Diagnostic Performance of AI-Based Cloud Software Regarding the Detection of Endodontic Findings on CBCT: A Single-Centre Cross-Sectional Validation Study

Background/Objectives: The aim of the present investigation was to validate the diagnostic performance of the AI-based dental cloud software Diagnocat® AIS (Version 1.0 (UDI: 860010268018), DGNCT LLC, Miami, FL, USA) regarding the detection possibilities of seven different endodontic findings on cone-beam computed tomography (CBCT) against a multi-rater consensus reference standard, and to characterize its calibration, threshold-optimized performance and clinical utility. Methods: 358 root-canal-treated teeth from 167 CBCT scans (167 patients) were retrospectively evaluated at a single private dental practice. From initially included 383 root-canal-treated teeth from 177 patients, 358 (93.5%) were recognized by the AI tool and entered the primary analysis. Two experienced dentists with a clinical focus on endodontics independently graded each tooth and disagreements were adjudicated by a senior expert. Seven different endodontic findings were evaluated: (i) apical (periapical) lesion; (ii) short root-canal filling (apical filling end >2 mm short of the radiographic apex); (iii) voids/lacunae in the root-canal filling; (iv) missed (un-instrumented/un-filled) canal; (v) overfilled root-canal filling (apical extrusion); (vi) apicoectomy (resected root apex with or without retrograde filling); and (vii) coronal restoration with a full-coverage crown. Diagnocat® output was binarized at the manufacturer-fixed 0.50 probability threshold; sensitivity, specificity, predictive values, accuracy, area under the curve AUC (ROC), Cohen κ and Gwet AC1 were computed with 95% cluster-bootstrap confidence intervals (cluster = scan). Threshold optimization, probability calibration, GEE-based subgroup analyses, and decision-curve analysis were pre-specified. Results: Diagnostic performance varied by finding. AUCs were 0.984 for missed canal, 0.917 for overfilled root canal, 0.902 for short root filling, 0.893 for crown, 0.864 for apical lesion, 0.857 for apicoectomy and 0.761 for voids in the root filling. Apical-lesion sensitivity rose from 33.6% for sub-millimeter lesions to ≥80% for lesion measuring 1–5 mm. Re-tuning the decision threshold raised missed-canal sensitivity from 69.6% to 97.5%. Decision-curve analysis confirmed positive benefits for missed canal and root-filling-quality findings. Conclusions: The AI tool Diagnocat® can be recommended as a focused screening adjunct in CBCT-based endodontic interpretation for missed canals, crowns, and gross root-filling-quality flaws. Sub-millimeter apical lesions and several less common findings (resorption, instrument fragment, retrograde filling) remain outside the reliable performance envelope of the current platform.