AI Uses Nocturnal Breathing Patterns to Detect Parkinson's Disease
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An artificial intelligence (AI) model can detect Parkinson's disease (PD), assess its severity, and track its progression using nocturnal breathing signals, new research suggests. In a large dataset study, the model accurately detected persons with PD from those without the disease with both high sensitivity and specificity. It also recognized persons with PD before they had received an actual diagnosis. The results highlight the model's potential as a digital PD biomarker, the investigators note.
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A key difficulty in PD drug development and disease management has been the lack of effective diagnostic biomarkers, the investigators note. They add that PD is typically diagnosed on the basis of clinical symptoms related mainly to motor functions, such as tremor and rigidity. However, motor symptoms tend to appear several years after disease onset, leading to late diagnosis. Previously, researchers have investigated the potential of detecting PD using cerebrospinal fluid and neuroimaging, but such methods are invasive and costly, and they require access to specialized medical centers.