深度学习
卷积神经网络
人工智能
灵敏度(控制系统)
置信区间
计算机科学
牙科
医学
人工神经网络
模式识别(心理学)
电子工程
内科学
工程类
作者
Xuan Zhang,Yuan Liu,Li Wen,Chao Liu,Deao Gu,Weibin Sun,Leiying Miao
出处
期刊:Oral Diseases
[Wiley]
日期:2020-12-19
卷期号:28 (1): 173-181
被引量:71
摘要
To develop and evaluate the performance of a deep learning system based on convolutional neural network (ConvNet) to detect dental caries from oral photographs.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.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.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.
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