Comprehensive Summary
This study aims to differentiate relapsing-remitting multiple sclerosis (RRMS) from myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD) using a combination of clinical/MRI algorithms, as well as a deep learning (DL) model. The research was performed by analyzing 406 MRI scans from patients diagnosed with either RRMS or MOGAD, and applying both traditional clinical/MRI algorithms and a CNN-based deep learning classifier to these data. The findings revealed that the clinical/MRI algorithm demonstrated high sensitivity but lower specificity, while the DL model showed the opposite: greater specificity but lower sensitivity. The combination of both methods improved diagnostic accuracy (86%) and significantly enhanced the model’s ability to distinguish between these two conditions. Additionally, probability attention maps (PAMs) provided valuable insights into the specific brain regions associated with each disease, shedding light on their distinct pathophysiological mechanisms. The study also suggests that integrating both clinical and imaging-based classifiers could improve clinical workflows and help identify at-risk patients more effectively.
Outcomes and Implications
This research is important because it addresses the clinical challenge of accurately differentiating between RRMS and MOGAD, two diseases with overlapping symptoms but differing management and prognosis. By combining clinical features with advanced imaging techniques like deep learning, the study offers a more reliable diagnostic approach, which can reduce misdiagnoses and unnecessary testing. In clinical practice, the integration of this dual classifier approach could significantly improve decision-making in patients with suspected CNS inflammatory diseases. Although the study has shown promising results, further prospective validation in diverse patient populations and real-world settings is required before these models can be routinely implemented. Once validated, these models could be used as part of an online diagnostic tool, helping clinicians assess complex cases more efficiently, potentially streamlining patient care and guiding MOG-Ab testing in the future.