ArticleNPJ digital medicine2025
A novel AI-powered radiographic analysis surpasses specialists in stage II-IV periodontitis detection: a multicenter diagnostic study.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 2 registered trials, which are not on this map. Cited by 10 papers.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Diagnostic Accuracy of Self-Reported Parameters, Oral and Systemic Biomarker Profile for the Detection of Periodontal Health and Disease
What is the Diagnostic Accuracy of a AI-clinical Knowledge Based Hybrid System for the Diagnosis of Stage II-IV Periodontitis Using OPG Images?A Multi-center Study
Who cites it
10 citing papers in PubMed.
- From Panoramic Radiographs to AI-Assisted Radiographic Periodontal Charting: Current Evidence and Future Perspectives.Dentistry journal · 2026Review
- Driving emerging technology to clinical impact.Journal of the American Dental Association (1939) · 2026Review
- Diagnostic and Therapeutic Approaches in Periodontology: From Traditional Concepts to Modern Innovations.Biomedicines · 2026Review
- Development and Validation of AI System for Tooth Detection and Diagnosis in Dental Radiographs.International dental journal · 2026Article
- Diagnostic Performance of Artificial Intelligence Models for Periodontitis Disease Detection Using Panoramic Radiographs: A Systematic Review.Dentistry journal · 2026Review
- Exploring the Potential of the PerioAI System to Support Periodontal Clinical Decision Making: A Proof-of-Principle Study.Journal of clinical periodontology · 2026Article
- Q-Bone system: an intelligent quantitative system for alveolar bone loss to assist the diagnosis of periodontitis - model development and validation.Journal of translational medicine · 2026Article
- Sonosensitized Hemoglobin Nanoparticles for Ultrasound-Triggered NO Gas Therapy of Periodontitis.Nano letters · 2026Article
- Fractal Analysis and Artificial Intelligence for Radiographic Detection of Periodontal Bone Loss: A Systematic Review.Diagnostics (Basel, Switzerland) · 2026Review
- Harnessing generative artificial intelligence for periodontitis prediction: a machine learning approach integrating systemic health indicators for precision oral health in resource-limited settings.Frontiers in dental medicine · 2026Article
Corrections and comments
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Authors and funding
15 authors.
Funding
No grant is acknowledged in the PubMed record.
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 and experience levels, enabling junior dentists to match specialist performance with AI support. Performance remained consistent in the multicenter evaluation, achieving >92.4% accuracy across all locations. HC-Net+'s ability to surpass specialist accuracy while making diagnostic expertise more accessible positions it as a transformative tool for precision dentistry. The diagnostic trials were registered at ClinicalTrial.gov (NCT05513599) on 08/23/2022 and (NCT06306677) on 03/12/2024.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.