Clinical Endoscopy

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

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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

Citations to this article as recorded by

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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Bimodal Chromoendoscopy with Confocal Laser Endomicroscopy for the Detection of Early Esophageal Squamous Cell Neoplasms: Piyapan Prueksapanich, Thanawat Luangsukrerk, Rapat Pittayanon, Anapat Sanpavat, Rungsun Rerknimitr; Clin Endosc 2019;52(2):144-151. Published online October 5, 2018; DOI: https://doi.org/10.5946/ce.2018.091

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Background
/Aims: This study aimed to evaluate the diagnostic accuracy of dual-focus narrow-band imaging (dNBI) and Lugol’schromoendoscopy (LCE) combined with probe-based confocal laser endomicroscopy (pCLE) to screen for esophageal squamous cell neoplasms (ESCNs) in patients with a history of head and neck cancer.
Methods
From March to August 2016, dNBI was performed. Next, LCE was performed, followed by pCLE and biopsy. Histology has historically been the gold standard to diagnose ESCN. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy of dNBI and LCE adjunct with pCLE were determined.
Results
Twenty-four patients were included. Ten ESCNs were found in 8 patients (33%). Forty percent of high-graded intraepithelial neoplasias and all low-grade intraepithelial neoplasias were overlooked by dNBI. The sensitivity, specificity, PPV, NPV, and accuracy of dNBI vs. LCE combined with pCLE were 50% vs. 80%, 62% vs. 67%, 36% vs. 44%, 75% vs. 91%, and 83% vs. 70%, respectively.
Conclusions
The use of dNBI to detect ESCN was suboptimal. LCE with pCLE following dNBI had additional value for detecting esophageal dysplasia not detected by dNBI. The use of pCLE to detect dNBI-missed lesions yielded a high NPV, while pCLE-guided biopsy could reduce the number of unnecessary biopsies.

Citations

Citations to this article as recorded by

Confocal Laser Endomicroscopy for Detection of Early Upper Gastrointestinal Cancer
Wei Han, Rui Kong, Nan Wang, Wen Bao, Xinli Mao, Jie Lu
Cancers.2023; 15(3): 776. CrossRef
Usefulness of Probe-Based Confocal Laser Endomicroscopy for Esophageal Squamous Cell Neoplasm
Sang Kil Lee
Clinical Endoscopy.2019; 52(2): 91. CrossRef
Confocal Laser Endomicroscopy as a Guidance Tool for Pleural Biopsies in Malignant Pleural Mesothelioma
Lizzy Wijmans, Paul Baas, Thomas E. Sieburgh, Daniel M. de Bruin, Petra M. Ghuijs, Marc J. van de Vijver, Peter I. Bonta, Jouke T. Annema
Chest.2019; 156(4): 754. CrossRef

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