This is certainly one of the most intriguing things now available: AI might analyze the data from your smartwatch to predict Parkinson’s disease up to seven years early. According to the findings of this study, which was carried out by the UK Dementia Research Institute (UKDRI) and the Neuroscience and Mental Health Innovation Institute (NMHII) at Cardiff University, this approach has the potential to be used as a novel screening tool for the condition.
Study Insights
It is possible that data collected by a smartwatch over the course of only one week might indicate indicators of Parkinson’s disease. Early detection and diagnosis of Parkinson’s disease can result in more effective treatment choices. According to the findings of a recent study, wearing a smartwatch may be able to detect Parkinson’s disease up to seven years earlier the main signs of the disease begin to present themselves.
The researchers looked at how quickly the subjects moved about during the trial. An artificial intelligence computer was able to precisely identify who would go on to get the disease by making use of an algorithm for machine learning.
Although the majority of individuals who are diagnosed with Parkinson’s begin to show symptoms around the age of 50, some people begin to experience symptoms as early as their 40s. It is possible that over fifty percent of the cells in the region of the brain that is afflicted have already perished before the characteristic symptoms begin to manifest, which is why a method of early identification is in such great demand.
Smartwatch data is easily accessible and low-cost; […] by using this type of data, we would potentially be able to identify individuals in the very early stages of Parkinson’s disease within the general population, explained study leader Dr. Cynthia Sandor, Emerging Leader at the UK DRI.
Over the course of one week, the researchers took many measurements to determine the individual’s average acceleration. They were able to utilize AI to identify people who would eventually go on to develop Parkinson’s disease by comparing the data from a subgroup of participants who had previously been diagnosed with Parkinson’s disease to the data from another group who obtained an official diagnosis up to seven years after their smartwatch data was gathered. This comparison took place between the two groups.
Artificial intelligence was able to differentiate between these people in the study and the control participants in the study. The researchers then demonstrated that it could be used to identify those individuals who are at risk in the general population.
Finally, the researchers came to the conclusion that this was a better indicator of whether or not a person will acquire Parkinson’s disease than any additional risk factor or any other early symptom that is commonly associated with the condition. Additionally, the model was able to forecast how much time would pass until a diagnosis could be made.
Because the researchers did not have a reference to another data set that was equivalent to the one they used in their investigation, they were unable to conduct a replication of their study using data from a different source.
