Lee Sang Won, Huz Kateryna, Gorelick Kayla, Li Jackie, Bina Thomas, Matsumura Satoko, Yin Noah, Zhang Nicholas, Anang Yvonne Naa Ardua, Sachadava Sanam, Servin-DeMarrais Helena I, McMahon Donald J, Lu Helen H, Yin Michael T, Wadhwa Sunil
Department of Biomedical Engineering, Columbia University, New York, NY, 10027, USA.
Division of Orthodontics, Columbia University College of Dental Medicine, New York, NY, 10032, USA.
BMC Oral Health. 2025 Mar 1;25(1):329. doi: 10.1186/s12903-025-05677-0.
Several commercial programs incorporate artificial intelligence in diagnosis, but very few dental professionals have been surveyed regarding its acceptability and usability. Furthermore, few have explored how these advances might be incorporated into routine practice.
Our team developed and implemented a deep learning (DL) model employing semantic segmentation neural networks and object detection networks to precisely identify alveolar bone crestal levels (ABCLs) and cemento-enamel junctions (CEJs) to measure change in alveolar crestal height (ACH). The model was trained and validated using a 550 bitewing radiograph dataset curated by an oral radiologist, setting a gold standard for ACH measurements. A twenty-question survey was created to compare the accuracy and efficiency of manual X-ray examination versus the application and to assess the acceptability and usability of the application.
In total, 56 different dental professionals classified severe (ACH > 5 mm) vs. non-severe (ACH ≤ 5 mm) periodontal bone loss on 35 calculable ACH measures. Dental professionals accurately identified between 35-87% of teeth with severe periodontal disease, whereas the artificial intelligence (AI) application achieved an 82-87% accuracy rate. Among the 65 participants who completed the acceptability and usability survey, more than half the participants (52%) were from an academic setting. Only 21% of participants reported that they already used automated or AI-based software in their practice to assist in reading of X-rays. The majority, 57%, stated that they only approximate when measuring bone levels and only 9% stated that they measure with a ruler. The survey indicated that 84% of participants agreed or strongly agreed with the AI application measurement of ACH. Furthermore, 56% of participants agreed that AI would be helpful in their professional setting.
Overall, the study demonstrates that an AI application for detecting alveolar bone has high acceptability among dental professionals and may provide benefits in time saving and increased clinical accuracy.
有几款商业程序在诊断中融入了人工智能，但针对牙科专业人员对其可接受性和可用性的调查却非常少。此外，很少有人探讨这些进展如何能融入日常实践。
我们的团队开发并实施了一种深度学习（DL）模型，该模型采用语义分割神经网络和目标检测网络来精确识别牙槽嵴顶水平（ABCLs）和牙骨质-釉质界（CEJs），以测量牙槽嵴高度（ACH）的变化。该模型使用由口腔放射科医生整理的550张咬合翼片X线数据集进行训练和验证，为ACH测量设定了金标准。创建了一项包含20个问题的调查，以比较手动X线检查与该应用程序的准确性和效率，并评估该应用程序的可接受性和可用性。
共有56名不同的牙科专业人员对35项可计算的ACH测量结果进行了严重（ACH＞5mm）与非严重（ACH≤5mm）牙周骨丧失的分类。牙科专业人员准确识别出35%-87%患有严重牙周疾病的牙齿，而人工智能（AI）应用程序的准确率为82%-87%。在完成可接受性和可用性调查的65名参与者中，超过一半的参与者（52%）来自学术机构。只有21%的参与者报告说他们在实践中已经使用自动化或基于AI的软件来辅助读取X线片。大多数人（57%）表示他们在测量骨水平时只是大致估算，只有9%的人表示他们用尺子测量。调查表明，84%的参与者同意或强烈同意AI应用程序对ACH的测量。此外，56%的参与者同意AI在他们的专业环境中会有所帮助。
总体而言，该研究表明，一种用于检测牙槽骨的AI应用程序在牙科专业人员中具有较高的可接受性，并且可能在节省时间和提高临床准确性方面带来益处。
