Intelligent image processing for neurodegenerative diseases: Advancing early detection and monitoring

Abstract

Objectives: Neurodegenerative diseases, such as Alzheimer's disease (AD), represent a significant and growing global health burden, characterised by progressive cognitive and motor deterioration. Traditional diagnostic methods often rely on subjective clinical assessments, which can be inefficient and lack sensitivity in early stages. This study aims to explore the potential of intelligent image processing and artificial intelligence (AI) in revolutionising the early detection and monitoring of neurodegenerative disorders. Specifically, we investigate how advanced computer vision techniques can analyse visual biomarkers, such as facial expressions, eye movements, and oral motor dynamics, to identify early signs of neurological decline before overt clinical symptoms manifest. We leverage these technologies to improve diagnostic accuracy, enable timely interventions, and enhance long-term disease management.

Method: Our approach integrates AI-driven image processing with medical diagnostics to objectively assess neurodegenerative progression. We employ deep learning algorithms and computer vision models to analyse visual data, extracting subtle patterns indicative of neurological impairment. Key techniques include facial expression recognition to detect emotional blunting, gaze tracking to identify irregular eye movements and kinematic analysis of mouth dynamics to assess speech-related motor deficits. These methods allow for the quantification of micro-expressions, movement abnormalities and reduced responsiveness, potential early biomarkers of neurodegeneration. We review recent advancements in AI-based medical imaging, focusing on their application in AD research, while also addressing data preprocessing, model training and validation strategies to ensure robustness and generalisability.

Results: Preliminary findings demonstrate that intelligent image processing can effectively detect early neurodegenerative changes with high precision. For instance, AI models analysing facial micro-expressions have identified emotional disturbances in preclinical AD stages, while eye-tracking algorithms have revealed gaze pattern anomalies correlated with cognitive decline. Additionally, automated mouth movement analysis has shown promise in capturing subtle motor impairments linked to disease progression. These results suggest that visual biomarkers, when processed through advanced AI systems, can complement traditional diagnostic tools, offering objective, scalable and non-invasive monitoring solutions. However, challenges such as dataset diversity, model interpretability and real-world applicability remain critical considerations for widespread clinical adoption.

Conclusions: Intelligent image processing holds transformative potential for advancing the early detection and monitoring of neurodegenerative diseases like AD. By harnessing AI and computer vision, this approach enables the identification of preclinical biomarkers, facilitating earlier and more accurate diagnoses. Despite its promise, further research is needed to address ethical concerns, ensure algorithmic fairness and validate these technologies across diverse populations. Future directions include integrating multimodal data and developing real-time monitoring systems for personalised care. Ultimately, the fusion of intelligent image processing with neurodegenerative disease research could significantly improve patient outcomes, reduce healthcare costs and pave the way for more effective therapeutic interventions.