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Machine learning vs human experts: sacroiliitis analysis from the RAPID-axSpA and C-OPTIMISE phase 3 axSpA trials
Fabian Proft1, Janis L Vahldiek2, Joeri Nicolaes3,4
1Department of Gastroenterology, Infectious Diseases and Rheumatology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.
Rheumatology Advances in Practice
|April 21, 2025
Summary
A new deep learning model accurately detects radiographic sacroiliitis in axial spondyloarthritis (axSpA) patients, potentially speeding up diagnosis and improving care. This AI tool shows promise in reducing variability in X-ray interpretations for axSpA.
Area of Science:
- Medical imaging analysis
- Artificial intelligence in healthcare
- Rheumatology and immunology
Background:
- Diagnosis of axial spondyloarthritis (axSpA) relies on identifying radiographic sacroiliitis.
- Conventional radiography interpretation suffers from significant interreader variability, impacting diagnostic accuracy and timeliness.
- Machine learning offers a potential solution to standardize and expedite axSpA diagnosis.
Purpose of the Study:
- To evaluate the performance of a deep learning (DL) model in detecting radiographic sacroiliitis in patients with axSpA.
- To assess the DL model's accuracy against expert readers in diverse clinical trial cohorts.
- To determine the potential of DL in reducing diagnostic variability and improving axSpA patient care pathways.
Main Methods:
- Retrospective analysis of radiographs from the RAPID-axSpA and C-OPTIMISE clinical trials.
- Utilized a DL model previously trained using a transfer learning approach on non-medical data.
- Model performance evaluated by comparing its readings to central expert readers, calculating sensitivity, specificity, and Cohen's kappa.
Main Results:
- The DL model demonstrated strong performance in the RAPID-axSpA cohort (82% sensitivity, 81% specificity, Cohen's κ=0.61) and good performance in the C-OPTIMISE cohort (90% sensitivity, 56% specificity, Cohen's κ=0.48).
- Model agreement with central readers was 82% for RAPID-axSpA and 75% for C-OPTIMISE.
- Results indicate the DL model closely matched expert reader performance in detecting radiographic sacroiliitis.
Conclusions:
- The evaluated deep learning model accurately detects radiographic sacroiliitis in axSpA patients across different clinical trial settings.
- This AI tool has the potential to expedite axSpA diagnosis, reduce healthcare resource utilization, and enhance patient management.
- The findings support the integration of DL models into clinical workflows for improved axSpA diagnosis and patient care.
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