Fig. 6: Correlations between key radiomics features and the annual rate of changes in cognition in longitudinal analysis. | npj Digital Medicine

Fig. 6: Correlations between key radiomics features and the annual rate of changes in cognition in longitudinal analysis.

From: Deep adaptive learning predicts and diagnoses CSVD-related cognitive decline using radiomics from T2-FLAIR: a multi-centre study

Fig. 6: Correlations between key radiomics features and the annual rate of changes in cognition in longitudinal analysis.The alternative text for this image may have been generated using AI.

The red dots represent subjects in the CSVD-CI group and the blue triangles represent subjects in the CSVD-nonCI group. The annual rate of changes in the MoCA was significantly related to the values of logarithm_glszm_GrayLevelNonUniformity (A), logarithm_glszm_LargeAreaEmphasis(B), logarithm_glszm_SizeZoneNonUniformity (C), logarithm_glszm_SizeZoneNonUniformityNormalized(D), logarithm_glszm_SmallAreaEmphasis (E), logarithm_glszm_ZoneEntropy(F), logarithm_glszm_ZoneVariance (G) and logarithm_glszm_ZonePercentage(H) in the CSVD-CI group. CSVD-CI cerebral small vessel disease patients with cognitive impairment, CSVD-nonCI cerebral small vessel disease patients without cognitive impairment, MoCA Montreal Cognitive Assessment. Figure 6A–H were created using GraphPad Prism 8.

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