Fingerprints as Predictors of Schizophrenia: A Deep Learning Study

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

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Abstract

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.

Item Type: Article
Uncontrolled Keywords: schizophrenia, machine learning, dermatoglyphics, diagnosis, artificial intelligence
Subjects: Subjects > Engineering
Subjects > Psychology
Divisions: Europe University of Atlantic > Research > Articles and books
Date Deposited: 05 Dec 2022 23:30
Last Modified: 05 Dec 2022 23:30
URI: https://repositorio.uneatlantico.es/id/eprint/4914

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