Color painting predicts clinical symptoms in chronic schizophrenia patients via deep learning
Abstract Background Individuals 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,...
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| Format: | Article |
| Language: | English |
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BMC
2021-10-01
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| Series: | BMC Psychiatry |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12888-021-03452-3 |
| _version_ | 1831681676476940288 |
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| author | Hui Shen Shui-Hua Wang Yi Zhang Haixia Wang Feng Li Molly V. Lucas Yu-Dong Zhang Yan Liu Ti-Fei Yuan |
| author_facet | Hui Shen Shui-Hua Wang Yi Zhang Haixia Wang Feng Li Molly V. Lucas Yu-Dong Zhang Yan Liu Ti-Fei Yuan |
| author_sort | Hui Shen |
| collection | DOAJ |
| description | Abstract Background Individuals 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. Methods In 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. Results The 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. Conclusion In 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. |
| first_indexed | 2024-12-20T06:02:28Z |
| format | Article |
| id | doaj.art-30c99737c3c1441b97d223dc6483d4c8 |
| institution | Directory Open Access Journal |
| issn | 1471-244X |
| language | English |
| last_indexed | 2024-12-20T06:02:28Z |
| publishDate | 2021-10-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Psychiatry |
| spelling | doaj.art-30c99737c3c1441b97d223dc6483d4c82022-12-21T19:50:52ZengBMCBMC Psychiatry1471-244X2021-10-0121111110.1186/s12888-021-03452-3Color painting predicts clinical symptoms in chronic schizophrenia patients via deep learningHui Shen0Shui-Hua Wang1Yi Zhang2Haixia Wang3Feng Li4Molly V. Lucas5Yu-Dong Zhang6Yan Liu7Ti-Fei Yuan8Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of MedicineSchool of Computing and Mathematical Sciences, University of LeicesterShanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of MedicineShanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of MedicineShanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of MedicineWu Tsai Neurosciences Institute, Stanford UniversitySchool of Computing and Mathematical Sciences, University of LeicesterShanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of MedicineShanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of MedicineAbstract Background Individuals 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. Methods In 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. Results The 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. Conclusion In 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.https://doi.org/10.1186/s12888-021-03452-3Deep learningResNetSchizophreniaColor perceptionPainting |
| spellingShingle | Hui Shen Shui-Hua Wang Yi Zhang Haixia Wang Feng Li Molly V. Lucas Yu-Dong Zhang Yan Liu Ti-Fei Yuan Color painting predicts clinical symptoms in chronic schizophrenia patients via deep learning BMC Psychiatry Deep learning ResNet Schizophrenia Color perception Painting |
| title | Color painting predicts clinical symptoms in chronic schizophrenia patients via deep learning |
| title_full | Color painting predicts clinical symptoms in chronic schizophrenia patients via deep learning |
| title_fullStr | Color painting predicts clinical symptoms in chronic schizophrenia patients via deep learning |
| title_full_unstemmed | Color painting predicts clinical symptoms in chronic schizophrenia patients via deep learning |
| title_short | Color painting predicts clinical symptoms in chronic schizophrenia patients via deep learning |
| title_sort | color painting predicts clinical symptoms in chronic schizophrenia patients via deep learning |
| topic | Deep learning ResNet Schizophrenia Color perception Painting |
| url | https://doi.org/10.1186/s12888-021-03452-3 |
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