Artificial intelligence–based rapid brain volumetry substantially improves differential diagnosis in dementia

Rudolph, Jan ; Rueckel, Johannes; Döpfert, Jörg; Ling, Wen Xin; Opalka, Jens; Brem, Christian; Hesse, Nina; Ingenerf, Maria; Koliogiannis, Vanessa; Solyanik, Olga; Hoppe, Boj F.; Zimmermann, Hanna; Flatz, Wilhelm; Forbrig, Robert; Patzig, Maximilian; Rauchmann, Boris‐Stephan; Perneczky, Robert; Peters, Oliver; Priller, Josef; Schneider, Anja; Fliessbach, Klaus; Hermann, Andreas; Wiltfang, Jens; Jessen, Frank; Düzel, Emrah; Buerger, Katharina; Teipel, Stefan; Laske, Christoph; Synofzik, Matthis; Spottke, Annika; Ewers, Michael; Dechent, Peter; Haynes, John‐Dylan; Levin, Johannes; Liebig, Thomas; Ricke, Jens; Ingrisch, Michael; Stoecklein, Sophia (2024): Artificial intelligence–based rapid brain volumetry substantially improves differential diagnosis in dementia. Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring, 16 (4): e70037. ISSN 2352-8729

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.

• Dokumententyp: Artikel (Klinikum der LMU)
• Organisationseinheit (Fakultäten): 07 Medizin > Klinikum der LMU München > Klinik und Poliklinik für Radiologie
• DFG-Fachsystematik der Wissenschaftsbereiche: Lebenswissenschaften
• DFG: Gefördert durch die Deutsche Forschungsgemeinschaft (DFG) - 491502892