Artificial Intelligence in Neurology: A Research Study on Machine Learning Applications in Brain Imaging, Diagnosis, and Prognosis

Abstract

Background: Neurological diseases present a heavy healthcare burden, and traditional diagnosis techniques are limited by accuracy and speed. Artificial intelligence, and especially machine learning, provides novel solutions for the improvement of brain imaging interpretation and prognosis forecasting.

Objective: To assess the effectiveness of machine learning models as a neurological imaging diagnostic and prognostic tool with special attention to statistical reliability, interpretability, and accuracy.

Methods: Brain imaging datasets were preprocessed, segmented, and enhanced using MRI, fMRI, CT, and PET.   CNNs, RNNs, SVMs, and ensemble solutions in the accepted practices were trained and checked.   Performance was measured by accuracy, F1-scores, measures of interpretability, and statistical significance testing.

Results: With testing accuracy of 89.5%, validation accuracy of 90.2%, and training accuracy of 95.6%, CNNs excelled among the models.  Other methods were beaten by F1-scores of 0.91.  With heatmaps in stroke datasets overlapping up to 91.5%, visualization aided interpretability.  With statistical analysis (p < 0.001), a +12.4% boost over baselines was seen. Clinical utility at the individual case level was demonstrated, with 90.7% agreement with radiologists and 92.1% accuracy in stroke predictions.

Conclusion: The CNN-based approaches to machine learning, i.e., provide helpful, comprehensible, and practical solutions to neurological imaging.   The models enhance the accuracy of the diagnosis, early disease identification, and prognostic decision-making.   The application of artificial intelligence to clinical neurology is a breakthrough solution to the problems of bias, data heterogeneity, compliance, and the enhancement of patient care.