Spontaneous Intracerebral hemorrhage (sICH) is a disease with high mortality and disability. Non-contrast computed tomography (NCCT) is the most commonly used imaging method in the diagnosis and treatment of sICH. This study aimed to develop a clinically useful prediction model for the short-term prognosis of sICH patients based on NCCT features using a machine learning model. We retrospectively collected data from sICH patients from four centers in China between January 2021 and June 2024, used data from three centers as training cohort to build the model, and another single center data for external validation. The NCCT imaging features were combined with the basic clinical characteristics of sICH patients as training features for machine learning. We developed and verified the effectiveness of five models: support vector machine (SVM), logistic regression (LR), random forest (RF), eXtreme Gradient Boosting (XGboost) and Light Gradient Boosting Machine (LightGBM). The clinical feature set, NCCT imaging feature set and fusion feature set were modeled separately and externally validated. The performance of machine learning models with different features was comprehensively evaluated using ROC curves, accuracy and other related indicators. The SHapley Additive exPlanations (SHAP) diagram was used to illustrate the importance of variables in the model, and the Sequential Forward Selection (SFS) was used to screen out the core features. Finally, a convenient and practical prognosis prediction platform was built based on the core features. This study is registered with ClinicalTrials.gov (NCT06535438). A total of 1091 sICH patients from three centers were included as the training cohort, and 102 patients from a single center were included as the external validation cohort. The LightGBM model showed the best performance in predicting the short-term prognosis of sICH patients, with an area under the receiver operating characteristic curve (AUROC) of 0.813 ± 0.012. The clinical feature cohort model (AUC: 0.822, 95% CI (0.763-0.881)), the NCCT imaging feature model (AUC: 0.770, 95% CI (0.704-0.835)) and the fusion model (AUC: 0.852, 95% CI (0.797-0.906)) were developed respectively. The external validation cohort were the clinical feature model (AUC: 0.792, 95% CI (0.689-0.894)), the NCCT imaging feature model (AUC: 0.746, 95% CI (0.637-0.855)), and the fusion feature (AUC: 0.796, 95% CI (0.694-0.897). Finally, the core factors obtained through screening, including Glasgow Coma Scale (GCS) score at admission, intraventricular hemorrhage (IVH), National Institutes of Health Stroke Scale (NIHSS) score at admission, hematoma volume, mean CT value, and black hole sign were incorporated into the model to generate a publicly accessible online platform (https://surge-ustc.shinyapps.io/multi_para_sih_prognosis/). The prediction model based on NCCT features established by the LightGBM model has a reliable predictive effect on the short-term prognosis of sICH patients and is of great clinical convenience and practicality. Funding provided by National Natural Science Foundation of China (82427808, 82171382, U23A20424, 82090034 and 82371201), the Anhui Province Clinical Medical Research Transformation Special Project (202204295107020006 and 202204295107020028) and Research Fund of Anhui Institute of translational medicine (2022zhyx-B11).

Background: White matter hyperintensities (WMH) are recognized as important imaging biomarkers associated with cognitive impairment. This study aimed to develop a machine learning model that combines clinical data and WMH radiomic features for improving the assessment of cognitive impairment Methods: A retrospective study was conducted on 303 patients with WMH. Clinical data and magnetic resonance imaging scans were collected, and cognitive function was evaluated using the Montreal Cognitive Assessment (MoCA). WMH lesions were segmented on T2 Fluid-Attenuated Inversion Recovery images using the SAM2UNET model, followed by the extraction of radiomic features from the segmented WMH regions. After feature selection with LASSO and recursive feature elimination (RFE), six machine learning models were developed, and the optimal model was identified. SHapley Additive exPlanations (SHAP) were applied to enhance the interpretability of the model. Model performance was evaluated using metrics such as the area under the receiver operating characteristic curve (AUROC), accuracy, precision, recall, and F1-score. Results: The integrated TabPFN model, utilizing 10 clinical and 5 radiomic features, achieved the superior overall predictive performance. The model yielded an AUROC of 0.842, an F1-score of 0.737, an accuracy of 0.754, a recall of 0.750, a precision of 0.724, and a specificity of 0.758, respectively. Calibration and decision curve analyses indicated good agreement and favorable clinical utility of the model in assessing cognitive impairment. Conclusion: This study established a reliable and interpretable TabPFN model integrating routine clinical and radiological data, offering a promising tool for the early detection and personalized management of cognitive impairment in community populations.

Background: Cognitive impairment is a prevalent condition among middle-aged and older adults and often progresses to dementia, posing substantial clinical and societal burdens. Early assessment of high-risk individuals is essential for timely intervention and management. This study aimed to develop a practical nomogram for the assessment of cognitive impairment in community-dwelling elderly populations. Methodology: This cross-sectional study recruited 581 participants between October 23 and November 8, 2023, comprising 465 assigned to the training cohort and 116 to the validation cohort. Demographic information, medical history, lifestyle, and biochemical parameters were collected using structured questionnaires. Cognitive impairment was assessed via the Montreal Cognitive Assessment (MoCA). Independent features were identified using LASSO regression followed by binary logistic regression, and a nomogram was constructed based on these variables. Model performance was evaluated by discrimination, calibration, and clinical utility using Receiver Operating Characteristic (ROC) curves, calibration plots, the Hosmer–Lemeshow test, and Decision Curve Analysis (DCA). Results: Cognitive impairment prevalence was 38.5% in the training and 32.8% in the validation cohort. Six features—sex, age, systolic blood pressure, homocysteine, fruit consumption, and family history of stroke—were integrated into the nomogram. The model demonstrated good discrimination (AUC 0.816 in training cohort; 0.796 in validation cohort) with satisfactory calibration and clinical applicability. Conclusion: The proposed nomogram provides a reliable and convenient tool for the early risk assessment of cognitive impairment in middle-aged and older adults, facilitating targeted prevention and personalized management in clinical and community settings. Its implementation may assist healthcare professionals in identifying high-risk individuals and mitigating progression toward dementia.