Feasibility Study of Artificial Intelligence Technology to Predict Placental Insuf-ficiency
- VernacularTitle:人工智能技术预测胎盘功能不全的可行性研究
- Author:
Jieyun CHEN
1
;
Min CHEN
;
Dunjin CHEN
Author Information
1. 广州医科大学附属第三医院:产前诊断(胎儿医学)科,广东 广州 510150
- Publication Type:Journal Article
- Keywords:
Placental insufficiency;
Artificial intelligence;
Uterine artery pulsatility index;
Placental volume;
Vas-cularization flow index
- From:
Journal of Practical Obstetrics and Gynecology
2024;40(12):992-998
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To explore the feasibility of artificial intelligence(AI)technology in predicting placental in-sufficiency in the first trimester.Methods:High risk pregnant women with singleton and anterior wall placenta were selected as the research subjects.Data of ultrasound parameters were collected in the first trimester,including u-terine arterial pulsatility index(UTPI)and placental vascular hemodynamics index(PVIs).Patients that developed into preeclampsia(PE)and fetal growth restriction(FGR)were defined as the placental insufficiency group.The placental images in the first trimester were asymmetrically amplified and divided into a training set and a testing set.The placental texture features were extracted from the training set images to create a placental insufficiency machine learning model based on ultrasound imaging technology.The model was validated using the testing set images,and the diagnostic test results of ultrasound and AI were compared.Results:A total of 164 pregnant women were included,with 147 normal cases and 17 cases with placental insufficiency.The sensitivity,specificity,positive predictive value,negative predictive value,positive likelihood ratio and negative likelihood ratio of the AI test set were 73.33％,60.85％,58.93％,74.87％,1.87 and 0.44,respectively.The area under the receiver operat-ing characteristic(ROC)curve was 0.67,with an accuracy rate of 58.93％,a recall rate of 73.33％,and an F1 score of 0.65.The comparison of diagnostic test results between ultrasound and AI:AI had the highest positive predictive value(58.93％),while other diagnostic test results were lower than PVIs.Conclusions:The positive predictive value of AI technology based on placental ultrasound texture analysis is superior to ultrasound parame-ters.Given that PVIs values are easily affected by many technical or physiological parameters,AI is a promising tool for predicting placental dysfunction.
