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Web-based AI application for enhanced dental disease diagnosis using advanced object detection integrated with
Hossein Sadr1,2, Mojdeh Nazari3,4, Mahsa Koochaki5
1Neuroscience Research Center, Trauma Institute, Guilan University of Medical Sciences, Rasht, Iran.
Oral Radiology
|January 13, 2026
Summary
This study introduces an AI-powered web application for automated dental disease detection from X-rays, improving diagnostic accuracy and efficiency. The YOLOv11-TAM model enhances early detection, aiding clinicians and potentially revolutionizing dental care.
Area of Science:
- Artificial Intelligence in Healthcare
- Medical Imaging Analysis
- Dental Diagnostics
Background:
- Accurate dental disease diagnosis is crucial but traditional panoramic X-ray analysis is time-consuming and prone to human error.
- Automating dental diagnostics can improve treatment outcomes and patient care.
- A novel web-based AI application is proposed to address these challenges.
Purpose of the Study:
- To develop and evaluate a web-based AI application for automated detection and diagnosis of dental diseases from panoramic X-ray images.
- To leverage the YOLOv11-TAM model for enhanced feature extraction and localization accuracy.
- To improve the efficiency and accessibility of dental diagnostics.
Main Methods:
- The system utilizes a user-friendly interface, PostgreSQL database, and a YOLOv11-TAM AI engine.
- The AI model was trained and validated on the DENTEX dataset (705 annotated panoramic X-rays).
- Architectural innovations in YOLOv11-TAM include C3k2 block, SPPF layer, and Transformer-based attention mechanisms.
Main Results:
- The customized YOLOv11-TAM model showed a ~15% precision increase and >12% localization accuracy improvement for periapical lesions over YOLOv11.
- High specificity (0.92) and superior detection of deep caries and periapical lesions were achieved.
- Usability study reported high user satisfaction (average >8), indicating intuitive design and seamless clinical integration.
Conclusions:
- The AI application offers a transformative approach to dental diagnostics, enhancing accuracy, efficiency, and accessibility.
- By reducing radiologist workload and enabling early disease detection, it can revolutionize dental healthcare.
- The solution shows particular promise for improving dental care in underserved regions.
