Generalizability of Deep Learning Classification of Spinal Osteoporotic Compression Fractures on Radiographs Using an Adaptation of the Modified-2 Algorithm-Based Qualitative Criteria
This research was (1) supported by the University of Washington Clinical Learning, Evidence, And Research (CLEAR) Center for Musculoskeletal Disorders, Administrative, Methodologic and Cores and NIAMS/NIH grant P30AR072572; and (2) supported in part by the General Electric-Association of University Radiologists Radiology Research Academic Fellowship (GERRAF), a career development award co-sponsored by General Electric Healthcare and the Association of University Radiologists.
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.
© 2023 The Association of University Radiologists. Published by Elsevier Inc.
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Dong, Q., Luo, G., Lane, N. E., Lui, L. Y., Marshall, L. M., Johnston, S. K., ... & Cross, N. M. (2023). Generalizability of Deep Learning Classification of Spinal Osteoporotic Compression Fractures on Radiographs Using an Adaptation of the Modified-2 Algorithm-Based Qualitative Criteria. Academic Radiology.