Evidence map›Paper›PMID 42866293›Full record

ReviewAnnals of medicine and surgery (2012)2026

Advancements in echocardiography for congenital heart defects (CHDs): a literature review.

Muhammad Abdullah Humayun, Carla Jammal, Anita Zhou, Ahila Ali, Hamza Maqbool, Aoun Zaib Khan, Amna Zaheer, Jamuna Shreshtha, Anum Akbar

Abstract readReview
In one paragraph

Review in Annals of medicine and surgery (2012), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Muhammad Abdullah HumayunDepartment of Informatics, Mayo Clinic, Arizona, USA.
Carla JammalMaster of Public Health (MPH), University of Warwick, Warwick Medical School, Coventry, UK.
Anita ZhouDepartment of Pediatrics, University of Nebraska Medical Center, Omaha, Nebraska, USA.
Ahila AliDepartment of Pediatrics, Dow Medical College, Karachi, Pakistan.
Hamza MaqboolDepartment of Pediatrics, Continental Medical College, Lahore Pakistan.
Aoun Zaib KhanDepartment of Pediatrics, Metro Health Pediatric Residency Program, Cleveland Ohio, USA.
Amna ZaheerDepartment of Pediatrics, Liaquat National Hospital and Medical College, Karachi, Pakistan.
Jamuna ShreshthaDepartment of Critical Care Medicine, Hospital for Advanced Medicine and Surgery, Kathmandu, Nepal.ORCID https://orcid.org/0009-0006-0701-4765
Anum AkbarDepartment of Pediatrics, University of Nebraska Medical Center, Omaha, Nebraska, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Congenital heart defects (CHDs) are the most prevalent congenital condition globally, affecting approximately 1% of live births and contributing to significant morbidity and mortality. While advancements in diagnostic imaging have improved early detection and management, CHDs remain a leading cause of infant mortality, particularly in low- and middle-income regions. Echocardiography has evolved as a cornerstone in CHD diagnosis, with traditional modalities such as 2D echocardiography, M-mode, and Doppler imaging providing essential structural and hemodynamic assessments. However, these conventional approaches have limitations in visualizing complex congenital anomalies. The emergence of advanced echocardiographic techniques, including three-dimensional echocardiography, fetal echocardiography, speckle tracking echocardiography (STE), and stress echocardiography (SE), has significantly enhanced diagnostic accuracy, preoperative planning, and long-term monitoring. Furthermore, recent innovations in artificial intelligence (AI), deep learning, digital signal processing, and contrast-enhanced echocardiography are revolutionizing the field by automating image analysis, improving image quality, and enabling predictive modeling for CHD prognosis. This review critically compares traditional and advanced echocardiographic modalities, evaluating their respective strengths, limitations, and impact on early CHD detection. Additionally, it explores emerging AI-driven technologies and their potential to optimize echocardiographic workflows, reduce diagnostic variability, and enhance patient outcomes. By synthesizing current evidence, this review aims to provide clinicians, researchers, and healthcare policymakers with insights into the evolving role of echocardiography in CHD diagnosis and management.

Indexed as

AI in cardiologyartificial intelligencecongenital heart defectscontrast echocardiographydigital signal processingechocardiographyfetal echocardiographypediatric cardiologyspeckle tracking echocardiographythree-dimensional echocardiography

Identifiers

PMID42866293
PMCPMC13657448

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.