Yoshitaka Tokai1, Toshiyuki Yoshio2, Kazuharu Aoyama3, Yoshimasa Horie1,4, Shoichi Yoshimizu1, Yusuke Horiuchi1, Akiyoshi Ishiyama1, Tomohiro Tsuchida1, Toshiaki Hirasawa1, Yuko Sakakibara5, Takuya Yamada6, Shinjiro Yamaguchi7, Junko Fujisaki1, Tomohiro Tada3,8. 1. Department of Gastroenterology, Cancer Institute Hospital, Japanese Foundation for Cancer Research, 3-8-31, Ariake, Koto, Tokyo, 135-8550, Japan. 2. Department of Gastroenterology, Cancer Institute Hospital, Japanese Foundation for Cancer Research, 3-8-31, Ariake, Koto, Tokyo, 135-8550, Japan. toshiyuki.yoshio@jfcr.or.jp. 3. AI Medical Service Inc., Toshima, Tokyo, Japan. 4. Division of Gastroenterology and Hepatology, Department of Internal Medicine, Toho University Ohashi Medical Center, Meguro, Tokyo, Japan. 5. Department of Gastroenterology, Osaka National Hospital, Osaka, Japan. 6. Department of Gastroenterology, Osaka Rosai Hospital, Sakai, Osaka, Japan. 7. Department of Gastroenterology, Kansai Rosai Hospital, Amagasaki, Hyogo, Japan. 8. Tada Tomohiro Institute of Gastroenterology and Proctology, Saitama, Japan.
Abstract
OBJECTIVES: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.
OBJECTIVES: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.