Fetal Cerebral Hemodynamic Changes in Preeclampsia Patients by Ultrasonic Imaging under Intelligent Algorithm

Abstract

This study was aimed at evaluating the adoption value of ultrasound imaging features on fetal cerebral hemodynamics in preeclampsia patients based on the partial difference algorithm and the hybrid segmentation network (HSegNet) algorithm. Forty pregnant women with preeclampsia diagnosed by ultrasound examination were selected as the research objects, and another forty normal pregnant women were selected as the control. Then, by using the partial differential algorithm, the imaging of fetal cerebral hemodynamics in preeclampsia patients was enhanced and optimized, and the general clinical data and experimental results were collected. The results showed that the automatic labeling of fetal cerebral artery in fetal middle cerebral artery (MCA) hemodynamic images was realized by HSegNet algorithm model, and the final accuracy was 97.3%, which had a good consistency with the manual annotation of doctors. Education level was a protective factor for preeclampsia (odds ratio (OR) = 0.535). Body mass index (BMI) and family history of hypertension during pregnancy were independent risk factors for preeclampsia (OR = 1.286, and 2.774, respectively). MCA end-diastolic volume (EDV) of preeclampsia fetuses was higher than that of normal fetuses. The MCA systolic-diastolic ratio (S/D), the pulsatility index (PI), and the resistive index (RI) in the preeclampsia group were significantly lower than those in the normal pregnancy group. The results showed that MCA PI, MCA RI, and MCA S/D had certain predictive values for the occurrence of adverse pregnancy outcomes ( $P<0.05$ ). In summary, the intelligent algorithm-based fetal MCA hemodynamic ultrasound image in the study could effectively predict pregnancy outcomes of patients and provide certain theoretical support for the subsequent reduction of adverse pregnancy outcomes in patients with preeclampsia.