Ahmed Walaa Magdy, Azhari Amr Ahmed, Fawaz Khaled Ahmed, Ahmed Hani M, Alsadah Zainab M, Majumdar Aritra, Carvalho Ricardo Marins
Assistant Professor, Department of Restorative Dentistry, Faculty of Dentistry, King Abdulaziz University, Jeddah, Saudi Arabia.
Assistant Professor, Department of Restorative Dentistry, Faculty of Dentistry, King Abdulaziz University, Jeddah, Saudi Arabia.
J Prosthet Dent. 2025 May;133(5):1326-1332. doi: 10.1016/j.prosdent.2023.07.013. Epub 2023 Aug 26.
Automated detection of dental caries could enhance early detection, save clinician time, and enrich treatment decisions. However, a reliable system is lacking.
The purpose of this study was to train a deep learning model and to assess its ability to detect and classify dental caries.
Bitewings radiographs with a 1876×1402-pixel resolution were collected, segmented, and anonymized with a radiographic image analysis software program and were identified and classified according to the modified King Abdulaziz University (KAU) classification for dental caries. The method was based on supervised learning algorithms trained on semantic segmentation tasks.
The mean score for the intersection-over-union of the model was 0.55 for proximal carious lesions on a 5-category segmentation assignment and a mean F1 score of 0.535 using 554 training samples.
The study validated the high potential for developing an accurate caries detection model that will expedite caries identification, assess clinician decision-making, and improve the quality of patient care.
龋齿的自动检测可以提高早期检测率、节省临床医生的时间并丰富治疗决策。然而，目前缺乏一个可靠的系统。
本研究的目的是训练一个深度学习模型，并评估其检测和分类龋齿的能力。
收集分辨率为1876×1402像素的咬合翼片X光片，使用放射图像分析软件程序进行分割、匿名处理，并根据改良的阿卜杜勒阿齐兹国王大学（KAU）龋齿分类法进行识别和分类。该方法基于在语义分割任务上训练的监督学习算法。
在一个5类分割任务中，模型的交并比平均得分为0.55，用于近端龋损，使用554个训练样本时，平均F1得分为0.535。
该研究验证了开发一个准确的龋齿检测模型的巨大潜力，该模型将加快龋齿识别、评估临床医生的决策并提高患者护理质量。
