Schizophrenia Diagnosis

Schizophrenia diagnosis is a complex challenge due to the disorder's heterogeneous symptoms and lack of objective biomarkers. Current research heavily utilizes machine learning, employing diverse model architectures such as convolutional neural networks (CNNs), recurrent neural networks (RNNs, particularly LSTMs), transformers, and generative adversarial networks (GANs) to analyze multimodal data including EEG, MRI (structural and functional), handwriting samples, speech, and even social media text. These advanced computational methods aim to improve diagnostic accuracy and provide insights into the underlying neurobiological mechanisms of schizophrenia, potentially leading to earlier and more precise interventions. The ultimate goal is to develop more reliable and accessible diagnostic tools to improve patient care and outcomes.