AI-Driven Innovations in Alzheimer's Disease: Integrating Early Diagnosis, Personalized Treatment, and Prognostic Modelling

• Published in: Ageing research reviews Vol. 101; p. 102497
• Main Authors: Kale, Mayur B., Wankhede, Nitu L., Pawar, Rupali S., Ballal, Suhas, Kumawat, Rohit, Goswami, Manish, Khalid, Mohammad, Taksande, Brijesh G., Upaganlawar, Aman B., Umekar, Milind J., Kopalli, Spandana Rajendra, Koppula, Sushruta
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.11.2024
• Subjects: CBA; genetic biomarkers; neuroimaging; AI; genetic biomarkers; AD; CBA; neuroimaging
• ISSN: 1568-1637; 1872-9649; 1872-9649
• DOI: 10.1016/j.arr.2024.102497

Alzheimer’s disease (AD) presents a significant challenge in neurodegenerative research and clinical practice due to its complex etiology and progressive nature. The integration of artificial intelligence (AI) into the diagnosis, treatment, and prognostic modelling of AD holds promising potential to transform the landscape of dementia care. This review explores recent advancements in AI applications across various stages of AD management. In early diagnosis, AI-enhanced neuroimaging techniques, including MRI, PET, and CT scans, enable precise detection of AD biomarkers. Machine learning models analyze these images to identify patterns indicative of early cognitive decline. Additionally, AI algorithms are employed to detect genetic and proteomic biomarkers, facilitating early intervention. Cognitive and behavioral assessments have also benefited from AI, with tools that enhance the accuracy of neuropsychological tests and analyze speech and language patterns for early signs of dementia. Personalized treatment strategies have been revolutionized by AI-driven approaches. In drug discovery, virtual screening and drug repurposing, guided by predictive modelling, accelerate the identification of effective treatments. AI also aids in tailoring therapeutic interventions by predicting individual responses to treatments and monitoring patient progress, allowing for dynamic adjustment of care plans. Prognostic modelling, another critical area, utilizes AI to predict disease progression through longitudinal data analysis and risk prediction models. The integration of multi-modal data, combining clinical, genetic, and imaging information, enhances the accuracy of these predictions. Deep learning techniques are particularly effective in fusing diverse data types to uncover new insights into disease mechanisms and progression. Despite these advancements, challenges remain, including ethical considerations, data privacy, and the need for seamless integration of AI tools into clinical workflows. This review underscores the transformative potential of AI in AD management while highlighting areas for future research and development. By leveraging AI, the healthcare community can improve early diagnosis, personalize treatments, and predict disease outcomes more accurately, ultimately enhancing the quality of life for individuals with AD. [Display omitted] •AI enhances Alzheimer's biomarker detection through neuroimaging and genetic/proteomic analysis.•Accelerates drug discovery and personalizes treatments by predicting individual responses.•Integrates multi-modal data to predict disease progression using deep learning.•Ethical, privacy, and clinical integration challenges require ongoing research.