Department of Surgery, Aga Khan University Hospital, Karachi, Pakistan.
MeDenTec, Karachi, Pakistan.
Sci Rep. 2024 Nov 5;14(1):26752. doi: 10.1038/s41598-024-78184-x.
This study evaluates the effectiveness of an Artificial Intelligence (AI)-based smartphone application designed for decay detection on intraoral photographs, comparing its performance to that of junior dentists. Conducted at The Aga Khan University Hospital, Karachi, Pakistan, this study utilized a dataset comprising 7,465 intraoral images, including both primary and secondary dentitions. These images were meticulously annotated by two experienced dentists and further verified by senior dentists. A YOLOv5s model was trained on this dataset and integrated into a smartphone application, while a Detection Transformer was also fine-tuned for comparative purposes. Explainable AI techniques were employed to assess the AI's decision-making processes. A sample of 70 photographs was used to directly compare the application's performance with that of junior dentists. Results showed that the YOLOv5s-based smartphone application achieved a precision of 90.7%, sensitivity of 85.6%, and an F1 score of 88.0% in detecting dental decay. In contrast, junior dentists achieved 83.3% precision, 64.1% sensitivity, and an F1 score of 72.4%. The study concludes that the YOLOv5s algorithm effectively detects dental decay on intraoral photographs and performs comparably to junior dentists. This application holds potential for aiding in the evaluation of the caries index within populations, thus contributing to efforts aimed at reducing the disease burden at the community level.
这项研究评估了一款基于人工智能（AI）的智能手机应用程序在口腔内照片上检测龋齿的效果，将其性能与初级牙医进行了比较。该研究在巴基斯坦卡拉奇的 Aga Khan 大学医院进行，使用了一个包含 7465 张口腔内图像的数据集，包括恒牙和乳牙。这些图像由两名经验丰富的牙医进行了仔细标注，并由资深牙医进一步验证。在这个数据集上训练了一个 YOLOv5s 模型，并将其集成到一个智能手机应用程序中，同时还对 Detection Transformer 进行了微调以进行比较。使用可解释的 AI 技术来评估 AI 的决策过程。使用 70 张照片样本直接比较应用程序和初级牙医的性能。结果表明，基于 YOLOv5s 的智能手机应用程序在检测口腔内龋齿方面的准确率为 90.7%，灵敏度为 85.6%，F1 得分为 88.0%。相比之下，初级牙医的准确率为 83.3%，灵敏度为 64.1%，F1 得分为 72.4%。该研究得出结论，YOLOv5s 算法可以有效地在口腔内照片上检测龋齿，并且与初级牙医的表现相当。该应用程序具有在人群中评估龋齿指数的潜力，从而有助于减少社区层面的疾病负担。
