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Incorporating Anatomical Awareness for Enhanced Generalizability and Progression Prediction in Deep Learning-Based Radiographic Sacroiliitis Detection
May 14, 2024, 4:43 a.m. | Felix J. Dorfner, Janis L. Vahldiek, Leonhard Donle, Andrei Zhukov, Lina Xu, Hartmut H\"antze, Marcus R. Makowski, Hugo J. W. L. Aerts, Fabian Proft,
cs.LG updates on arXiv.org arxiv.org
Abstract: Purpose: To examine whether incorporating anatomical awareness into a deep learning model can improve generalizability and enable prediction of disease progression.
Methods: This retrospective multicenter study included conventional pelvic radiographs of 4 different patient cohorts focusing on axial spondyloarthritis (axSpA) collected at university and community hospitals. The first cohort, which consisted of 1483 radiographs, was split into training (n=1261) and validation (n=222) sets. The other cohorts comprising 436, 340, and 163 patients, respectively, were used …
abstract arxiv cs.cv cs.lg deep learning detection disease patient prediction retrospective study type
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