An Engineering Framework for NDD and NDEVD Identification in the Human Brain: Critical Analysis and its Treatment Through AI Therapy

Stressful daily routine and fast moving lifestyle have resulted in the brain malfunctionality leading to a global healthcare problem. This situation has generated the need to understand the brain structure and its functionalities in the context of health, diseases and disorders.Identification of brain malfunctionality is a critical area of neuroscience and medical diagnostics research. An array of disease identification and advanced methods including neuroimaging, genetic testing, biomarker detection, and machine learning methods have been recognized.Neuroimaging devices offer cross-sectional vision, generating high-resolution structural and functional information regarding brain abnormalities.They speed up the early diagnosis of diseases such as Alzheimers, Parkinsons, and brain tumors. Genetic research identifies mutations, and susceptibility genes to determine neurodegenerative or neuropsychiatry disorders.Molecular and biochemical markers, offer non-invasive diagnostic tools to closely follow disease progression and treatment response. Machine Learning Algorithms, especially Deep-Learning are immensely useful in analyzing large neuroimaging data, enhancing diagnostic accuracy and forecasting patient outcomes.Brought together in synergy, these methods by clinicians, medical scientists, and pedagogues have devised a prototype that can enable early detection, improved diagnostic specificity, by obviating misdiagnosis and providing customized treatments.This kind of framework is helpful in identifying new diseases and their symptoms for improved diagnosis and improved patient care.