Neuroscience Meets AI: Advancements in Brain Disorder Diagnosis and Treatment

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Manjit Sandhu, Animesh Dey, Raja Gulfam Shaikh, Jeewant Choudhry, Ajay Paithane, Rakesh Sahebrao, Jadhav

Abstract

The application of artificial intelligence techniques in the diagnosis and treatment of brain disorders using EHR, neuroimaging, and genomics is described. To this large dataset containing clinical histories, imaging data and genetic data of patients with Alzheimer’s disease, Parkinson’s disease, epilepsy and schizophrenia, we applied Support Vector Machines, Random Forests, k-Nearest Neighbors, Convolutional Neural Networks and Recurrent Neural Networks. The disease states were classified and forecasted using machine learning, for imaging data, Convolutional Neural Networks and for electroencephalogram signals, Recurrent Neural Networks. epilepsy:s had high diagnostic accuracy; they were even more accurate than conventional approaches in some cases, including 91 percent accuracy in the diagnosis of epilepsy; and AI-based treatment plans had a better prognosis for patients. These observations show how AI can revolutionalise neuroscience by improving the diagnostic precision and personalising the treatments. But there are some challenges that are still unsolved, for instance, the problem of data heterogeneity, the interpretability of the models, and the ethical concerns which should be addressed by researchers, clinicians, and developers of technologies. 

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