Oral and Maxillofacial Radiology, Faculty of Dentistry, Selcuk University, Turkey.
Electrical Electronics Engineering, Faculty of Technology, Selcuk University, Turkey.
Niger J Clin Pract. 2023 Aug;26(8):1085-1090. doi: 10.4103/njcp.njcp_624_22.
The aim of the present study was to evaluate the effectiveness of an artificial intelligence (AI) system in the detection of roots with apical periodontitis (AP) on digital panoramic radiographs.
Three hundred and six panoramic radiographs containing 400 roots with AP (an equal number for both jaws) were used to test the diagnostic performance of an AI system. Panoramic radiographs of the patients were selected with the terms 'apical lesion' and 'apical periodontitis' from the archive and then with the agreement of two oral and maxillofacial radiologists. The radiologists also carried out the grouping and determination of the lesion borders. A deep learning (DL) model was built and the diagnostic performance of the model was evaluated by using recall, precision, and F measure.
The recall, precision, and F-measure scores were 0.98, 0.56, and 0.71, respectively. While the number of roots with AP detected correctly in the mandible was 169 of 200 roots, it was only 56 of 200 roots in the maxilla. Only four roots without AP were incorrectly identified as those with AP.
The DL method developed for the automatic detection of AP on digital panoramic radiographs showed high recall, precision, and F measure values for the mandible, but low values for the maxilla, especially for the widened periodontal ligament (PL)/uncertain AP.
本研究旨在评估人工智能（AI）系统在数字全景片上检测根尖周炎（AP）根的有效性。
使用 306 张包含 400 个有 AP 根（上下颌各 200 个）的全景片来测试 AI 系统的诊断性能。从档案中使用“根尖病变”和“根尖周炎”术语选择患者的全景片，然后由两位口腔颌面放射科医生达成一致。放射科医生还进行了分组和病变边界的确定。构建了深度学习（DL）模型，并通过召回率、精确率和 F 度量来评估模型的诊断性能。
召回率、精确率和 F 度量得分分别为 0.98、0.56 和 0.71。虽然下颌骨中有 200 个 AP 根中有 169 个被正确检测到，但上颌骨中只有 200 个中有 56 个。仅有 4 个没有 AP 的根被错误地识别为有 AP。
为自动检测数字全景片上的 AP 而开发的 DL 方法在检测下颌骨时表现出较高的召回率、精确率和 F 度量值，但在检测上颌骨时，尤其是在牙周膜（PL）增宽和不确定的 AP 时，值较低。
