ArticleScientific reports2020
Evaluation of deep convolutional neural networks for automatic classification of common maternal fetal ultrasound planes.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 67 papers, 1 of them a synthesis that pooled it.
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Who cites it
67 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial Intelligence-Assisted Echocardiographic Image-Analysis for the Diagnosis of Fetal Congenital Heart Disease: A Systematic Review and Meta-Analysis.Reviews in cardiovascular medicine · 2025Pooled it
- A wavelet-enhanced state space model for multi-disease diagnosis across chest X-ray, CT, and MRI.Quantitative imaging in medicine and surgery · 2026Article
- Artificial intelligence in assisted localization of fetal ultrasound standard planes: a narrative review.Quantitative imaging in medicine and surgery · 2026Review
- Enhanced Fetal Plane Classification in Ultrasound Imaging via Prototypical Networks and Few-Shot Learning.Journal of imaging informatics in medicine · 2026Article
- A visually grounded language model for fetal ultrasound understanding.Nature biomedical engineering · 2026Article
- Review
- FetalCLIP: a visual-language foundation model for fetal ultrasound image analysis.NPJ digital medicine · 2026Article
- Artificial intelligence-assisted quality assessment of mid-trimester ultrasound examinations using large vision-language models.BMC pregnancy and childbirth · 2026Article
- A multicentre benchmark dataset for comprehensive landmark-based fetal ultrasound biometry.Scientific reports · 2026Article
- Beyond Computer-Aided Diagnosis: Artificial Intelligence as a "Digital Mentor" for POCUS Image Acquisition and Quality Assurance: A Narrative Review.Diagnostics (Basel, Switzerland) · 2026Review
- Artificial Intelligence-Based Automated Assessment of the Four-Chamber View in Fetal Cardiac Ultrasound Videos.Bioengineering (Basel, Switzerland) · 2026Article
- FHP-ClsNet: a model for fetal head position classification based on intrapartum ultrasound.Frontiers in medicine · 2026Article
- Cross-domain dynamic routing decoders for multi-domain generalization in ultrasound imaging.Quantitative imaging in medicine and surgery · 2026Article
- SS_CASE_UNet: an attention-enhanced semi-supervised framework for fetal cerebellum segmentation in ultrasound images.Scientific reports · 2025Article
- Benchmarking Vision LLMs in fetal ultrasound interpretation: a five-point expert evaluation of standard vs. custom prompts.Biomedical engineering online · 2025Article
- Automated and accurate view classification of fetal echocardiography using convolutional neural networks.Quantitative imaging in medicine and surgery · 2025Article
- A multi-modal AI framework integrating Siamese networks and few-shot learning for early fetal health risk assessment.MethodsX · 2025Article
- On the public dissemination and open sourcing of ultrasound resources, datasets and deep learning models.NPJ digital medicine · 2025Article
- Dynamic graph-based quantum feature selection for accurate fetal plane classification in ultrasound imaging.Scientific reports · 2025Article
- From pretraining to privacy: federated ultrasound foundation model with self-supervised learning.NPJ digital medicine · 2025Article
7 more citing papers are in PubMed but not listed here.
Corrections and comments
- Erratum issued
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The goal of this study was to evaluate the maturity of current Deep Learning classification techniques for their application in a real maternal-fetal clinical environment. A large dataset of routinely acquired maternal-fetal screening ultrasound images (which will be made publicly available) was collected from two different hospitals by several operators and ultrasound machines. All images were manually labeled by an expert maternal fetal clinician. Images were divided into 6 classes: four of the most widely used fetal anatomical planes (Abdomen, Brain, Femur and Thorax), the mother's cervix (widely used for prematurity screening) and a general category to include any other less common image plane. Fetal brain images were further categorized into the 3 most common fetal brain planes (Trans-thalamic, Trans-cerebellum, Trans-ventricular) to judge fine grain categorization performance. The final dataset is comprised of over 12,400 images from 1,792 patients, making it the largest ultrasound dataset to date. We then evaluated a wide variety of state-of-the-art deep Convolutional Neural Networks on this dataset and analyzed results in depth, comparing the computational models to research technicians, which are the ones currently performing the task daily. Results indicate for the first time that computational models have similar performance compared to humans when classifying common planes in human fetal examination. However, the dataset leaves the door open on future research to further improve results, especially on fine-grained plane categorization.
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