BACKGROUND: Despite evolving treatments, functional recovery in patients with large vessel occlusion stroke remains variable and outcome prediction challenging. Can we improve estimation of functional outcome with interpretable deep learning models using clinical and magnetic resonance imaging data? METHODS: In this observational study, we collected data of 222 patients with middle cerebral artery M1 segment occlusion who received mechanical thrombectomy. In a 5-fold cross validation, we evaluated interpretable deep learning models for predicting functional outcome in terms of modified Rankin scale at 3 months using clinical variables, diffusion weighted imaging and perfusion weighted imaging, and a combination thereof. Based on 50 test patients, we compared model performances to those of 5 experienced stroke neurologists. Prediction performance for ordinal (modified Rankin scale score, 0–6) and binary (modified Rankin scale score, 0–2 versus 3–6) functional outcome was assessed using discrimination and calibration measures like area under the receiver operating characteristic curve and accuracy (percentage of correctly classified patients). RESULTS: In the cross validation, the model based on clinical variables and diffusion weighted imaging achieved the highest binary prediction performance (area under the receiver operating characteristic curve, 0.766 [0.727–0.803]). Performance of models using clinical variables or diffusion weighted imaging only was lower. Adding perfusion weighted imaging did not improve outcome prediction. On the test set of 50 patients, binary prediction performance between model (accuracy, 60% [55.4%–64.4%]) and neurologists (accuracy, 60% [55.8%–64.21%]) was similar when using clinical data. However, models significantly outperformed neurologists when imaging data were provided, alone or in combination with clinical variables (accuracy, 72% [67.8%–76%] versus 64% [59.8%–68.4%] with clinical and imaging data). Prediction performance of neurologists with comparable experience varied strongly. CONCLUSIONS: We hypothesize that early prediction of functional outcome in large vessel occlusion stroke patients may be significantly improved if neurologists are supported by interpretable deep learning models.

Background and purpose If patients presenting with large vessel occlusions (LVO) and mild symptoms should be treated with endvoascular treatment (EVT) remains unclear. Aims of this study were (1) assessing the safety and technical efficacy of EVT in patients with NIHSS < 8 as opposed to a comparison group of patients presenting with NIHSS ≥ 8 and (2) evaluation of the clinical effect of reperfusion in patients with NIHSS < 8. Methods Patients included into the retrospective multicenter BEYOND-SWIFT registry (NCT03496064) were analyzed. Clinical effect of achieving successful reperfusion (defined as modified Thrombolysis in Cerebral Infarction grade 2b/3) in patients presenting with NIHSS < 8 (N = 193) was evaluated using multivariable logistic regression analyses (displayed as adjusted Odds Ratios, aOR and 95% confidence intervals, 95%-CI). Primary outcome was excellent functional outcome (modified Rankin Scale, mRS 0–1) at day 90. Safety and efficacy of mechanical thrombectomy in patients with NIHSS < 8 was compared to patients presenting with NIHSS ≥ 8 (N = 1423). Results Among patients with NIHSS < 8 (N = 193, 77/193, 39.9% receiving pre-interventional IV-tPA), successful reperfusion was significantly related to mRS 0–1 (aOR 3.217, 95%-CI 1.174–8.816) and reduced the chances of non-hemorrhagic neurological worsening (aOR 0.194, 95%-CI 0.050–0.756) after adjusting for prespecified confounders. In interaction analyses, the relative merits of achieving successful reperfusion were mostly comparable between patients presenting with NIHSS < 8 and NIHSS ≥ 8 as evidenced by non-significantly different aOR. Interventional safety and efficacy metrics were similar between patients with NIHSS < 8 and NIHSS ≥ 8. Conclusions Achieving successful reperfusion is beneficial in patients with persisting LVO presenting with NIHSS < 8 and reduces the risk of non-hemorrhagic neurological worsening.

Background and ObjectivesEnlarged perivascular spaces (EPVSs) in the basal ganglia (BG-EPVS) are an important marker of cerebral small vessel disease (cSVD), and EPVS in the centrum semiovale (CSO-...

Background/Objectives: Minor neurocognitive disorder (minor NCD) represents a heterogeneous and potentially modifiable stage along the continuum from normal aging to dementia, offering a critical window for targeted health promotion interventions. Early identification of individuals at increased risk of progression is essential for implementing preventive strategies that may delay functional decline. This study developed a transparent machine learning (ML) framework to predict diagnostic change from minor to major NCD at 12 and 24 months using baseline demographic, clinical, and multidomain neuropsychological data. Methods: A retrospective cohort of 162 memory clinic patients was analyzed using a rigorously controlled pipeline incorporating nested stratified cross-validation, SMOTE-based imbalance correction, and sequential forward feature selection. Logistic regression, support vector machines (SVMs), and XGBoost were evaluated, with SHapley Additive exPlanations (SHAPs) applied to ensure interpretability. Results: SVM achieved the most balanced predictive performance at both 12 months (accuracy = 0.90) and 24 months (accuracy = 0.81). Short-term progression was primarily driven by subtle multidomain cognitive inefficiencies, while longer-term risk reflected continued cognitive vulnerability modulated by metabolic factors such as diabetes. Conclusions: These findings highlight the potential of explainable ML as a health promotion tool and suggest that explainable ML can uncover clinically meaningful cognitive risk signatures at the earliest stages of NCD. By identifying modifiable systemic contributors alongside cognitive risk profiles, this framework supports precision-oriented preventive strategies and proactive longitudinal monitoring in minor NCD.