Revolutionary Test Offers New Hope for Parkinson’s Patients As It Can Accurately Classify Four Subtypes of the Disease With 95% Accuracy.

Parkinson’s is a neurodegenerative disorder affecting movement and cognitive functions. The manifestation and progression of symptoms can differ among individuals due to the diverse biological triggers of the disease.

Previously, there wasn’t a reliable method to distinguish between its subtypes. Consequently, individuals received generalized diagnoses, hindering their access to specialized care, treatments, and support.

Pioneering Test Sets New Standards in Parkinson’s Detection

However, a groundbreaking study unveiled today in Nature Machine Intelligence reveals a promising development. Scientists from the Francis Crick Institute and UCL Queen Square Institute of Neurology, in collaboration with tech firm Faculty AI, have demonstrated the potential of computer algorithms in categorizing four distinct subtypes of Parkinson’s. Impressively, one model achieved a 95% accuracy rate, signifying a leap towards individualized treatments and drug research.

The pathology of Parkinson’s is attributed to the abnormal folding of crucial proteins and the malfunctioning of mitochondria – cells’ powerhouses. While most Parkinson’s cases develop spontaneously, some have genetic roots.

In this innovative study, they developed stem cells from patients and chemically induced four Parkinson’s variants. These variants were then visualized in intricate detail, capturing key cell structures like lysosomes – responsible for cellular waste management. Using these images, they equipped a computer program to identify each variant. This trained program could subsequently determine the disease subtype from unfamiliar images.

Interestingly, mitochondria and lysosomes emerged as pivotal indicators for subtype identification, underscoring their role in the disease’s onset. However, other cellular areas, like the cell nucleus, were also influential. Some elements of the images remain unexplained but are still crucial.

James Evans, a doctoral candidate at the Crick and UCL, and co-author alongside Karishma D’Sa and Gurvir Virdi, shared, “Now that we use more advanced image techniques, we generate vast quantities of data, much of which is discarded when we manually select a few features of interest. Using AI in this study enabled us to evaluate a larger number of cell features, and assess the importance of these features in discerning disease subtype.

“Using deep learning, we were able to extract much more information from our images than with conventional image analysis. We now hope to expand this approach to understand how these cellular mechanisms contribute to other subtypes of Parkinson’s.”

Sonia Gandhi, deputy research head and leader of the Neurodegeneration Biology Laboratory at the Crick, expressed, “We understand many of the processes that are causing Parkinson’s in people’s brains. But, while they are alive, we have no way of knowing which mechanism is happening, and therefore can’t give precise treatments.

“We don’t currently have treatments which make a huge difference in the progression of Parkinson’s disease. Using a model of the patient’s own neurons, and combining this with large numbers of images, we generated an algorithm to classify certain subtypes – a powerful approach that could open the door to identifying disease subtypes in life. Taking this one step further, our platform would allow us to first test drugs in stem cell models, and predict whether a patient’s brain cells would be likely to respond to a drug, before enrolling into clinical trials. The hope is that one day this could lead to fundamental changes in how we deliver personalized medicine.”

Remarkably, this initiative began during the pandemic disruptions, prompting the entire team to master Python coding – a skill they are now harnessing in ongoing projects.

The research team’s forthcoming plans encompass exploring Parkinson’s subtypes linked to other genetic variations and investigating if non-genetic cases can be similarly categorized.

Image Credit: Shutterstock