Automated caries detection with smartphone color photography using machine learning

Duc Long Duong, Malitha Humayun Kabir, Rong Fu Kuo

研究成果: Article同行評審

29 引文 斯高帕斯(Scopus)


Untreated caries is significant problem that affected billion people over the world. Therefore, the appropriate method and accuracy of caries detection in clinical decision-making in dental practices as well as in oral epidemiology or caries research, are required urgently. The aim of this study was to introduce a computational algorithm that can automate recognize carious lesions on tooth occlusal surfaces in smartphone images according to International Caries Detection and Assessment System (ICDAS). From a group of extracted teeth, 620 unrestored molars/premolars were photographed using smartphone. The obtained images were evaluated for caries diagnosis with the ICDAS II codes, and were labeled into three classes: “No Surface Change” (NSC); “Visually Non-Cavitated” (VNC); “Cavitated” (C). Then, a two steps detection scheme using Support Vector Machine (SVM) has been proposed: “C versus (VNC + NSC)” classification, and “VNC versus NSC” classification. The accuracy, sensitivity, and specificity of best model were 92.37%, 88.1%, and 96.6% for “C versus (VNC + NSC),” whereas they were 83.33%, 82.2%, and 66.7% for “VNC versus NSC.” Although the proposed SVM system required further improvement and verification, with the data only imaged from the smartphone, it performed an auspicious potential for clinical diagnostics with reasonable accuracy and minimal cost.

期刊Health Informatics Journal
出版狀態Published - 2021

All Science Journal Classification (ASJC) codes

  • 健康資訊學


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