Multi-classification of Grading Stages for Osteoporosis Using Abdominal Computed Tomography with Clinical Variables: Application of Deep Learning with a Convolutional Neural Network

Abstract

Abstract Background: Osteoporosis is a significant global health concern and is often undetected until a fracture occurs. To improve early detection, a deep learning (DL) model was developed to classify osteoporosis stages using abdominal computed tomography (CT) scans. Materials and Methods: This study retrospectively collected data from scanned contrast-enhanced abdominal CT. A total of 3,012 acquired CT scan data DL models were constructed for using image data, demographic information, and multi-modality data, respectively. The three groups were defined according to T-score [normal (T-Score ≥ –1.0), osteopenia (–2.5 < T-Score < –1.0), and osteoporosis (T-Score ≤ –2.5)] derived from dual-energy X-ray absorptiometry and assessed by a qualified radiologist. In the DL process, we used the Gradient-Weighted Class Activation Mapping (Grad-CAM) technique to identify features and accurately interpret clinical areas. Results: Of the 3,012 data sets, the results of the multimodal dataset models showed the highest area under the receiver operating characteristic curve (AUC) (0.94) and accuracy (ACC) (0.80), while the image data model showed an AUC of 0.93 and an ACC of 0.79. The model using demographic information independently showed the worst performance with an AUC of 0.85 and an ACC of 0.68. The sensitivity and specificity for the multimodal model are 0.80 and 0.90, respectively, while the demographic data model scored 0.69 and 0.84. In addition, the Grad-CAM identified informative extracted features through the convolutional neural network, indicating femoral neck was the most common cause of femoral fractures across all three grading stages. Conclusions: We developed a DL model for the multi-classification of osteoporosis using real-world clinical data, combining CT-scanned images with variables. This implies that DL can be fully applied to medical data for the classification stage of osteoporosis. Our results suggest that abdominal CT could be important in osteoporosis screening and could lead to appropriate treatment for the reduction of osteoporotic fractures.