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    Application of artificial intelligence using convolutional neural networks in determining the invasion depth of esophageal squamous cell carcinoma. Tokai Yoshitaka,Yoshio Toshiyuki,Aoyama Kazuharu,Horie Yoshimasa,Yoshimizu Shoichi,Horiuchi Yusuke,Ishiyama Akiyoshi,Tsuchida Tomohiro,Hirasawa Toshiaki,Sakakibara Yuko,Yamada Takuya,Yamaguchi Shinjiro,Fujisaki Junko,Tada Tomohiro Esophagus : official journal of the Japan Esophageal Society 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. 10.1007/s10388-020-00716-x
    Diagnosis using deep-learning artificial intelligence based on the endocytoscopic observation of the esophagus. Kumagai Youichi,Takubo Kaiyo,Kawada Kenro,Aoyama Kazuharu,Endo Yuma,Ozawa Tsuyoshi,Hirasawa Toshiaki,Yoshio Toshiyuki,Ishihara Soichiro,Fujishiro Mitsuhiro,Tamaru Jun-Ichi,Mochiki Erito,Ishida Hideyuki,Tada Tomohiro Esophagus : official journal of the Japan Esophageal Society BACKGROUND AND AIMS:The endocytoscopic system (ECS) helps in virtual realization of histology and can aid in confirming histological diagnosis in vivo. We propose replacing biopsy-based histology for esophageal squamous cell carcinoma (ESCC) by using the ECS. We applied deep-learning artificial intelligence (AI) to analyse ECS images of the esophagus to determine whether AI can support endoscopists for the replacement of biopsy-based histology. METHODS:A convolutional neural network-based AI was constructed based on GoogLeNet and trained using 4715 ECS images of the esophagus (1141 malignant and 3574 non-malignant images). To evaluate the diagnostic accuracy of the AI, an independent test set of 1520 ECS images, collected from 55 consecutive patients (27 ESCCs and 28 benign esophageal lesions) were examined. RESULTS:On the basis of the receiver-operating characteristic curve analysis, the areas under the curve of the total images, higher magnification pictures, and lower magnification pictures were 0.85, 0.90, and 0.72, respectively. The AI correctly diagnosed 25 of the 27 ESCC cases, with an overall sensitivity of 92.6%. Twenty-five of the 28 non-cancerous lesions were diagnosed as non-malignant, with a specificity of 89.3% and an overall accuracy of 90.9%. Two cases of malignant lesions, misdiagnosed as non-malignant by the AI, were correctly diagnosed as malignant by the endoscopist. Among the 3 cases of non-cancerous lesions diagnosed as malignant by the AI, 2 were of radiation-related esophagitis and one was of gastroesophageal reflux disease. CONCLUSION:AI is expected to support endoscopists in diagnosing ESCC based on ECS images without biopsy-based histological reference. 10.1007/s10388-018-0651-7