Application of artificial intelligence using convolutional neural networks in determining the invasion depth of esophageal squamous cell carcinoma

• DOI: 10.1007/s10388-020-00716-x
• Publication Date: 2020-01-24T19:04:11Z
• Authors: Yoshitaka Tokai; Toshiyuki Yoshio; Kazuharu Aoyama; Yoshimasa Horie; Shoichi Yoshimizu; Yusuke Horiuchi; Akiyoshi Ishiyama; Tomohiro Tsuchida; Toshiaki Hirasawa; Yuko Sakakibara; Takuya Yamada; Shinjiro Yamaguchi; Junko Fujisaki; Tomohiro Tada
• Keywords: Aged, 80 and over; Male; Endoscopic Mucosal Resection; Esophageal Neoplasms; Reproducibility of Results; Middle Aged; Sensitivity and Specificity; 3. Good health; 03 medical and health sciences; Deep Learning; 0302 clinical medicine; Japan; Artificial Intelligence; Area Under Curve; Outcome Assessment, Health Care; Preoperative Care; Humans; Female; Neoplasm Invasiveness; Esophageal Squamous Cell Carcinoma; Neural Networks, Computer; Aged; Retrospective Studies

In Japan, endoscopic resection (ER) is often used to treat esophageal squamous cell carcinoma (ESCC) when invasion depths are diagnosed as EP-SM1, whereas ESCC cases deeper than SM2 are treated by surgical operation or chemoradiotherapy. Therefore, it is crucial to determine the invasion depth of ESCC via preoperative endoscopic examination. Recently, rapid progress in the utilization of artificial intelligence (AI) with deep learning in medical fields has been achieved. In this study, we demonstrate the diagnostic ability of AI to measure ESCC invasion depth.We retrospectively collected 1751 training images of ESCC at the Cancer Institute Hospital, Japan. We developed an AI-diagnostic system of convolutional neural networks using deep learning techniques with these images. Subsequently, 291 test images were prepared and reviewed by the AI-diagnostic system and 13 board-certified endoscopists to evaluate the diagnostic accuracy.The AI-diagnostic system detected 95.5% (279/291) of the ESCC in test images in 10 s, analyzed the 279 images and correctly estimated the invasion depth of ESCC with a sensitivity of 84.1% and accuracy of 80.9% in 6 s. The accuracy score of this system exceeded those of 12 out of 13 board-certified endoscopists, and its area under the curve (AUC) was greater than the AUCs of all endoscopists.The AI-diagnostic system demonstrated a higher diagnostic accuracy for ESCC invasion depth than those of endoscopists and, therefore, can be potentially used in ESCC diagnostics.