Clinical and translational gastroenterology · 2020
Application of Convolutional Neural Networks for Detection of Superficial Nonampullary Duodenal Epithelial Tumors in Esophagogastroduodenoscopic Images.
Оценочное исследованиеСтатьяНазвание и аннотация: Перевод пока не подготовлен
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Inoue Shuntaro, Shichijo Satoki, Aoyama Kazuharu, Kono Mitsuhiro, Fukuda Hiromu, Shimamoto Yusaku, Nakagawa Kentaro, Ohmori Masayasu, Iwagami Hiroyoshi, Matsuno Kenshi, Iwatsubo Taro, Nakahira Hiroko, Matsuura Noriko, Maekawa Akira, Kanesaka Takashi, Yamamoto Sachiko, Takeuchi Yoji, Higashino Koji, Uedo Noriya, Ishihara Ryu, Tada Tomohiro
Аннотация
OBJECTIVES: A superficial nonampullary duodenal epithelial tumor (SNADET) is defined as a mucosal or submucosal sporadic tumor of the duodenum that does not arise from the papilla of Vater. SNADETs rarely metastasize to the lymph nodes, and most can be treated endoscopically. However, SNADETs are sometimes missed during esophagogastroduodenoscopic examination. In this study, we constructed a convolutional neural network (CNN) and evaluated its ability to detect SNADETs.
METHODS: A deep CNN was pretrained and fine-tuned using a training data set of the endoscopic images of SNADETs (duodenal adenomas [N = 65] and high-grade dysplasias [HGDs] [N = 31] [total 531 images]). The CNN evaluated a separate set of images from 26 adenomas, 8 HGDs, and 681 normal tissue (total 1,080 images). The gold standard for both the training data set and test data set was a "true diagnosis" made by board-certified endoscopists and pathologists. A detected tumor was marked with a rectangular frame on the endoscopic image. If it overlapped at least a part of the "true tumor" diagnosed by board-certified endoscopists, the CNN was considered to have "detected" the SNADET.
RESULTS: The trained CNN detected 94.7% (378 of 399) of SNADETs on an image basis (94% [280 of 298] of adenomas and 100% [101 of 101] of HGDs) and 100% on a tumor basis. The time needed for screening the 399 images containing SNADETs and all 1,080 images (including normal images) was 12 and 31 seconds, respectively.
DISCUSSION: We used a novel algorithm to construct a CNN for detecting SNADETs in a short time.
METHODS: A deep CNN was pretrained and fine-tuned using a training data set of the endoscopic images of SNADETs (duodenal adenomas [N = 65] and high-grade dysplasias [HGDs] [N = 31] [total 531 images]). The CNN evaluated a separate set of images from 26 adenomas, 8 HGDs, and 681 normal tissue (total 1,080 images). The gold standard for both the training data set and test data set was a "true diagnosis" made by board-certified endoscopists and pathologists. A detected tumor was marked with a rectangular frame on the endoscopic image. If it overlapped at least a part of the "true tumor" diagnosed by board-certified endoscopists, the CNN was considered to have "detected" the SNADET.
RESULTS: The trained CNN detected 94.7% (378 of 399) of SNADETs on an image basis (94% [280 of 298] of adenomas and 100% [101 of 101] of HGDs) and 100% on a tumor basis. The time needed for screening the 399 images containing SNADETs and all 1,080 images (including normal images) was 12 and 31 seconds, respectively.
DISCUSSION: We used a novel algorithm to construct a CNN for detecting SNADETs in a short time.