Fig. 2: Performance of the deep learning model for the detection of cognitive impairment in CSVD patients from three cohorts. | npj Digital Medicine

Fig. 2: Performance of the deep learning model for the detection of cognitive impairment in CSVD patients from three cohorts.

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

Fig. 2: Performance of the deep learning model for the detection of cognitive impairment in CSVD patients from three cohorts.The alternative text for this image may have been generated using AI.

A ROC curves of five-fold cross-validation results for diagnosing cognitive impairment in CSVD patients from the training cohort. B The validation datasets of the training cohort were stratified into different subgroups, and there was no significant difference in the detection efficacy of the model between different age levels, different education levels, different genders, different severities of WMH or patients with and without CMBs (DeLong’s test, all P > 0.05). The AUC value for cognitive impairment was significantly higher in CSVD patients without LI than in CSVD patients with LI (DeLong’s test, P < 0.001). C ROC curve for detecting cognitive impairment in CSVD patients from the hospital-based external validation cohort. D ROC curve for detecting cognitive impairment in CSVD patients from the community cohort. **p < 0.001. AUC area under the curve, CMBs cerebral microbleeds, CSVD cerebral small vessel disease, edu education, LI lacunar infarction, ROC receiver operating characteristic, WMH white matter hyperintensity. Figure 2A, C and D were created using Python (v3.11); Fig. 2B was created using GraphPad Prism 8.

Back to article page