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Rationale and Objectives: Spinal osteoporotic compression fractures (OCFs) can be an early biomarker for osteoporosis but are often subtle, incidental, and underreported. To ensure early diagnosis and treatment of osteoporosis, we aimed to build a deep learning vertebral body classifier for OCFs as a critical component of our future automated opportunistic screening tool., Materials and Methods: We retrospectively assembled a local dataset, including 1790 subjects and 15,050 vertebral bodies (thoracic and lumbar). Each vertebral body was annotated using an adaption of the modified-2 algorithm-based qualitative criteria. The Osteoporotic Fractures in Men (MrOS) Study dataset provided thoracic and lumbar spine radiographs of 5994 men from six clinical centers. Using both datasets, five deep learning algorithms were trained to classify each individual vertebral body of the spine radiographs. Classification performance was compared for these models using multiple metrics, including the area under the receiver operating characteristic curve (AUC-ROC), sensitivity, specificity, and positive predictive value (PPV)., Results: Our best model, built with ensemble averaging, achieved an AUC-ROC of 0.948 and 0.936 on the local dataset's test set and the MrOS dataset's test set, respectively. After setting the cutoff threshold to prioritize PPV, this model achieved a sensitivity of 54.5% and 47.8%, a specificity of 99.7% and 99.6%, and a PPV of 89.8% and 94.8%., Conclusion: Our model achieved an AUC-ROC>0.90 on both datasets. This testing shows some generalizability to real-world clinical datasets and a suitable performance for a future opportunistic osteoporosis screening tool., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Nathan Cross, Qifei Dong, Sandra Johnston, Jessica Perry, and David Haynor report financial support was provided by the National Institute of Arthritis and Musculoskeletal and Skin Diseases. Gang Luo reports financial support was provided by the National Institute of Arthritis and Musculoskeletal and Skin Diseases and National Heart Lung and Blood Institute. Jeffrey Jarvik reports financial support was provided by the National Institute of Arthritis and Musculoskeletal and Skin Diseases and reports a relationship with the General Electric-Association of University Radiologists Radiology Research Academic Fellowship (GERRAF) that includes travel reimbursement. Nathan Cross reports financial support was provided by the General Electric-Association of University Radiologists Radiology Research Academic Fellowship (GERRAF). Nathan M. Cross, Jeffrey Jarvik, David R. Haynor, Gang Luo, Sandra Johnston, Qifei Dong, Jonathan Renslo, Brian Chang, and Jessica Perry have patent #63/463823 pending to the University of Washington., (Copyright © 2023 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)

Rationale and Objectives: Osteoporosis affects 9% of individuals over 50 in the United States and 200 million women globally. Spinal osteoporotic compression fractures (OCFs), an osteoporosis biomarker, are often incidental and under-reported. Accurate automated opportunistic OCF screening can increase the diagnosis rate and ensure adequate treatment. We aimed to develop a deep learning classifier for OCFs, a critical component of our future automated opportunistic screening tool., Materials and Methods: The dataset from the Osteoporotic Fractures in Men Study comprised 4461 subjects and 15,524 spine radiographs. This dataset was split by subject: 76.5% training, 8.5% validation, and 15% testing. From the radiographs, 100,409 vertebral bodies were extracted, each assigned one of two labels adapted from the Genant semiquantitative system: moderate to severe fracture vs. normal/trace/mild fracture. GoogLeNet, a deep learning model, was trained to classify the vertebral bodies. The classification threshold on the predicted probability of OCF outputted by GoogLeNet was set to prioritize the positive predictive value (PPV) while balancing it with the sensitivity. Vertebral bodies with the top 0.75% predicted probabilities were classified as moderate to severe fracture., Results: Our model yielded a sensitivity of 59.8%, a PPV of 91.2%, and an F 1 score of 0.72. The areas under the receiver operating characteristic curve (AUC-ROC) and the precision-recall curve were 0.99 and 0.82, respectively., Conclusion: Our model classified vertebral bodies with an AUC-ROC of 0.99, providing a critical component for our future automated opportunistic screening tool. This could lead to earlier detection and treatment of OCFs., (Copyright © 2022 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)