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Peerapon Vateekul 2 Articles
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
Graphical AbstractGraphical Abstract AbstractAbstract PDFSupplementary MaterialPubReaderePub
/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.
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.
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%.
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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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
AbstractAbstract PDFSupplementary MaterialPubReaderePub
/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.
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.
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.
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 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; : 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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