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Deep learning system for the differential diagnosis of oral mucosal lesions through clinical photographic imaging.
J Dent Sci
January 2025
School of Dentistry, College of Medicine, National Cheng Kung University, Tainan, Taiwan.
Background/purpose: Oral mucosal lesions are associated with a variety of pathological conditions. Most deep-learning-based convolutional neural network (CNN) systems for computer-aided diagnosis of oral lesions have typically concentrated on determining limited aspects of differential diagnosis. This study aimed to develop a CNN-based diagnostic model capable of classifying clinical photographs of oral ulcerative and associated lesions into five different diagnoses, thereby assisting clinicians in making accurate differential diagnoses.
View Article and Find Full Text PDFEstablishment and evaluation of a deep learning-based tooth wear severity grading system using intraoral photographs.
J Dent Sci
January 2025
First Clinical Division, Peking University School and Hospital of Stomatology & National Center for Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices & Beijing Key Laboratory of Digital Stomatology & NHC Key Laboratory of Digital Stomatology & NMPA Key Laboratory for Dental Materials, Beijing, China.
Background/purpose: Artificial intelligence (AI) can assist in medical diagnosis owing to its high accuracy and efficiency. This study aimed to develop a diagnostic system for automatically determining the degree of tooth wear (TW) using intraoral photographs with deep learning.
Materials And Methods: The study included 388 intraoral photographs.
Determination of periodontal bone loss in panoramic radiographs through artificial intelligence analysis.
J Dent Sci
January 2025
School of Medicine, Chung Shan Medical University, Taichung, Taiwan.
Artificial intelligence measurement of multi-layer tooth structures using semantic segmentation and computer vision.
J Dent Sci
January 2025
Department of Dentistry, National Taiwan University Hospital, College of Medicine, National Taiwan University, Taipei, Taiwan.
Automatic classification of temporomandibular joint disorders by magnetic resonance imaging and convolutional neural networks.
J Dent Sci
January 2025
Institute of Statistics, National Yang Ming Chiao Tung University, Hsinchu City, Taiwan.
Background/purpose: In this study, we utilized magnetic resonance imaging data of the temporomandibular joint, collected from the Division of Oral and Maxillofacial Surgery at Taipei Veterans General Hospital. Our research focuses on the classification and severity analysis of temporomandibular joint disease using convolutional neural networks.
Materials And Methods: In gray-scale image series, the most critical features often lie within the articular disc cartilage, situated at the junction of the temporal bone and the condyles.
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