucosal dissection training: a prospective observational study conducted at a single center in Japan: Takahito Toba, Tsuyoshi Ishii, Nobuyuki Sato, Akira Nogami, Aya Hojo, Ryo Shimizu, Ai Fujimoto, Takahisa Matsuda; Received April 30, 2024 Accepted June 3, 2024 Published online November 4, 2024; DOI: https://doi.org/10.5946/ce.2024.108 [Epub ahead of print]

Graphical Abstract Abstract PDF PubReader ePub: Background
/Aims: The efficacy of endoscopic submucosal dissection (ESD) for early-stage gastric cancer is well established. However, its acquisition is challenging owing to its complexity. In Japan, G-Master is a novel ex vivo gastric ESD training model. The effectiveness of training using G-Master is unknown. This study evaluated the efficacy of gastric ESD training using the G-Master to evaluate trainees’ learning curves and performance.
Methods
Four trainees completed 30 ESD training sessions using the G-Master, and procedure time, resection area, resection completion, en-bloc resection requirement, and perforation occurrence were measured. Resection speed was the primary endpoint, and learning curves were evaluated using the Cumulative Sum (CUSUM) method.
Results
All trainees completed the resection and en-bloc resection of the lesion without any intraoperative perforations. The learning curves covered three phases: initial growth, plateau, and late growth. The transition from phase 1 to phase 2 required a median of 10 sessions. Each trainee completed 30 training sessions in approximately 4 months.
Conclusions
Gastric ESD training using the G-Master is a simple, fast, and effective method for pre-ESD training in clinical practice. It is recommended that at least 10 training sessions be conducted.

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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

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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.

Citations

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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

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E-learning system to improve the endoscopic diagnosis of early gastric cancer: Kenshi Yao, Takashi Yao, Noriya Uedo, Hisashi Doyama, Hideki Ishikawa, Satoshi Nimura, Yuichi Takahashi; Clin Endosc 2024;57(3):283-292. Published online August 3, 2023; DOI: https://doi.org/10.5946/ce.2023.087

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We developed three e-learning systems for endoscopists to acquire the necessary skills to improve the diagnosis of early gastric cancer (EGC) and demonstrated their usefulness using randomized controlled trials. The subjects of the three e-learning systems were “detection”, “characterization”, and “preoperative assessment”. The contents of each e-learning system included “technique”, “knowledge”, and “obtaining experience”. All e-learning systems proved useful for endoscopists to learn how to diagnose EGC. Lecture videos describing “the technique” and “the knowledge” can be beneficial. In addition, repeating 100 self-study cases allows learners to gain “experience” and improve their diagnostic skills further. Web-based e-learning systems have more advantages than other teaching methods because the number of participants is unlimited. Histopathological diagnosis is the gold standard for the diagnosis of gastric cancer. Therefore, we developed a comprehensive diagnostic algorithm to standardize the histopathological diagnosis of gastric cancer. Once we have successfully shown that this algorithm is helpful for the accurate histopathological diagnosis of cancer, we will complete a series of e-learning systems designed to assess EGC accurately.

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Pitfalls in Endoscopic Submucosal Dissection for Early Gastric Cancer with Papillary Adenocarcinoma
Gwang Ha Kim
Gut and Liver.2024; 18(3): 368. 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.

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Endoskopische Therapie von Barrett-Neoplasien und Magenfrühkarzinomen
Florian Berreth, Jan Peveling-Oberhag, Jörg G. Albert
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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; : 1. 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
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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
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Jean-Francois Rey
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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
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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 Articles

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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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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The Learning Curve for Peroral Endoscopic Myotomy in Latin America: A Slide to the Right?: Michel Kahaleh, Amy Tyberg, Supriya Suresh, Arnon Lambroza, Fernando Rodriguez Casas, Mario Rey, Jose Nieto, Guadalupe Ma Martínez, Felipe Zamarripa, Vitor Arantes, Maria G Porfilio, Monica Gaidhane, Pietro Familiari, Juan Carlos Carames, Romulo Vargas-Rubio, Raul Canadas, Albis Hani, Guillermo Munoz, Bismarck Castillo, Eduardo T Moura, Farias F Galileu, Hannah P Lukashok, Carlos Robles-Medranda, Eduardo G de Moura; Clin Endosc 2021;54(5):701-705. Published online June 3, 2021; DOI: https://doi.org/10.5946/ce.2020.290

Abstract PDF PubReader ePub

Background
/Aims: Peroral endoscopic myotomy (POEM) has been increasingly used for achalasia in Latin America, where Chagas disease is prevalent, and this makes POEM more challenging. The aim of this study was to determine the learning curve for POEM in Latin America.
Methods
Patients undergoing POEM in Latin America with a single operator were included from a prospective registry over 4 years. Non-linear regression and cumulative sum control chart (CUSUM) analyses were conducted for the learning curve.
Results
A total of 125 patients were included (52% male; mean age, 59 years), of which 80 had type II achalasia (64%), and 38 had Chagas disease (30%). The average pre-procedure and post-procedure Eckardt scores were 6.79 and 1.87, respectively. Technical success was achieved in 93.5% of patients, and clinical success was achieved in 88.8%. Adverse events occurred in 27 patients (22%) and included bleeding (4 patients), pneumothorax (4 patients), mucosal perforation (13 patients), mediastinitis (2 patients), and leakage (4 patients).
The CUSUM chart showed a median procedure time of 97 min (range, 45-196 min), which was achieved at the 61st procedure. Procedure duration progressively decreased, with the last 10 procedures under 50 min approaching a plateau (p-value <0.01).
Conclusions
Mastering POEM in Latin America requires approximately 61 procedures for both POEM efficiency and to accomplish the procedure within 97 minutes.

Citations

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Experiencia en miotomía endoscópica peroral en un centro de Bogotá, Colombia, entre 2018 y 2022
Tatiana P Barragan Briceño, Paola Stephany Gonzalez Ausique, Carlos Fernando Fuentes Díaz, Jesús Antonio Rodríguez Fajardo, Maria Camila Gomez Ayala
Revista colombiana de Gastroenterología.2024; 39(2): 146. CrossRef
Miotomía endoscópica por vía oral (POEM) como tratamiento para la acalasia pediátrica: estudio multicéntrico y primeros resultados
Carlos Leganés Villanueva, Eduardo Albéniz Arbizu, Ilaria Goruppi, Nuria Brun Lozano, Federica Bianchi, Alberto Pérez Martínez, Sheyla Montori Pina, Ada Yessenia Molina Caballero, Marianette Murzi, Federico Betroletti, Fermin Estremera, Susana Boronat Gue
Gastroenterología y Hepatología.2024; : 502262. CrossRef
Learning curve for esophageal peroral endoscopic myotomy: a systematic review and meta-analysis
Srinivas R. Puli, Mihir S. Wagh, David Forcione, Harishankar Gopakumar
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Amir Mari, Fadi Abu Baker, Rinaldo Pellicano, Tawfik Khoury
Journal of Clinical Medicine.2021; 10(16): 3607. CrossRef
Issues to be Considered for Learning Curve for Peroral Endoscopic Myotomy
Hironari Shiwaku, Haruhiro Inoue
Clinical Endoscopy.2021; 54(5): 625. CrossRef

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Confirming Whether Fine Needle Biopsy Device Shortens the Learning Curve of Endoscopic Ultrasound-Guided Tissue Acquisition Without Rapid Onsite Evaluation: Meng-Ying Lin, Cheng-Lin Wu, Mitsuhiro Kida, Wei-Lun Chang, Bor-Shyang Sheu; Clin Endosc 2021;54(3):420-427. Published online May 28, 2021; DOI: https://doi.org/10.5946/ce.2020.184

Abstract PDF PubReader ePub

Background
/Aims: Endoscopic ultrasonography (EUS)-guided tissue acquisition requires a long learning curve. We aimed to compare the skill maturation curves between fine needle aspiration (FNA) and biopsy (FNB) for tissue acquisition.
Methods
The initial 60 procedures performed by the trainee endosonographer (30 FNA vs. 30 FNB) were consecutively enrolled. The difference in procedure performance was compared between the two groups. Learning curves were assessed. Twenty additional cases were subsequently enrolled to assess the consistency of performance in the FNB group.
Results
The FNB group acquired larger tissue samples (2.35 vs. 0.70 mm2; p<0.001) with lower blood content (p=0.001) and higher tissue quality (p=0.017) compared with the FNA group. In addition, the FNB group required less needle pass to establish a diagnosis (2.43 vs. 2.97; p=0.006). A threshold diagnostic sensitivity of ≥80% was achieved after performing 10 FNB procedures. The number of needle passes significantly decreased after conducting 20 FNB procedures (1.80 vs. 2.70; p=0.041). The diagnostic sensitivity and number of needle passes remained the same in the subsequent FNB procedures. By contrast, this skill maturation phenomenon was not observed after performing 30 FNA procedures.
Conclusions
In EUS-guided tissue acquisition, the FNB needle was more efficient and thus shortened the learning curve of EUSguided tissue acquisition in trainee endosonographers.

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Identification of Endosonographic Features that Compromise EUS-FNB Diagnostic Accuracy in Pancreatic Masses
Hsueh-Chien Chiang, Chien-Jui Huang, Yao-Shen Wang, Chun-Te Lee, Meng-Ying Lin, Wei-Lun Chang
Digestive Diseases and Sciences.2024; 69(11): 4302. CrossRef
Tissue Quality Comparison Between Heparinized Wet Suction and Dry Suction in Endoscopic Ultrasound-Fine Needle Biopsy of Solid Pancreatic Masses: A Randomized Crossover Study
Meng-Ying Lin, Cheng-Lin Wu, Yung-Yeh Su, Chien-Jui Huang, Wei-Lun Chang, Bor-Shyang Sheu
Gut and Liver.2023; 17(2): 318. CrossRef
Factors Affecting the Learning Curve in the Endoscopic Ultrasound-Guided Sampling of Solid Pancreatic Lesions: A Prospective Study
Marcel Razpotnik, Simona Bota, Mathilde Kutilek, Gerolf Essler, Christian Urak, Julian Prosenz, Jutta Weber-Eibel, Andreas Maieron, Markus Peck-Radosavljevic
Gut and Liver.2023; 17(2): 308. CrossRef
Investigation into the content of red material in EUS-guided pancreatic cancer biopsies
Meng-Ying Lin, Yung-Yeh Su, Yu-Ting Yu, Chien-Jui Huang, Bor-Shyang Sheu, Wei-Lun Chang
Gastrointestinal Endoscopy.2023; 97(6): 1083. CrossRef

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Three-Dimensional Flexible Endoscopy Can Facilitate Efficient and Reliable Endoscopic Hand Suturing: An ex-vivo Study: Jun Omori, Osamu Goto, Kazutoshi Higuchi, Takamitsu Umeda, Naohiko Akimoto, Masahiro Suzuki, Kumiko Kirita, Eriko Koizumi, Hiroto Noda, Teppei Akimoto, Mitsuru Kaise, Katsuhiko Iwakiri; Clin Endosc 2020;53(3):334-338. Published online April 24, 2020; DOI: https://doi.org/10.5946/ce.2019.207

Abstract PDF Supplementary Material PubReader ePub

Background
/Aims: Three-dimensional (3D) flexible endoscopy, a new imaging modality that provides a stereoscopic view, can facilitate endoscopic hand suturing (EHS), a novel intraluminal suturing technique. This ex-vivo pilot study evaluated the usefulness of 3D endoscopy in EHS.
Methods
Four endoscopists (two certified, two non-certified) performed EHS in six sessions on a soft resin pad. Each session involved five stitches, under alternating 3D and two-dimensional (2D) conditions. Suturing time (sec/session), changes in suturing time, and accuracy of suturing were compared between 2D and 3D conditions.
Results
The mean suturing time was shorter in 3D than in 2D (9.8±3.4 min/session vs. 11.2±5.1 min/session) conditions and EHS was completed faster in 3D conditions, particularly by non-certified endoscopists. The suturing speed increased as the 3D sessions progressed. Error rates (failure to grasp the needle, failure to thread the needle, and puncture retrial) in the 3D condition were lower than those in the 2D condition, whereas there was no apparent difference in deviation distance.
Conclusions
3D endoscopy may contribute to increasing the speed and accuracy of EHS in a short time period. Stereoscopic viewing during 3D endoscopy may help in efficient skill acquisition for EHS, particularly among novice endoscopists.

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Future Directions for Robotic Endoscopy–Artificial Intelligence (AI), Three-Dimensional (3D) Imaging, and Natural Orifice Transluminal Endoscopic Surgery (NOTES)
Cem Simsek, Hung Leng Kaan, Hiroyuki Aihara
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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.

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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.

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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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Original Article

Learning Curve of Capsule Endoscopy: Korean Gut Image Study Group, Yun Jeong Lim, Young Sung Joo, Dae Young Jung, Byong Duk Ye, Ji Hyun Kim, Jae Hee Cheon, Seong Eun Kim, Jae Hyuk Do, Byung Ik Jang, Jeong Seop Moon, Jin Oh Kim, Hoon Jae Chun, Myung-Gyu Choi; Clin Endosc 2013;46(6):633-636. Published online November 19, 2013; DOI: https://doi.org/10.5946/ce.2013.46.6.633

Abstract PDF PubReader ePub

Background/Aims

Capsule endoscopy (CE) has become an important tool for the diagnosis of small bowel disease. Although CE does not require the skill of endoscope insertion, the images should be interpreted by a person with experience in assessing images of the gastrointestinal mucosa. This investigation aimed to document the number of cases needed by trainees to gain the necessary experience for CE competency.

Methods

Fifteen cases were distributed to 12 trainees with no previous experience of CE during their gastroenterology training as clinical fellows. Twelve trainees and an expert were asked to read CE images from one patient each week for 15 weeks. The diagnosis was reported using five categories (no abnormalities detected, small bowel erosion or ulcer, small bowel tumor, Crohn disease, and active small bowel bleeding with no identifiable source). We then examined, using the κ coefficient, how the degree of mean agreements between the trainees and the expert changed as the training progressed each week.

Results

The agreement rate of CE diagnosis increased as the frequencies of interpretation increased. Most of the mean κ coefficients were >0.60 and >0.80 after week 9 and 11, respectively.

Conclusions

Experience with approximately 10 cases of CE is appropriate for trainees to attain CE competency.

Citations

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