Objective To observe and the clinical characteristics in children with acute traumatic bi-lateral eip dural hematomas,analyze the formation mehc ansi ms and explore the early diagnso is strategies and effective surgical treatment methods.Methods From August 2004 to December 2013,21 cases(17 males,4 females) of pediatric patients with acute traumatic bilateral epidural hematomas were treated in our hospital, and the clinical materials,imaging data and prognosis were summarized and analyzed.Results The 21 pedi-atric patients were aged from 4 months to 16 years,the mean ga e was 6.88 years.Bilateral single epidural hematoma across the superior sagittal sinus was found in 5 cases,bilateral double or more hematomas in both sides of the midline were observed in 16 cases.Initial CT scan showed simultaneous bilateral hematomas in 12 cases;while in 9 cases of bilateral hematomas were found with a delayed onset by the reviewed CT.The admitting glasgow coma score was between 13-15 in 8 cases,9-12 in 11 cases and 3-8 in 2 cases.Surgi-cal treatment was performed in 8 cases,including 7 cases with bilateral operations and 1 case with unilateral operation.Thirteen cases were managed consevr atively.Exec pt one death case, all other children were well er covered to normal lives.Conclusion Delayed contra-lateral epidural hematomas following evacuation of a prior EDH can lead to poor outcome because of usually delayed discovery.Early dynamic CT scans can detect bilateral epidural hematomas and observe their changes in time.Early detectiona nd prompt individual treat-ment should be applied in children with bilateral epidural hematomas to get good prognosis.

Objective Previous studies have shown that the triglyceride-glucose (TyG) index reflects insulin resistance and predicts the risk of ischemic stroke. Massive cerebral infarction, as a type of ischemic stroke with poor prognosis, is often more prone to complications and sequelae. However, no studies have yet explored the relationship between the TyG index and massive cerebral infarction. This study aims to investigate the role of the TyG index in assessing the infarct area in ischemic stroke. Methods This retrospective study included 212 adult patients with ischemic stroke diagnosed by cranial magnetic resonance imaging and admitted to our hospital from January 2020 to June 2024. The patients were divided into massive cerebral infarction group and non-massive cerebral infarction group according to the study purpose. Univariable and multivariable logistic regression analyses were performed on each group using SPSS 26 software. The receiver operating characteristic curves were analyzed for the prediction models. Results Variables with P＜0.2 in the univariable logistic regression analysis were included in the multivariable logistic regression analysis. The results showed that age (OR 1.043, 95% CI 1.004-1.084, P＜0.05), National Institutes of Health Stroke Scale score (OR 22.986, 95% CI 8.679-60.882, P＜0.001), and TyG index (OR 1.729, 95% CI 1.017-2.941, P＜0.05) were significant predictors. The area under the receiver operating characteristic curve of the predictive model was 0.868 (P＜0.001), indicating high predictive performance. Conclusion The TyG index is an independent risk factor for massive cerebral infarction. The combination of the TyG index with clinical laboratory indicators can effectively predict massive cerebral infarction.

Objective Previous studies have shown that the triglyceride-glucose (TyG) index reflects insulin resistance and predicts the risk of ischemic stroke. Massive cerebral infarction, as a type of ischemic stroke with poor prognosis, is often more prone to complications and sequelae. However, no studies have yet explored the relationship between the TyG index and massive cerebral infarction. This study aims to investigate the role of the TyG index in assessing the infarct area in ischemic stroke. Methods This retrospective study included 212 adult patients with ischemic stroke diagnosed by cranial magnetic resonance imaging and admitted to our hospital from January 2020 to June 2024. The patients were divided into massive cerebral infarction group and non-massive cerebral infarction group according to the study purpose. Univariable and multivariable logistic regression analyses were performed on each group using SPSS 26 software. The receiver operating characteristic curves were analyzed for the prediction models. Results Variables with P＜0.2 in the univariable logistic regression analysis were included in the multivariable logistic regression analysis. The results showed that age (OR 1.043, 95% CI 1.004-1.084, P＜0.05), National Institutes of Health Stroke Scale score (OR 22.986, 95% CI 8.679-60.882, P＜0.001), and TyG index (OR 1.729, 95% CI 1.017-2.941, P＜0.05) were significant predictors. The area under the receiver operating characteristic curve of the predictive model was 0.868 (P＜0.001), indicating high predictive performance. Conclusion The TyG index is an independent risk factor for massive cerebral infarction. The combination of the TyG index with clinical laboratory indicators can effectively predict massive cerebral infarction.

Objective To explore the accuracy of artificial intelligence-based diagnosis of ovarian malignant tumors and the identification of benign and malignant tumors under transabdominal scanning and transvaginal scanning methods. Methods A dataset of transabdominal and transvaginal two-dimensional ultrasound images was used and the images were preprocessed to enhance quality. The region of interest was segmented and divided into a training set and a test set. A convolutional neural network (CNN) was trained on the images in the training set, and the accuracy of the model on the test set was calculated. Results Transvaginal scanning was 14% more accurate in diagnosing malignant ovarian tumors than transabdo-minal scanning on the test set. For identifying the benign and malignant ovarian tumors containing cystic components, a mixture of transvaginal and transabdominal scanning increased the accuracy by 9.7% over transabdominal scanning alone. Conclusion CNN can identify ovarian malignant tumors under both scanning methods, but the accuracy of transvaginal scanning is higher than that of transabdominal scanning, and the CNN model has a higher accuracy in identifying benign and malignant ovarian tumors under transvaginal scanning.