Exploring AI’s Role in Managing Neurodegenerative Disorders: Possibilities and Hurdles

Abstract

Neurodegenerative diseases are an ever-growing type of a disease which is very common in elderly people. With the increase in the population of older people (aged 65 and above), these diseases have increased in number by a considerable rate. Currently there is no cure for neurodegenerative diseases. A patient who has developed one of these diseases is diagnosed and given certain medications which can help them to slow down the symptoms, but the diseases are incurable. The diseases include Alzheimer’s disease, Parkinson’s disease, Lewy body dementia, frontotemporal disease, multiple sclerosis, amyotrophic lateral sclerosis, Huntington’s disease, Chromatic Traumatic Encephalopathy, progressive supranuclear palsy and others. These diseases include various symptoms like depression, anxiety, losing control over muscle movement, degradation in performance, eventually depending on others for daily tasks, difficulty in remembering things and several other symptoms. Diagnosing these diseases is a long and tedious method and it requires a lot of money. Identification of these type of diseases is itself a big task and different expensive tests like CT scan and MRI scan are prescribed to the patients to find out the type of biomarker present in their brain. After analysis the type of biomarker and why the nerve cells in the brain and the spinal cord are degrading, doctors conclude about the type of disease a patient might be suffering from. Several drugs are prescribed to the patients based on their condition. One type of a drug may not suit two patients suffering from the same type of disease. Hence, finding out the type of disease a patient is going through along with the type of medicine that might suit them is not an easy task. Hence, with the help of AI, researchers are trying to find ways to diagnose and prescribe the patients in an easy, cost-effective manner with less invasiveness. Machine Learning model like Random Forest comes handy when trying to identify the type of marker present in the brain cells of the patients. With the help of the already existing datasets along with the diagnosis data of the patient, the model can easily predict about the type of biomarker present thereby giving an easy and mostly accurate result about the type of biomarker present. Random forest algorithm, along with deep learning modules is helping to predict and give faster and accurate results on the type of diseases. These models are rigorously being trained with every time new datasets being added, which gives us more accurate results. With the help of AI, automated drug prediction for patients, early diagnosis, biomarker prediction, course and progression of a disease can be predicted easily and with the advancement in research, AI is proving to be promising enough so become more and more efficient and reliable. Using AI in the field of neurodegenerative diseases has made it a lot easier to monitor the diseases and help deal with them in a better manner. Using Ensemble based models and ADNI databases which offers multi-modal data, makes it easier for AI to predict neurodegenerative diseases at an early stage, find the diseases’ course and progression, and also prescribe the patients with the type of drugs that will suit their body. However, as there as many opportunities to use AI in the field of neurodegenerative diseases, it also has its own challenges.