首页|Hebei North University Reports Findings in Pulmonary Embolism (Prediction of sho rt-term adverse clinical outcomes of acute pulmonary embolism using conventional machine learning and deep Learning based on CTPA images)
Hebei North University Reports Findings in Pulmonary Embolism (Prediction of sho rt-term adverse clinical outcomes of acute pulmonary embolism using conventional machine learning and deep Learning based on CTPA images)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Lung Diseases and Cond itions - Pulmonary Embolism is the subject of a report. According to news report ing out of Hebei, People’s Republic of China, by NewsRx editors, research stated , “To explore the predictive value of traditional machine learning (ML) and deep learning (DL) algorithms based on computed tomography pulmonary angiography (CT PA) images for short-term adverse outcomes in patients with acute pulmonary embo lism (APE). This retrospective study enrolled 132 patients with APE confirmed by CTPA.” Our news journalists obtained a quote from the research from Hebei North Univers ity, “Thrombus segmentation and texture feature extraction was performed using 3 D-Slicer software. The least absolute shrinkage and selection operator (LASSO) a lgorithm was used for feature dimensionality reduction and selection, with optim al l values determined using leave-one-fold cross-validation to identify texture features with non-zero coefficients. ML models (logistic regression, random for est, decision tree, support vector machine) and DL models (ResNet 50 and Vgg 19) were used to construct the prediction models. Model performance was evaluated u sing receiver operating characteristic (ROC) curves and the area under the curve (AUC). The cohort included 84 patients in the good prognosis group and 48 patie nts in the poor prognosis group. Univariate and multivariate logistic regression analyses showed that diabetes, RV/LV 1.0, and Qanadli index form independent ri sk factors predicting poor prognosis in patients with APE(P <0.05). A total of 750 texture features were extracted, with 4 key features iden tified through screening. There was a weak positive correlation between texture features and clinical parameters. ROC curves analysis demonstrated AUC values of 0.85 (0.78-0.92), 0.76 (0.67-0.84), and 0.89 (0.83-0.95) for the clinical, text ure feature, and combined models, respectively. In the ML models, the random for est model achieved the highest AUC (0.85), and the support vector machine model achieved the lowest AUC (0.62). And the AUCs for the DL models (ResNet 50 and Vg g 19) were 0.91 (95%CI: 0.90-0.92) and 0.94(95%CI: 0.9 3-0.95), respectively. Vgg 19 model demonstrated exceptional precision (0.93), r ecall (0.76), specificity (0.95) and F1 score (0.84).”
HebeiPeople’s Republic of ChinaAsiaCardiovascular Diseases and ConditionsCyborgsEmbolismEmbolism and Thrombo sisEmerging TechnologiesHealth and MedicineLung Diseases and ConditionsM achine LearningPulmonary EmbolismRespiratory Tract Diseases and ConditionsRisk and PreventionSupport Vector MachinesVascular Diseases and ConditionsVector Machines
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