Earlier than any doctor: Artificial intelligence recognizes Parkinson’s

US engineers show that a neural network based on breathing patterns can detect the neurological disease Parkinson’s disease before typical symptoms appear.

Artificial intelligence detects Parkinson’s disease based on breathing patterns during sleep.

Photo: Panthermedia.net/agsandrew

In Germany alone, 400,000 patients are affected by Parkinson’s disease, and the trend is rising. The disease causes tremors, slow movements and stiff muscles. In many cases, it is only recognized when people show typical motor symptoms. Parkinson’s disease can only be detected using complex diagnostics. Over the years, researchers have looked for ways to detect the disease using CSF and imaging techniques. However, these methods are painful, invasive, expensive – and can only be carried out in specialized medical centers with a neurological department. They are neither suitable for routine diagnostics nor for close follow-up of patients.

However, early diagnosis could improve the quality of life of those affected. In addition, researchers for clinical studies are looking for patients who are at the beginning of the disease. It is hoped that it would be easier to intervene with medication here than in later stages. Now there is renewed hope for progress. Because researchers at the Massachusetts Institute of Technology (MIT) in Cambridge, Massachusetts, have shown that Parkinson’s disease can be identified simply by reading a person’s breathing patterns: this is possible using an inexpensive, quick and non-invasive method. If the technology proves itself in clinical use, many people could be examined in a short time.

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