Evaluation of artificial intelligence for detecting periapical pathosis on cone‐beam computed tomography scans

• Published in: International endodontic journal 2020-05, Vol.53 (5), p.680-689
• Main Authors: Orhan, K., Bayrakdar, I. S., Ezhov, M., Kravtsov, A., Özyürek, T.
• Format: Article
• Language: English
• Subjects: Artificial Intelligence; Computed tomography; Cone-Beam Computed Tomography; deep learning; Dentistry; Humans; Jaw; Lesions; Mandible; Maxilla; Neural networks; Periapical Diseases; periapical pathology; Reproducibility of Results; Segmentation; Synapses; Teeth; Tooth Root
• ISSN: 0143-2885; 1365-2591
• DOI: 10.1111/iej.13265

Aim To verify the diagnostic performance of an artificial intelligence system based on the deep convolutional neural network method to detect periapical pathosis on cone‐beam computed tomography (CBCT) images. Methodology images of 153 periapical lesions obtained from 109 patients were included. The specific area of the jaw and teeth associated with the periapical lesions were then determined by a human observer. Lesion volumes were calculated using the manual segmentation methods using Fujifilm‐Synapse 3D software (Fujifilm Medical Systems, Tokyo, Japan). The neural network was then used to determine (i) whether the lesion could be detected; (ii) if the lesion was detected, where it was localized (maxilla, mandible or specific tooth); and (iii) lesion volume. Manual segmentation and artificial intelligence (AI) (Diagnocat Inc., San Francisco, CA, USA) methods were compared using Wilcoxon signed rank test and Bland–Altman analysis. Results The deep convolutional neural network system was successful in detecting teeth and numbering specific teeth. Only one tooth was incorrectly identified. The AI system was able to detect 142 of a total of 153 periapical lesions. The reliability of correctly detecting a periapical lesion was 92.8%. The deep convolutional neural network volumetric measurements of the lesions were similar to those with manual segmentation. There was no significant difference between the two measurement methods (P > 0.05). Conclusions Volume measurements performed by humans and by AI systems were comparable to each other. AI systems based on deep learning methods can be useful for detecting periapical pathosis on CBCT images for clinical application.