A YOLO-V5 approach for the evaluation of normal fillings and overhanging fillings: an artificial intelligence study

• Published in: Brazilian oral research Vol. 38; p. e098
• Main Authors: Akgül, Nilgün, Yilmaz, Cemile, Bilgir, Elif, Çelik, Özer, Baydar, Oğuzhan, Bayrakdar, İbrahim Şevki
• Format: Journal Article
• Language: English
• Published: Brazil Sociedade Brasileira de Pesquisa Odontológica - SBPqO 01.01.2024; Sociedade Brasileira de Pesquisa Odontológica
• Subjects: Algorithms; Artifical Intelligence; Artificial Intelligence; Deep Learning; Dental Restoration, Permanent - methods; Dentistry; DENTISTRY, ORAL SURGERY & MEDICINE; Humans; Original Research/Imaginology; Radiography, Panoramic; Reference Values; Reproducibility of Results; Radiography, Panoramic; Dentistry; Artifical Intelligence; Deep Learning
• ISSN: 1806-8324; 1807-3107; 1807-3107
• DOI: 10.1590/1807-3107bor-2024.vol38.0098

Dental fillings, frequently used in dentistry to address various dental tissue issues, may pose problems when not aligned with the anatomical contours and physiology of dental and periodontal tissues. Our study aims to detect the prevalence and distribution of normal and overhanging filling restorations using a deep CNN architecture trained through supervised learning, on panoramic radiography images. A total of 10480 fillings and 2491 overhanging fillings were labeled using CranioCatch software from 2473 and 1850 images, respectively. After the data obtaining phase, validation (80%), training 10%), and test-groups (10%) were formed from images for both labelling. The YOLOv5x architecture was used to develop the AI model. The model's performance was assessed through a confusion matrix and sensitivity, precision, and F1 score values of the model were calculated. For filling, sensitivity is 0.95, precision is 0.97, and F1 score is 0.96; for overhanging were determined to be 0.86, 0.89, and 0.87, respectively. The results demonstrate the capacity of the YOLOv5 algorithm to segment dental radiographs efficiently and accurately and demonstrate proficiency in detecting and distinguishing between normal and overhanging filling restorations.