Automated detection of active and structural MRI lesions in the sacroiliac joints in axial spondyloarthritis: training and validation across 3 phase III clinical trial datasets.
OBJECTIVES: This study aims to assess the performance of a fully automated deep learning (DL) system for detecting active and structural magnetic resonance imaging (MRI) lesions of. METHODS: A 2-stage automated pipeline was developed to delineate left and right SIJs and detect 5 MRI-defined lesion types: 1 active lesion: bone marrow oedema (BMO), 4 structural .
OBJECTIVES: This study aims to assess the performance of a fully automated deep learning (DL) system for detecting active and structural magnetic resonance imaging (MRI) lesions of. METHODS: A 2-stage automated pipeline was developed to delineate left and right SIJs and detect 5 MRI-defined lesion types: 1 active lesion: bone marrow oedema (BMO), 4 structural .
What the study was
- Study design
- Clinical Trial Phase III
- Category
- Diagnostics
- Maturity
- Potentially Practice-Changing
- Journal
- Annals of the rheumatic diseases
Why it surfaced
HIGH priority (score 9/10); study design: Clinical Trial Phase III; topic: Sentinel; flag: PROMISING_PRELIMINARY
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