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In Zhonghua kou qiang yi xue za zhi = Zhonghua kouqiang yixue zazhi = Chinese journal of stomatology

Objective: To establish and to evaluate a computer-aided system based on deep-learning for detection and diagnosis of dental approximal caries on periapical radiographs. Methods: One hundred and sixty human premolars and molars extracted for orthodontic or periodontal reasons were obtained from Department of Oral and Maxillofacial Surgery, Affiliated Stomatological Hospital, Fujian Medical University. A total of 160 periapical radiographic images were divided into a training dataset (n=80) and a test dataset (n=80). A deep-learning based computer-aided caries diagnosis system was established and trained. The performances of computer-aided diagnosis system and human observer were compared using receiver operating characteristic (ROC) curves, precision-recall (P-R) curves, the area under the curve (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). The AUC values of human observers and caries diagnosis system was compared by using an online statistical tool (SPSSAU 20.0). Chi-square test was used to analyze the differences between human observers and caries diagnosis system (ɑ=0.05). Results: The AUC values of human observers and caries diagnosis system were 0.729 (95%CI: 0.650-0.808) and 0.762 (95%CI: 0.685-0.839), respectively (P>0.05). No significant differences were found for the specificity, PPV and NPV between the caries diagnosis system and human observers (P all>0.05). The caries diagnosis system was significantly more sensitive in detecting dental proximal caries than human observers (P<0.05). For the diagnosis of level-1 caries (caries limited to outer 1/2 of enamel), the sensitivity of human observers and computer-aided detection system were 27% and 77%, respectively (P<0.05). Conclusions: The computer-aided diagnosis system provided similar accuracy as human observers and significantly better sensitivity than human observers, especially for shallow caries in enamel.

Lin X J, Zhang D, Huang M Y, Cheng H, Yu H

2020-Sep-09

Deep learning, Dental caries, Diagnosis, computer-assisted, X-ray periapical radiography