Neural Network Analysis of MRI Scans for FND Diagnosis
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Background Functional Neurological Disorder (FND) currently lacks a definitive method of diagnosis, leading to an extremely high rate of misdiagnosis. Methods This project aimed to address the question of improving diagnostic accuracy for FND by utilizing logistic regression models and neural networks, integrating patient MRI data and clinical history to differentiate FND from other neurological disorders. MRI scans were first pre-processed through noise reduction and feature engineering, and then used to train two types of models: logistic regression for general neurological disorder classification and a neural network specifically for FND diagnosis. The diagnostic performance was measured using the ROC AUC metric, with additional evaluation through accuracy, precision, recall, and the F1 score. Results & Conclusions By targeting the most relevant variables from the MRI data, both models demonstrated high efficacy, with the neural network showing a 92% accuracy rate in FND classification.
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