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要旨:
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 (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).
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.
