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Quantitative assessment of alpha-synuclein pathology with AI
Parkinson’s disease (PD) is the second most common neurodegenerative disorder after Alzheimer’s. PD affects around 10 million people worldwide. α-synuclein is a neuronal protein that is linked genetically and neuropathologically to PD.
Researchers at Lundbeck trained deep learning neural networks to build AI models for performing an objective, quantitative assessment of α-synuclein pathology and to generate a spatiotemporal map of pathology spread in mouse brains.
Overview of study:
- Traditionally neuronal cells have been counted from samples by stereological methods
- With Aiforia Create the researchers trained a deep learning AI model to quantify α-synuclein positive neurons and thus the total area of pathology
- They were then able to develop an anatomical map of relevant brain regions
Results:
- The cell count produced by Aiforia correlated with manual methods
- The first study was a test performed on pS129-α-synuclein positive staining in the Substantia Nigra and Amygdala
- The manual cell counts (with ImageJ) were compared to the Aiforia quantification, with a significant Pearson correlation (p-value <0.0001)
- Aiforia quantitation showed to be a fast and accurate method
