Clinical Endoscopy

Original Article

Impact of expert-curated video training data on computer-aided detection of sessile serrated lesions: a retrospective study in Korea: Jaehee Han, Sang-Il Oh, Piljoo Kim, Kyung-Nam Kim; Received December 22, 2025 Accepted April 7, 2026 Published online August 20, 2026; DOI: https://doi.org/10.5946/ce.2025.471 [Epub ahead of print]

Graphical Abstract Abstract PDF Supplementary Material: Background
/Aims: Computer-aided detection (CADe) improves adenoma detection; however, its performance for sessile serrated lesion (SSL) detection remains inconsistent. We hypothesized that models trained on expert-curated, histopathologically-confirmed datasets would improve SSL detection compared to models trained on public datasets without verified labels.
Methods
Two CADe models with identical Visual Geometry Group 16 architectures were trained using different datasets: Model A on public datasets (~54,000 frames) and model B on a histopathologically-confirmed private dataset (~120,000 frames) derived from examinations performed by endoscopists with adenoma detection rates of >35%. Validation was conducted using 31 independent colonoscopy videos obtained from another endoscopist. Diagnostic performance was evaluated using event- and frame-based analyses.
Results
Model B demonstrated a significantly higher event-based sensitivity than model A (99.7% vs. 39.9%, p<0.001). The detection of SSLs was markedly improved with model B. Frame-based sensitivity and F1-score were also higher for model B. Although model B generated more false positives per video (2.55 vs. 0.16, p<0.001), these alerts were brief and unlikely to meaningfully interfere with the endoscopic workflow.
Conclusions
CADe models trained on expert-curated, histopathologically-confirmed datasets showed improved detection of neoplastic colorectal lesions, including SSLs, compared to models trained on public datasets. These findings highlight the importance of clinically curated datasets for optimizing the CADe performance for subtle colorectal lesions.

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Review

The cutting-edge evolution of artificial intelligence-assisted capsule endoscopy: Dong Jun Oh, Yun Jeong Lim; Received November 12, 2025 Accepted January 20, 2026 Published online May 4, 2026; DOI: https://doi.org/10.5946/ce.2025.418 [Epub ahead of print]

Abstract PDF: Since its introduction in 2000, capsule endoscopy (CE) has transformed gastrointestinal (GI) diagnostics by enabling noninvasive visualization of the entire GI tract using a swallowable capsule. However, CE still has several limitations, including long reading times, inter-reader variability, and missed lesions due to poor image quality or incomplete examinations. Recent advances in artificial intelligence (AI) have significantly improved CE interpretation. Deep-learning models, particularly convolutional neural networks, can detect small-bowel lesions with accuracy comparable to that of expert endoscopists, while greatly reducing reading time. AI algorithms can also provide objective assessments of small-bowel cleanliness. Transformer-based models can further enhance video-level analysis by recognizing global patterns and sequential relationships among CE images. In addition, foundation models demonstrate high adaptability and robust performance across different CE systems and a wide range of lesion types. Future AI-assisted CE reading is expected to integrate real-time image analysis, autonomous capsule movement, and multimodal sensing technologies to create an intelligent diagnostic platform. Ultimately, AI is transforming CE into an efficient, reliable, and data-driven diagnostic tool suitable for diverse clinical settings. Furthermore, AI-assisted CE is extending its clinical utility beyond small-bowel lesion detection to the comprehensive evaluation of the stomach and colon.

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Original Article

Enhancing lymph node diagnosis: integrating deep learning with endoscopic ultrasonography: a retrospective study in China: Zijun Fan, Zhenyun Gong, Run Bao, Qinkai Li, Wei Wu, Liming Xu, Junbo Li, Xinze Li, Guilian Cheng, Duanmin Hu; Clin Endosc 2025;58(6):918-927. Published online October 24, 2025; DOI: https://doi.org/10.5946/ce.2025.113

Graphical Abstract Abstract PDF

Background
/Aims: Lymphadenopathy presents diagnostic challenges, particularly for the mediastinal and intra-abdominal lymph nodes (LNs). Endoscopic ultrasonography (EUS) has emerged as a tool for LN detection; however, its accuracy varies. To enhance the diagnostic performance and minimize medical costs, assisting LN assessment using EUS is necessary. Machine learning (ML) offers potential for medical image analysis. This study aimed to develop an ML model for classifying mediastinal and intra-abdominal LNs using gastrointestinal EUS.
Methods
EUS images of mediastinal and intra-abdominal LNs were randomly split into training and validation datasets. U-Net was selected for LN segmentation, and six deep-learning architectures were combined with the k-nearest-neighbor algorithm for LN classification. Physicians, comprising one expert group and one trainee group, reviewed the validation dataset and made individual diagnoses. A logistic regression model was generated based on LN features. We compared the diagnostic yields of ML, expert and trainee groups, logistic regression analysis, and a combination of the various methods mentioned above for diagnosing LNs.
Results
In total, 93 patients were enrolled, providing 630 images. The ResNet-50+logistic regression analysis+expert group achieved the best F1 score and sensitivity of 0.89 and 100.0%, respectively. Paired comparisons revealed that the combination outperformed both experts and trainees in terms of the area under the curve (p<0.01).
Conclusions
ML assists in predicting the mediastinal and intra-abdominal LNs based on gastrointestinal EUS images, particularly when combined with expert expertise and logistic regression models.

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Artificial intelligence in endoscopic ultrasound for lymph node diagnosis: perspective on an evolving frontier
Piyapoom Pakvisal, Rungsun Rerknimitr
Clinical Endoscopy.2025; 58(6): 862. CrossRef

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Reviews

Recent technological advances in video capsule endoscopy: a comprehensive review: Minjee Kim, Hyun Joo Jang; Clin Endosc 2026;59(2):182-193. Published online September 29, 2025; DOI: https://doi.org/10.5946/ce.2025.135

Abstract PDF

Video capsule endoscopy (VCE) originally revolutionized gastrointestinal imaging by providing a noninvasive method for evaluating small bowel diseases. Recent technological innovations, including enhanced imaging systems, artificial intelligence (AI), and improved localization, have significantly improved VCE’s diagnostic accuracy, efficiency, and clinical utility. This review aims to summarize and evaluate recent technological advances in VCE, focusing on system comparisons, image enhancement, localization technologies, and AI-assisted lesion detection.

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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

Abstract PDF

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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Role of CADx in colonoscopy: lessons from real-life studies
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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

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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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

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.

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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

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.

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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

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

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

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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