An Insight into the Role of Artificial Intelligence in the Early Diagnosis of Alzheimer's Disease.

Autor:	Verma RK; Department of Pharmacy Practice, School of Pharmacy, International Medical University-Bukit Jalil 57000, Kuala Lumpur, Malaysia., Pandey M; Department of Pharmaceutical Technology, School of Pharmacy, International Medical University- Bukit Jalil 57000, Kuala Lumpur, Malaysia., Chawla P; Department of Pharmaceutical Chemistry and Analysis, ISF College of Pharmacy, Moga, Punjab, India., Choudhury H; Department of Pharmaceutical Technology, School of Pharmacy, International Medical University- Bukit Jalil 57000, Kuala Lumpur, Malaysia., Mayuren J; Department of Pharmaceutical Technology, School of Pharmacy, International Medical University- Bukit Jalil 57000, Kuala Lumpur, Malaysia., Bhattamisra SK; Department of Life sciences, School of Pharmacy, International Medical University- Bukit Jalil 57000, Kuala Lumpur, Malaysia., Gorain B; School of Pharmacy, Faculty of Health and Medical Sciences, Taylor's University, Subang Jaya, Selangor 47500, Malaysia., Raja MAG; Department of Pharmaceutics, Faculty of Pharmacy, Northern Border University, Saudi Arabia., Amjad MW; Department of Pharmaceutics, Faculty of Pharmacy, Northern Border University, Saudi Arabia., Obaidur Rahman S; Department of Pharmaceutical Medicine, School of Pharmaceutical Education and Research, Jamia Humdard, New Delhi, India.
Jazyk:	angličtina
Zdroj:	CNS & neurological disorders drug targets [CNS Neurol Disord Drug Targets] 2022; Vol. 21 (10), pp. 901-912.
DOI:	10.2174/1871527320666210512014505
Abstrakt:	Background: The complication of Alzheimer's disease (AD) has made the development of its therapeutic a challenging task. Even after decades of research, we have achieved no more than a few years of symptomatic relief. The inability to diagnose the disease early is the major hurdle behind its treatment. Several studies have aimed to identify potential biomarkers that can be detected in body fluids (CSF, blood, urine, etc.) or assessed by neuroimaging (i.e., PET and MRI). However, the clinical implementation of these biomarkers is incomplete as they cannot be validated. Methods: This study aimed to overcome the limitation of using artificial intelligence along with technical tools that have been extensively investigated for AD diagnosis. For developing a promising artificial intelligence strategy that can diagnose AD early, it is critical to supervise neuropsychological outcomes and imaging-based readouts with a proper clinical review. Conclusion: Profound knowledge, a large data pool, and detailed investigations are required for the successful implementation of this tool. This review will enlighten various aspects of early diagnosis of AD using artificial intelligence. (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)
Databáze:	MEDLINE
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