Prediction Model of Spinal Osteoporosis Using Lumbar Spine X-Ray from Transfer Learning Deep Convolutional Neural Networks
- Author:
Jae Min KIM
1
;
Joon Bum WOO
;
Hae Yu KIM
Author Information
- Publication Type:Clinical Article
- From: The Nerve 2024;10(2):98-106
- CountryRepublic of Korea
- Language:English
-
Abstract:
Objective:Osteoporosis is highly prevalent among older adults and women. This condition leads to a deterioration in bone mineral density and microarchitecture, significantly increasing the risk of fractures. Additionally, osteoporosis commonly results in complications such as screw loosening and non-union during spinal surgery. Deep-learning algorithms have now achieved an accuracy comparable to the current human margin of error. Therefore, this study explored the potential of using transfer learning in deep learning algorithms to predict, diagnose, and screen for osteoporosis using commonly obtained sagittal spine X-rays from patients with spinal conditions.
Methods:We retrospectively evaluated 2,300 consecutive patients who underwent dual energy X-ray absorptiometry (DXA) and lumbar sagittal plain X-ray exams between 2013 and 2021. The exclusion criteria included: (1) a gap of more than 1 year between the DXA and X-ray exams; (2) vertebrae that had undergone vertebroplasty; (3) lack of spine anterior-posterior DXA; and (4) images that were unassessable. Ultimately, 256 patients (images) were included in the study. Transfer learning was applied using convolutional neural network (CNN) techniques, specifically visual geometry group (VGG) 16, VGG 19, ResNet50, and Xception.
Results:The most accurate CNN model in the training group was ResNet50, with an accuracy of 0.95. ResNet50 showed the best performance, with an accuracy of 0.82, precision of 0.80, recall of 0.86, and F1-score of 0.83. Additionally, its area under the curve (0.76) was higher than that other CNN models. The confusion matrix for ResNet50’s performance displayed the outcomes for images predicted as osteoporosis (n=12) among the test data osteoporosis images (n=14)
Conclusion:Artificial intelligence (AI) technology employing deep learning techniques is significantly nearing human capabilities in the role of diagnostic assistance. The diagnosis of osteoporosis using bone mineral density is expected to evolve into a comprehensive diagnostic aid or decision-making tool with the integration of AI in the future.
