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▎Edited by WuXi AppTec Content Team
Parkinson's disease is the second most common neurodegenerative disease worldwide after Alzheimer's disease, afflicting millions of people
The study's leader, Professor Dina Katabi of MIT, said that as early as 1817, when Dr.
▲The research can collect the breathing signal of the user during sleep in two ways, and use the AI model to output the diagnosis result (Image source: Reference [1])
Then, the neural network of the system can analyze the collected breathing signal
.
The neural network learned how to predict quantitative EEG based on breathing patterns, and output the predictions
.
In an evaluation of 7,687 participants (including 757 patients with Parkinson's disease), the system not only detected Parkinson's disease with high sensitivity and accuracy, but also predicted disease severity and tracked disease progression
.
As a result, users do not need to go out or implant invasive devices, and can be assessed for Parkinson's disease on a daily basis while sleeping at home
.
Professor Katabi said that this progress has important implications for Parkinson's disease drug development and clinical care
.
"For drug development, our results could lead to significantly shorter clinical trial cycles and fewer participants, ultimately accelerating the development of new treatments
.
For clinical care, many people have limited access to medical resources, such as living in impoverished areas, or moving It is inconvenient and difficult to leave the house, and this means that these people can be assessed
.
”
Source of cover image: 123RF References: [1] Yang, Y.
, Yuan, Y.
, Zhang, G.
et al.
Artificial intelligence-enabled detection and assessment of Parkinson's disease using nocturnal breathing signals.
Nat Med (2022).
https ://doi.
org/10.
1038/s41591-022-01932-x[2] Artificial intelligence model can detect Parkinson's from breathing patterns.
Retrieved Aug 25th, 2022 from
Parkinson's disease is the second most common neurodegenerative disease worldwide after Alzheimer's disease, afflicting millions of people
The study's leader, Professor Dina Katabi of MIT, said that as early as 1817, when Dr.
▲The research can collect the breathing signal of the user during sleep in two ways, and use the AI model to output the diagnosis result (Image source: Reference [1])
Then, the neural network of the system can analyze the collected breathing signal
.
The neural network learned how to predict quantitative EEG based on breathing patterns, and output the predictions
.
In an evaluation of 7,687 participants (including 757 patients with Parkinson's disease), the system not only detected Parkinson's disease with high sensitivity and accuracy, but also predicted disease severity and tracked disease progression
.
As a result, users do not need to go out or implant invasive devices, and can be assessed for Parkinson's disease on a daily basis while sleeping at home
.
Professor Katabi said that this progress has important implications for Parkinson's disease drug development and clinical care
.
"For drug development, our results could lead to significantly shorter clinical trial cycles and fewer participants, ultimately accelerating the development of new treatments
.
For clinical care, many people have limited access to medical resources, such as living in impoverished areas, or moving It is inconvenient and difficult to leave the house, and this means that these people can be assessed
.
”
Source of cover image: 123RF References: [1] Yang, Y.
, Yuan, Y.
, Zhang, G.
et al.
Artificial intelligence-enabled detection and assessment of Parkinson's disease using nocturnal breathing signals.
Nat Med (2022).
https ://doi.
org/10.
1038/s41591-022-01932-x[2] Artificial intelligence model can detect Parkinson's from breathing patterns.
Retrieved Aug 25th, 2022 from
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