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Parkinson’s disease subtypes revealed using machine learning models

Use of machine learning has enabled scientists to accurately predict four subtypes of Parkinson’s disease based on images of patient-derived stem cells.

Parkinson’s disease is a neurodegenerative condition that affects both movement and cognition. Symptoms and progression vary based on the underlying disease subtype, although it has not been possible to accurately differentiate between these subtypes.

This may well change in the near future as a team based at the Francis Crick Institute and UCL Queen Square Institute of Neurology, together with the technology company Faculty AI, have shown that machine learning can accurately predict subtypes of Parkinson’s disease using images of patient-derived stem cells.

The work, which was published in the journal Nature Machine intelligence, generated a machine learning-based model that could simultaneously predict the presence of Parkinson’s disease as well as its primary mechanistic subtype in human neurons.

In the study, researchers generated stem cells from a patients’ own cells and chemically created four different subtypes of Parkinson’s disease: two involving pathways leading to toxic build-up of the protein α-synuclein and two involving pathways leading to defunct mitochondria. Together, this created a ’human’ model of the disease.

Next, researchers imaged these disease models and ‘trained’ the machine learning algorithm to recognise each subtype, from which it was then able to predict the particular subtype.

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Prediction of Parkinson’s disease subtype

The machine learning model enabled researchers to accurately identify a disease state from a healthy control state.

With quantitative cellular profile-based classifiers, the models were able to achieve an accuracy of 82%. In contrast, image-based deep neural networks could predict control and four distinct disease subtypes with an accuracy of 95%.

The machine learning-trained classifiers were able to achieve a level of accuracy across all subtypes, using the organellar features of the mitochondria, with the additional contribution of the lysosomes, confirming the biological importance of these pathways in Parkinson’s disease.

James Evans, a PhD student at the Francis Crick Institute and UCL, and co-first author with Karishma D’Sa and Gurvir Virdi, said: ‘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, assistant research director and group leader of the Neurodegeneration Biology Laboratory at the Francis Crick Institute, who was also involved in the study, said: ‘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 personalised medicine.‘

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