Fingerprints as Predictors of Schizophrenia: A Deep Learning Study.
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 algorit...
| Publicado en: | Schizophrenia Bulletin Vol. 49; no. 3; pp. 738 - 746 |
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| Autores principales: | , , , , , , , , , , , , , , , , , , , |
| Formato: | pictorial research tables/charts Journal Article |
| Publicado: |
Oxford University Press / USA
May2023
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=163492126&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 163492126 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 05867614 2CZ jtl: Schizophrenia Bulletin issn: 05867614 maglogo: N pubinfo: dt: May2023 vid: 49 iid: 3 pid: 622 pub: Oxford University Press / USA artinfo: ui: 163492126 163492126 163492126 10.1093/schbul/sbac173 163492126 ppf: 738 ppct: 8 formats: tig: atl: Fingerprints as Predictors of Schizophrenia: A Deep Learning Study. aug: au: 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 Olmo-Romero, Francisco del 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 Mestre, Mar Fatjó-Vilas affil: FIDMAG Germanes Hospitalàries Research Foundation , Barcelona , Spain sug: subj: Fingerprints Schizophrenia Risk Factors Risk Assessment Deep Learning Algorithms Human Psychotic Disorders Diagnosis Neural Networks (Computer) Exploratory Research Machine Learning Dermatoglyphics Artificial Intelligence Central Nervous System ab: 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. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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