The effect of a deep-learning tool on dentists' performances in detecting apical radiolucencies on periapical radiographs.

Abstract

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.