%T Fingerprints as Predictors of Schizophrenia: A Deep Learning Study
%X Background and Hypothesis
The existing developmental bond between fingerprint generation and growth of the central nervous system points to a potential use of fingerprints as risk markers in schizophrenia. However, the high complexity of fingerprints geometrical patterns may require flexible algorithms capable of characterizing such complexity.

Study Design
Based on an initial sample of scanned fingerprints from 612 patients with a diagnosis of non-affective psychosis and 844 healthy subjects, we have built deep learning classification algorithms based on convolutional neural networks. Previously, the general architecture of the network was chosen from exploratory fittings carried out with an independent fingerprint dataset from the National Institute of Standards and Technology. The network architecture was then applied for building classification algorithms (patients vs controls) based on single fingers and multi-input models. Unbiased estimates of classification accuracy were obtained by applying a 5-fold cross-validation scheme.

Study Results
The highest level of accuracy from networks based on single fingers was achieved by the right thumb network (weighted validation accuracy = 68%), while the highest accuracy from the multi-input models was attained by the model that simultaneously used images from the left thumb, index and middle fingers (weighted validation accuracy = 70%).

Conclusion
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.
%D 2022
%L uneatlantico4914
%R doi:10.1093/schbul/sbac173
%J Schizophrenia Bulletin
%A Raymond Salvador
%A María Ángeles García-León
%A Isabel Feria-Raposo
%A Carlota Botillo-Martín
%A Carlos Martín-Lorenzo
%A Carmen Corte-Souto
%A Tania Aguilar-Valero
%A David Gil Sanz
%A David Porta-Pelayo
%A Manuel Martín-Carrasco
%A Francisco del Olmo-Romero
%A Jose Maria Santiago-Bautista
%A Pilar Herrero-Muñecas
%A Eglee Castillo-Oramas
%A Jesús Larrubia-Romero
%A Zoila Rios-Alvarado
%A José Antonio Larraz-Romeo
%A Maria Guardiola-Ripoll
%A Carmen Almodóvar-Payá
%A Mar Fatjó-Vilas Mestre
%A Salvador Sarró
%A Peter J McKenna
%A Emilio González-Pablos
%A Emilio Negro-González
%A Eva María Castells Bescos
%A Elena Felipe Martínez
%A Paula Muñoz Hermoso
%A Cora Camaño Serna
%A Carlos Rebolleda Gil
%A Carmen Feliz Muñoz
%A Paula Sevillano De La Fuente
%A Manuel Sánchez Perez
%A Izascun Arrece Iriondo
%A José Vicente Jauregui Berecibar
%A Ana Domínguez Panchón
%A Alfredo Felices de la Fuente
%A Clara Bosque Gabarre
%A Edith Pomarol-Clotet
%K schizophrenia, machine learning, dermatoglyphics, diagnosis, artificial intelligence