Color painting predicts clinical symptoms in chronic schizophrenia patients via deep learning

Shen, Hui; Wang, Shuihua; Zhang, Yi; Wang, Haixia; Li, Feng; Lucas, Molly V.; Zhang, Yudong; Yan, Liu; Yuan, Ti‐Fei

doi:10.1186/s12888-021-03452-3

Cited by 5 publications

(2 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Using a neural network analysis, the investigators could reliably predict the results of the Positive and Negative Syndrome Scale (PANSS) assessment using drawings made by people with schizophrenia. The model could predict high scores both in the general scale and the subscales [42] .…”

Section: Resultsmentioning

confidence: 91%

Machine learning techniques in diagnostics and prediction of the clinical features of schizophrenia: a narrative review

Gashkarimov,

Sultanova,

Efremov

et al. 2023

Consortium Psychiatricum

View full text Add to dashboard Cite

BACKGROUND: Schizophrenia is a severe psychiatric disorder associated with a significant negative impact. Early diagnosis and treatment of schizophrenia has a favorable effect on the clinical outcome and patients quality of life. In this context, machine learning techniques open up new opportunities for a more accurate diagnosis and prediction of the clinical features of this illness. AIM: This literature review is aimed to search for information on the use of machine learning techniques in the prediction and diagnosis of schizophrenia and the determination of its clinical features. METHODS: The Google Scholar, PubMed, and eLIBRARY.ru databases were used to search for relevant data. The review included articles that had been published not earlier than January 1, 2010, and not later than March 31, 2023. Combinations of the following keywords were applied for search queries: machine learning, deep learning, schizophrenia, neural network, predictors, artificial intelligence, diagnostics, suicide, depressive, insomnia, and cognitive. Original articles regardless of their design were included in the review. Descriptive analysis was used to summarize the retrieved data. RESULTS: Machine learning techniques are widely used in the functional assessment of patients with schizophrenia. They are used for interpretation of MRI, EEG, and actigraphy findings. Also, models created using machine learning algorithms can analyze speech, behavior, and the creativity of people and these data can be used for the diagnosis of psychiatric disorders. It has been found that different machine learning-based models can help specialists predict and diagnose schizophrenia based on medical history and genetic data, as well as epigenetic information. Machine learning techniques can also be used to build effective models that can help specialists diagnose and predict clinical manifestations and complications of schizophrenia, such as insomnia, depressive symptoms, suicide risk, aggressive behavior, and changes in cognitive functions over time. CONCLUSION: Machine learning techniques play an important role in psychiatry, as they have been used in models that help specialists in the diagnosis of schizophrenia and determination of its clinical features. The use of machine learning algorithms is one of the most promising direction in psychiatry, and it can significantly improve the effectiveness of the diagnosis and treatment of schizophrenia.

show abstract

Section: Resultsmentioning

confidence: 91%

Machine learning techniques in diagnostics and prediction of the clinical features of schizophrenia: a narrative review

Gashkarimov,

Sultanova,

Efremov

et al. 2023

Consortium Psychiatricum

View full text Add to dashboard Cite

show abstract

“…Shen H et al 2021 [37] The experiment involved the analysis of color paintings using advanced computational techniques to differentiate between schizophrenia patients and healthy controls and predict symptom severity in schizophrenia patients.…”

Section: [24]mentioning

confidence: 99%

Artificial Intelligence and Machine Learning in Neuroregeneration: A Systematic Review

Mulpuri,

Konda,

Gadde

et al. 2024

Cureus

View full text Add to dashboard Cite

Artificial intelligence (AI) and machine learning (ML) show promise in various medical domains, including medical imaging, precise diagnoses, and pharmaceutical research. In neuroscience and neurosurgery, AI/ML advancements enhance brain-computer interfaces, neuroprosthetics, and surgical planning. They are poised to revolutionize neuroregeneration by unraveling the nervous system's complexities. However, research on AI/ML in neuroregeneration is fragmented, necessitating a comprehensive review. Adhering to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) recommendations, 19 English-language papers focusing on AI/ML in neuroregeneration were selected from a total of 247. Two researchers independently conducted data extraction and quality assessment using the Mixed Methods Appraisal Tool (MMAT) 2018. Eight studies were deemed high quality, 10 moderate, and four low. Primary goals included diagnosing neurological disorders (35%), robotic rehabilitation (18%), and drug discovery (12% each). Methods ranged from analyzing imaging data (24%) to animal models (24%) and electronic health records (12%). Deep learning accounted for 41% of AI/ML techniques, while standard ML algorithms constituted 29%. The review underscores the growing interest in AI/ML for neuroregenerative medicine, with increasing publications. These technologies aid in diagnosing diseases and facilitating functional recovery through robotics and targeted stimulation. AI-driven drug discovery holds promise for identifying neuroregenerative therapies. Nonetheless, addressing existing limitations remains crucial in this rapidly evolving field.

show abstract

A systematic literature review and analysis of deep learning algorithms in mental disorders

Arji,

Erfannia,

alirezaei

et al. 2023

Informatics in Medicine Unlocked

View full text Add to dashboard Cite

Color painting predicts clinical symptoms in chronic schizophrenia patients via deep learning

Cited by 5 publications

References 20 publications

Machine learning techniques in diagnostics and prediction of the clinical features of schizophrenia: a narrative review

Machine learning techniques in diagnostics and prediction of the clinical features of schizophrenia: a narrative review

Artificial Intelligence and Machine Learning in Neuroregeneration: A Systematic Review

A systematic literature review and analysis of deep learning algorithms in mental disorders

Contact Info

Product

Resources

About