N. G. Raghavendra Rao1, Gurinderdeep Singh2, Arvind R. Bhagat Patil3, T. Naga Aparna4, Shanmugam Vippamakula5, Sudhahar Dharmalingam6, D. Kumarasamyraja7, Vinod Kumar8*
1Professor, Department of Pharmaceutics, KIET School of Pharmacy, KIET Group of Institutions, Delhi-NCR, Muradnagar, Ghaziabad-201206, UP. India.
2Assistant Professor, Department of Pharmaceutical Sciences and Drug Research, Punjabi University Patiala, India.
3Dean, Yeshwantrao Chavan College of Engineering, Nagpur, India.
4Associate Professor, Sri Indu Institute of Pharmacy, Sheriguda, Ibrahimpatnam, India.
5Professor, M B School of Pharmaceutical Sciences, (Erstwhile Sree Vidyanikethan College of Pharmacy), Mohan Babu University, A.Rangampet, Tirupati - 517102, India
6Professor & Head, Department of Pharmaceutical Chemistry and Analysis, Nehru College of Pharmacy (affiliated to Kerala University of Health Sciences, Thrissur) Pampady, Nila Gardens, Thiruvilwamala, Thrissur Dist, Kerala - 680588, India.
7Professor, Department of Pharmaceutics, PGP College of pharmaceutical science and research institute, Namakkal Tamilnadu, Affiliated by The Tamilnadu Dr.M.G.R.Medical University, Chennai, Tamilnadu, India.
8Associate Professor, G D Goenka University, Gurugram, Sohna, India.
* Address for Correspondence:
Vinod Kumar
Associate Professor, G D Goenka University, Gurugram, Sohna, India.
Email: Vksingh38@yahoo.com
ORCID ID: 0000-0002-1510-8132
Abstract
The numerous and varied forms of neurodegenerative illnesses provide a considerable challenge to contemporary healthcare. The emergence of artificial intelligence has fundamentally changed the diagnostic picture by providing effective and early means of identifying these crippling illnesses. As a subset of computational intelligence, machine-learning algorithms have become very effective tools for the analysis of large datasets that include genetic, imaging, and clinical data. Moreover, multi-modal data integration, which includes information from brain imaging (MRI, PET scans), genetic profiles, and clinical evaluations, is made easier by computational intelligence. A thorough knowledge of the course of the illness is made possible by this consolidative method, which also facilitates the creation of predictive models for early medical evaluation and outcome prediction. Furthermore, there has been a great deal of promise shown by the use of artificial intelligence to neuroimaging analysis. Sophisticated image processing methods combined with machine learning algorithms make it possible to identify functional and structural anomalies in the brain, which often act as early indicators of neurodegenerative diseases. This chapter examines how computational intelligence plays a critical role in improving the diagnosis of neurodegenerative diseases such as Parkinson's, Alzheimer's, etc. To sum up, computational intelligence provides a revolutionary approach for improving the identification of neurodegenerative illnesses. In the battle against these difficult disorders, embracing and improving these computational techniques will surely pave the path for more individualized therapy and more therapies that are successful.
Keywords Neurodegenerative Disorders, Computational Biology, Technology
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Advances in Computational Biology for Diagnosing Neurodegenerative Diseases: A Comprehensive Review.pdf