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Background: Brain automatic segmentation methods have shown reliable results in measuring brain structure volumes and white matter lesions. Several studies showed that automated segmentation provides greater benefits than risks for patients affected by neurological diseases. However, few automated segmentation software are currently approved by regulatory agencies such as the US Food and Drug Administration (FDA). Here we present the methods and performance of QyScore®, an automated imaging analysis tool certified in Europe (CE marked) and US (FDA cleared) for the automatic volumetry of Grey and White Matter (GM and WM respectively) Hippocampus (HP), Amygdala (AM) and White Matter Hyperintensity (WMH). Method: A comprehensive database (N = 210) coming from several cohorts of patients with neurodegenerative diseases was established. The reliability measures considered for the validation of QyScore® imaging markers were: Dice Similarity Coefficient (DSC) and the Relative Volume Difference (RVD) for the HP, the AM, the GM and the WM volumes, DSC and the F1 metrics for the WMH. For each reliability index we identified thresholds and we hypothesized that DSC/F1 scores obtained using QyScore® markers were superior to the threshold, while RVD scores were inferior to the threshold. In addition, QyScore® performances were compared with the ones obtained from gold standard methods (manual segmentation) through regression analysis and Bland‐Altman plots. Result: Comparison with reliability measures are described in Table 1. QyScore® compared with manual segmentation methods provides reliable performances for the segmentation of the grey and white matter volumes, hippocampal and amygdala volumes, as well as white matter hyperintensity volume as described in Figure 1. Conclusion: QyScore® represents a reliable and user‐friendly tool, supporting the diagnosis made by clinicians for the early detection of neurological diseases such as MCI and AD dementia. [ABSTRACT FROM AUTHOR]

Background: Diagnostic criteria for Alzheimer's disease (AD) acknowledge a key role of imaging markers such as hippocampus and amygdala volumes, for early diagnosis. Here we aim at assessing the diagnostic accuracy of QyScore® medial temporal lobe atrophy markers in distinguishing AD dementia and Mild Cognitive Impairment (MCI) individuals from healthy controls (HC) in US and EU cohorts. Method: 3DT1 images were selected from different cohorts (ADNI, MEMORA, NACC, OASIS3) and analyzed using the QyScore® automatic segmentation pipeline. Brain volumes of the hippocampus and the amygdala were normalized to the total intracranial volume. Analyses of variance were performed to compare volumetric measurements among clinical groups. Areas under the curve were calculated to identify the diagnostic accuracy of QyScore® markers in discriminating AD and MCI from HC. Result: The sample was composed of 425 AD, 909 MCI and 993 HC with a higher prevalence of men and Apoe Ɛ4 carriers in each group. Analysis of variance showed a significant difference in hippocampal and amygdala volumes among groups. Preliminary results on the area under the curve (AUC) showed a good diagnostic accuracy in discriminating AD vs HC and MCI versus HC both for the hippocampal volumes (ADvsHC: AUC=0.86, 95%CI: 0.832‐0.877; MCIvsHC: AUC =0.75, 95%Ci: 0.726‐0.770), and the amygdala volumes (ADvsHC: AUC =0.77, 95%CI: 0.737‐0.791; MCIvsHC: AUC =0.70,95%CI 95%: 0.675‐0.720). Conclusion: Our results highlight the good diagnostic performance of QyScore® markers, and support the relevance of implementing these markers in the diagnostic workflow of AD patients. [ABSTRACT FROM AUTHOR]