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Artificial Intelligence–based Rapid Brain Volumetry Substantially Improves Differential Diagnosis in Dementia

MCML Authors

Boj Friedrich Hoppe

Dr.

→ Group Michael Ingrisch
Clinical Data Science in Radiology

Michael Ingrisch

Prof. Dr.

Core PI

Clinical Data Science in Radiology

Abstract

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.

article RRD+24

Alzheimer's and Dementia

16.e70037. Oct. 2024.

Authors

J. Rudolph • J. Rueckel • J. Döpfert • W. X. Ling • J. Opalka • C. Brem • N. Hesse • M. Ingenerf • V. Koliogiannis • O. Solyanik • B. F. Hoppe • H. Zimmermann • W. Flatz • R. Forbrig • M. Patzig • B.-S. Rauchmann • R. Perneczky • O. Peters • J. Priller • A. Schneider • K. Fliessbach • A. Hermann • J. Wiltfang • F. Jessen • E. Düzel • K. Buerger • S. Teipel • C. Laske • M. Synofzik • A. Spottke • M. Ewers • P. Dechent • J.-D. Haynes • J. Levin • T. Liebig • J. Ricke • M. Ingrisch • S. Stoecklein