Journal Article10.1055/A-1229-0920
A deep learning-based system for identifying differentiation status and delineating the margins of early gastric cancer in magnifying narrow-band imaging endoscopy.
Tingsheng Ling,Lianlian Wu,Yiwei Fu,Qinwei Xu,Ping An,Jun Zhang,Shan Hu,Yiyun Chen,Xinqi He,Jing Wang,Xi Chen,Jie Zhou,Youming Xu,Xiaoping Zou,Honggang Yu +14 more
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TL;DR: A real-time system for accurately identifying differentiation status and delineating margins of EGC in Magnifying Narrow-band Imaging (ME-NBI) endoscopy is developed and achieved a superior performance when compared with experts, and was successfully tested in real EGC videos.
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Abstract: Background Accurate identification of the differentiation status and margins for early gastric cancer (EGC) is critical for determining the surgical strategy and achieving curative resection in EGC patients. The aim of this study was to develop a real-time system to accurately identify differentiation status and delineate the margins of EGC on magnifying narrow-band imaging (ME-NBI) endoscopy. Methods 2217 images from 145 EGC patients and 1870 images from 139 EGC patients were retrospectively collected to train and test the first convolutional neural network (CNN1) to identify EGC differentiation status. The performance of CNN1 was then compared with that of experts using 882 images from 58 EGC patients. Finally, 928 images from 132 EGC patients and 742 images from 87 EGC patients were used to train and test CNN2 to delineate the EGC margins. Results The system correctly predicted the differentiation status of EGCs with an accuracy of 83.3 % (95 % confidence interval [CI] 81.5 % – 84.9 %) in the testing dataset. In the man – machine contest, CNN1 performed significantly better than the five experts (86.2 %, 95 %CI 75.1 % – 92.8 % vs. 69.7 %, 95 %CI 64.1 % – 74.7 %). For delineating EGC margins, the system achieved an accuracy of 82.7 % (95 %CI 78.6 % – 86.1 %) in differentiated EGC and 88.1 % (95 %CI 84.2 % – 91.1 %) in undifferentiated EGC under an overlap ratio of 0.80. In unprocessed EGC videos, the system achieved real-time diagnosis of EGC differentiation status and EGC margin delineation in ME-NBI endoscopy. Conclusion We developed a deep learning-based system to accurately identify differentiation status and delineate the margins of EGC in ME-NBI endoscopy. This system achieved superior performance when compared with experts and was successfully tested in real EGC videos.
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Artificial Intelligence in Endoscopy.
TL;DR: A comprehensive review of the use of deep learning in the field of GI endoscopy can be found in this article, where a total of 149 original articles pertaining to AI (27 articles in esophagus, 30 articles in stomach, 29 articles in CE, and 63 articles in colon) were identified.
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Deep learning system compared with expert endoscopists in predicting early gastric cancer and its invasion depth and differentiation status (with videos)
01 Jan 2022
TL;DR: Wang et al. as mentioned in this paper developed and validated a deep learning-based system that covers various aspects of early gastric cancer (EGC) diagnosis, including detecting gastric neoplasm, identifying EGC, and predicting EGC invasion depth and differentiation status.
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Kyoto international consensus report on anatomy, pathophysiology and clinical significance of the gastro-oesophageal junction
Kentaro Sugano,Stuart J. Spechler,Emad M. El-Omar,Kenneth E.L. McColl,Kaiyo Takubo,Takuji Gotoda,Mitsuhiro Fujishiro,K. Iijima,Haruhiro Inoue,Takashi Kawai,Yoshikazu Kinoshita,Hiroto Miwa,Ken-ichi Mukaisho,Kazunari Murakami,Yasuyuki Seto,Hisao Tajiri,Shobna Bhatia,Myung-Gyu Choi,Rebecca C. Fitzgerald,Kwong Ming Fock,Khean-Lee Goh,Khek Yu Ho,Varocha Mahachai,Maria O'Donovan,Robert D. Odze,Richard M. Peek,Massimo Rugge,Prateek Sharma,Jose D. Sollano,Michael Vieth,Justin C.Y. Wu,Ming-Shiang Wu,Duo-Wu Zou,Michio Kaminishi,Peter Malfertheiner +34 more
TL;DR: This international consensus on the new definitions of BO, GOJ and the GOJZ will be instrumental in future studies aiming to resolve many issues on this important anatomic area and hopefully will lead to better classification and management of the diseases surrounding the GoJ.
Application of artificial intelligence for improving early detection and prediction of therapeutic outcomes for gastric cancer in the era of precision oncology.
TL;DR: In this paper , a review summarizes the application of artificial intelligence algorithms to multi-dimensional data including clinical and follow-up information, conventional images (endoscope, histopathology, and computed tomography (CT)), molecular biomarkers, etc.
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Efficient Gastrointestinal Disease Classification Using Pretrained Deep Convolutional Neural Network
TL;DR: In this article , an efficient GI tract disease classification technique is proposed which utilizes an optimized brightness-controlled contrast-enhancement method to improve the contrast of the WCE images, which shows an overall improvement of 15.26% in accuracy, 13.3% in precision, 16.77% in recall rate, and 15.18% in F-measure.
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