Doppler Ultrasound Imaging Combined with Fetal Heart Detection in Predicting Fetal Distress in Pregnancy-Induced Hypertension under the Guidance of Artificial Intelligence Algorithm

Abstract

This study was to improve the feasibility and economic benefits of intelligent medical system Doppler ultrasound (DUS) imaging technology combined with fetal heart detection to predict the fetal distress in pregnancy-induced hypertension (PIH), so as to reduce the risk of deterioration of the patient’s condition. The characteristics of DUS images were analyzed, and a diffusion filter reducing the specificity was adopted to improve the smooth speckle noise of DUS images. 120 pregnant women in hospital were the subjects of the study, all of whom received ultrasound cord blood flow testing and fetal heart monitoring. 88 PIH patients with fetal distress were diagnosed and included in the observation group, and 32 healthy pregnant women tested during the same period were identified as the control group. Clinical data were reviewed and analyzed. The diagnostic rates of fetal distress by simple fetal heart monitoring and DUS detection combined with fetal heart monitoring were compared. The results showed that 26.7% of fetal distress were diagnosed by fetal heart monitoring alone, and 73.3% of fetal distress were diagnosed by combined testing, so the diagnostic accuracy of the combined detection method was greatly higher than the single fetal heart detection ( P < 0.05 ). The Pulsatility index (PI), resistance index (RI), and S/D values detected by the umbilical artery in the observation group were 1.48, 0.85, and 4.31, respectively. The PI, RI, and S/D values detected by the umbilical artery in the control group were 0.96, 0.64, and 3.59, respectively. The results of arterial detection were significantly higher than those of the control group, and the difference was of significant scientific significance ( P < 0.05 ). In summary, the PI and RI values of the middle cerebral artery (MCA) detected by DUS diagnosis can effectively reflect the current status of the fetus in the uterus and reduce the mortality of the fetus. The images guided by DUS imaging technology can clearly show the current status of the fetus in the uterus, effectively improve the medical diagnostic efficiency, and have important reference value for the development of intelligent medical equipment.

Development of Artificial Intelligence Image Classification Models for Determination of Umbilical Cord Vascular Anomalies

“…7,9 Next, AI and ML have been applied to measurement and fetal risk assessments. 6,9,37 Finally, there was an AI model looking at umbilical cord coiling but when analyzing the figures included both 2v and 3v cords to classify coiling index. 8 The inclusion of 2v umbilical cords to calculate coiling index could skew results.…”

“…First, there has been segmentation or “coloring” of the different anatomies to aid in identification and quantification 7,9 . Next, AI and ML have been applied to measurement and fetal risk assessments 6,9,37 . Finally, there was an AI model looking at umbilical cord coiling but when analyzing the figures included both 2v and 3v cords to classify coiling index 8 .…”

“…Current applications of AI/ML include: predicting fetal distress in pregnancy-induced hypertension, classification of amniotic fluid levels, fetal anatomy colorization, and the umbilical coiling index. 2,[6][7][8][9] A practical application of AI image classification should involve increased maternal-fetal risk and be of added benefit to sonographers and obstetricians. Therefore, a clinical exemplar using standard ultrasound images that correlates to increased maternal-fetal risks should be pursued.…”

“…Some of these models are simple mathematical equations while others use automated feedback loop mechanisms originally theorized several decades ago. Current applications of AI/ML include: predicting fetal distress in pregnancy‐induced hypertension, classification of amniotic fluid levels, fetal anatomy colorization, and the umbilical coiling index 2,6–9 . A practical application of AI image classification should involve increased maternal‐fetal risk and be of added benefit to sonographers and obstetricians.…”

A deep learning mixed-data type approach for the classification of FHR signals

“…The contribution of Su Liu et al ( Liu S et al, 2021 ) stresses the importance of integrating echo images with cardiotocographic data for improving the classification of fetal states. The goal of the study was to improve the feasibility and economic benefits of an artificial intelligence based medical system when Doppler ultrasound (DUS) imaging technology are combined with fetal heart detection to predict the fetal distress in pregnancy-induced hypertension (PIH).…”