期刊
JOURNAL OF DENTAL RESEARCH
卷 99, 期 12, 页码 1363-1367出版社
SAGE PUBLICATIONS INC
DOI: 10.1177/0022034520936950
关键词
automatic diagnosis; cone beam computed tomography; diagnostic accuracy; disease classification; lesion detection; single-shot detection
资金
- National Research Foundation of Korea - Ministry of Science and ICT of South Korea [2019R1C1C1009881]
- National Research Foundation of Korea [2019R1C1C1009881] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
The purpose of this study was to develop a diagnostic tool to automatically detect temporomandibular joint osteoarthritis (TMJOA) from cone beam computed tomography (CBCT) images with artificial intelligence. CBCT images of patients diagnosed with temporomandibular disorder were included for image preparation. Single-shot detection, an object detection model, was trained with 3,514 sagittal CBCT images of the temporomandibular joint that showed signs of osseous changes in the mandibular condyle. The region of interest (condylar head) was defined and classified into 2 categories-indeterminate for TMJOA and TMJOA-according to image analysis criteria for the diagnosis of temporomandibular disorder. The model was tested with 2 sets of 300 images in total. The average accuracy, precision, recall, and F1 score over the 2 test sets were 0.86, 0.85, 0.84, and 0.84, respectively. Automated detection of TMJOA from sagittal CBCT images is possible by using a deep neural networks model. It may be used to support clinicians with diagnosis and decision making for treatments of TMJOA.
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