PDF(Pubmed)
Abstract：
Background/Objectives: Periapical lesions (PLs) are frequently detected in dental radiology. Accurate diagnosis of these lesions is essential for proper treatment planning. Imaging techniques such as orthopantomogram (OPG) and cone-beam CT (CBCT) imaging are used to identify PLs. The aim of this study was to assess the diagnostic accuracy of artificial intelligence (AI) software Diagnocat for PL detection in OPG and CBCT images. Methods: The study included 49 patients, totaling 1223 teeth. Both OPG and CBCT images were analyzed by AI software and by three experienced clinicians. All the images were obtained in one patient cohort, and findings were compared to the consensus of human readers using CBCT. The AI\'s diagnostic accuracy was compared to a reference method, calculating sensitivity, specificity, accuracy, positive predictive value (PPV), negative predictive value (NPV), and F1 score. Results: The AI\'s sensitivity for OPG images was 33.33% with an F1 score of 32.73%. For CBCT images, the AI\'s sensitivity was 77.78% with an F1 score of 84.00%. The AI\'s specificity was over 98% for both OPG and CBCT images. Conclusions: The AI demonstrated high sensitivity and high specificity in detecting PLs in CBCT images but lower sensitivity in OPG images.
摘要：
背景/目的：根尖周病变（PLs）在牙科放射学中经常被发现。这些病变的准确诊断对于正确的治疗计划至关重要。使用成像技术(例如,正像图(OPG)和锥形束CT(CBCT)成像)来识别PL。这项研究的目的是评估人工智能（AI）软件Diagnocat在OPG和CBCT图像中进行PL检测的诊断准确性。方法：本研究包括49例患者，总共1223颗牙齿。OPG和CBCT图像均通过AI软件和三名经验丰富的临床医生进行分析。所有图像都是在一个患者队列中获得的，并将研究结果与使用CBCT的人类读者的共识进行了比较。将AI的诊断准确性与参考方法进行了比较，计算灵敏度,特异性，准确度,阳性预测值(PPV)，负预测值(NPV)，F1得分。结果：AI对OPG图像的敏感度为33.33%，F1评分为32.73%。对于CBCT图像，AI的敏感性为77.78%，F1评分为84.00%。OPG和CBCT图像的AI特异性均超过98%。结论：AI在CBCT图像中检测PLs具有较高的敏感性和特异性，但在OPG图像中具有较低的敏感性。
