Diagnostic Accuracy of a Commercial AI-based Platform in Evaluating Endodontic Treatment Outcomes on Periapical Radiographs Using CBCT as the Reference Standard

Abstract

INTRODUCTION: Artificial intelligence (AI) has shown promise in dental diagnostics; however, its accuracy in assessing endodontic treatment outcomes compared to experienced clinicians remains unclear. This study evaluated the performance of an AI-driven platform (Diagnocat) against experienced clinicians in assessing endodontic treatment outcomes on periapical radiographs, using cone-beam computed tomography as the reference standard. METHODS: This retrospective diagnostic accuracy study analyzed 376 teeth (860 roots) from 4 prospective clinical trials. Treatment outcomes were assessed using periapical radiographs, independently evaluated by 2 calibrated endodontists and the AI-driven platform. Cone-beam computed tomography scans served as the reference standard. Sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve were calculated. RESULTS: The AI-driven platform demonstrated higher sensitivity but lower specificity than clinicians at both tooth (sensitivity: 67.3% vs 49.3%, P < .001; specificity: 82.3% vs 92.5%, P < .001) and root levels (sensitivity: 54.3% vs 43.8%, P = .003; specificity: 86.7% vs 94.5%, P < .001). Overall accuracy was comparable at the tooth level (AI: 76.3%, clinicians: 75.3%, P = .716) but slightly lower for the AI-driven platform at the root level (78.5% vs 81.6%, P = .021). Receiver operating characteristic curve analysis showed comparable area under the curve values between the AI-driven platform and clinicians at both tooth (0.75 vs 0.71) and root levels (0.71 vs 0.69). CONCLUSIONS: While the AI-driven platform demonstrated potential as an adjunctive tool for assessing endodontic treatment outcomes, particularly in detecting lesions that might be missed by human assessment, its lower specificity highlights the need for clinical oversight to prevent overdiagnosis.

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