Ibraheem Wael I, Jain Saurabh, Ayoub Mohammed Naji, Namazi Mohammed Ahmed, Alfaqih Amjad Ismail, Aggarwal Aparna, Meshni Abdullah A, Almarghlani Ammar, Alhumaidan Abdulkareem Abdullah
Department of Preventive Dental Sciences, College of Dentistry, Jazan University, Jazan 45142, Saudi Arabia.
Department of Prosthetic Dental Sciences, College of Dentistry, Jazan University, Jazan 45142, Saudi Arabia.
Diagnostics (Basel). 2025 Jun 4;15(11):1432. doi: 10.3390/diagnostics15111432.
The purpose of the study is to evaluate the diagnostic accuracy of artificial intelligence (AI) software in detecting a common set of periodontal and restorative conditions, including marginal bone loss, dental caries, periapical lesions, calculus, endodontic treatment, crowns, restorations, and open crown margins, using intraoral periapical radiographs. Additionally, the study will assess how this AI software influences the diagnostic accuracy of dentists with varying levels of experience in identifying these conditions. A total of three hundred digital IOPARs representing 1030 teeth were selected based on predetermined selection criteria. The parameters assessed included (a) calculus, (b) periapical radiolucency, (c) caries, (d) marginal bone loss, (e) type of restorative (filling) material, (f) type of crown retainer material, and (g) detection of open crown margins. Two oral radiologists performed the initial diagnosis of the selected radiographs and independently labeled all the predefined parameters for the provided IOPARs under standardized conditions. This data served as reference data. A pre-trained AI-based computer-aided detection ("CADe") software (Second Opinion, version 1.1) was used for the detection of the predefined features. The reports generated by the AI software were compared with the reference data to evaluate the diagnostic accuracy of the AI software. In the second phase of the study, thirty dental interns and thirty dental specialists were randomly selected. Each participant was randomly assigned five IOPARs and was asked to detect and diagnose the predefined conditions. Subsequently, all the participants were requested to reassess the IOPARs, this time with the assistance of the AI software. All the data was recorded using a self-designed Performa. The sensitivity of the AI software in detecting caries, periapical lesions, crowns, open crown margins, restoration, endodontic treatment, calculus, and marginal bone loss was 91.0%, 86.6%, 97.1%, 82.6%, 89.3%, 93.4%, 80.2%, and 91.1%, respectively. The specificity of the AI software in detected caries, periapical lesions, crowns, open crown margins, restoration, endodontic treatment, calculus, and marginal bone loss was 87%, 98.3%, 99.6%, 91.9%, 96.4%, 99.3%, 97.8%, and 93.1%, respectively. The differences between the AI software and radiologist diagnoses of caries, periapical lesions, crowns, open crown margins, restoration, endodontic treatment, calculus, and marginal bone loss were statistically significant (all values < 0.0001). The results showed that the diagnostic accuracy of operators (interns and specialists) with AI software revealed higher accuracy, sensitivity, and specificity in detecting caries, PA lesions, restoration, endodontic treatment, calculus, and marginal bone loss compared to that without using AI software. There were variations in the improvements in the diagnostic accuracy of interns and dental specialists. Within the limitations of the study, it can be concluded that the tested AI software has high accuracy in detecting the tested dental conditions in IOPARs. The use of AI software enhanced the diagnostic capabilities of dental operators. The present study used AI software to detect a clinically useful set of periodontal and restorative conditions, which can help dental operators in fast and accurate diagnosis and provide high-quality treatment to their patients.
本研究的目的是使用口腔根尖片评估人工智能（AI）软件在检测一组常见的牙周和修复情况时的诊断准确性，这些情况包括边缘骨丧失、龋齿、根尖周病变、牙结石、根管治疗、牙冠、修复体以及开放的牙冠边缘。此外，该研究还将评估这种AI软件如何影响不同经验水平的牙医在识别这些情况时的诊断准确性。根据预先确定的选择标准，共选取了代表1030颗牙齿的300张数字化口腔根尖片。评估的参数包括：（a）牙结石，（b）根尖周透射区，（c）龋齿，（d）边缘骨丧失，（e）修复（充填）材料类型，（f）牙冠固位材料类型，以及（g）开放牙冠边缘的检测。两名口腔放射科医生对所选的根尖片进行初步诊断，并在标准化条件下为提供的口腔根尖片独立标记所有预定义参数。该数据用作参考数据。使用一个预先训练的基于AI的计算机辅助检测（“CADe”）软件（Second Opinion，版本1.1）来检测预定义特征。将AI软件生成的报告与参考数据进行比较，以评估AI软件的诊断准确性。在研究的第二阶段，随机选择了30名牙科实习生和30名牙科专家。每位参与者被随机分配5张口腔根尖片，并被要求检测和诊断预定义情况。随后，要求所有参与者重新评估这些口腔根尖片，这次在AI软件的协助下进行。所有数据均使用自行设计的表格记录。AI软件在检测龋齿、根尖周病变、牙冠、开放牙冠边缘、修复体、根管治疗、牙结石和边缘骨丧失方面的灵敏度分别为91.0%、86.6%、97.1%、82.6%、89.3%、93.4%、80.2%和91.1%。AI软件在检测龋齿、根尖周病变、牙冠、开放牙冠边缘、修复体、根管治疗、牙结石和边缘骨丧失方面的特异度分别为87%、98.3%、99.6%、91.9%、96.4%、99.3%、97.8%和93.1%。AI软件与放射科医生在龋齿、根尖周病变、牙冠、开放牙冠边缘、修复体、根管治疗、牙结石和边缘骨丧失诊断方面的差异具有统计学意义（所有p值<0.0001）。结果表明，与不使用AI软件相比，使用AI软件的操作人员（实习生和专家）在检测龋齿、根尖周病变、修复体、根管治疗、牙结石和边缘骨丧失方面显示出更高的准确性、灵敏度和特异度。实习生和牙科专家在诊断准确性的提高方面存在差异。在本研究的局限性范围内，可以得出结论，所测试的AI软件在检测口腔根尖片中的测试牙科情况方面具有很高的准确性。AI软件的使用增强了牙科操作人员的诊断能力。本研究使用AI软件检测了一组临床上有用的牙周和修复情况，这可以帮助牙科操作人员进行快速准确的诊断，并为患者提供高质量的治疗。
