Towards fracture risk assessment by deep-learning-based classification of prevalent vertebral fractures

Eren B. Yilmaz; Tobias Fricke; Julian Laue; Constanze Polzer; Sam Sedaghat; Jan-Bernd Hövener; Claus-Christian Glüer; Carsten Meyer

doi:10.1117/12.2653526

7 April 2023 Towards fracture risk assessment by deep-learning-based classification of prevalent vertebral fractures

Eren B. Yilmaz, Tobias Fricke, Julian Laue, Constanze Polzer, Sam Sedaghat, Jan-Bernd Hövener, Claus-Christian Glüer, Carsten Meyer

Author Affiliations +

Proceedings Volume 12465, Medical Imaging 2023: Computer-Aided Diagnosis; 124651D (2023) https://doi.org/10.1117/12.2653526
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

Abstract

Osteoporosis is a complex multifactorial skeletal disease and has become a major socioeconomic issue, causing tremendous hospitalization and rehabilitation costs. In addition to age, premature menopause or the use of oral corticosteroids, vertebral fractures are regarded as main risk factors for developing osteoporosis and associated fractures. In this work, we adapt an existing automated pipeline to classify fracture grades of individual vertebrae and the spine as a whole: First, vertebral body centers were identified on CT images by a hierarchical neural network. Next, the fracture grades of individual vertebrae were processed by a multi-head, feed-forward convolutional neural network. The sum of the classified grades was then evaluated according to the German radiology guidelines. A hyperparameter search on validation data showed the most promising results for an output configuration based on three sequentially applied binary classification outputs trained using binary cross-entropy: Grade 0–1 vs 2–3, and 0 vs 1–3, and 0–2 vs 3. In a cross-validation setting on 159 low-dose CT images, our pipeline accurately classified patients to have sum-scores ≥ 2 with sensitivities and specificities of 90 % ± 5.0 % and 87 % ± 2.7 %, respectively. As our method was based on classifying individual vertebrae, we were able to provide both the fracture position and severity to enhance transparency, interpretability and usability.

Conference Presentation

Citation Download Citation

Eren B. Yilmaz, Tobias Fricke, Julian Laue, Constanze Polzer, Sam Sedaghat, Jan-Bernd Hövener, Claus-Christian Glüer, and Carsten Meyer "Towards fracture risk assessment by deep-learning-based classification of prevalent vertebral fractures", Proc. SPIE 12465, Medical Imaging 2023: Computer-Aided Diagnosis, 124651D (7 April 2023); https://doi.org/10.1117/12.2653526

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