Fingerprints as Predictors of Schizophrenia: A Deep Learning Study

Autor: 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, Santiago-Bautista, Jose Maria, Herrero-Muñecas, Pilar, Castillo-Oramas, Eglee, Larrubia-Romero, Jesús, Rios-Alvarado, Zoila, Larraz-Romeo, José Antonio, Guardiola-Ripoll, Maria, Almodóvar-Payá, Carmen, Fatjó-Vilas Mestre, Mar, Sarró, Salvador, McKenna, Peter J, HHFingerprints Group, Pomarol-Clotet, Edith
Přispěvatelé: Instituto de Salud Carlos III, Ministerio de Ciencia e Innovación (España), Unión Europea. Fondo Europeo de Desarrollo Regional (FEDER/ERDF), Centro de Investigación Biomédica en Red - CIBERSAM (Salud Mental), Government of Catalonia (España)
Rok vydání: 2022
Předmět:
Zdroj: Schizophrenia Bulletin. 49:738-745
ISSN: 1745-1701
0586-7614
Popis: 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. This work was supported by several grants funded by the Instituto de Salud Carlos III and the Spanish Ministry of Science and Innovation (co-funded by the European Regional Development Fund/European Social Fund “Investing in your future”): Miguel Servet Research Contract (CPII13/00018 to RS, CPII16/00018 to EP-C, CP20/00072 to MF-V), PFIS Contract (FI19/0352 to MG-R). Research Mobility programme (MV18/00054 to EP-C), Research Projects (PI18/00877 and PI21/00525 to RS). It has also been supported by the Centro de Investigación Biomédica en Red de Salud Mental and the Generalitat de Catalunya: 2014SGR1573 and 2017SGR1365 to EP-C and SLT008/18/00206 to IF-R from the Departament de Salut. The authors have declared that there are no conflicts of interest in relation to the subject of this study. Sí
Databáze: OpenAIRE
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