A Radiomics Model Based on CT Images Combined with Multiple Machine Learning Models to Predict the Prognosis of Spontaneous Intracerebral Hemorrhage.

OBJECTIVE: We aimed to construct 3 predictive models, including a clinical model, a radiomics model, and a combined model, to forecast the discharge prognosis of patients with intracerebral hemorrhage on admission. METHODS: A retrospective study was conducted, involving a total of 161 patients with intracerebral hemorrhage (ICH). At a ratio of 7:3, 115 of these patients were assigned to the training cohort, and 46 of these patients were assigned to the validation cohort. To produce the radionics signature and pick the features to use in its construction, the least absolute shrinkage and selection operator (LASSO) regression was applied. Five machine models were applied, and the optimal model was chosen to construct the radionics model. A clinical model was constructed using univariate and stepwise analysis to identify independent risk variables for poor outcomes at discharge. A predictive combined model nomogram was generated by integrating the clinical model and radiomics model. The performance of the nomogram was assessed in the training cohort and validated in the validation cohort. Analyses of the receiver operating characteristic curve (ROC), the calibration curve, and the decision curve were performed to assess the performance of the combined model. RESULTS: This study encompassed a cohort of 161 individuals diagnosed with intracerebral hemorrhage (ICH), consisting of 110 males and 51 females. Utilizing the modified Rankin Scale (mRS) at discharge, the analysis revealed that 89 patients (55.3%) had a good prognosis, while 72 patients (44.7%) had a poor prognosis. Only 8 out of 1130 radiomics features were selected and used in conjunction with the LR algorithm to develop the radiomics model. Sex, IVH, GCS score, and ICH volume were determined to be independent predictors of poor outcomes at the time of discharge. The AUC values of the combined model, radiomics model, and clinical model were 0.8583, 0.8364, and 0.7579 in the training cohort, and 0.9153, 0.8692, and 0.7114 in the validation cohort, respectively. The combined model nomogram exhibited good calibration and clinical benefit in both the training and validation cohorts. The decision curve analysis (DCA) displays that the combined model obtained the highest net benefit compared to the radiomics model and clinics model in the training cohort. CONCLUSIONS: The combined model demonstrates reliability and efficacy in predicting the discharge prognosis of ICH, enabling physicians to perform individualized risk assessments, and make optimal choices about patients with ICH.

Application of Esmolol to Control Heart Rate and Artificial Intelligence Reconstruction Analysis for Rapid Zero-Cooperation Emergency Coronary CTA.

Objective: To investigate a rapid and effective method for the examination of coronary CTA in emergency patients requiring coronary CTA examination, who have faster heart rate (≧80 bpm) or cannot cooperate with the examination due to the inability of breath holding at poor physical conditions. Methods: Before coronary CTA examination, with the ECG monitoring, intravenous injection of esmolol was given to achieve rapid heart rate reduction. Without the patient's cooperation, coronary CTA examination was then performed in a quick and effective manner using the 640-slice high-speed CT. The diagnosis report was obtained through the subsequent reconstruction analysis using artificial intelligence software. Conclusion: Esmolol injection can rapidly reduce the heart rate of normal people during exercise and at rest, and the steady blood concentration can be reached in 2 minutes. The half-life is about 5 minutes, with short duration and fewer side effects on patients. The diagnostic rate of coronary artery segment using (excellent + good) CTA image of the patients with esmolol and artificial intelligence analysis in the experimental group was 95.4%, while the diagnostic rate was 91.1% in the control group, and there was no significant statistical difference between the two groups (P > 0.05). Esmolol injection can rapidly reduce heart rate in patients with high heart rate, without holding breath or long-term preoperative preparation; the combination with the analysis of subsequent artificial intelligence reconstruction is a new method for rapid and effective coronary CTA examination in all patients.