Automated Detection of TMJ Osteoarthritis Based on Artificial Intelligence.
K S LeeH J KwakJ M OhN JhaYoon-Ji KimW KimU B BaikJ J RyuPublished in: Journal of dental research (2020)
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.
Keyphrases
- deep learning
- artificial intelligence
- cone beam computed tomography
- convolutional neural network
- machine learning
- big data
- loop mediated isothermal amplification
- label free
- real time pcr
- image quality
- rheumatoid arthritis
- optical coherence tomography
- end stage renal disease
- decision making
- neural network
- chronic kidney disease
- palliative care
- magnetic resonance
- computed tomography
- newly diagnosed
- sensitive detection
- molecularly imprinted
- solid phase extraction