AI-enhanced diagnosis of very late-onset schizophrenia-like psychosis: A step toward preventing dementia in older adults
The rapid aging of the global population, projected to reach 2.1 billion individuals aged 60 and older by 2050, is associated with an increased prevalence of mental health conditions, particularly dementia and psychosis. Among these, very late-onset schizophrenia-like psychosis (VLOSLP), defined as...
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Elsevier
2025-09-01
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| Series: | Neuroscience Informatics |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S277252862500038X |
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| author | Ali Allahgholi Ava Mazhari |
| author_facet | Ali Allahgholi Ava Mazhari |
| author_sort | Ali Allahgholi |
| collection | DOAJ |
| description | The rapid aging of the global population, projected to reach 2.1 billion individuals aged 60 and older by 2050, is associated with an increased prevalence of mental health conditions, particularly dementia and psychosis. Among these, very late-onset schizophrenia-like psychosis (VLOSLP), defined as occurring after age 60, poses significant diagnostic challenges due to overlapping neurobiological changes and medical conditions common in older adults. Studies have indicated a higher risk of dementia in patients with VLOSLP, emphasizing the necessity for ongoing symptom monitoring. In recent years, artificial intelligence (AI), particularly deep learning (DL) and machine learning (ML), has shown promise in enhancing disease diagnosis through advanced medical imaging techniques. This study aims to classify VLOSLP using MRI images from patients aged 60 and older, obtained from the COBRE and MCICshare databases via the SchizoConnect platform. To address the challenge of limited data, synthetic images were generated using Generative Adversarial Networks (GAN) following preprocessing techniques. These images were then classified using a Support Vector Machine (SVM) classifier, with feature extraction performed through Zernike moments. The findings achieved an area under the curve (AUC) of 0.98, contributing to more accurate diagnoses of VLOSLP and facilitating better management and monitoring of this complex condition in the aging population. |
| format | Article |
| id | doaj-art-e65ed201366c4b61b54eba69058d2fa5 |
| institution | Kabale University |
| issn | 2772-5286 |
| language | English |
| publishDate | 2025-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Neuroscience Informatics |
| spelling | doaj-art-e65ed201366c4b61b54eba69058d2fa52025-08-22T04:58:51ZengElsevierNeuroscience Informatics2772-52862025-09-015310022310.1016/j.neuri.2025.100223AI-enhanced diagnosis of very late-onset schizophrenia-like psychosis: A step toward preventing dementia in older adultsAli Allahgholi0Ava Mazhari1Faculty of Medical Sciences and Technologies, Biomedical Engineering Department, Science and Research Branch, Islamic Azad University, Tehran, IranDepartment of Biomedical Engineering, Amirkabir University of Technology (Tehran Polytechnique), Tehran, Iran; Corresponding author.The rapid aging of the global population, projected to reach 2.1 billion individuals aged 60 and older by 2050, is associated with an increased prevalence of mental health conditions, particularly dementia and psychosis. Among these, very late-onset schizophrenia-like psychosis (VLOSLP), defined as occurring after age 60, poses significant diagnostic challenges due to overlapping neurobiological changes and medical conditions common in older adults. Studies have indicated a higher risk of dementia in patients with VLOSLP, emphasizing the necessity for ongoing symptom monitoring. In recent years, artificial intelligence (AI), particularly deep learning (DL) and machine learning (ML), has shown promise in enhancing disease diagnosis through advanced medical imaging techniques. This study aims to classify VLOSLP using MRI images from patients aged 60 and older, obtained from the COBRE and MCICshare databases via the SchizoConnect platform. To address the challenge of limited data, synthetic images were generated using Generative Adversarial Networks (GAN) following preprocessing techniques. These images were then classified using a Support Vector Machine (SVM) classifier, with feature extraction performed through Zernike moments. The findings achieved an area under the curve (AUC) of 0.98, contributing to more accurate diagnoses of VLOSLP and facilitating better management and monitoring of this complex condition in the aging population.http://www.sciencedirect.com/science/article/pii/S277252862500038XAging populationVery late-onset schizophrenia-like psychosis (VLOSLP)Artificial intelligence in psychiatryGenerative adversarial networks (GAN)Machine learning in mental health diagnosis |
| spellingShingle | Ali Allahgholi Ava Mazhari AI-enhanced diagnosis of very late-onset schizophrenia-like psychosis: A step toward preventing dementia in older adults Neuroscience Informatics Aging population Very late-onset schizophrenia-like psychosis (VLOSLP) Artificial intelligence in psychiatry Generative adversarial networks (GAN) Machine learning in mental health diagnosis |
| title | AI-enhanced diagnosis of very late-onset schizophrenia-like psychosis: A step toward preventing dementia in older adults |
| title_full | AI-enhanced diagnosis of very late-onset schizophrenia-like psychosis: A step toward preventing dementia in older adults |
| title_fullStr | AI-enhanced diagnosis of very late-onset schizophrenia-like psychosis: A step toward preventing dementia in older adults |
| title_full_unstemmed | AI-enhanced diagnosis of very late-onset schizophrenia-like psychosis: A step toward preventing dementia in older adults |
| title_short | AI-enhanced diagnosis of very late-onset schizophrenia-like psychosis: A step toward preventing dementia in older adults |
| title_sort | ai enhanced diagnosis of very late onset schizophrenia like psychosis a step toward preventing dementia in older adults |
| topic | Aging population Very late-onset schizophrenia-like psychosis (VLOSLP) Artificial intelligence in psychiatry Generative adversarial networks (GAN) Machine learning in mental health diagnosis |
| url | http://www.sciencedirect.com/science/article/pii/S277252862500038X |
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