ReviewBiomimetics (Basel, Switzerland)2023
Enhancing Fetal Anomaly Detection in Ultrasonography Images: A Review of Machine Learning-Based Approaches.
Review in Biomimetics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 2 of them syntheses that pooled it.
What it found
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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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
14 citing papers in PubMed, 2 syntheses or guidelines pooled it, 33 citations in OpenAlex.
- AI-guided meta-analysis of non-invasive prenatal testing platforms for trisomy 21 screening: comparative evaluation of cffDNA and fetal cell-based approaches.BMC pregnancy and childbirth · 2026Pooled it
- Communicating neurological prognosis in the prenatal period: a narrative review and practice guidelines.Pediatric research · 2025Guideline
- Revolutionizing cleft lip and palate management through artificial intelligence: a scoping review.Oral and maxillofacial surgery · 2025Article
- Progress in the Application of Artificial Intelligence in Ultrasound-Assisted Medical Diagnosis.Bioengineering (Basel, Switzerland) · 2025Review
- Advancements in machine learning and biomarker integration for prenatal Down syndrome screening.Turkish journal of obstetrics and gynecology · 2025Article
- Equity and timeliness as factors in the effectiveness of an ethical prenatal sequencing service: reflections from parents and professionals.European journal of human genetics : EJHG · 2025Article
- Article
- Artificial intelligence-driven framework for improving prenatal screening for congenital heart disease in rural Nebraska.Frontiers in pediatrics · 2025Article
- Research advancements in the Use of artificial intelligence for prenatal diagnosis of neural tube defects.Frontiers in pediatrics · 2025Review
- Burden of Fetal Anomalies among Antenatal Mothers Attending a Public Referral Hospital: A Mixed Cohort Study.International journal of MCH and AIDS · 2025Article
- Clinically guided adaptive contrast adjustment for fetal plane classification: a modular plug-and-play solution.Frontiers in physiology · 2025Article
- Novel neural network classification of maternal fetal ultrasound planes through optimized feature selection.BMC medical imaging · 2024Article
- Ensemble machine learning framework for predicting maternal health risk during pregnancy.Scientific reports · 2024Article
- Enhancing Obstetric Ultrasonography With Artificial Intelligence in Resource-Limited Settings.JAMA · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Fetal development is a critical phase in prenatal care, demanding the timely identification of anomalies in ultrasound images to safeguard the well-being of both the unborn child and the mother. Medical imaging has played a pivotal role in detecting fetal abnormalities and malformations. However, despite significant advances in ultrasound technology, the accurate identification of irregularities in prenatal images continues to pose considerable challenges, often necessitating substantial time and expertise from medical professionals. In this review, we go through recent developments in machine learning (ML) methods applied to fetal ultrasound images. Specifically, we focus on a range of ML algorithms employed in the context of fetal ultrasound, encompassing tasks such as image classification, object recognition, and segmentation. We highlight how these innovative approaches can enhance ultrasound-based fetal anomaly detection and provide insights for future research and clinical implementations. Furthermore, we emphasize the need for further research in this domain where future investigations can contribute to more effective ultrasound-based fetal anomaly detection.
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What OpenQuestion holds
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.