Parkinson’s disease (PD) is the fastest growing neurological disease in the world. Currently, there is no single test or biomarker that can diagnose PD or monitor disease progression. However, a new study utilizing artificial intelligence may change the way we track Parkinson’s.
A lot like a warning system, a biomarker (short for biological marker) is used to help measure what is going on in the body. For example, the A1C blood test can help detect prediabetes. Early detection is important for PD. Research shows that there are multiple advantages associated with the early diagnosis and treatment of PD, including slowing or delaying disease progression and maintaining quality of life.
The challenge, so far, has been the lack of biomarkers for early PD diagnosis. Usually, PD is only diagnosed years after early signs appear— when movement symptoms (such as tremor, rigidity, and difficulty walking) are present. However, a new study may have found an early biomarker for PD.
In a groundbreaking study published in the journal Nature Medicine, “Artificial intelligence-enabled detection and assessment of Parkinson's disease using nocturnal breathing signals” (Yang et al., 2022), the authors developed an artificial intelligence (AI)-enabled system that could reliably:
Wirelessly identify people who have PD from their breathing patterns during sleep
Accurately assess people’s PD disease severity
Track PD progression over time
Xray scan of lungs
Study authors point out that the relationship between Parkinson’s disease and breathing is documented in various studies. Further, the authors note that this breathing link has been reinforced by more recent PD studies that go a step further, reporting that degeneration in the brainstem helps control breathing, issues of respiratory muscle weakness, and sleep breathing disorders. According to the study, “Since breathing and sleep are impacted early in the development of PD, we anticipate that our AI model can potentially recognize individuals with PD before their actual diagnosis” (Yang et al., 2022, p. 2207).
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