Footprints Project

Differential diagnosis in schizophrenia, bipolar disorder and autism using fingerprints, genomics and Deep Learning algorithms

One main hypothesis about schizophrenia suggests that prenatal neurodevelopmental alterations, influenced by genetics and environment, may be reflected in fingerprint patterns. Using AI tools, our team developed fingerprint-based algorithms that distinguish people with schizophrenia from healthy controls with 70% accuracy. Validation confirmed their effectiveness for schizophrenia but showed limitations for differentiating from bipolar disorder. The next step is to improve diagnostic accuracy by combining larger samples, genomic data, and new algorithms to support early and precise psychosis diagnosis.

PI: Edith Pomarol-Clotet

Research group: Applied Multimodal Neuroimaging Group

Institution: Fundación para la Investigación y la Docencia María Angustias Giménez (FIDMAG Research Foundation)

Visit the group website here.

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