Endoscopic images by a single-shot multibox detector for the identification of early cancerous lesions in the esophagus: A pilot study

Yao Kuang Wang, Hao Yi Syu, Yi Hsun Chen, Chen Shuan Chung, Yu Sheng Tseng, Shinn-Ying Ho, Chien Wei Huang*, I. Chen Wu, Hsiang Chen Wang

*此作品的通信作者

研究成果: Article同行評審

8 引文 斯高帕斯(Scopus)

摘要

Diagnosis of early esophageal neoplasia, including dysplasia and superficial cancer, is a great challenge for endoscopists. Recently, the application of artificial intelligence (AI) using deep learning in the endoscopic field has made significant advancements in diagnosing gastrointestinal cancers. In the present study, we constructed a single-shot multibox detector using a convolutional neural network for diagnosing different histological grades of esophageal neoplasms and evaluated the diagnostic accuracy of this computer-aided system. A total of 936 endoscopic images were used as training images, and these images included 498 white-light imaging (WLI) and 438 narrowband imaging (NBI) images. The esophageal neoplasms were divided into three classifications: squamous low-grade dysplasia, squamous high-grade dysplasia, and squamous cell carcinoma, based on pathological diagnosis. This AI system analyzed 264 test images in 10 s, and the sensitivity, specificity, and diagnostic accuracy of this system in detecting esophageal neoplasms were 96.2%, 70.4%, and 90.9%, respectively. The accuracy of this AI system in differentiating the histological grade of esophageal neoplasms was 92%. Our system showed better accuracy in diagnosing NBI (95%) than WLI (89%) images. Our results showed the great potential of AI systems in identifying esophageal neoplasms as well as differentiating histological grades.

原文English
文章編號321
頁(從 - 到)1-12
頁數12
期刊Cancers
13
發行號2
DOIs
出版狀態Published - 2 1月 2021

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