Neuroscientist Joan Compte describes benefits of AI in Parkinson’s Disease image analysis

The neurodegenerative disease, Parkinson’s, is commonly detected using brain scans and analyzed manually. We interviewed Joan, neuroscience researcher, on the benefits of AI in PD research.
Written by Aiforia

What is PD?

Parkinson’s disease (PD) is a neurological disorder characterized by the degeneration of dopamine releasing regions in the brain. The loss of dopamine leads to a chronic and progressive decrease in movement, coordination, and cognition. While there is no cure, an early diagnosis promotes treatment efficacy and prolongs a higher quality of life.

Traditional stereological methods in PD research are time-consuming and involve tedious manual work, sometimes leading to error-prone analysis. Extrapolating data from samples also involves inter- and intra-observer bias meaning final conclusions from varying samples can vary greatly between researchers and projects.

Artificial Intelligence (AI)-assisted image analysis increases speed and scalability of projects, saving researchers valuable time to focus their efforts on more complex and higher level challenges. Large data management and analysis consistency eliminates variation from random sampling, leading to improved statistical accuracy and reproducible results for future PD research. Read more about how deep learning AI is a promising alternative to stereology here.

Predoctoral researcher Joan Compte has implemented AI models to save time in his work using Aiforia Create. We interviewed him on the use of AI in PD research and his experience using Aiforia’s software without prior coding knowledge.

Interview: Joan Compte, neuroscience predoctoral researcher

 

Blog images Joan Comte

What is your research focus on?

Joan: My research is focused on Parkinson's Disease.

Why did you decide to use AI for your current research project?

We decided to use AI for our projects because cell counting (of whatever staining) is very time-consuming. Besides, stereological methods provide just estimated numbers, which can lead to slightly error-prone analysis. Additionally, some stainings are very faint and difficult in the manual procedure. AI, like the one of Aiforia, helps to get rid of all these drawbacks and ensures a robust and efficient method to standardize and accelerate object counting. 

How did you find getting started with the Aiforia Create AI software?

Working with Aiforia is very accessible for every user. Without any previous knowledge on AI or programming/code, the user can directly start training a specific algorithm for any of his/her objects. The interface is very intuitive and it gets very simple to begin accumulating hundreds of annotations. Besides, the annotation assistant tool makes the first proto-algorithms gain a lot of quality very quickly. On top of it, customer care of Aiforia is always ready to help with whatever inquiry and give always the best solutions. 

What is the biggest advantage of using AI to your work?

Aiforia makes life much simpler! Countings that could last for months are performed in seconds once your algorithm is ready. Besides, the different available tools optimize the whole procedure, reaching high-quality algorithms in just 1-2 months.

References

Rumman M, Tasneem AN, et al. Joint 7th International Conference on Informatics, Electronics & Vision (ICIEV), 2018

Schmitz C, Hof PR, Neuroscience, 2005

Souza JWM, Alves SSA, et al. Computational Intelligence and Neuroscience, 2018 

Questions?

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