Clinical Endoscopy

Original Articles

Assessing the potential of artificial intelligence to enhance colonoscopy adenoma detection in clinical practice: a prospective observational trial: Søren Nicolaj Rønborg, Suresh Ujjal, Rasmus Kroijer, Magnus Ploug; Received February 19, 2024 Accepted May 23, 2024 Published online August 23, 2024; DOI: https://doi.org/10.5946/ce.2024.038 [Epub ahead of print]

Graphical Abstract Abstract PDF PubReader ePub: Background
/Aim: This study aimed to evaluate the effectiveness of the GI Genius (Medtronic) module in clinical practice, focusing on the adenoma detection rate (ADR) during colonoscopy. Computer-aided polyp detection (CADe) systems using artificial intelligence have been shown to improve adenoma detection in controlled trials. However, the effectiveness of these systems in clinical practice has recently been questioned.
Methods
This single-center prospective observational study was conducted at the University Hospital of Southern Denmark and included all individuals referred for colonoscopy between November 2020 and January 2021. The primary outcome was ADR, comparing patients examined with CADe to those examined without it. The selection of patients to be examined with the CADe module was completely random.
Results
A total of 502 patients were analyzed (318 in the control group and 184 in the CADe group). The overall ADR was 32.1% with a slight increase in the CADe group (34.7% vs. 30.5%). Multivariable analysis showed a very modest and statistically insignificant increase in ADR (risk ratio, 1.12; 95% confidence interval, 0.88–1.43).
Conclusions
The use of CADe in clinical practice did not increase ADR with statistical significance when compared to colonoscopy without CADe. These findings suggest that the impact of CADe systems in everyday clinical practice are modest.

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Effectiveness of a novel artificial intelligence-assisted colonoscopy system for adenoma detection: a prospective, propensity score-matched, non-randomized controlled study in Korea: Jung-Bin Park, Jung Ho Bae; Received June 24, 2024 Accepted July 21, 2024 Published online August 5, 2024; DOI: https://doi.org/10.5946/ce.2024.168 [Epub ahead of print]

Graphical Abstract Abstract PDF PubReader ePub: Background
/Aims: The real-world effectiveness of computer-aided detection (CADe) systems during colonoscopies remains uncertain. We assessed the effectiveness of the novel CADe system, ENdoscopy as AI-powered Device (ENAD), in enhancing the adenoma detection rate (ADR) and other quality indicators in real-world clinical practice.
Methods
We enrolled patients who underwent elective colonoscopies between May 2022 and October 2022 at a tertiary healthcare center. Standard colonoscopy (SC) was compared to ENAD-assisted colonoscopy. Eight experienced endoscopists performed the procedures in randomly assigned CADe- and non-CADe-assisted rooms. The primary outcome was a comparison of ADR between the ENAD and SC groups.
Results
A total of 1,758 sex- and age-matched patients were included and evenly distributed into two groups. The ENAD group had a significantly higher ADR (45.1% vs. 38.8%, p=0.010), higher sessile serrated lesion detection rate (SSLDR) (5.7% vs. 2.5%, p=0.001), higher mean number of adenomas per colonoscopy (APC) (0.78±1.17 vs. 0.61±0.99; incidence risk ratio, 1.27; 95% confidence interval, 1.13–1.42), and longer withdrawal time (9.0±3.4 vs. 8.3±3.1, p<0.001) than the SC group. However, the mean withdrawal times were not significantly different between the two groups in cases where no polyps were detected (6.9±1.7 vs. 6.7±1.7, p=0.058).
Conclusions
ENAD-assisted colonoscopy significantly improved the ADR, APC, and SSLDR in real-world clinical practice, particularly for smaller and nonpolypoid adenomas.

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Review

As how artificial intelligence is revolutionizing endoscopy: Jean-Francois Rey; Clin Endosc 2024;57(3):302-308. Published online March 8, 2024; DOI: https://doi.org/10.5946/ce.2023.230

Abstract PDF PubReader ePub

With incessant advances in information technology and its implications in all domains of our lives, artificial intelligence (AI) has emerged as a requirement for improved machine performance. This brings forth the query of how this can benefit endoscopists and improve both diagnostic and therapeutic endoscopy in each part of the gastrointestinal tract. Additionally, it also raises the question of the recent benefits and clinical usefulness of this new technology in daily endoscopic practice. There are two main categories of AI systems: computer-assisted detection (CADe) for lesion detection and computer-assisted diagnosis (CADx) for optical biopsy and lesion characterization. Quality assurance is the next step in the complete monitoring of high-quality colonoscopies. In all cases, computer-aided endoscopy is used, as the overall results rely on the physician. Video capsule endoscopy is a unique example in which a computer operates a device, stores multiple images, and performs an accurate diagnosis. While there are many expectations, we need to standardize and assess various software packages. It is important for healthcare providers to support this new development and make its use an obligation in daily clinical practice. In summary, AI represents a breakthrough in digestive endoscopy. Screening for gastric and colonic cancer detection should be improved, particularly outside expert centers. Prospective and multicenter trials are mandatory before introducing new software into clinical practice.

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Deep Learning-Based Real-Time Organ Localization and Transit Time Estimation in Wireless Capsule Endoscopy
Seung-Joo Nam, Gwiseong Moon, Jung-Hwan Park, Yoon Kim, Yun Jeong Lim, Hyun-Soo Choi
Biomedicines.2024; 12(8): 1704. CrossRef

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Original Article

Performance comparison between two computer-aided detection colonoscopy models by trainees using different false positive thresholds: a cross-sectional study in Thailand: Kasenee Tiankanon, Julalak Karuehardsuwan, Satimai Aniwan, Parit Mekaroonkamol, Panukorn Sunthornwechapong, Huttakan Navadurong, Kittithat Tantitanawat, Krittaya Mekritthikrai, Salin Samutrangsi, Peerapon Vateekul, Rungsun Rerknimitr; Clin Endosc 2024;57(2):217-225. Published online February 7, 2024; DOI: https://doi.org/10.5946/ce.2023.145

Graphical Abstract Abstract PDF Supplementary Material PubReader ePub: Background
/Aims: This study aims to compare polyp detection performance of “Deep-GI,” a newly developed artificial intelligence (AI) model, to a previously validated AI model computer-aided polyp detection (CADe) using various false positive (FP) thresholds and determining the best threshold for each model.
Methods
Colonoscopy videos were collected prospectively and reviewed by three expert endoscopists (gold standard), trainees, CADe (CAD EYE; Fujifilm Corp.), and Deep-GI. Polyp detection sensitivity (PDS), polyp miss rates (PMR), and false-positive alarm rates (FPR) were compared among the three groups using different FP thresholds for the duration of bounding boxes appearing on the screen.
Results
In total, 170 colonoscopy videos were used in this study. Deep-GI showed the highest PDS (99.4% vs. 85.4% vs. 66.7%, p<0.01) and the lowest PMR (0.6% vs. 14.6% vs. 33.3%, p<0.01) when compared to CADe and trainees, respectively. Compared to CADe, Deep-GI demonstrated lower FPR at FP thresholds of ≥0.5 (12.1 vs. 22.4) and ≥1 second (4.4 vs. 6.8) (both p<0.05). However, when the threshold was raised to ≥1.5 seconds, the FPR became comparable (2 vs. 2.4, p=0.3), while the PMR increased from 2% to 10%.
Conclusions
Compared to CADe, Deep-GI demonstrated a higher PDS with significantly lower FPR at ≥0.5- and ≥1-second thresholds. At the ≥1.5-second threshold, both systems showed comparable FPR with increased PMR.

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Reviews

Application of artificial intelligence for diagnosis of early gastric cancer based on magnifying endoscopy with narrow-band imaging: Yusuke Horiuchi, Toshiaki Hirasawa, Junko Fujisaki; Clin Endosc 2024;57(1):11-17. Published online January 5, 2024; DOI: https://doi.org/10.5946/ce.2023.173

Abstract PDF PubReader ePub

Although magnifying endoscopy with narrow-band imaging is the standard diagnostic test for gastric cancer, diagnosing gastric cancer using this technology requires considerable skill. Artificial intelligence has superior image recognition, and its usefulness in endoscopic image diagnosis has been reported in many cases. The diagnostic performance (accuracy, sensitivity, and specificity) of artificial intelligence using magnifying endoscopy with narrow band still images and videos for gastric cancer was higher than that of expert endoscopists, suggesting the usefulness of artificial intelligence in diagnosing gastric cancer. Histological diagnosis of gastric cancer using artificial intelligence is also promising. However, previous studies on the use of artificial intelligence to diagnose gastric cancer were small-scale; thus, large-scale studies are necessary to examine whether a high diagnostic performance can be achieved. In addition, the diagnosis of gastric cancer using artificial intelligence has not yet become widespread in clinical practice, and further research is necessary. Therefore, in the future, artificial intelligence must be further developed as an instrument, and its diagnostic performance is expected to improve with the accumulation of numerous cases nationwide.

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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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Computer-aided polyp characterization in colonoscopy: sufficient performance or not?: Natalie Halvorsen, Yuichi Mori; Clin Endosc 2024;57(1):18-23. Published online January 5, 2024; DOI: https://doi.org/10.5946/ce.2023.092

Abstract PDF PubReader ePub: Computer-assisted polyp characterization (computer-aided diagnosis, CADx) facilitates optical diagnosis during colonoscopy. Several studies have demonstrated high sensitivity and specificity of CADx tools in identifying neoplastic changes in colorectal polyps. To implement CADx tools in colonoscopy, there is a need to confirm whether these tools satisfy the threshold levels that are required to introduce optical diagnosis strategies such as “diagnose-and-leave,” “resect-and-discard” or “DISCARD-lite.” In this article, we review the available data from prospective trials regarding the effect of multiple CADx tools and discuss whether they meet these thresholds.

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Advanced endoscopic imaging for detection of Barrett’s esophagus: Netanel Zilberstein, Michelle Godbee, Neal A. Mehta, Irving Waxman; Clin Endosc 2024;57(1):1-10. Published online January 5, 2024; DOI: https://doi.org/10.5946/ce.2023.031

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Barrett’s esophagus (BE) is the precursor to esophageal adenocarcinoma (EAC), and is caused by chronic gastroesophageal reflux. BE can progress over time from metaplasia to dysplasia, and eventually to EAC. EAC is associated with a poor prognosis, often due to advanced disease at the time of diagnosis. However, if BE is diagnosed early, pharmacologic and endoscopic treatments can prevent progression to EAC. The current standard of care for BE surveillance utilizes the Seattle protocol. Unfortunately, a sizable proportion of early EAC and BE-related high-grade dysplasia (HGD) are missed due to poor adherence to the Seattle protocol and sampling errors. New modalities using artificial intelligence (AI) have been proposed to improve the detection of early EAC and BE-related HGD. This review will focus on AI technology and its application to various endoscopic modalities such as high-definition white light endoscopy, narrow-band imaging, and volumetric laser endomicroscopy.

Citations

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Advancements in Barrett's esophagus detection: The role of artificial intelligence and its implications
Sara Massironi
World Journal of Gastroenterology.2024; 30(11): 1494. CrossRef

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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; 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.

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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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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

Abstract PDF PubReader ePub

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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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; : 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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Recent developments in small bowel endoscopy: the “black box” is now open!: Luigina Vanessa Alemanni, Stefano Fabbri, Emanuele Rondonotti, Alessandro Mussetto; Clin Endosc 2022;55(4):473-479. Published online July 14, 2022; DOI: https://doi.org/10.5946/ce.2022.113

Abstract PDF PubReader ePub

Over the last few years, capsule endoscopy has been established as a fundamental device in the practicing gastroenterologist’s toolbox. Its utilization in diagnostic algorithms for suspected small bowel bleeding, Crohn’s disease, and small bowel tumors has been approved by several guidelines. The advent of double-balloon enteroscopy has significantly increased the therapeutic possibilities and release of multiple devices (single-balloon enteroscopy and spiral enteroscopy) aimed at improving the performance of small bowel enteroscopy. Recently, some important innovations have appeared in the small bowel endoscopy scene, providing further improvement to its evolution. Artificial intelligence in capsule endoscopy should increase diagnostic accuracy and reading efficiency, and the introduction of motorized spiral enteroscopy into clinical practice could also improve the therapeutic yield. This review focuses on the most recent studies on artificial-intelligence-assisted capsule endoscopy and motorized spiral enteroscopy.

Citations

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Deep learning–based lesion detection and severity grading of small-bowel Crohn’s disease ulcers on double-balloon endoscopy images
Wanqing Xie, Jing Hu, Pengcheng Liang, Qiao Mei, Aodi Wang, Qiuyuan Liu, Xiaofeng Liu, Juan Wu, Xiaodong Yang, Nannan Zhu, Bingqing Bai, Yiqing Mei, Zhen Liang, Wei Han, Mingmei Cheng
Gastrointestinal Endoscopy.2024; 99(5): 767. CrossRef
Capsule Endoscopy for the Diagnosis of Suspected Small Bowel Bleeding
P. P. Polyakov, A. Ya. Alimetov, A. V. Onopriev, A. V. Avakimyan, A. Kh. Kade, S. A. Zanin, E. S. Zanina, Z. S. Popov, A. I. Trofimenko, Z. T. Jndoyan, A. A. Avagimyan
Innovative Medicine of Kuban.2023; (3): 121. CrossRef
Outcomes of Double Balloon-Enteroscopy in Elderly vs. Adult Patients: A Retrospective 16-Year Single-Centre Study
Margherita Trebbi, Cesare Casadei, Silvia Dari, Andrea Buzzi, Mario Brancaccio, Valentina Feletti, Alessandro Mussetto
Diagnostics.2023; 13(6): 1112. 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;[Epub] CrossRef
As how artificial intelligence is revolutionizing endoscopy
Jean-Francois Rey
Clinical Endoscopy.2024; 57(3): 302. 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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Systematic Review and Meta-Analysis

Does computer-aided diagnostic endoscopy improve the detection of commonly missed polyps? A meta-analysis: Arun Sivananthan, Scarlet Nazarian, Lakshmana Ayaru, Kinesh Patel, Hutan Ashrafian, Ara Darzi, Nisha Patel; Clin Endosc 2022;55(3):355-364. Published online May 12, 2022; DOI: https://doi.org/10.5946/ce.2021.228

Abstract PDF PubReader ePub

Background
/Aims: Colonoscopy is the gold standard diagnostic method for colorectal neoplasia, allowing detection and resection of adenomatous polyps; however, significant proportions of adenomas are missed. Computer-aided detection (CADe) systems in endoscopy are currently available to help identify lesions. Diminutive (≤5 mm) and nonpedunculated polyps are most commonly missed. This meta-analysis aimed to assess whether CADe systems can improve the real-time detection of these commonly missed lesions.
Methods
A comprehensive literature search was performed. Randomized controlled trials evaluating CADe systems categorized by morphology and lesion size were included. The mean number of polyps and adenomas per patient was derived. Independent proportions and their differences were calculated using DerSimonian and Laird random-effects modeling.
Results
Seven studies, including 2,595 CADe-assisted colonoscopies and 2,622 conventional colonoscopies, were analyzed. CADe-assisted colonoscopy demonstrated an 80% increase in the mean number of diminutive adenomas detected per patient compared with conventional colonoscopy (0.31 vs. 0.17; effect size, 0.13; 95% confidence interval [CI], 0.09–0.18); it also demonstrated a 91.7% increase in the mean number of nonpedunculated adenomas detected per patient (0.32 vs. 0.19; effect size, 0.05; 95% CI, 0.02–0.07).
Conclusions
CADe-assisted endoscopy significantly improved the detection of most commonly missed adenomas. Although this method is a potentially exciting technology, limitations still apply to current data, prompting the need for further real-time studies.

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
As how artificial intelligence is revolutionizing endoscopy
Jean-Francois Rey
Clinical Endoscopy.2024; 57(3): 302. CrossRef
Eye tracking technology in endoscopy: Looking to the future
Arun Sivananthan, Jabed Ahmed, Alexandros Kogkas, George Mylonas, Ara Darzi, Nisha Patel
Digestive Endoscopy.2023; 35(3): 314. CrossRef
Artificial intelligence and the push for small adenomas: all we need?
Katharina Zimmermann-Fraedrich, Thomas Rösch
Endoscopy.2023; 55(04): 320. CrossRef
Recent advances in devices and technologies that might prove revolutionary for colonoscopy procedures
Jonathan S. Galati, Kevin Lin, Seth A. Gross
Expert Review of Medical Devices.2023; 20(12): 1087. CrossRef
Detecting colorectal lesions with image-enhanced endoscopy: an updated review from clinical trials
Mizuki Nagai, Sho Suzuki, Yohei Minato, Fumiaki Ishibashi, Kentaro Mochida, Ken Ohata, Tetsuo Morishita
Clinical Endoscopy.2023; 56(5): 553. CrossRef
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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
The Role of Artificial Intelligence in Colorectal Cancer Screening: Lesion Detection and Lesion Characterization
Edward Young, Louisa Edwards, Rajvinder Singh
Cancers.2023; 15(21): 5126. CrossRef
Artificial intelligence for colorectal neoplasia detection during colonoscopy: a systematic review and meta-analysis of randomized clinical trials
Shenghan Lou, Fenqi Du, Wenjie Song, Yixiu Xia, Xinyu Yue, Da Yang, Binbin Cui, Yanlong Liu, Peng Han
eClinicalMedicine.2023; 66: 102341. CrossRef
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Jochen Weigt
United European Gastroenterology Journal.2022; 10(8): 793. 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
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Malinda Vania, Bayu Adhi Tama, Hasan Maulahela, Sunghoon Lim
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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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Artificial Intelligence-Based Colorectal Polyp Histology Prediction by Using Narrow-Band Image-Magnifying Colonoscopy: Istvan Racz, Andras Horvath, Noemi Kranitz, Gyongyi Kiss, Henriett Regoczi, Zoltan Horvath; Clin Endosc 2022;55(1):113-121. Published online September 23, 2021; DOI: https://doi.org/10.5946/ce.2021.149

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Background
/Aims: We have been developing artificial intelligence based polyp histology prediction (AIPHP) method to classify Narrow Band Imaging (NBI) magnifying colonoscopy images to predict the hyperplastic or neoplastic histology of polyps. Our aim was to analyze the accuracy of AIPHP and narrow-band imaging international colorectal endoscopic (NICE) classification based histology predictions and also to compare the results of the two methods.
Methods
We studied 373 colorectal polyp samples taken by polypectomy from 279 patients. The documented NBI still images were analyzed by the AIPHP method and by the NICE classification parallel. The AIPHP software was created by machine learning method. The software measures five geometrical and color features on the endoscopic image.
Results
The accuracy of AIPHP was 86.6% (323/373) in total of polyps. We compared the AIPHP accuracy results for diminutive and non-diminutive polyps (82.1% vs. 92.2%; p=0.0032). The accuracy of the hyperplastic histology prediction was significantly better by NICE compared to AIPHP method both in the diminutive polyps (n=207) (95.2% vs. 82.1%) (p<0.001) and also in all evaluated polyps (n=373) (97.1% vs. 86.6%) (p<0.001)
Conclusions
Our artificial intelligence based polyp histology prediction software could predict histology with high accuracy only in the large size polyp subgroup.

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Review

Artificial Intelligence in Lower Gastrointestinal Endoscopy: The Current Status and Future Perspective: Sebastian Manuel Milluzzo, Paola Cesaro, Leonardo Minelli Grazioli, Nicola Olivari, Cristiano Spada; Clin Endosc 2021;54(3):329-339. Published online January 13, 2021; DOI: https://doi.org/10.5946/ce.2020.082

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The present manuscript aims to review the history, recent advances, evidence, and challenges of artificial intelligence (AI) in colonoscopy. Although it is mainly focused on polyp detection and characterization, it also considers other potential applications (i.e., inflammatory bowel disease) and future perspectives. Some of the most recent algorithms show promising results that are similar to human expert performance. The integration of AI in routine clinical practice will be challenging, with significant issues to overcome (i.e., regulatory, reimbursement). Medico-legal issues will also need to be addressed. With the exception of an AI system that is already available in selected countries (GI Genius; Medtronic, Minneapolis, MN, USA), the majority of the technology is still in its infancy and has not yet been proven to reach a sufficient diagnostic performance to be adopted in the clinical practice. However, larger players will enter the arena of AI in the next few months.

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Focused Review Series: Present and Future of Diagnosis and Managements of Small Bowel Diseases Exploiting Artificial Intelligence and Advanced Endoscopy

A New Active Locomotion Capsule Endoscopy under Magnetic Control and Automated Reading Program: Dong Jun Oh, Kwang Seop Kim, Yun Jeong Lim; Clin Endosc 2020;53(4):395-401. Published online July 30, 2020; DOI: https://doi.org/10.5946/ce.2020.127

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Capsule endoscopy (CE) is the first-line diagnostic modality for detecting small bowel lesions. CE is non-invasive and does not require sedation, but its movements cannot be controlled, it requires a long time for interpretation, and it has lower image quality compared to wired endoscopy. With the rapid advancement of technology, several methods to solve these problems have been developed. This article describes the ongoing developments regarding external CE locomotion using magnetic force, artificial intelligence-based interpretation, and image-enhancing technologies with the CE system.

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The Future of Capsule Endoscopy: The Role of Artificial Intelligence and Other Technical Advancements: Young Joo Yang; Clin Endosc 2020;53(4):387-394. Published online July 16, 2020; DOI: https://doi.org/10.5946/ce.2020.133

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Capsule endoscopy has revolutionized the management of small-bowel diseases owing to its convenience and noninvasiveness. Capsule endoscopy is a common method for the evaluation of obscure gastrointestinal bleeding, Crohn’s disease, small-bowel tumors, and polyposis syndrome. However, the laborious reading process, oversight of small-bowel lesions, and lack of locomotion are major obstacles to expanding its application. Along with recent advances in artificial intelligence, several studies have reported the promising performance of convolutional neural network systems for the diagnosis of various small-bowel lesions including erosion/ulcers, angioectasias, polyps, and bleeding lesions, which have reduced the time needed for capsule endoscopy interpretation. Furthermore, colon capsule endoscopy and capsule endoscopy locomotion driven by magnetic force have been investigated for clinical application, and various capsule endoscopy prototypes for active locomotion, biopsy, or therapeutic approaches have been introduced. In this review, we will discuss the recent advancements in artificial intelligence in the field of capsule endoscopy, as well as studies on other technological improvements in capsule endoscopy.

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Focused Review Series: Application of Artificial Intelligences 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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Lesion-Based Convolutional Neural Network in Diagnosis of Early Gastric Cancer: Hong Jin Yoon, Jie-Hyun Kim; Clin Endosc 2020;53(2):127-131. Published online March 30, 2020; DOI: https://doi.org/10.5946/ce.2020.046

Abstract PDF PubReader ePub

Diagnosis and evaluation of early gastric cancer (EGC) using endoscopic images is significantly important; however, it has some limitations. In several studies, the application of convolutional neural network (CNN) greatly enhanced the effectiveness of endoscopy. To maximize clinical usefulness, it is important to determine the optimal method of applying CNN for each organ and disease. Lesion�-based CNN is a type of deep learning model designed to learn the entire lesion from endoscopic images. This review describes the application of lesion-based CNN technology in diagnosis of EGC.

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Artificial Intelligence in Gastrointestinal Endoscopy: Alexander P. Abadir, Mohammed Fahad Ali, William Karnes, Jason B. Samarasena; Clin Endosc 2020;53(2):132-141. Published online March 30, 2020; DOI: https://doi.org/10.5946/ce.2020.038

Abstract PDF PubReader ePub

Artificial intelligence (AI) is rapidly integrating into modern technology and clinical practice. Although in its nascency, AI has become a hot topic of investigation for applications in clinical practice. Multiple fields of medicine have embraced the possibility of a future with AI assisting in diagnosis and pathology applications.
In the field of gastroenterology, AI has been studied as a tool to assist in risk stratification, diagnosis, and pathologic identification. Specifically, AI has become of great interest in endoscopy as a technology with substantial potential to revolutionize the practice of a modern gastroenterologist. From cancer screening to automated report generation, AI has touched upon all aspects of modern endoscopy.
Here, we review landmark AI developments in endoscopy. Starting with broad definitions to develop understanding, we will summarize the current state of AI research and its potential applications. With innovation developing rapidly, this article touches upon the remarkable advances in AI-assisted endoscopy since its initial evaluation at the turn of the millennium, and the potential impact these AI models may have on the modern clinical practice. As with any discussion of new technology, its limitations must also be understood to apply clinical AI tools successfully.

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Review

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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