ArticleSensors (Basel, Switzerland)2021
Deep Learning-Based Computer-Aided Fetal Echocardiography: Application to Heart Standard View Segmentation for Congenital Heart Defects Detection.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 2 of them syntheses that pooled it.
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Who cites it
22 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Effectiveness of machine learning for diagnosis and prognostic prediction of congenital heart disease: a systematic review and meta-analysis.BMC pediatrics · 2026Pooled 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
- Confounder-Matched Deep Learning on Cardiac CT for the Diagnosis of Tetralogy of Fallot: A Proof of Concept.Diagnostics (Basel, Switzerland) · 2026Article
- Neural Computing Advancements in Cardiac Imaging: A Review of Deep Learning Approaches for Heart Disease Diagnosis.Journal of imaging · 2026Review
- Deep Learning-Driven Innovations in Echocardiography: Taxonomy, Clinical Impact, Challenges, and Opportunities.Annals of biomedical engineering · 2026Review
- Article
- Role of Artificial Intelligence in Congenital Heart Disease and Interventions.Journal of the Society for Cardiovascular Angiography & Interventions · 2025Review
- Automated Assessment of the Pulmonary Artery-to-Ascending Aorta Ratio in Fetal Cardiac Ultrasound Screening Using Artificial Intelligence.Bioengineering (Basel, Switzerland) · 2024Article
- Optimizing Object Detection Algorithms for Congenital Heart Diseases in Echocardiography: Exploring Bounding Box Sizes and Data Augmentation Techniques.Reviews in cardiovascular medicine · 2024Article
- Incidence and Antenatal Detection of Congenital Heart Malformations-Data from a Tertiary Obstetric Romanian Center.Diagnostics (Basel, Switzerland) · 2024Article
- A deep learning framework for identifying and segmenting three vessels in fetal heart ultrasound images.Biomedical engineering online · 2024Article
- AI supported fetal echocardiography with quality assessment.Scientific reports · 2024Article
- Artificial Intelligence in Obstetric Anomaly Scan: Heart and Brain.Life (Basel, Switzerland) · 2024Review
- Combining deep learning and intelligent biometry to extract ultrasound standard planes and assess early gestational weeks.European radiology · 2023Article
- Enhancing Fetal Anomaly Detection in Ultrasonography Images: A Review of Machine Learning-Based Approaches.Biomimetics (Basel, Switzerland) · 2023Review
- 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
- Automatic echocardiographic anomalies interpretation using a stacked residual-dense network model.BMC bioinformatics · 2023Article
- A multicenter study on two-stage transfer learning model for duct-dependent CHDs screening in fetal echocardiography.NPJ digital medicine · 2023Article
- Review
- Artificial Intelligence in Pediatric Cardiology: A Scoping Review.Journal of clinical medicine · 2022Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Accurate segmentation of fetal heart in echocardiography images is essential for detecting the structural abnormalities such as congenital heart defects (CHDs). Due to the wide variations attributed to different factors, such as maternal obesity, abdominal scars, amniotic fluid volume, and great vessel connections, this process is still a challenging problem. CHDs detection with expertise in general are substandard; the accuracy of measurements remains highly dependent on humans' training, skills, and experience. To make such a process automatic, this study proposes deep learning-based computer-aided fetal heart echocardiography examinations with an instance segmentation approach, which inherently segments the four standard heart views and detects the defect simultaneously. We conducted several experiments with 1149 fetal heart images for predicting 24 objects, including four shapes of fetal heart standard views, 17 objects of heart-chambers in each view, and three cases of congenital heart defect. The result showed that the proposed model performed satisfactory performance for standard views segmentation, with a 79.97% intersection over union and 89.70% Dice coefficient similarity. It also performed well in the CHDs detection, with mean average precision around 98.30% for intra-patient variation and 82.42% for inter-patient variation. We believe that automatic segmentation and detection techniques could make an important contribution toward improving congenital heart disease diagnosis rates.
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