ArticleIEEE transactions on ultrasonics, ferroelectrics, and frequency control2021
Fully Automated 3-D Ultrasound Segmentation of the Placenta, Amniotic Fluid, and Fetus for Early Pregnancy Assessment.
Article in IEEE transactions on ultrasonics, ferroelectrics, and frequency control, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05326191 (First-trimester Placental Ultrasound Study), which is not on this map. Cited by 18 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
First-trimester Placental Ultrasound Study (First PLUS): an Observational Cohort Study to Assess the Clinical Utility of the OxNNet Toolkit for the Prediction of Adverse Pregnancy Outcomes.
Who cites it
18 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Ultrasound evaluation of placental volume and placental vascular indices in women developing hypertensive disorders in pregnancy compared to controls: a systematic review and meta-analysis.European heart journal. Quality of care & clinical outcomes · 2026Pooled it
- Artificial Intelligence in Imaging in the First Trimester of Pregnancy: A Systematic Review.Fetal diagnosis and therapy · 2024Pooled it
- Reference charts for first-trimester placental three-dimensional fractional moving blood volume derived using OxNNet.Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology · 2026Observational
- First-trimester Placental Ultrasound (FirstPLUS) study: prediction of fetal growth restriction using OxNNet-derived first-trimester placental volume.Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology · 2026Observational
- Reference charts for first-trimester placental volume derived using OxNNet.Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology · 2025Observational
- An AI method to predict pregnancy loss by extracting biological indicators from embryo ultrasound recordings in early pregnancy.Scientific reports · 2025Article
- Automatic Human Embryo Volume Measurement in First Trimester Ultrasound From the Rotterdam Periconception Cohort: Quantitative and Qualitative Evaluation of Artificial Intelligence.Journal of medical Internet research · 2025Article
- PRISM Lite: A lightweight model for interactive 3D placenta segmentation in ultrasound.Proceedings of SPIE--the International Society for Optical Engineering · 2025Article
- 3D Single Vessel Fractional Moving Blood Volume (3D-svFMBV): Fully Automated Tissue Perfusion Estimation Using Ultrasound.IEEE transactions on medical imaging · 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
- The human placenta project: Funded studies, imaging technologies, and future directions.Placenta · 2023Review
- AFNet Algorithm for Automatic Amniotic Fluid Segmentation from Fetal MRI.Bioengineering (Basel, Switzerland) · 2023Article
- Multi-centre deep learning for placenta segmentation in obstetric ultrasound with multi-observer and cross-country generalization.Scientific reports · 2023Article
- Deep learning based automatic segmentation of the placenta and uterine cavity on prenatal MR images.Proceedings of SPIE--the International Society for Optical Engineering · 2023Article
- Placenta segmentation in ultrasound imaging: Addressing sources of uncertainty and limited field-of-view.Medical image analysis · 2023Article
- Toward deep observation: A systematic survey on artificial intelligence techniques to monitor fetus via ultrasound images.iScience · 2022Article
- Amniotic Fluid Classification and Artificial Intelligence: Challenges and Opportunities.Sensors (Basel, Switzerland) · 2022Review
- A 10-Year Retrospective Review of Prenatal Applications, Current Challenges and Future Prospects of Three-Dimensional Sonoangiography.Diagnostics (Basel, Switzerland) · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
Volumetric placental measurement using 3-D ultrasound has proven clinical utility in predicting adverse pregnancy outcomes. However, this metric cannot currently be employed as part of a screening test due to a lack of robust and real-time segmentation tools. We present a multiclass (MC) convolutional neural network (CNN) developed to segment the placenta, amniotic fluid, and fetus. The ground-truth data set consisted of 2093 labeled placental volumes augmented by 300 volumes with placenta, amniotic fluid, and fetus annotated. A two-pathway, hybrid (HB) model using transfer learning, a modified loss function, and exponential average weighting was developed and demonstrated the best performance for placental segmentation (PS), achieving a Dice similarity coefficient (DSC) of 0.84- and 0.38-mm average Hausdorff distances (HDAV). The use of a dual-pathway architecture improved the PS by 0.03 DSC and reduced HDAV by 0.27 mm compared with a naïve MC model. The incorporation of exponential weighting produced a further small improvement in DSC by 0.01 and a reduction of HDAV by 0.44 mm. Per volume inference using the FCNN took 7-8 s. This method should enable clinically relevant morphometric measurements (such as volume and total surface area) to be automatically generated for the placenta, amniotic fluid, and fetus. The ready availability of such metrics makes a population-based screening test for adverse pregnancy outcomes possible.
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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.