ArticleBMC psychiatry2021
Color painting predicts clinical symptoms in chronic schizophrenia patients via deep learning.
Article in BMC psychiatry, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 9 citations in OpenAlex.
- Application of artificial intelligence in schizophrenia rehabilitation management: a systematic scoping review.Translational psychiatry · 2026Article
- Validation of the Scale of Distinctive Elements in the Drawings of Subjects with Psychotic Symptoms: A Study Protocol.Advances in experimental medicine and biology · 2026Article
- Observational
- Artificial Intelligence and Machine Learning in Neuroregeneration: A Systematic Review.Cureus · 2024Review
- Machine learning techniques in diagnostics and prediction of the clinical features of schizophrenia: a narrative review.Consortium psychiatricum · 2023Review
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Authors and funding
9 authors at 5 institutions in 3 countries.
Funding
Abstract
backgroundIndividuals with psychiatric disorders perceive the world differently. Previous studies indicated impaired color vision and weakened color discrimination ability in psychotic patients. Examining the paintings from psychotic patients can measure the visual-motor function. However, few studies examined the potential changes in the color painting behavior in these individuals. The current study aims to discriminate schizophrenia patients from healthy controls (HCs) and predict PANSS scores of schizophrenia patients according to their paintings.
methodsIn the present study, we retrospectively analyzed the paintings colored by 281 chronic schizophrenia patients and 35 HCs. The images were scanned and processed using series of computational analyses.
resultsThe results showed that schizophrenia patients tend to use less color and exhibit different strokes compared to HCs. Using a deep learning residual neural network (ResNet), we were able to discriminate patients from HCs with over 90% accuracy. Further, we developed a novel convolutional neural network to predict PANSS positive, negative, general psychopathology, and total scores. The Root Mean Square Error (RMSE) of the prediction was low, which indicates higher accuracy of prediction.
conclusionIn conclusion, the deep learning paradigm showed the large potential to discriminate schizophrenia patients from HCs based on color paintings. Besides, this color painting-based paradigm can effectively predict clinical symptom severity for chronic schizophrenia patients. The color paintings by schizophrenia patients show potential as a tool for clinical diagnosis and prognosis. These findings show potential as a tool for clinical diagnosis and prognosis among schizophrenia patients.
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