背景/目标:本研究的目的是评估AI驱动平台Diagnocat的诊断准确性,用于使用锥形束计算机断层扫描(CBCT)图像评估牙髓治疗结果。方法:连续55例患者(男15例,女40例,包括年龄12-70岁)的CBCT成像。使用Diagnocat的AI平台分析CBCT图像,它评估了填充概率等参数,足够的闭塞,足够的密度,过量填充,填充中的空隙,短填充,和根管数量。这些图像也由两位经验丰富的人类读者进行了评估。诊断准确性指标(准确性、精度,召回,和F1评分)被评估并与读者的共识进行比较,作为参考标准。结果:AI平台对大多数参数表现出很高的诊断准确性,填充概率的完美分数(准确性,精度,召回,F1=100%)。足够的闭塞显示中等性能(准确率=84.1%,精度=66.7%,召回率=92.3%,F1=77.4%)。足够的密度(准确度=95.5%,精度,召回,F1=97.2%),过量填充(准确度=95.5%,精度=86.7%,召回=100%,F1=92.9%),和短填充物(准确度=95.5%,精度=100%,召回率=86.7%,F1=92.9%)也表现出强劲的性能。AI在填充检测中的空隙性能(准确率=88.6%,精度=88.9%,召回率=66.7%,和F1=76.2%)强调了需要改进的地方。结论:AI平台Diagnocat在使用CBCT图像评估牙髓治疗结果方面显示出较高的诊断准确性,表明其作为牙科放射学有价值的工具的潜力。
Background/Objectives: The aim of this study was to assess the diagnostic accuracy of the AI-driven platform Diagnocat for evaluating endodontic treatment outcomes using cone beam computed tomography (CBCT) images. Methods: A total of 55 consecutive patients (15 males and 40 females, aged 12-70 years) referred for CBCT imaging were included. CBCT images were analyzed using Diagnocat\'s AI platform, which assessed parameters such as the probability of filling, adequate obturation, adequate density, overfilling, voids in filling, short filling, and root canal number. The images were also evaluated by two experienced human readers. Diagnostic accuracy metrics (accuracy, precision, recall, and F1 score) were assessed and compared to the readers\' consensus, which served as the reference standard. Results: The AI platform demonstrated high diagnostic accuracy for most parameters, with perfect scores for the probability of filling (accuracy, precision, recall, F1 = 100%). Adequate obturation showed moderate performance (accuracy = 84.1%, precision = 66.7%, recall = 92.3%, and F1 = 77.4%). Adequate density (accuracy = 95.5%, precision, recall, and F1 = 97.2%), overfilling (accuracy = 95.5%, precision = 86.7%, recall = 100%, and F1 = 92.9%), and short fillings (accuracy = 95.5%, precision = 100%, recall = 86.7%, and F1 = 92.9%) also exhibited strong performance. The performance of AI for voids in filling detection (accuracy = 88.6%, precision = 88.9%, recall = 66.7%, and F1 = 76.2%) highlighted areas for improvement. Conclusions: The AI platform Diagnocat showed high diagnostic accuracy in evaluating endodontic treatment outcomes using CBCT images, indicating its potential as a valuable tool in dental radiology.