A novel AI-powered radiographic analysis surpasses specialists in stage II-IV periodontitis detection: a multicenter diagnostic study.

ABSTRACT: Missed periodontitis diagnoses are common, and AI dental radiography systems based on clinical standards can enhance reliable detection. We introduce and evaluate HC-Net+, a deep-learning model that mimics clinical pathways while integrating localized tooth lesion analyses with broad contextual understanding. This is the first AI model developed from orthopantomograms (OPGs) linked to clinical diagnoses, pre-trained and fine-tuned with 10,881 OPGs, and tested with dual benchmarking against 382 clinically labeled OPGs covering 10,198 teeth and 760 radiographically labeled OPGs from four diverse international centers. It outperformed periodontal specialists' diagnostic accuracy (AUROC: 94.2% vs. 85.6%, p < 0.01). The system significantly improved early periodontitis detection across training

SUBMITTER: Li Y 

PROVIDER: S-EPMC12627451 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature