Diagnostic Performance of Artificial Intelligence in the Detection of Dental Diseases on Panoramic Radiographs
Year: 2026
Type: article
Abstract: Aim To evaluate the diagnostic performance of an artificial intelligence (AI) system in detecting common dental diseases on panoramic radiographs and to compare its findings with expert manual interpretation. Background AI has gained increasing attention in dental radiology as a potential tool for supporting image interpretation. Although several studies have reported promising results, the real-world diagnostic performance of AI systems across diverse dental pathologies remains insufficiently validated. Methods This retrospective diagnostic accuracy study analyzed 100 panoramic radiographs using the Second Dentists ® AI software (Velmeni Inc., USA). AI-generated binary outputs were compared with consensus diagnoses established by two experienced oral and maxillofacial radiologists blinded to AI results. Analysis was performed per radiograph, allowing multiple coexisting conditions. Diagnostic performance was assessed using sensitivity, specificity, precision, accuracy, F1-score, and agreement measures with 95% confidence intervals. Results The AI system demonstrated high diagnostic performance for missing teeth (MT), fixed prosthesis (FiP), root stumps (RS), and dental caries (DC). Lower sensitivity was observed for periapical pathology (PP), which accounted for the majority of false-negative (FN) findings. Agreement between the AI and expert interpretation was high for most categories, although it was reduced for subtle lesions. Conclusion AI shows potential as an adjunctive tool for interpreting panoramic radiographs, particularly for well-defined dental conditions. However, reduced sensitivity for subtle pathologies, methodological constraints, and limited external validation highlight the need for cautious clinical implementation and further multicenter evaluation.
Authors: Vidhi Jain, Esther J., Grashika Alone, Hannah Mathai, Akshta Derashri, Jay Bhojwani
Institution: College of Dental Sciences & Research Centre
Language: English
Subfield: Oral Surgery
Field: Dentistry
Domain: Health Sciences
Open Access status: hybrid
OpenAlex ID: W7161650760