Scientists have made significant progress in using artificial intelligence (AI) to track the progression of Parkinson's disease, enabling more accurate diagnosis and treatment adjustment. AI-based systems can now quantify motor symptoms of this neurological disease through the analysis of video recordings taken on smartphones and tablets, representing a revolution in how the disease is monitored and treated.

New systems, developed at the University of California, San Francisco (UCSF), as well as at the University of Florida, use machine learning to analyze patient movements, allowing for more precise measurement of Parkinson's disease symptoms. These systems not only enable quantitative assessment but also reveal subtle changes in motor functions that might otherwise go unnoticed during standard clinical examinations.

Quantification of symptoms through video analysis
Traditional methods of tracking Parkinson's disease rely on subjective assessments by neurologists during clinical exams. Patients perform a series of standard movements, such as tapping fingers or walking, while doctors evaluate them based on visual assessment. However, these methods have their limitations, as changes in symptoms may manifest between doctor visits and are often too subtle to be detected by the naked eye.

Artificial intelligence, integrated into new systems, analyzes video recordings of patients, identifying key movement characteristics such as speed and precision of hand or walking movements. Based on this data, the system can assign scores reflecting the severity of the disease. This data not only helps doctors adjust treatments but also enables continuous patient monitoring outside clinical settings, which has previously been difficult to achieve.

Enhanced diagnostics and treatment
By implementing these advanced AI systems, Parkinson's disease diagnostics become not only more accurate but also faster. The systems allow for the detection of changes in movements indicating disease progression even before symptoms become visible to clinicians. In a study at the University of Florida, using AI to analyze patient video recordings demonstrated the system's ability to detect small changes in finger movements, which could be crucial for early intervention. This approach allows for more precise tracking of treatment effectiveness and adjustment in real time, potentially significantly improving patient quality of life.

Application in other neurological disorders
Although these systems were primarily developed for Parkinson's disease, their application could be extended to other neurological disorders. For example, similar AI systems could be used to monitor motor impairments caused by stroke, multiple sclerosis, or traumatic brain injuries. This would enable the quantification of symptoms in ways that have previously been unimaginable in clinical practice, providing doctors with a powerful tool for monitoring and treating a wide range of neurological conditions.

Advanced technology for early detection
With advances in video analysis, researchers have also developed AI systems that can predict the development of Parkinson's disease years before the first symptoms appear. By using advanced biomarkers, these systems analyze patients' blood samples to identify those at high risk of developing the disease. This technology, still in the research phase, has the potential to transform how Parkinson's disease is diagnosed and treated, allowing for early initiation of therapy and perhaps even disease prevention.

The future of AI in medical practice
The development of AI systems for monitoring and treating Parkinson's disease is just the beginning. As technology continues to advance, we can expect these tools to be increasingly applied in medical practice. From personalized medicine to early disease detection, AI has the potential to completely transform how doctors approach diagnosis and treatment. Given the speed at which this technology is evolving, many experts believe that within a few years we will witness widespread adoption of these systems in clinical practice, enabling faster, more accurate, and more effective medical care.

Source: University of California