Radiologists face an ever-growing volume of MRI scans with limited capacity to review them quickly. Delayed tumor detection directly impacts patient outcomes. Junior radiologists and clinics in under-resourced settings lacked the specialist support needed for confident neurological interpretation.

We trained a 3D convolutional neural network on a dataset of thousands of annotated MRI scans, achieving over 94% sensitivity for gliomas, meningiomas, and pituitary tumors. The model processes a full 3D MRI scan in under 90 seconds and outputs a detailed heatmap overlaid on the original scan, showing the precise location, estimated size, and shape of any detected anomalies.

In clinical validation studies, NeuroScan AI reduced average diagnostic turnaround time from 3 days to under 4 hours. False negative rates were reduced by 31% compared to baseline junior radiologist performance, and the system was accepted as a decision-support tool by two partner hospital networks.

This project can be expanded to detect additional neurological conditions, including multiple sclerosis lesions and early indicators of Alzheimer's disease. Natural language processing capabilities can also be integrated to automatically generate draft radiology reports, further reducing diagnostic workload for clinical teams.