Objective To address the limitations of conventional physics-informed neural network(PINN)in handling hemodynamic boundary constraints,an improved hard boundary-constrained PINN(HBC-PINN)framework was proposed to achieve precise prediction of blood flow fields within stenotic arteries.Methods An idealized stenosed vessel geometry model was established and computational fluid dynamic simulation was performed to obtain a validation dataset.Appropriate boundary dependent trial functions were designed according to the hard constraint method to embed the flow boundary conditions into the network output.Thus,an HBC-PINN model with the hard boundary constraint method was constructed to predict the velocity field and pressure field of stenosed blood flow.Meanwhile,an original PINN model with the soft constraint method was also built for comparison.By evaluating the accuracy of the two models on the validation dataset,the capability of the HBC-PINN model to simulate hemodynamics without using any labeled data for training was verified.Results The effectiveness of the HBC-PINN method in predicting hemodynamic parameters in stenosed blood flow tasks was validated.The relative L2 errors of the flow velocity and pressure predicted by the HBC-PINN in two different stenosis scenarios were both lower than 0.5％,representing an improvement of over 48.8％in accuracy compared to the original PINN model.Additionally,the prediction accuracy of the transverse velocity also increased by more than 35.4％.Conclusions Implementing hard constraints on boundary conditions in the PINN modeling process can effectively improve the prediction accuracy of hemodynamic parameters and the efficiency of model solving.

Objective To address the limitations of conventional physics-informed neural network(PINN)in handling hemodynamic boundary constraints,an improved hard boundary-constrained PINN(HBC-PINN)framework was proposed to achieve precise prediction of blood flow fields within stenotic arteries.Methods An idealized stenosed vessel geometry model was established and computational fluid dynamic simulation was performed to obtain a validation dataset.Appropriate boundary dependent trial functions were designed according to the hard constraint method to embed the flow boundary conditions into the network output.Thus,an HBC-PINN model with the hard boundary constraint method was constructed to predict the velocity field and pressure field of stenosed blood flow.Meanwhile,an original PINN model with the soft constraint method was also built for comparison.By evaluating the accuracy of the two models on the validation dataset,the capability of the HBC-PINN model to simulate hemodynamics without using any labeled data for training was verified.Results The effectiveness of the HBC-PINN method in predicting hemodynamic parameters in stenosed blood flow tasks was validated.The relative L2 errors of the flow velocity and pressure predicted by the HBC-PINN in two different stenosis scenarios were both lower than 0.5％,representing an improvement of over 48.8％in accuracy compared to the original PINN model.Additionally,the prediction accuracy of the transverse velocity also increased by more than 35.4％.Conclusions Implementing hard constraints on boundary conditions in the PINN modeling process can effectively improve the prediction accuracy of hemodynamic parameters and the efficiency of model solving.

Objective:To investigate the effect of long-term oral aspirin on the changes in the aneurysm sac and persistent type Ⅱ endoleak after endovascular aortic repair (EVAR) of infrarenal abdominal aortic aneurysms based on propensity score-matched analysis.Methods:A retrospective cohort study was used to analyze the clinical data of 133 patients with infrarenal abdominal aortic aneurysms treated with EVAR from January 2019 to December 2021 in the Department of Vascular Surgery, Nanjing Drum Tower Hospital. There were 113 males and 20 females, aged (74.8±7.2) years (range: 59 to 95 years). Patients were divided into the group receiving aspirin ( n=80) and the group not taking aspirin ( n=53) based on whether they took aspirin regularly for a long time after surgery. The two groups were matched in a 1∶1 ratio using propensity score matching and the caliper value was 0.05. Cumulative probability curve was plotted using the Kaplan-Meier method and the Log-rank test was used to compare the differences in primary endpoint events (enlargement of the aneurysm sac, occurrence of persistent type Ⅱ endoleak) and secondary endpoint events (adverse cardiovascular events and clinically relevant bleeding events) between the two groups. Results:The follow-up time was (38.4±11.8) months (range: 30 to 58 months). Among the 133 patients, a total of 25 cases (18.8%) suffered enlargement of the aneurysm sac, including 20 cases in the group receiving aspirin and 5 cases in the group not taking aspirin; 35 cases (26.3%) suffered persistent type Ⅱ endoleak, including 26 cases in the group receiving aspirin and 9 cases in the group not taking aspirin. Adverse cardiovascular events occurred in 11 cases (8.3%) and clinically relevant bleeding events were reported in 5 cases (3.8%). A matched cohort was established after propensity score matching, resulting in 32 cases per group. The survival analysis found that the rate of aneurysm sac enlargement was significantly higher in the group receiving aspirin than that in the group not taking aspirin (Log-rank test: P=0.010), and the incidence of persistent type Ⅱ endoleak was significantly higher than that in the group not taking aspirin (Log-rank test: P=0.019). The incidence of adverse cardiovascular events and clinically relevant bleeding events were not significantly different in two groups (Log-rank test: P=0.061, P=0.286). Conclusions:The risk of aneurysm sac expansion and persistent type Ⅱ endoleak were significantly higher in patients taking long-term aspirin after EVAR than in the group not taking asprin. Therefore, high-risk abdominal aortic aneurysm patients who are prone to aneurysm sac expansion should be evaluated in advance so that the risks and benefits of surgery can be comprehensively evaluated and treatment strategies can be optimized.

Objective:To investigate the effect of long-term oral aspirin on the changes in the aneurysm sac and persistent type Ⅱ endoleak after endovascular aortic repair (EVAR) of infrarenal abdominal aortic aneurysms based on propensity score-matched analysis.Methods:A retrospective cohort study was used to analyze the clinical data of 133 patients with infrarenal abdominal aortic aneurysms treated with EVAR from January 2019 to December 2021 in the Department of Vascular Surgery, Nanjing Drum Tower Hospital. There were 113 males and 20 females, aged (74.8±7.2) years (range: 59 to 95 years). Patients were divided into the group receiving aspirin ( n=80) and the group not taking aspirin ( n=53) based on whether they took aspirin regularly for a long time after surgery. The two groups were matched in a 1∶1 ratio using propensity score matching and the caliper value was 0.05. Cumulative probability curve was plotted using the Kaplan-Meier method and the Log-rank test was used to compare the differences in primary endpoint events (enlargement of the aneurysm sac, occurrence of persistent type Ⅱ endoleak) and secondary endpoint events (adverse cardiovascular events and clinically relevant bleeding events) between the two groups. Results:The follow-up time was (38.4±11.8) months (range: 30 to 58 months). Among the 133 patients, a total of 25 cases (18.8%) suffered enlargement of the aneurysm sac, including 20 cases in the group receiving aspirin and 5 cases in the group not taking aspirin; 35 cases (26.3%) suffered persistent type Ⅱ endoleak, including 26 cases in the group receiving aspirin and 9 cases in the group not taking aspirin. Adverse cardiovascular events occurred in 11 cases (8.3%) and clinically relevant bleeding events were reported in 5 cases (3.8%). A matched cohort was established after propensity score matching, resulting in 32 cases per group. The survival analysis found that the rate of aneurysm sac enlargement was significantly higher in the group receiving aspirin than that in the group not taking aspirin (Log-rank test: P=0.010), and the incidence of persistent type Ⅱ endoleak was significantly higher than that in the group not taking aspirin (Log-rank test: P=0.019). The incidence of adverse cardiovascular events and clinically relevant bleeding events were not significantly different in two groups (Log-rank test: P=0.061, P=0.286). Conclusions:The risk of aneurysm sac expansion and persistent type Ⅱ endoleak were significantly higher in patients taking long-term aspirin after EVAR than in the group not taking asprin. Therefore, high-risk abdominal aortic aneurysm patients who are prone to aneurysm sac expansion should be evaluated in advance so that the risks and benefits of surgery can be comprehensively evaluated and treatment strategies can be optimized.

Objective To predict the constitutive parameters of a superelastic model of plantar soft tissues based on random forest(RF)and backpropagation(BP)neural network algorithms to improve the efficiency and accuracy of the method for obtaining constitutive parameters.Methods First,a finite element model for a spherical indentation experiment of plantar soft tissues was established,and the spherical indentation experiment process was simulated to obtain a dataset of nonlinear displacement and indentation force,divided into training and testing sets.The established RF and BP neural network(BPNN)models were trained separately.The constitutive parameters of plantar soft tissues were predicted using experimental data.Finally,the mean square error(MSE)and coefficient of determination(R2)were introduced to evaluate the accuracy of the model prediction,and the effectiveness of the model was verified by comparison with the experimental curves.Results Combining the RF and BPNN models with finite element simulation was an effective and accurate method for determining the superelastic constitutive parameters of plantar soft tissues.After training,the MSE of the RF model reached 1.370 2×10-3,and R2 was 0.982 9,whereas the MSE of the BPNN model reached 4.858 1×10-5,and R2 was 0.999 3.The inverse-determined constitutive parameters of the plantar soft tissues suitable for simulation were obtained.The calculated response curves for the two predicted sets of constitutive parameters were in good agreement with the experimental curves.Conclusions The prediction accuracy for the superelastic constitutive parameters of plantar soft tissues based on an artificial intelligence algorithm model is high,and the relevant research results can be applied to study other mechanical properties of plantar soft tissues.This study provides a new method for obtaining the constitutive parameters of plantar soft tissues and helps to quickly diagnose clinical problems,such as plantar soft tissue lesions.

Objective To investigate the application of machine learning to predict the hemodynamic parameters of combined stenotic left coronary artery(LCA)aneurysms.Methods Parameterized modeling and simulation based on the geometric parameter range of combined stenosis LCA aneurysms in clinical statistics were conducted.The obtained simulation data was used as the dataset,and two common machine learning models were constructed and trained for optimization to predict two key hemodynamic parameters:wall shear stress(WSS)and pressure.By comparing and analyzing the performances of these models on the training and testing sets,the accuracy of each model was evaluated,and the effectiveness of the data-driven prediction of hemodynamic parameters for LCA aneurysms with concomitant stenosis was verified.Results The effectiveness of machine learning method in inverting the hemodynamic parameters of aneurysms was determined.For WSS prediction,the trained deep learning model and random forest model achieved mean squared error(MSE),mean absolute error(MAE),and determination coefficient R2 of 0.052 8,0.032 2,0.988 3,and 0.078 2,0.046 3,and 0.976 6,respectively.For pressure prediction,the accuracies of the deep learning models and random forest models were comparable,with MSE,MAE,and R2 of 4.67×10-6,3×10-4,0.999 7,and 1.07×10-5,5×10-4,and 0.999 3,respectively.Conclusions Machine learning methods show high accuracy in predicting the hemodynamic parameters of combined stenotic coronary artery aneurysm models.The predictive accuracy of the model,computational efficiency,and needs of the application scenarios need to be considered in machine learning prediction so that the appropriate model can be selected according to the specific situation.This study has clinical significance,helping doctors to more accurately evaluate a patient's condition and provide new ideas and method for the diagnosis and treatment of cardiovascular diseases.

Objective:To investigate the study of the correlation between C-reactive protein to albumin ratio (CAR) and restenosis after stenting in patients with lower extremity atherosclerotic occlusive disease(LEASO).Methods:The clinical data of 95 patients with LEASO admitted to the Department of Vascular Surgery of Nanjing Drum Tower Hospital from June 2020 to December 2022 were retrospectively analyzed. There were 67 males and 28 females,aged (73.1±9.4) years (range:51 to 92 years). The patients were classified into the restenosis group ( n=61) and the patency group ( n=34) according to the CT angiography results. Independent sample t test,Mann-Whitney U test and χ2 test were used to compare the data between two groups. Risk factors for restenosis after femoropopliteal artery stenting in patients with LEASO were analyzed using multivariate Cox regression. The relationship between preoperative CAR level and restenosis after stent placement was analyzed. Subject operating characteristic(ROC) curves of CAR were plotted to assess the predictive value of CAR for restenosis after stenting,and the results were expressed as area under the curve (AUC). Results:The aortoiliac calcification grade,number of stents,length of stents,C-reactive protein and CAR levels in restenosis group were higher than those in the patency group,and the serum albumin level was lower than that in the patency group(all P<0.05). And the results of multifactorial Cox regression analysis showed that higher pre-procedure CAR level and lower ABI value was an independent risk factor for in-stent restenosis. The AUC of the ROC curve for restenosis was 0.737(95% CI:0.617 to 0.856),the AUC of the ROC curve for 12-month restenosis was 0.709(95% CI:0.602 to 0.815), and the AUC of the ROC curve for 24-month restenosis was 0.702(95% CI:0.594 to 0.811). Conclusion:Higher pre-procedural CAR levels in patients with LEASO is risk factor for in-stent restenosis,and CAR has a predictive value for restenosis after lower extremity arterial stent dilatation and angioplasty.

Objective:To investigate the study of the correlation between C-reactive protein to albumin ratio (CAR) and restenosis after stenting in patients with lower extremity atherosclerotic occlusive disease(LEASO).Methods:The clinical data of 95 patients with LEASO admitted to the Department of Vascular Surgery of Nanjing Drum Tower Hospital from June 2020 to December 2022 were retrospectively analyzed. There were 67 males and 28 females,aged (73.1±9.4) years (range:51 to 92 years). The patients were classified into the restenosis group ( n=61) and the patency group ( n=34) according to the CT angiography results. Independent sample t test,Mann-Whitney U test and χ2 test were used to compare the data between two groups. Risk factors for restenosis after femoropopliteal artery stenting in patients with LEASO were analyzed using multivariate Cox regression. The relationship between preoperative CAR level and restenosis after stent placement was analyzed. Subject operating characteristic(ROC) curves of CAR were plotted to assess the predictive value of CAR for restenosis after stenting,and the results were expressed as area under the curve (AUC). Results:The aortoiliac calcification grade,number of stents,length of stents,C-reactive protein and CAR levels in restenosis group were higher than those in the patency group,and the serum albumin level was lower than that in the patency group(all P<0.05). And the results of multifactorial Cox regression analysis showed that higher pre-procedure CAR level and lower ABI value was an independent risk factor for in-stent restenosis. The AUC of the ROC curve for restenosis was 0.737(95% CI:0.617 to 0.856),the AUC of the ROC curve for 12-month restenosis was 0.709(95% CI:0.602 to 0.815), and the AUC of the ROC curve for 24-month restenosis was 0.702(95% CI:0.594 to 0.811). Conclusion:Higher pre-procedural CAR levels in patients with LEASO is risk factor for in-stent restenosis,and CAR has a predictive value for restenosis after lower extremity arterial stent dilatation and angioplasty.