• Yoshiko Ariji, Motoki Fukuda, Yoshitaka Kise, Michihito Nozawa, Yudai Yanashita, Hiroshi Fujita, Akitoshi Katsumata, and Eiichiro Ariji.
• Associate Proffessor, Department of Oral and Maxillofacial Radiology, Aichi-Gakuin University School of dentistry, Nagoya, Japan. Electronic address: Yoshiko@dpc.agu.ac.jp.
• Oral Surg Oral Med Oral Pathol Oral Radiol. 2019 May 1; 127 (5): 458-463.

ObjectiveAlthough the deep learning system has been applied to interpretation of medical images, its application to the diagnosis of cervical lymph nodes in patients with oral cancer has not yet been reported. The purpose of this study was to evaluate the performance of deep learning image classification for diagnosis of lymph node metastasis.Study DesignThe imaging data used for evaluation consisted of computed tomography (CT) images of 127 histologically proven positive cervical lymph nodes and 314 histologically proven negative lymph nodes from 45 patients with oral squamous cell carcinoma. The performance of a deep learning image classification system for the diagnosis of lymph node metastasis on CT images was compared with the diagnostic interpretations of 2 experienced radiologists by using the Mann-Whitney U test and χ2 analysis.ResultsThe performance of the deep learning image classification system resulted in accuracy of 78.2%, sensitivity of 75.4%, specificity of 81.0%, positive predictive value of 79.9%, negative predictive value of 77.1%, and area under the receiver operating characteristic curve of 0.80. These values were not significantly different from those found by the radiologists.ConclusionsThe deep learning system yielded diagnostic results similar to those of the radiologists, which suggests that this system may be valuable for diagnostic support.Copyright © 2018 Elsevier Inc. All rights reserved.

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