Department of General Dental Sciences, Marquette University School of Dentistry, Milwaukee, WI, United States.
Denti.AI Technology Inc, Toronto, Canada.
Dentomaxillofac Radiol. 2022 Sep 1;51(7):20220122. doi: 10.1259/dmfr.20220122. Epub 2022 Sep 12.
To determine the efficacy of a deep-learning (DL) tool in assisting dentists in detecting apical radiolucencies on periapical radiographs.
Sixty-eight intraoral periapical radiographs with CBCT-proven presence or absence of apical radiolucencies were selected to serve as the testing subset. Eight readers examined the subset, denoted the positions of apical radiolucencies, and used a 5-point confidence scale to score each radiolucency. The same subset was assessed by readers under two conditions: with and without Denti.AI DL tool predictions. For the two sessions, the performance of the readers was compared. The comparison was performed with the alternate free response receiver operating characteristic (AFROC) methodology.
Localization of lesion accuracy (AFROC-AUC), specificity and sensitivity (by lesion) detection demonstrated improvements in the DL aided session in comparison with the unaided reading session. Subgroup performance analysis revealed an increase in sensitivity for small radiolucencies and in radiolucencies located apical to endodontically treated teeth..
The study revealed that the DL technology (Denti.AI) enhances dental professionals' abilities to detect apical radiolucencies on intraoral radiographs.
DL tools have the potential to improve diagnostic efficacy of dentists in identifying apical radiolucencies on periapical radiographs.
评估深度学习（DL）工具辅助牙医检测根尖周放射影像上根尖透影区的效能。
选择 68 张经 CBCT 证实存在或不存在根尖透影区的口腔根尖周放射影像作为测试子集。8 位读者检查了该子集，标注了根尖透影区的位置，并使用 5 分置信度评分对每个透影区进行评分。读者在两种条件下对同一子集进行评估：有和没有 Denti.AI DL 工具预测。对两次评估，比较了读者的表现。比较采用了交替自由响应接收器操作特征（AFROC）方法。
与无辅助阅读相比，在 DL 辅助阅读时，病变定位准确性（AFROC-AUC）、特异性和敏感性（按病变）检测得到改善。亚组表现分析显示，对小的透影区和位于根管治疗牙上方的透影区的敏感性有所提高。
研究表明，DL 技术（Denti.AI）增强了牙科专业人员在口腔放射影像上检测根尖透影区的能力。
DL 工具有可能提高牙医在根尖周放射影像上识别根尖透影区的诊断效能。
