A Deep Learning Approach for Mandibular Condyle Segmentation on Ultrasonography.
Authors
Affiliations (6)
Affiliations (6)
- Department of Oral Diagnosis and Radiology, Faculty of Dentistry, Marmara University, Başıbüyük Sağlık Yerleşkesi Başıbüyük Yolu 9/3, 34854, Maltepe, Istanbul, Turkey. [email protected].
- Private Clinic, İstanbul, Turkey.
- Department of Oral Diagnosis and Radiology, Faculty of Dentistry, Kahramanmaraş Sutcu Imam University, Kahramanmaraş, Turkey.
- Department of Oral Diagnosis and Radiology, Faculty of Dentistry, Eskişehir Osmangazi University, Eskişehir, Turkey.
- Department of Oral Diagnosis and Radiology, Faculty of Dentistry, Marmara University, Başıbüyük Sağlık Yerleşkesi Başıbüyük Yolu 9/3, 34854, Maltepe, Istanbul, Turkey.
- Department of Mathematics and Computer, Faculty of Science and Letters, Eskişehir Osmangazi University, Eskişehir, Turkey.
Abstract
Deep learning techniques have demonstrated potential in various fields, including segmentation, and have recently been applied to medical image processing. This study aims to develop and evaluate computer-based diagnostic software designed to assess the segmentation of the mandibular condyle in ultrasound images. A total of 668 retrospective ultrasound images of anonymous adult mandibular condyles were analyzed. The CranioCatch labeling program (CranioCatch, Eskişehir, Turkey) was utilized to annotate the mandibular condyle using a polygonal labeling method. These annotations were subsequently reviewed and validated by experts in oral and maxillofacial radiology. In this study, all test images were detected and segmented using the YOLOv8 deep learning artificial intelligence (AI) model. When evaluating the model's performance in image estimation, it achieved an F1 score of 0.93, a sensitivity of 0.90, and a precision of 0.96. The automatic segmentation of the mandibular condyle from ultrasound images presents a promising application of artificial intelligence. This approach can help surgeons, radiologists, and other specialists save time in the diagnostic process.