Fingerprints as Predictors of Schizophrenia: A Deep Learning Study.

Raymond SalvadorMaría Ángeles García-LeónIsabel Feria-RaposoCarlota Botillo-MartínCarlos Martín-LorenzoCarmen Corte-SoutoTania Aguilar-ValeroDavid Gil-SanzDavid Porta-PelayoManuel Martín-CarrascoFrancisco Del Olmo-RomeroJose Maria Santiago-BautistaPilar Herrero-MuñecasEglee Castillo-OramasJesús Larrubia-RomeroZoila Rios-AlvaradoJosé Antonio Larraz-RomeoMaria Guardiola-RipollCarmen Almodóvar-PayáMar Fatjó-Vilas MestreSalvador SarróPeter J McKennanull nullEdith Pomarol-Clotet

Published in: Schizophrenia bulletin (2022)

Although fitted models were based on data from patients with a well established diagnosis, since fingerprints remain lifelong stable after birth, our results imply that fingerprints may be applied as early predictors of psychosis. Specially, if they are used in high prevalence subpopulations such as those of individuals at high risk for psychosis.

Keyphrases

deep learning
bipolar disorder
risk factors
big data
machine learning
gestational age
convolutional neural network
preterm birth