To determine the efficacy of a deep learning 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 deep learning 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 deep-leaning 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 deep-learning technology (Denti.AI) enhances dental professionals' abilities to detect apical radiolucencies on intraoral radiographs.”

Deep learning tools have the potential to improve diagnostic efficacy of dentists in identifying apical radiolucencies on periapical radiographs.