ArticleFrontiers in bioengineering and biotechnology2020
Grade Prediction of Bleeding Volume in Cesarean Section of Patients With Pernicious Placenta Previa Based on Deep Learning.
Article in Frontiers in bioengineering and biotechnology, 2020. 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, 18 citations in OpenAlex.
- Roles and applications of artificial intelligence in fetal and placental MRI: a literature review.BMC pregnancy and childbirth · 2026Pooled it
- Predicting placenta accreta spectrum antenatally: a deep learning-radiomics dual-model based on intraoperative clinical and histopathologic criteria.BMC medical imaging · 2026Article
- Machine Learning for Intraoperative Bleeding Prediction in Patients Undergoing Surgery: Scoping Review.JMIR medical informatics · 2026Article
- Radiomic study of antenatal prediction of severe placenta accreta spectrum from MRI.The British journal of radiology · 2024Article
- Application of intraoperative abdominal aortic balloon occlusion for bleeding during cesarean section in pernicious placenta previa.American journal of translational research · 2024Article
- Risk factors of emergency cesarean section in pregnant women with severe placenta accreta spectrum: a retrospective cohort study.Frontiers in medicine · 2023Article
- Clinical value of serum VEGF and sFlt-1 in pernicious placenta previa.Annals of medicine · 2021Article
- Prenatal prediction and typing of placental invasion using MRI deep and radiomic features.Biomedical engineering online · 2021Article
- Effect of carboprost tromethamine injection combined with modified B-lynch suture and carboprost methylate suppositories in parturients with placenta previa.American journal of translational research · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In order to predict the amount of bleeding in the cesarean section of the patients with Pernicious Placenta Previa (PPP), this study proposed an automatic blood loss prediction method based on Magnetic Resonance Imaging (MRI) uterus image. Firstly, the DeepLab-V3 + network was used to segment the original MRI abdominal image to obtain the uterine region image. Then, the uterine region image and the corresponding blood loss data were trained by Visual Geometry Group Network-16 (VGGNet-16) network. The classification model of blood loss level was obtained. Using a dataset of 82 positive samples and 128 negative samples, the proposed method achieved accuracy, sensitivity and specificity of 75.61, 73.75, and 77.46% respectively. The experimental results showed that this method can not only automatically identify the uterine region of pregnant women, but also objectively determine the level of intraoperative bleeding. Therefore, this method has the potential to reduce the workload of the attending physician and improve the accuracy of experts' judgment on the level of bleeding during cesarean section, so as to select the corresponding hemostasis measures.
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Registered trials
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.