Effectiveness of opportunistic osteoporosis screening on chest CT using the DCNN model

• Published in: BMC musculoskeletal disorders Vol. 25; no. 1; p. 176
• Main Authors: Pan, Jing, Lin, Peng-Cheng, Gong, Shen-Chu, Wang, Ze, Cao, Rui, Lv, Yuan, Zhang, Kun, Wang, Lin
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 27.02.2024; BioMed Central; BMC
• Subjects: Artificial intelligence; Bone density; Bone mineral density; Calcification; Chest; Chest CT; Classification; Computed tomography; CT imaging; Datasets; Deep learning; Diagnosis; Fractures; Labeling; Machine learning; Medical diagnosis; Medical examination; Neural networks; Osteopenia; Osteoporosis; Quantitative CT; Risk factors; Segmentation; Software; Vertebrae; China; Deep learning; Osteoporosis; Bone mineral density; Chest CT; Quantitative CT
• ISSN: 1471-2474; 1471-2474
• DOI: 10.1186/s12891-024-07297-1

To develop and evaluate a deep learning model based on chest CT that achieves favorable performance on opportunistic osteoporosis screening using the lumbar 1 + lumbar 2 vertebral bodies fusion feature images, and explore the feasibility and effectiveness of the model based on the lumbar 1 vertebral body alone. The chest CT images of 1048 health check subjects from January 2021 to June were retrospectively collected as the internal dataset (the segmentation model: 548 for training, 100 for tuning and 400 for test. The classification model: 530 for training, 100 for validation and 418 for test set). The subjects were divided into three categories according to the quantitative CT measurements, namely, normal, osteopenia and osteoporosis. First, a deep learning-based segmentation model was constructed, and the dice similarity coefficient(DSC) was used to compare the consistency between the model and manual labelling. Then, two classification models were established, namely, (i) model 1 (fusion feature construction of lumbar vertebral bodies 1 and 2) and (ii) model 2 (feature construction of lumbar 1 alone). Receiver operating characteristic curves were used to evaluate the diagnostic efficacy of the models, and the Delong test was used to compare the areas under the curve. When the number of images in the training set was 300, the DSC value was 0.951 ± 0.030 in the test set. The results showed that the model 1 diagnosing normal, osteopenia and osteoporosis achieved an AUC of 0.990, 0.952 and 0.980; the model 2 diagnosing normal, osteopenia and osteoporosis achieved an AUC of 0.983, 0.940 and 0.978. The Delong test showed that there was no significant difference in area under the curve (AUC) values between the osteopenia group and osteoporosis group (P = 0.210, 0.546), while the AUC value of normal model 2 was higher than that of model 1 (0.990 vs. 0.983, P = 0.033). This study proposed a chest CT deep learning model that achieves favorable performance on opportunistic osteoporosis screening using the lumbar 1 + lumbar 2 vertebral bodies fusion feature images. We further constructed the comparable model based on the lumbar 1 vertebra alone which can shorten the scan length, reduce the radiation dose received by patients, and reduce the training cost of technologists.