Accuracy of AI-driven susceptibility map-weighted MRI analyses to differentiate neurodegenerative from non-neurodegenerative parkinsonism

Motivation: Susceptibility map-weighted imaging (SMWI) of the substantia nigra is a novel MRI sequence that has the potential to aid the diagnosis of patients with clinically uncertain parkinsonian syndromes (CUPS). Goal(s): To investigate the accuracy of AI-driven automated SMWI software in a clinically relevant population. Approach: We acquired SMWI in patients who received a dopamine transporter (DAT)-SPECT because of CUPS. The diagnostic software (Heuron IPD) results were compared with the DAT-SPECT results as a reference. Results: Preliminary analysis of 120 patients demonstrated an accuracy of 88% for the diagnostic software to differentiate neurodegenerative from non-neurodegenerative parkinsonism in patients who presented with CUPS. Impact: Susceptibility map-weighted imaging (SMWI) demonstrates a diagnostic accuracy of 88% in patients with clinically uncertain parkinsonian syndromes (CUPS). These findings are promising for the use of SWMI as diagnostic marker and warrant prospective studies in CUPS patients.