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MIT researchers have developed an AI module that can detect Parkinson's disease with the breathing patterns of the patient with the appearance of a Wi-Fi router using a neural network to discern the presence and severity of one of the fastest-growing neurological diseases in the world. The fastest-growing neurological disease in the world, Parkinson's is the second-most common neurological disorder. The study was inspired by 200-year-old observations from James Parkinson, the first doctor to clinically catalog signs of degenerative neurological disease.
Artificial intelligence tool that can analyze changes in nighttime breathing to detect and track the progression of the disease, which causes tremors and other serious issues with movement. Testing the AI model on an independent dataset it was able to diagnose Parkinson's patients with 86% accuracy from just one night of data. The AI was able to accurately flag Parkinson's using one night of breathing data collected from a belt worn around the abdomen.
The new AI model device can be installed in the patient's room at ease. It also monitors the progress of the disease apart from simple diagnosis. The study shows that the AI model can track a Parkinson's patient over 12 months and correlate changes in breathing patterns with increases in disease severity. the team developed a device with the appearance of a home Wi-Fi router, but instead of providing internet access, it extracts the subject's breathing patterns without any bodily contact.
The study will be needed to show that AI can reliably diagnose Parkinson's at an earlier stage and track its progression. The researchers have already developed a wall-mounted device that can be used to monitor patients in their homes. This AI-based system for detecting PD, predicting disease severity, and tracking disease progression over time using nocturnal breathing. It is also expected that this device might detect other neurological disorders in the near
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