Applying deep learning techniques to identify tonsilloliths in panoramic radiography.
Authors
Affiliations (5)
Affiliations (5)
- Faculty of Dentistry, Department of Dentomaxillofacial Radiology, Dicle University, Sur, Diyarbakır, Turkey. [email protected].
- Faculty of Dentistry, Department of Dentomaxillofacial Radiology, Fırat University, Elazığ, Turkey.
- Çağla Danacı, Institute of Science, Department of Software Engineering, Fırat University, 23200, Elazığ, Turkey.
- Faculty of Technology, Department of Software Engineering, Sivas Republic University, Sivas, Turkey.
- Seda Arslan Tuncer, Faculty of Engineering, Department of Software Engineering, Fırat University, Elazığ, Turkey.
Abstract
Tonsilloliths can be seen on panoramic radiographs (PRs) as deposits located on the middle portion of the ramus of the mandible. Although tonsilloliths are clinically harmless, the high risk of misdiagnosis leads to unnecessary advanced examinations and interventions, thus jeopardizing patient safety and increasing unnecessary resource use in the healthcare system. Therefore, this study aims to meet an important clinical need by providing accurate and rapid diagnostic support. The dataset consisted of a total of 275 PRs, with 125 PRs lacking tonsillolith and 150 PRs having tonsillolith. ResNet and EfficientNet CNN models were assessed during the model selection process. An evaluation was conducted to analyze the learning capacity, intricacy, and compatibility of each model with the problem at hand. The effectiveness of the models was evaluated using accuracy, recall, precision, and F1 score measures following the training phase. Both the ResNet18 and EfficientNetB0 models were able to differentiate between tonsillolith-present and tonsillolith-absent conditions with an average accuracy of 89%. ResNet101 demonstrated underperformance when contrasted with other models. EfficientNetB1 exhibits satisfactory accuracy in both categories. The EfficientNetB0 model exhibits a 93% precision, 87% recall, 90% F1 score, and 89% accuracy. This study indicates that implementing AI-powered deep learning techniques would significantly improve the clinical diagnosis of tonsilloliths.