Journal Article

Artificial intelligence substantially improves differential diagnosis of dementia-added diagnostic value of rapid brain volumetry

Peters-, Oliver, Ingrisch, Michael, Brem, Christian, Ruckel, Johannes, Scheffler, Klaus, Hesse, Nina, Rauchmann, Boris, Dopfert, Jorg, Fliessbach, Klaus, Koliogiannis, Vanessa, Laske, Christoph, Synofzik, Matthis, Dechent, Peter, Haynes, John-Dylan, Forbrig, Robert, Schneider-, Anja, Spottke, A, Ricke, Jens, Burger, Katharina, Ling, Wen Xin, Zimmermann, Hanna, Ingenerf, Maria, Ewers, Michael, Duzel, Emrah, Stocklein, Sophia, Hermann, Andreas, Flatz, Wilhelm, Solyanik, Olga, Patzig, Maximilian, Jessen, Frank, Teipelo, Stefan, Rudolph, Jan, Priller--, Josef, Wiltfang, Jens, Opalka, Jens

2021

Overview

Background: Brain volumetry is a key aspect in dementia diagnostics. We applied an artificial intelligence (AI) system based on a Convolutional Neural Network (CNN) which aims to perform lobe-separated rapid brain volumetry (< 1/2 h) of three-dimensional T1-weighted magnetic resonance imaging (MRI) with automated segmentation as well as comparison to age- and gender-adapted percentiles. Our aim was to quantify the added value in the differential diagnostics of dementia. Methods: A total of 55 patients-17 with confirmed diagnosis of Alzheimer's disease (AD), 18 with confirmed diagnosis of frontotemporal dementia (FTD) and 20 healthy controls-received T1-weighted three-dimensional magnetization prepared-rapid gradient echo (MP-RAGE) MRI. Images were retrospectively assessed by one board-certified neuroradiologist (BCNR) and two radiology residents (RR)-one of whom had received 6 months of neuroradiology training (RR1). All cases were evaluated in a two-step reading process-beginning without AI-support and followed by an AI-supported reading (AI tool: md-brain version 3.3.0). For each subject, the suspected diagnostic category (AD, FTD and healthy controls) was determined using a likelihood score (0-5), adding up to a sum of 5 for all three diagnostic categories. Individual reader performance with and without AI support was statistically evaluated using receiver operating characteristics (ROC). Results: AI support substantially improved AD diagnosis in all three readers. The effect was most pronounced for RR2 who had not undergone neuroradiology training (area under the curve [AUC] without AI support [- AI]: 0.629, AI supported [+ AI]: 0.885). But, even for the BCNR, a substantial benefit was measurable (AUCs: BCNR--AI: 0.827, + AI: 0.882; RR1--AI: 0.713, + AI: 0.834). In diagnosing FTD RR2 improved with AI support (AUCs:--AI: 0.610, + AI: 0.754), while BCNR and RR1 had comparable reading performances with and without AI support (AUCs: BCNR--AI: 0.843, + AI: 0.828; RR1--AI: 0.865, + AI: 0.868). Discussion: Even experienced BCNR can improve their diagnostic accuracy for AD by using AI based rapid brain volumetry and comparison with the age- and gender-matched reference cohorts. In diagnosing FTD, especially radiologists who are less experienced in dementia differential diagnosis can strongly benefit from AI support. Conclusion: AI support in the radiological work-up of dementia patients is feasible and can substantially improve diagnostic accuracy, which might lead to earlier diagnosis and therefore optimized patient management.

• Publisher: Springer
• Subject: Advertising executives / Alzheimer's disease / Artificial intelligence / Diagnosis / Diagnostic imaging / Magnetization / Political activity / Political aspects