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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11736322 | PMC |
| http://dx.doi.org/10.5009/gnl250001 | DOI Listing |
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Deep learning system for the differential diagnosis of oral mucosal lesions through clinical photographic imaging.
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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.
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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.
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Department of Dentistry, National Taiwan University Hospital, College of Medicine, National Taiwan University, Taipei, Taiwan.
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Institute of Statistics, National Yang Ming Chiao Tung University, Hsinchu City, Taiwan.
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