INTRODUCTION

This study evaluates the clinical value of a deep learning-based artificial intelligence (AI) system that performs rapid brain volumetry with automatic lobe segmentation and age- and sex-adjusted percentile comparisons.

METHODS

Fifty-five patients-17 with Alzheimer's disease (AD), 18 with frontotemporal dementia (FTD), and 20 healthy controls-underwent cranial magnetic resonance imaging scans. Two board-certified neuroradiologists (BCNR), two board-certified radiologists (BCR), and three radiology residents (RR) assessed the scans twice: first without AI support and then with AI assistance.

RESULTS

AI significantly improved diagnostic accuracy for AD (area under the curve -AI: 0.800, +AI: 0.926,  < 0.05), with increased correct diagnoses ( < 0.01) and reduced errors ( < 0.03). BCR and RR showed notable performance gains (BCR:  < 0.04; RR:  < 0.02). For the diagnosis FTD, overall consensus ( < 0.01), BCNR ( < 0.02), and BCR ( < 0.05) recorded significantly more correct diagnoses.

DISCUSSION

AI-assisted volumetry improves diagnostic performance in differentiating AD and FTD, benefiting all reader groups, including BCNR.

HIGHLIGHTS

Artificial intelligence (AI)-supported brain volumetry significantly improved the diagnostic accuracy for Alzheimer's disease (AD) and frontotemporal dementia (FTD), with notable performance gains across radiologists of varying expertise levels.The presented AI tool is readily clinically available and reduces brain volumetry processing time from 12 to 24 hours to under 5 minutes, with full integration into picture archiving and communication systems, streamlining the workflow and facilitating real-time clinical decision making.AI-supported rapid brain volumetry has the potential to improve early diagnosis and to improve patient management.