Brainbow Portfolio Cover

2020 / Italy

Neural networks for health. Brainbow allows early diagnosis of neurological and neurodegenerative diseases through accurate, quantitative and accessible software tools.





The complex early diagnosis of Autism.

Autism (prevalence ~1,5%) is a neurodevelopment specific disorder that compromises the neuropsychological development of the child. Although early signs appears even before 12 months of age, it may take up to 4 years to have a conclusive diagnosis.


The answer is in the brain waves.

Brainbow develops a neural network-based algorithm aimed to support early screening and diagnosis of neurological disorders, through machine learning analysis of the electroencephalogram (EEG). This will reduce the diagnosis time of years, anticipating the start and outcome of treatments.

Ask Stefano

Can you tell us how

BRAINBOW was born?

Brainbow stems from research in deep learning algorithms at the Semeion research center, where researchers, who specialize in discovering and testing new mathematical models and algorithms with a focus on Adaptive Artificial Systems, have developed an algorithm able to extrapolate diagnostic indications from a biomarker found in the electroencephalogram of patients with the autism condition.

Stefano Gay - Brainbow

Can you tell us how

BRAINBOW met DayOne?

It was the researchers themselves who, knowing Day One’s work, approached us. At the time, in fact, only a very first proof of concept had been done regarding the possibility of using the new algorithm to analyze EEG traces, but it was not yet clear what path this would take to be validated and become a product. The extremely complementary skills made the collaboration fruitful and allowed a leap forward both at the level of validation and product concept, allowing also to expand the network in the clinical, financial and academic fields, transforming Brainbow into one of the companies on the launch pad in the AI Biotech field.

What Top 3 Tips

would you give to

future innovators ?

I can think of three pieces of advice in particular that I would like to share:
1) build on a solid foundation when your innovation has one or more clear use-cases .and at least a first clear technical validation.
2) create a network of people who are knowledgeable and experienced in your field, who can help you have solid metrics for your choices, and a clear vision of your field outside of academia.
3) Don’t be afraid to make mistakes. Often Plan A is only the first attempt; the one that will really work lies between the T and Z.