APPLICATION OF CONVOLUTIONAL NEURAL NETWORKS IN ASSESSING MENTAL HEALTH AND FUNCTIONAL REHABILITATION OF ELDERLY PATIENTS WITH COGNITIVE IMPAIRMENTS IN PSYCHIATRIC CARE

Abstract

Objective: This study explores the application of convolutional neural network (CNN) technology in analyzing clinical data of elderly patients with cognitive impairments and mental disabilities, with a particular focus on its potential role in sports rehabilitation, physical therapy, and functional recovery interventions. Methods: By integrating neuroimaging, genetics, and behavioral data, CNN models were employed to enhance the early detection and diagnosis of dementia. Medical imaging data, including MRI and CT scans, as well as behavioral and motor function assessments, were processed using CNN-based deep learning techniques. The study examined the ability of CNN to identify subtle neurophysiological changes and movement dysfunction patterns associated with dementia-related cognitive decline. Results: Findings demonstrated that CNN models effectively recognized brain structure abnormalities and mobility impairments related to dementia with high accuracy, sensitivity, and specificity. Moreover, CNN-based analysis provided valuable insights into functional decline, gait abnormalities, and neuromuscular coordination deficits, which are critical for designing exercise-based rehabilitation programs for elderly patients. However, challenges such as data quality, model interpretability, and privacy concerns remain barriers to clinical integration. Conclusion: The study underscores the potential of CNN technology in sports rehabilitation and physical therapy by enabling early detection of neurocognitive decline and movement disorders in elderly individuals with psychiatric conditions. Future research should focus on enhancing model generalization, interpretability, and integration with multi-modal health data to support personalized rehabilitation and exercise-based interventions for dementia patients. These findings provide a foundation for developing AI-assisted rehabilitation programs that optimize physical activity strategies to improve cognitive and motor function in aging populations.