Introduction The aim of this study was to use a Deep Learning (DL) algorithm for the automated segmentation of cone-beam computed tomographic (CBCT) images and the detection of periapical lesions. Methods Limited field of view CBCT volumes (n = 20) containing 61 roots with and without lesions were segmented clinician dependent versus using the DL approach based on a U-Net architecture. Segmentation labeled each voxel as 1 of 5 categories: lesion'' (periapical lesion), tooth structure,’’ bone,'' restorative materials,’’ and ``background.’’ Repeated splits of all images into a training set and a validation set based on 5-fold cross validation were performed using Deep Learning segmentation (DLS), and te results were averaged. DLS versus clinical-dependent segmentation was assessed by dichotomized lesion detection accuracy evaluating sensitivity, specificity, positive predictive value, negative predictive value, and voxel-matching accuracy using the DICE index for each of the 5 labels. Results DLS lesion detection accuracy was 0.93 with specificity of 0.88, positive predictive value of 0.87, and negative predictive value of 0.93. The overall cumulative DICE indexes for the individual labels were lesion = 0.52, tooth structure = 0.74, bone = 0.78, restorative materias = 0.58, and background = 0.95. The cumulative DICE index for all actual true lesions was 0.67. Conclusions This DL algorithm trained in a limited CBCT environment showed excellent results in lesion detection accuracy. Overall voxel-matching accuracy may be benefited by enhanced versions of artificial intelligence.