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Deep Neural Networks for Image-Based Dietary Assessment
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Osteoporosis detection in panoramic radiographs using a deep convolutional neural network-based computer-assisted
Jae-Seo Lee1, Shyam Adhikari2, Liu Liu1
1Department of Oral and Maxillofacial Radiology, School of Dentistry, Dental Science Research Institute, Chonnam National University, Gwangju, South Korea.
Dento Maxillo Facial Radiology
|July 14, 2018
Summary
A deep convolutional neural network (DCNN) computer-assisted diagnosis (CAD) system accurately detected osteoporosis on panoramic radiographs. This AI tool shows high agreement with expert radiologists, aiding early detection in dental settings.
Area of Science:
- Dentistry
- Radiology
- Artificial Intelligence in Medicine
Background:
- Osteoporosis diagnosis often relies on clinical assessment and bone density scans.
- Panoramic radiographs offer a readily available imaging modality for dental patients.
- Early detection of osteoporosis can prevent fractures and improve patient outcomes.
Purpose of the Study:
- To evaluate the diagnostic performance of a deep convolutional neural network (DCNN)-based computer-assisted diagnosis (CAD) system for osteoporosis detection.
- To compare the DCNN-CAD system's accuracy against diagnoses made by experienced oral and maxillofacial radiologists.
- To assess the potential of DCNN-CAD for early osteoporosis identification in dental practice.
Main Methods:
- 1268 female patients' panoramic radiographs were analyzed.
- Radiologists diagnosed osteoporosis based on mandibular inferior cortex erosion.
- Three DCNN models (SC-DCNN, SC-DCNN Augment, MC-DCNN) were applied to 200 radiographs for performance testing.
- Receiver operating characteristic (ROC) analysis was used to assess diagnostic performance.
Main Results:
- The area under the curve (AUC) values for the DCNN models were: SC-DCNN (0.9763), SC-DCNN Augment (0.9991), and MC-DCNN (0.9987).
- All tested DCNN models demonstrated high diagnostic accuracy in detecting osteoporosis.
- The DCNN-based CAD system showed strong agreement with expert radiologists' diagnoses.
Conclusions:
- The DCNN-based CAD system exhibits high diagnostic performance for osteoporosis detection on panoramic radiographs.
- This AI tool can assist dentists in the early identification of osteoporosis.
- Facilitating early referral of asymptomatic osteoporosis patients to medical professionals is a key benefit.
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