Schizophrenia is a serious mental disorder in which people interpret reality abnormally.
Schizophrenia may result in some combination of hallucinations, delusions, and extremely disordered thinking and behavior that impairs daily functioning and can be disabling.
People with schizophrenia require lifelong treatment.
In a study from the FIDMAG Germanes Hospitalàries Research Foundation in Barcelona, scientists found fingerprint images have the potential as predictors of schizophrenia.
They built deep learning classification algorithms based on convolutional neural networks using a sample of fingerprints from 612 patients with a diagnosis of nonaffective psychosis and 844 healthy people
The general architecture of the network was chosen previously and then applied for building classification algorithms for patients versus controls based on single fingers and multi-input models.
The researchers found that the right thumb network achieved the highest level of accuracy from networks based on single fingers (weighted validation accuracy, 68%).
A model simultaneously using images from the left thumb, index, and middle fingers had the highest accuracy for multi-input models (weighted validation accuracy, 70%).
This is the first study analyzing the potential utility of fingerprint images to automatically diagnose schizophrenia through deep learning, and our results support the feasibility of such an approach.
Moreover, the lifelong stability of fingerprints also supports their potential value as predictors of the risk of psychosis, especially if combined with other sources of data.
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The study was conducted by Raymond Salvador et al and published in Schizophrenia Bulletin.
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