Clinical Neuroradiology, 31(Supplement 1), 21-22. (2021)
J. Rudolph, J. Rückel, J. Döpfert, X. Ling, J. Opalka, C. Brem et al.
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.
Materials & 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 (MPRAGE) 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: mdbrain 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).
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).
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.