Artículo Materias > Ingeniería
Materias > Psicología Universidad Europea del Atlántico > Investigación > Artículos y libros Abierto Inglés 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. metadata Salvador, Raymond; García-León, María Ángeles; Feria-Raposo, Isabel; Botillo-Martín, Carlota; Martín-Lorenzo, Carlos; Corte-Souto, Carmen; Aguilar-Valero, Tania; Gil Sanz, David; Porta-Pelayo, David; Martín-Carrasco, Manuel; del Olmo-Romero, Francisco; Maria Santiago-Bautista, Jose; Herrero-Muñecas, Pilar; Castillo-Oramas, Eglee; Larrubia-Romero, Jesús; Rios-Alvarado, Zoila; Antonio Larraz-Romeo, José; Guardiola-Ripoll, Maria; Almodóvar-Payá, Carmen; Fatjó-Vilas Mestre, Mar; Sarró, Salvador; McKenna, Peter J; González-Pablos, Emilio; Negro-González, Emilio; María Castells Bescos, Eva; Felipe Martínez, Elena; Muñoz Hermoso, Paula; Camaño Serna, Cora; Rebolleda Gil, Carlos; Feliz Muñoz, Carmen; Sevillano De La Fuente, Paula; Sánchez Perez, Manuel; Arrece Iriondo, Izascun; Vicente Jauregui Berecibar, José; Domínguez Panchón, Ana; Felices de la Fuente, Alfredo; Bosque Gabarre, Clara y Pomarol-Clotet, Edith mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, david.gil@uneatlantico.es, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR (2022) Fingerprints as Predictors of Schizophrenia: A Deep Learning Study. Schizophrenia Bulletin. ISSN 0586-7614