Artificial Intelligence and Machine Learning Models for Diagnosing Neurodegenerative Disorders

Abstract

Gradual loss of neuron tissues lying within brain causes abnormality in cognitive and motor which are further responsible for developing neurodegenerative disorders. With the increasing prevalence of these disorders, there is a growing need for accurate and reliable diagnosis, as well as effective treatment strategies. Artificial Intelligence and Machine learning demonstrated great improvement in diagnosing such disorders. Keeping such scenario in mind, AI and ML models can be trained to analyze large datasets of medical imaging and clinical data to identify patterns and biomarkers associated with neurodegenerative disorders. These models can also be used to predict disease progression and response to treatment, enabling personalized care for patients. Some of the Artificial Intelligence (AI) and Machine Learning (ML) models that have been developed for neurodegenerative disorders include deep learning algorithms, graphical convolutional networks etc. for analyzing a variety of data, including structural and functional neuroimaging, genomic data, and electronic health records. While these models have shown promise in improving the diagnosis and management of neurodegenerative disorders, there are also challenges that need to be addressed. These include issues related to data quality, model interpretability, and ethical considerations. Overall, AI and ML models have the potential to revolutionize the field of neurodegenerative disorders, providing clinicians with new tools to improve patient outcomes and enhance our understanding of these devastating diseases.