TY  - JOUR
N2  - 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.
SN  - 0586-7614
TI  - Fingerprints as Predictors of Schizophrenia: A Deep Learning Study
A1  - Salvador, Raymond
A1  - García-León, María Ángeles
A1  - Feria-Raposo, Isabel
A1  - Botillo-Martín, Carlota
A1  - Martín-Lorenzo, Carlos
A1  - Corte-Souto, Carmen
A1  - Aguilar-Valero, Tania
A1  - Gil Sanz, David
A1  - Porta-Pelayo, David
A1  - Martín-Carrasco, Manuel
A1  - del Olmo-Romero, Francisco
A1  - Maria Santiago-Bautista, Jose
A1  - Herrero-Muñecas, Pilar
A1  - Castillo-Oramas, Eglee
A1  - Larrubia-Romero, Jesús
A1  - Rios-Alvarado, Zoila
A1  - Antonio Larraz-Romeo, José
A1  - Guardiola-Ripoll, Maria
A1  - Almodóvar-Payá, Carmen
A1  - Fatjó-Vilas Mestre, Mar
A1  - Sarró, Salvador
A1  - McKenna, Peter J
A1  - González-Pablos, Emilio
A1  - Negro-González, Emilio
A1  - María Castells Bescos, Eva
A1  - Felipe Martínez, Elena
A1  - Muñoz Hermoso, Paula
A1  - Camaño Serna, Cora
A1  - Rebolleda Gil, Carlos
A1  - Feliz Muñoz, Carmen
A1  - Sevillano De La Fuente, Paula
A1  - Sánchez Perez, Manuel
A1  - Arrece Iriondo, Izascun
A1  - Vicente Jauregui Berecibar, José
A1  - Domínguez Panchón, Ana
A1  - Felices de la Fuente, Alfredo
A1  - Bosque Gabarre, Clara
A1  - Pomarol-Clotet, Edith
JF  - Schizophrenia Bulletin
ID  - uneatlantico4914
UR  - http://doi.org/10.1093/schbul/sbac173
KW  - schizophrenia
KW  -  machine learning
KW  -  dermatoglyphics
KW  -  diagnosis
KW  -  artificial intelligence
AV  - public
Y1  - 2022///
ER  -