Dentomaxillofac Radiol. 2025 Jan 20:twaf004. doi: 10.1093/dmfr/twaf004. Online ahead of print.
ABSTRACT
OBJECTIVES: The aim of this technical report was to assess whether the “Radiological Report” tool within the Artificial Intelligence (AI) software Diagnocat can achieve a satisfactory level of performance comparable to that of experienced dentomaxillofacial radiologists in interpreting cone-beam CT scans.
METHODS: Ten cone-beam CT scans were carefully selected and analyzed using the AI tool, and they were also evaluated by two dentomaxillofacial radiologists. Observations related to tooth numeration, alterations in dental crowns, roots, and periodontal tissues were documented and subsequently compared to the AI findings. Kappa statistics, along with their corresponding 95% confidence intervals, were calculated to ascertain the degree of agreement.
RESULTS: The agreement between the AI tool and the radiologists ranged from substantial to nearly perfect for identifying teeth, determining the number of roots and canals, assessing crown conditions, and detecting endodontic treatments. However, for tasks such as classifying bone loss, identifying posts, evaluating the quality of fillings, and appraising the situation of periodontal spaces, the agreement was deemed slight.
CONCLUSIONS: The “radiological report” tool of the Diagnocat demonstrates satisfactory performance in reliably identifying teeth, roots, canals, assessing crown conditions, and detecting endodontic treatment. However, further investigations are needed to evaluate the tool’s effectiveness in diagnosing posts, assessing the condition and quality of fillings, and determining the status of periodontal spaces.
PMID:39832268 | DOI:10.1093/dmfr/twaf004
