@article{uneatlantico4914,
            year = {2022},
          author = {Raymond Salvador and Mar{\'i}a {\'A}ngeles Garc{\'i}a-Le{\'o}n and Isabel Feria-Raposo and Carlota Botillo-Mart{\'i}n and Carlos Mart{\'i}n-Lorenzo and Carmen Corte-Souto and Tania Aguilar-Valero and David Gil Sanz and David Porta-Pelayo and Manuel Mart{\'i}n-Carrasco and Francisco del Olmo-Romero and Jose Maria Santiago-Bautista and Pilar Herrero-Mu{\~n}ecas and Eglee Castillo-Oramas and Jes{\'u}s Larrubia-Romero and Zoila Rios-Alvarado and Jos{\'e} Antonio Larraz-Romeo and Maria Guardiola-Ripoll and Carmen Almod{\'o}var-Pay{\'a} and Mar Fatj{\'o}-Vilas Mestre and Salvador Sarr{\'o} and Peter J McKenna and Emilio Gonz{\'a}lez-Pablos and Emilio Negro-Gonz{\'a}lez and Eva Mar{\'i}a Castells Bescos and Elena Felipe Mart{\'i}nez and Paula Mu{\~n}oz Hermoso and Cora Cama{\~n}o Serna and Carlos Rebolleda Gil and Carmen Feliz Mu{\~n}oz and Paula Sevillano De La Fuente and Manuel S{\'a}nchez Perez and Izascun Arrece Iriondo and Jos{\'e} Vicente Jauregui Berecibar and Ana Dom{\'i}nguez Panch{\'o}n and Alfredo Felices de la Fuente and Clara Bosque Gabarre and Edith Pomarol-Clotet},
         journal = {Schizophrenia Bulletin},
           title = {Fingerprints as Predictors of Schizophrenia: A Deep Learning Study},
        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.},
        keywords = {schizophrenia, machine learning, dermatoglyphics, diagnosis, artificial intelligence},
             url = {http://repositorio.uneatlantico.es/id/eprint/4914/}
}