Xuan Zhang1, Yuan Liang2, Wen Li3, Chao Liu4, Deao Gu4, Weibin Sun1, Leiying Miao3. 1. Department of Periodontology, Nanjing Stomatological Hospital, Medical School of Nanjing University, Nanjing, China. 2. University of California, Los Angeles, CA, USA. 3. Department of Endodontics, Nanjing Stomatological Hospital, Medical School of Nanjing University, Nanjing, China. 4. Department of Orthodontics, Nanjing Stomatological Hospital, Medical School of Nanjing University, Nanjing, China.
Abstract
OBJECTIVES: To develop and evaluate the performance of a deep learning system based on convolutional neural network (ConvNet) to detect dental caries from oral photographs. METHODS: 3,932 oral photographs obtained from 625 volunteers with consumer cameras were included for the development and evaluation of the model. A deep ConvNet was developed by adapting from Single Shot MultiBox Detector. The hard negative mining algorithm was applied to automatically train the model. The model was evaluated for: (i) classification accuracy for telling the existence of dental caries from a photograph and (ii) localization accuracy for locations of predicted dental caries. RESULTS: The system exhibited a classification area under the curve (AUC) of 85.65% (95% confidence interval: 82.48% to 88.71%). The model also achieved an image-wise sensitivity of 81.90%, and a box-wise sensitivity of 64.60% at a high-sensitivity operating point. The hard negative mining algorithm significantly boosted both classification (p < .001) and localization (p < .001) performance of the model by reducing false-positive predictions. CONCLUSIONS: The deep learning model is promising to detect dental caries on oral photographs captured with consumer cameras. It can be useful for enabling the preliminary and cost-effective screening of dental caries among large populations.
OBJECTIVES: To develop and evaluate the performance of a deep learning system based on convolutional neural network (ConvNet) to detect dental caries from oral photographs. METHODS: 3,932 oral photographs obtained from 625 volunteers with consumer cameras were included for the development and evaluation of the model. A deep ConvNet was developed by adapting from Single Shot MultiBox Detector. The hard negative mining algorithm was applied to automatically train the model. The model was evaluated for: (i) classification accuracy for telling the existence of dental caries from a photograph and (ii) localization accuracy for locations of predicted dental caries. RESULTS: The system exhibited a classification area under the curve (AUC) of 85.65% (95% confidence interval: 82.48% to 88.71%). The model also achieved an image-wise sensitivity of 81.90%, and a box-wise sensitivity of 64.60% at a high-sensitivity operating point. The hard negative mining algorithm significantly boosted both classification (p < .001) and localization (p < .001) performance of the model by reducing false-positive predictions. CONCLUSIONS: The deep learning model is promising to detect dental caries on oral photographs captured with consumer cameras. It can be useful for enabling the preliminary and cost-effective screening of dental caries among large populations.
Authors: Baichen Ding; Zhuo Zhang; Yiran Liang; Weiwei Wang; Siwei Hao; Ze Meng; Lian Guan; Ying Hu; Bin Guo; Runlian Zhao; Yan Lv Journal: Ann Transl Med Date: 2021-11