Bridging AI and Neurology: Deep Learning for Accurate Diagnosis and Personalised Recommendations

One such degenerative ailment is Parkinson's disease (PD) which is a brain disorder that progresses as an individual ages and affects many across the globe. Hence, early diagnosis and detection of the condition are necessary to help in the management of its symptoms and the speed at which it advances. This project proposes the development of a system for diagnosing PD and analysis of its symptoms via the imaging techniques and other patient data bereft of the biological examination. The system first employs a Convolutional Neural Network (CNN) algorithm to analyze certain brain images (MRI) for diagnosis of Parkinson's. Upon identification of the disease, the system also looks into the symptoms of the patient to grade the severity of the disease from early stages to more advanced cases. Moreover, it specifies peculiar types of parkinsonism, which can be characterized as a dominating tremor type, rigidity type, or mixed-parkinsonism. It also recommends treatment options and interventions, preventative measures, and other health professionals in order to help the health worker put together a total care package for the patient. This concept seeks to combine a typical imaging paradigm and symptom assessment so as to enhance the diagnosis and management of PD. Thus increasing the prognosis as well as the quality of the patient's life.