Published in
Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring, Volume 16, Issue 4, https://doi.org/10.1002/dad2.70037
Authors
Rudolph, J. et al
Abstract
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, p < 0.05), with increased correct diagnoses (p < 0.01) and reduced errors (p < 0.03). BCR and RR showed notable performance gains (BCR: p < 0.04; RR: p < 0.02). For the diagnosis FTD, overall consensus (p < 0.01), BCNR (p < 0.02), and BCR (p < 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.
