Dataset Information

Artificial intelligence-enabled detection and assessment of Parkinson's disease using nocturnal breathing signals.

ABSTRACT: There are currently no effective biomarkers for diagnosing Parkinson's disease (PD) or tracking its progression. Here, we developed an artificial intelligence (AI) model to detect PD and track its progression from nocturnal breathing signals. 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. The AI model can detect PD with an area-under-the-curve of 0.90 and 0.85 on held-out and external test sets, respectively. The AI model can also estimate PD severity and progression in accordance with the Movement Disorder Society Unified Parkinson's Disease Rating Scale (R = 0.94, P = 3.6 × 10^-25). The AI model uses an attention layer that allows for interpreting its predictions with respect to sleep and electroencephalogram. Moreover, 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. Our study demonstrates the feasibility of objective, noninvasive, at-home assessment of PD, and also provides initial evidence that this AI model may be useful for risk assessment before clinical diagnosis.

SUBMITTER: Yang Y

PROVIDER: S-EPMC9556299 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature

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Artificial intelligence-enabled detection and assessment of Parkinson's disease using nocturnal breathing signals.

Yang Yuzhe Y Yuan Yuan Y Zhang Guo G Wang Hao H Chen Ying-Cong YC Liu Yingcheng Y Tarolli Christopher G CG Crepeau Daniel D Bukartyk Jan J Junna Mithri R MR Videnovic Aleksandar A Ellis Terry D TD Lipford Melissa C MC Dorsey Ray R Katabi Dina D

Nature medicine 20220822 10

There are currently no effective biomarkers for diagnosing Parkinson's disease (PD) or tracking its progression. Here, we developed an artificial intelligence (AI) model to detect PD and track its progression from nocturnal breathing signals. 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. The AI model can detect PD with an area-under-the-curve of 0.90 and 0.85 on held-out and ex ...[more]

PMID: 35995955

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Dataset Information

Artificial intelligence-enabled detection and assessment of Parkinson's disease using nocturnal breathing signals.

Publications

Artificial intelligence-enabled detection and assessment of Parkinson's disease using nocturnal breathing signals.

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