aceAccuracy of transfer learning in predicting peri-implantitis onset from intraoral radiograph: A multi-center longitudinal retrospective study
Abstract Background The growing application of artificial intelligence in dentistry has opened new opportunities for predicting peri-implantitis (PI) using convolutional neural networks (CNNs). Transfer learning, where knowledge from a model pre-trained on a large dataset is adapted to a new, domain-specific task, may enhance performance, particularly when medical datasets are limited. This study evaluated the ability of several pre-trained CNN architectures applied to baseline 2D intraoral radiographs to predic…
Non-hybrid feature extraction on pre-trained CNNs reached AUCs of 0.74 to 0.83 in predicting later peri-implantitis from a routine baseline radiograph, suggesting a possible aid to early risk stratification.
Fine-tuning strategies performed modestly or poorly on the same task, with AUCs generally between 0.42 and 0.69, and the authors say larger prospective studies are needed before clinical adoption.
The dataset is small, with 195 implants in 108 patients, the design is retrospective, and the authors state that larger prospective studies are needed before clinical adoption.
Evidence
- Peer-reviewedClinical Oral Investigations2026-10-19
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Truvace Impact Record TRV-2026-1885, v1: “Accuracy of transfer learning in predicting peri-implantitis onset from intraoral radiograph: A multi-center longitudinal retrospective study.” Truvace, 2026-10-19. /record/TRV-2026-1885 (accessed at citation time). sha256 d589e367d256dc2c…
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