Artificial Intelligence-Enabled Detection and Assessment of Parkinson's Disease
using Nocturnal Breathing Signals

Nature Medicine 2022

Yuzhe Yang1     Yuan Yuan1     Guo Zhang1     Hao Wang2     Ying-Cong Chen1     Yingcheng Liu1     Christopher Tarolli3     Daniel Crepeau4
Jan Bukartyk4     Mithri Junna4     Aleksandar Videnovic5     Terry Ellis6     Melissa Lipford4     Ray Dorsey3     Dina Katabi1
1MIT CSAIL     2Rutgers University     3University of Rochester Medical Center     4Mayo Clinic     5Massachusetts General Hospital     6Boston University    


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). 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.


Artificial Intelligence-Enabled Detection and Assessment of Parkinson's Disease using Nocturnal Breathing Signals
Yuzhe Yang, Yuan Yuan, Guo Zhang, Hao Wang, Ying-Cong Chen, Yingcheng Liu, Christopher Tarolli, Daniel Crepeau, Jan Bukartyk, Mithri Junna, Aleksandar Videnovic, Terry Ellis, Melissa Lipford, Ray Dorsey, Dina Katabi
Nature Medicine, 28, 2207–2215 (2022)
Selected as one of the Ten Notable Advances in 2022 by Nature Medicine
[Paper]  •  [Poster]  •  [Talk]  •  [Core AI Technique (DIR)]  •  [Core AI Technique (MDLT)]  •  [BibTeX]


Invited Talk @ Stanford MedAI

Invited Talk @ The AI Talks, NTU & NUS

Core AI/ML Techniques & Breakthroughs

Deep Imbalanced Regression

Delving into Deep Imbalanced Regression
Yuzhe Yang, Kaiwen Zha, Ying-Cong Chen, Hao Wang, and Dina Katabi
International Conference on Machine Learning (ICML 2021), Long Talk
[Project Page]  •  [Paper]  •  [Code]  •  [Blog Post]

Multi-Domain Long-Tailed Recognition

On Multi-Domain Long-Tailed Recognition, Generalization and Beyond
Yuzhe Yang, Hao Wang, and Dina Katabi
European Conference on Computer Vision (ECCV 2022)
[Project Page]  •  [Paper]  •  [Code]  •  [Blog Post]



  title={Artificial intelligence-enabled detection and assessment of Parkinson’s disease using nocturnal breathing signals},
  author={Yang, Yuzhe and Yuan, Yuan and Zhang, Guo and Wang, Hao and Chen, Ying-Cong and Liu, Yingcheng and Tarolli, Christopher G and Crepeau, Daniel and Bukartyk, Jan and Junna, Mithri R and others},
  journal={Nature Medicine},
  publisher={Nature Publishing Group}

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