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Artificial intelligence-enabled detection and assessment of Parkinson’s disease

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Reference: Yang, Y et al Nature Medicine (2022)

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PD is the fastest-growing neurological disease in the world. Over 1 million people in the US are living with PD as of 2020, resulting in an economic burden of $52 billion per year.

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A key difficulty in PD drug development and disease management is the lack of effective diagnostic biomarkers.

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Scientists developed an artificial intelligence (AI) model to detect PD and track its progression from nocturnal breathing signals.

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The model was evaluated on a large dataset comprising 7,671 individuals, using data from several hospitals in the United States, as well as multiple public datasets.

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The model can assess PD in the home setting in a touchless manner, by extracting breathing from radio waves that bounce off a person’s body during sleep.

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They evaluated the accuracy of diagnosing PD from one night of nocturnal breathing. The AI model detects PD with high accuracy.

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They further investigated whether the accuracy improves by combining several nights from the same individual.

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They validated their AI model on an external testset (n = 1,920 nights from 1,920 subjects out of which 644 have PD) from an independent hospital not involved during model development.

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They evaluate the ability of their model to produce a PD severity score that correlates well with the MDS-UPDRS by analyzing the patients’ nocturnal breathing.

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BeGenomics.com

Artificial intelligence-enabled detection and assessment of Parkinson’s disease

BeGenomics.com

Artificial intelligence-enabled detection and assessment of Parkinson’s disease

BeGenomics.com

Artificial intelligence-enabled detection and assessment of Parkinson’s disease

BeGenomics.com

Artificial intelligence-enabled detection and assessment of Parkinson’s disease