Artificial intelligence system for identifying dental implants from intraoral radiographs: A diagnostic accuracy study.

STATEMENT OF PROBLEM Identifying an implant system from radiographic images is essential for managing prosthetic complications but often relies on a clinician's subjective assessment and incomplete patient records. The increasing number of implant systems worldwide complicates accurate recognition and delays treatment procedures.

Purpose

This study aimed to evaluate the diagnostic performance of a commercially available artificial intelligence software program in identifying different dental implant models from periapical radiographs. MATERIAL AND

Methods

An observational retrospective study was conducted using 720 anonymized periapical radiographs representing 12 implant systems. Each radiograph was uploaded into the MovumStudio implant identification module, and a response was considered correct when the software program identified the implant brand or model as the first option with ≥70% confidence. Descriptive and inferential statistics were performed to assess accuracy, sensitivity, specificity, predictive values, the Cohen kappa coefficient, and the area under the receiver operating characteristic curves (α=.05).

Results

The artificial intelligence system correctly identified 589 of 674 valid intraoral radiographs, achieving an overall accuracy of 87.4% (95% confidence interval, 84.9% to 89.9%) and a Cohen kappa value of 0.864, indicating almost perfect agreement. Sensitivity and specificity were ≥90% for most implant systems, with the area under the receiver operating characteristic curve above 0.90, except for the GMI Frontier system (area under the curve=0.55). The mean response time was 8.42 ±5.96 seconds.

Conclusions

The MovumStudio implant identification module demonstrated high diagnostic accuracy and reliability for recognizing most implant systems from periapical radiographs, offering a rapid, user-independent tool to support clinical decision-making.