ArticleSchizophrenia bulletin2023
Fingerprints as Predictors of Schizophrenia: A Deep Learning Study.
Article in Schizophrenia bulletin, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Fingerprints Encode Risk for Bipolarity.Bipolar disorders · 2026Article
- Epiphenomena in psychosis: how perinatal stressful events shape long-term outcomes.Molecular psychiatry · 2025Review
- Genetic and Neurodevelopmental Markers in Schizophrenia-Spectrum Disorders: Analysis of the Combined Role of theBiomedicines · 2024Article
- Diagnosis of mental disorders using machine learning: Literature review and bibliometric mapping from 2012 to 2023.Heliyon · 2024Review
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Authors and funding
24 authors.
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No grant is acknowledged in the PubMed record.
Abstract
background and hypothesisThe 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
designBased 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
resultsThe 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%).
conclusionAlthough 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.
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