ArticleBMC pregnancy and childbirth2022
Automated prediction of early spontaneous miscarriage based on the analyzing ultrasonographic gestational sac imaging by the convolutional neural network: a case-control and cohort study.
Article in BMC pregnancy and childbirth, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it, 17 citations in OpenAlex.
- Artificial Intelligence in Imaging in the First Trimester of Pregnancy: A Systematic Review.Fetal diagnosis and therapy · 2024Pooled it
- Artificial Intelligence in Recurrent Pregnancy Loss: From Risk Prediction to ART Translation.Journal of clinical medicine · 2026Review
- Emerging applications of artificial intelligence for obstetric ultrasound: A scoping review.International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics · 2026Article
- Gestational sac ellipticity index measured at ≤ 7 weeks' gestation as a predictor of first-trimester embryonic demise: a retrospective cohort study.Archives of gynecology and obstetrics · 2026Article
- ProtoMAP: prototypical network based few-shot learning for missed abortion prediction.BMC medical informatics and decision making · 2025Article
- An AI method to predict pregnancy loss by extracting biological indicators from embryo ultrasound recordings in early pregnancy.Scientific reports · 2025Article
- The predictive value of ultrasound markers for pregnancy outcomes in recurrent pregnancy loss: a retrospective study.Scientific reports · 2024Article
- Deep learning-based automation for segmentation and biometric measurement of the gestational sac in ultrasound images.Frontiers in pediatrics · 2024Article
- Evolving the Era of 5D Ultrasound? A Systematic Literature Review on the Applications for Artificial Intelligence Ultrasound Imaging in Obstetrics and Gynecology.Journal of clinical medicine · 2023Review
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Authors and funding
10 authors at 3 institutions in 1 country.
Funding
Abstract
backgroundIt is challenging to predict the outcome of the pregnancy when fetal heart activity is detected in early pregnancy. However, an accurate prediction is of importance for obstetricians as it helps to provide appropriate consultancy and determine the frequency of ultrasound examinations. The purpose of this study was to investigate the role of the convolutional neural network (CNN) in the prediction of spontaneous miscarriage risk through the analysis of early ultrasound gestational sac images.
methodsA total of 2196 ultrasound images from 1098 women with early singleton pregnancies of gestational age between 6 and 8 weeks were used for training a CNN for the prediction of the miscarriage in the retrospective study. The patients who had positive fetal cardiac activity on their first ultrasound but then experienced a miscarriage were enrolled. The control group was randomly selected in the same database from the fetuses confirmed to be normal during follow-up. Diagnostic performance of the algorithm was validated and tested in two separate test sets of 136 patients with 272 images, respectively. Performance in prediction of the miscarriage was compared between the CNN and the manual measurement of ultrasound characteristics in the prospective study.
resultsThe accuracy of the predictive model was 80.32% and 78.1% in the retrospective and prospective study, respectively. The area under the receiver operating characteristic curve (AUC) for classification was 0.857 (95% confidence interval [CI], 0.793-0.922) in the retrospective study and 0.885 (95%CI, 0.846-0.925) in the prospective study, respectively. Correspondingly, the predictive power of the CNN was higher compared with manual ultrasound characteristics, for which the AUCs of the crown-rump length combined with fetal heart rate was 0.687 (95%CI, 0.587-0.775).
conclusionsThe CNN model showed high accuracy for predicting miscarriage through the analysis of early pregnancy ultrasound images and achieved better performance than that of manual measurement.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.