Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Clinical validation of artificial intelligence-based single-subject morphometry without normative reference database
1
Zitationen
6
Autoren
2025
Jahr
Abstract
BACKGROUND: validated a convolutional neural network (CNN)-based VBM which does not rely on a normative reference database. OBJECTIVE: Clinical validation of CNN-based VBM. METHODS: F-fluorodeoxyglucose (FDG) served as reference standard. RESULTS: Repeated-measures ANOVA revealed a significant impact of the VBM method on the visual detection of any neurodegenerative disease (p < 0.001). Balanced accuracy/sensitivity/specificity were 80.4/86.3/74.5% for CNN-based VBM versus 75.7/79.5/71.8% for conventional VBM. Differentiation between AD and non-AD typical atrophy patterns did not differ between both VBM methods (p = 0.871). CONCLUSIONS: CNN-based VBM provides clinically useful accuracy for the detection of neurodegeneration-suspect atrophy with higher sensitivity than conventional VBM with a mixed-scanner normative reference database and without compromising specificity.
Ähnliche Arbeiten
Ultrastructural Characterization of the Lower Motor System in a Mouse Model of Krabbe Disease
2016 · 13.268 Zit.
Staging of brain pathology related to sporadic Parkinson’s disease
2002 · 10.634 Zit.
Accuracy of clinical diagnosis of idiopathic Parkinson's disease: a clinico-pathological study of 100 cases.
1992 · 10.391 Zit.
α-Synuclein in Lewy bodies
1997 · 8.302 Zit.
Mutation in the α-Synuclein Gene Identified in Families with Parkinson's Disease
1997 · 8.199 Zit.