Revolutionizing Early Diagnosis: The Role of Artificial Intelligence in Neurodegenerative Disorders

Abstract

There has been an increase in the prevalence of neurodegenerative diseases among the elderly population in recent years. AI methods can help with fast and accurate diagnosis of numerous diseases, enabling quick interventions and personalized treatment plans. Predicting prognosis and disease progression, these algorithms provide valuable information on disease pathways and clinical decision making. AI powered image analysis techniques detect biomarkers linked to illnesses by recognizing subtle alterations in the brain's structure and operation. Clinical trials and drug development rely on biomarkers like these as they allow for quick intervention and provide unbiased evaluations of treatment success. AI has the great capacity to change how we identify, and treat neurodegenerative diseases, playing a crucial role in advancing successful treatments and enhancing patient care. There are few treatment choices available for numerous neurodegenerative disorders because there are not enough early diagnostic methods that enable timely administration of medications to damaged neurons before they die. Although significant progress has been achieved in neurodegenerative disease biomarkers, it is still uncertain if the biomarkers identified so far can be beneficial for early detection. Furthermore, the trustworthiness of these biomarkers has been unsatisfactory, because of the significant differences between the tissues typically utilized for biomarker identification and predominantly affected neurons. This article discusses the possible effectiveness of unusual epigenetic and resulting transcriptional changes as indicators of early neurodegenerative disease, and offers insights into finding and using these indicators in neural samples from patients to single cell level, which significantly improves the accuracy of using biomarkers.