2026-08-05T16:13:20Zhttps://repisalud.isciii.es/rest/oai/request

oai:repisalud.isciii.es:20.500.12105/158932025-05-12T07:18:48Zcom_20.500.12105_2052com_20.500.12105_2051col_20.500.12105_19618

00925njm 22002777a 4500 dc Salvador, Raymond author García-León, María Ángeles author Feria-Raposo, Isabel author Botillo-Martín, Carlota author Martín-Lorenzo, Carlos author Corte-Souto, Carmen author Aguilar-Valero, Tania author Gil-Sanz, David author Porta-Pelayo, David author Martín-Carrasco, Manuel author Del Olmo-Romero, Francisco author Santiago-Bautista, Jose Maria author Herrero-Muñecas, Pilar author Castillo-Oramas, Eglee author Larrubia-Romero, Jesús author Rios-Alvarado, Zoila author Larraz-Romeo, José Antonio author Guardiola-Ripoll, Maria author Almodóvar-Payá, Carmen author Fatjó-Vilas Mestre, Mar author Sarró, Salvador author McKenna, Peter J author HHFingerprints Group author Pomarol-Clotet, Edith author 2023-05-03 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. Schizophr Bull. 2023 May 3;49(3):738-745. 10.1093/schbul/sbac173 1745-1701 Schizophrenia bulletin 36444899 http://hdl.handle.net/20.500.12105/15893 Artificial intelligence Dermatoglyphics Diagnosis Machine learning Schizophrenia Fingerprints as Predictors of Schizophrenia: A Deep Learning Study