Lundbeck case study: quantitative assessment with AI

Preclinical scientists at Lundbeck used AI for the quantitative assessment of alpha-synuclein pathology, increasing the speed and accuracy of analysis.
Written by Aiforia

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


  • 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

alpha-synuclein AI assisted pathology

Watch the on-demand webinar

"Accelerating preclinical assessment: artificial intelligence for the quantitative assessment of alpha-synuclein pathology".