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

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.

Introduction

Globally, congenital heart defects (CHDs) are considered the most prevalent congenital condition^[1^]. Nearly 1% of all live births and up to 10% of stillbirths globally are affected by CHD, making them the most common type of birth defect^[2,3^]. The overall prevalence of CHD is notably higher in females, both during childhood and adulthood^[4^]. Males are at a much higher risk of being born with severe CHD, while females are more likely to have milder forms of the condition. In high-income countries (HICs), it is now the second leading cause of infant death^[5^]. CHDs occur in about one in every 110 births in the United States^[6,7^]. Earlier studies have estimated that there are about 250 000 deaths each year worldwide due to CHDs, and around 1.35 million babies are born globally with CHDs each year^[8^]. The global prevalence of CHDs at birth was estimated to be about 1.8 cases per 100 live births in 2017, reflecting a 4.2% increase since 1990. This rise is partly due to the inclusion of data from low- and middle-socio-demographic index (SDI) regions that had not been previously accounted for, as well as biological and social factors that still require further investigation^[9^]. At the same time, the prevalence of ventricular septal defect (VSD) and atrial septal defects (ASD) has been declining, likely because a higher proportion of these conditions are being treated through repairs or have naturally regressed, along with the effects of an aging population. As a result, the proportion of people with CHD aged 15 years and older has increased from about 23% in 1990 to over 28% in 2017. By 2017, an estimated 12 million people worldwide were living with CHD, marking an 18.7% rise from the 10.1 million in 1990^[1^]. The decline in mortality varied significantly by SDI, with countries in the low-SDI group showing only a 6% decrease in mortality since 1990, while middle-, high-middle-, and high-SDI regions saw declines of more than 50%. Despite some progress, mortality rates remain significantly higher in low- and middle-SDI regions compared to high-income countries.

HIGHLIGHTS

• Echocardiography is very important for the diagnosis and follow-up of congenital heart defects (CHDs).

• Advanced modalities like 3D, fetal, stress, and transesophageal echocardiography improve the assessment of CHDs.

• Artificial intelligence, machine learning, and digital signal processing are emerging tools that may enhance accuracy and workflow efficiency.

• Speckle tracking and contrast echocardiography add functional and structural details beyond conventional imaging.

• The review summarizes the strengths, limitations, and future directions of echocardiography in congenital cardiology.

Severe CHDs are typically noticed soon after birth or in the first few months. In contrast, less severe CHDs might not be detected until later in childhood. Early identification is necessary for enhancing the overall outcome^[10^]. Several diagnostic modalities have been utilized in diagnosing CHDs, including chest X-ray, genetic testing, cardiac MRI, pulse oximetry, and echocardiography, with the latter serving as a key tool for both initial evaluation and long-term management^[11^]. A study demonstrated that for most children undergoing primary complete repair, echocardiography alone is a precise tool for diagnosing major CHDs, potentially reducing the need for routine diagnostic catheterization^[12^]. Echocardiography demonstrated 95% accuracy in diagnosing conditions before surgery, and any errors made did not cause any surgical complications^[12^]. However, despite significant advancements in echocardiography, the morbidity and mortality associated with CHDs continue to be a concern, highlighting the need for further improvements in echocardiography to enable early detection and improve patient outcomes. The aim of this literature review is to critically compare traditional and current echocardiographic approaches in terms of their advantages, limitations, and diagnostic accuracy. Additionally, it will explore ongoing advancements in echocardiography and their potential impact on improving the early detection and management of CHDs. This analysis will provide valuable insights for clinicians, researchers, and healthcare policymakers to optimize diagnostic strategies and guide future developments in cardiac imaging^[13^].

Method

This review follows a narrative literature review approach, systematically gathering and analyzing existing literature on echocardiographic techniques used in CHD diagnosis and management. A structured search was conducted using the following electronic databases: PubMed, Google Scholar, Science Direct, and the American College of Cardiology (ACC) Library. The search included peer-reviewed journal articles, clinical guidelines, conference proceedings, and expert consensus statements published between 2000 and 2025. To ensure comprehensive coverage, manual searches of reference lists in relevant studies were also performed.

The inclusion criteria for this review encompass studies that explore the role of traditional echocardiography, including two-dimensional (2D), M-mode, and Doppler imaging, as well as advanced echocardiographic modalities such as three-dimensional (3D) echocardiography, fetal echocardiography, speckle tracking echocardiography, and contrast-enhanced ultrasound. Research investigating the application of artificial intelligence (AI), machine learning (ML), deep learning (DL), and digital signal processing (DSP) in echocardiography is also included. Additionally, studies providing comparative analyses of different echocardiographic techniques in diagnosing CHD are considered. Only articles published in English with a focus on pediatric and congenital cardiology are eligible for inclusion. The exclusion criteria comprise studies that primarily focus on adult-acquired heart disease rather than CHD. Papers that discuss echocardiography without relevance to congenital conditions are also excluded. Furthermore, non-peer-reviewed articles, letters to the editor, and opinion pieces lacking supporting data are not considered. Studies that do not provide substantial data on diagnostic accuracy, advantages, or limitations of echocardiographic modalities are similarly excluded from this review.

A structured approach was employed to extract and synthesize relevant findings from the selected studies, focusing on several key thematic areas. These areas included the historical evolution of echocardiography in the diagnosis of CHD, the comparative effectiveness of traditional echocardiographic techniques, and emerging innovations such as AI, ML, and DSP. Additionally, the analysis examined the clinical applications of echocardiography in CHD management and explored potential future directions in the field. The findings were categorized based on several criteria. First, they were classified according to the specific echocardiographic modality, including 2D echocardiography, Doppler imaging, and 3D echocardiography. Second, studies were grouped based on their clinical applications, such as fetal, neonatal, or pediatric echocardiography. Third, research focusing on technological advancements, including AI integration and automated image processing, was separately analyzed. Lastly, findings were assessed based on comparative diagnostic accuracy and the limitations of different echocardiographic techniques.

Echocardiography in CHD diagnosis

The origins of echocardiography can be traced back to around 1949 with Dr. Wolf-Dieter Kiedel, who used ultrasound transmission to obtain volumetric measurements^[14^]. It was in 1953 that the first use of reflected ultrasound waves to image the heart in a one-dimensional view – A-mode – was conducted by Dr. Carl Hellmuth Hertz and Dr. Inge Edler^[15,16^]. Following the creation of A-mode for echocardiography, M-mode was subsequently created by Dr. Hertz and Dr. Edler in 1953, with the use of M-mode beginning in 1968^[12,13^]. While modes such as A-mode and M-mode helped physician-scientists to gather data about heart function, Dr. Hertz envisioned viewing the heart in two dimensions, which was eventually completed in 1967 when the first 2D echocardiograms, done in real-time, were conducted by Dr. Nicholas Bom and Dr. Lancée^[14^].

After the development of 2D echocardiography, other modes of echocardiography emerged. Around 1955–1956, Dr. Shiego Satomura pioneered the use of Doppler echocardiography; Doppler echocardiography was later utilized from the mid-1970s and beyond to measure values such as blood flow and pressure gradients^[17^]. 2D echocardiography was later advanced to 3D echocardiography with the successful use of technology in the 1990s by Dr. Olaf von Ramm^[18^].

Other developments include transthoracic and transesophageal echocardiograms, exercise stress echocardiograms, and fetal echocardiograms. Transthoracic and transesophageal echocardiograms first emerged in the 1970s with full maturation of the technology occurring several decades later through the incorporation of better imaging, frame rates, and 3D elements^[17^]. Exercise stress echocardiograms began to be implemented into care by 1979 and beyond, examining ischemia of the heart after exercise-induced stress^[19^]. Fetal echocardiograms emerged in the 1960s with Dr. Wang’s use of echocardiography to examine fetal hearts and matured as a technology closer to the 1970s^[20^]. The 1980s marked the beginning of using fetal echocardiograms to examine CHD, a use which was further developed and standardized by Dr. Lindsey Allan and other doctors in the field^[20^]. Overall, echocardiography as a technology has matured into various forms of use since its initial creation in the early 1900s. This timeline is presented in Figure 1^[21^].

Figure 1.

Timeline of the key advancements in echocardiography technology (created using BioRender)^[21^].

Comparison of traditional with current echo approaches

Standard echocardiography

Common echocardiography methods, like 2D imaging, M-mode, and Doppler imaging, are key tools for evaluating cardiac anatomy, function, and hemodynamics. These fundamental procedures are essential to a thorough cardiac evaluation and frequently precede the use of more advanced imaging techniques.

2D echocardiography

2D echocardiography is the most widely used method of evaluating patients with CHDs. It is particularly effective in identifying congenital abnormalities such as VSDs, ASDs, and patent ductus arteriosus (PDA)^[22^]. Additionally, 2D echocardiography assesses the size of the heart chambers, anomalies in wall motion, and the overall function of the ventricles^[23^]. This modality can identify secondary complications that are frequently observed in cyanotic congenital heart abnormalities, such as pulmonary artery dilatation and right ventricular (RV) hypertrophy^[24–26^]. Examples of cyanotic congenital cardiac anomalies that can be assessed by 2D echocardiography include tetralogy of Fallot (TOF), transposition of the great arteries (TGA), and double outlet right ventricle (DORV)^[27^]. In patients with congenitally corrected TGA or atrioventricular septal defects (AVSDs), 3D echocardiography has further enhanced the diagnostic potential of 2D echocardiography by allowing for more accurate visualization of complex cardiac structures^[28^]. M-mode and Doppler techniques have also been used to further increase the diagnostic accuracy of 2D echocardiography. Color Doppler combined with 2D imaging, for example, allows clinicians to assess valve regurgitation, identify intracardiac shunting, and observe blood flow patterns. These combined techniques provide a more complete assessment of both structural and functional issues in CHD patients^[29–31^]. Notwithstanding its benefits, a double-outlet right ventricle or aberrant pulmonary venous connections can make it difficult for 2D echocardiography to accurately detect complicated anatomical linkages in situations of severe congenital heart disease. Thus, sophisticated imaging techniques like cardiac MRI, CT angiography, or 3D echocardiography would be required to corroborate the diagnostic findings in these circumstances.

M-mode echocardiography

Although motion mode echocardiography (M-mode echocardiography) is accurate in measuring cardiac structure and function throughout time, it remains a crucial feature of echocardiographic evaluation in congenital heart diseases (CHDs). In contrast to 2D echocardiography, M-mode captures cardiac motion along a single line of the ultrasound beam, enabling physicians to evaluate dynamic variations in ventricular function, wall thickness, and chamber size over the course of the cardiac cycle^[32,33^]. M-mode is widely used to assess left ventricular (LV) function by measuring ejection fraction, fractional shortening, left ventricular end-diastolic dimension (LVEDD), and end-systolic dimension (LVESD)^[34^]. Patients with CHD who have ventricular hypertrophy, dilatation, or dysfunction as a result of diseases such as TOF or VSDs require these measurements for follow-up. Due to its excellent temporal resolution, M-mode is especially useful for evaluating tiny cardiac structures and fast heart rates in newborns and babies with CHDs^[35^]. For instance, it is used to measure the diameters of the aortic and pulmonary valves, which are essential in the diagnosis of valvular anomalies, and to track septal motion in atrial or VSDs^[36^]. M-mode’s diagnostic usefulness is enhanced when combined with 2D and Doppler echocardiography. M-mode, for instance, can validate abnormal septal motion observed on 2D echocardiography, which is frequently associated with paradoxical septal motion in pericardial effusion or RV volume overload in ASD^[37^].

Doppler echocardiography

Doppler echocardiography is a vital part of routine echocardiographic assessment of cardiac hemodynamics, valvular function, intracardiac pressures, and blood flow abnormalities^[38^]. Doppler echocardiography can be used to examine the diseased heart and obtain more direct hemodynamic measurements without the need for cardiac catheterization^[39^]. This helps the physician describe a CHD more precisely. This method enables the evaluation of hemodynamic abnormalities, such as valvular stenosis, regurgitation, and intracardiac shunting, which are commonly found in CHD^[23^]. Doppler echocardiography is also useful for estimating pulmonary artery pressures and determining collateral circulation, both of which are crucial in patients with pulmonary atresia^[40^]. Doppler echocardiography comes in three primary varieties: Color Doppler, Pulsed-Wave (PW), and Continuous-Wave (CW). While CW Doppler is unable to pinpoint the flow, it can quantify high-velocity flows, such as those found in aortic stenosis (AS) and valvular regurgitation. PW Doppler, though limited by aliasing at high velocities, is helpful for evaluating diastolic function since it can quantify low-velocity flows at specific locations ^[41^]. Blood flow can be visually represented by Color Doppler, which can detect flow reversals, shunts, and regurgitant jets^[42^]. By measuring the vena contracta, jet size, and flow convergence, it is important for determining the degree of valvular regurgitation; nevertheless, it is impacted by variables including the transducer frequency, Nyquist limit (the maximum measurable velocity in PW and Color Doppler echocardiography), and gain settings^[42^].

3D echocardiography

The diagnosis and evaluation of complex CHDs have significantly improved over the past 20 years due to advancements in echocardiography, particularly 3D echocardiography^[43^]. This technique provides volumetric and dynamic visualization of cardiac structures in real-time, enhancing spatial understanding of anatomical relationships, valve function, and heart size^[44^]. 3D echocardiography offers superior spatial resolution compared to traditional 2D echocardiography, which only generates cross-sectional images. This capability allows for comprehensive volumetric reconstruction of the heart, which is crucial for assessing ventricular morphology, valvular abnormalities, and septal defects^[43^]. Furthermore, this technique enables precise measurements of chamber sizes and heart function, which is particularly beneficial for conditions such as TOF, hypoplastic left heart syndrome (HLHS), and AVSDs, where accurate visualization is essential for surgical planning^[45^]. Furthermore, real-time 3D echocardiography (RT-3DE) enhances the dynamic examination of ventricular performance and valve function, supporting the preoperative assessment of complex outflow tract obstructions and TGA^[46^]. In addition to structural assessment, 3D Echo offers better imaging of coaptation zones, leaflet motion, and valve morphology. This provides notable benefits over 2D imaging for congenital valve abnormalities such as Ebstein’s anomaly and bicuspid aortic valve. Additionally, it facilitates precise quantification of valve stenosis and regurgitation, enabling more accurate grading in situations such as mitral valve disease, pulmonary stenosis, and AS^[47,48^]. Additionally, 3D Echo is essential for assessing prosthetic valves and postoperative results, especially in valvular repairs and postoperative Fontan circulation, where identifying paravalvular leaks, prosthetic stenosis, and thrombus formation is critical ^[43^]. Nevertheless, despite its benefits, 3D Echo has several drawbacks, such as slower spatial resolution than cardiac MRI and CT, a greater risk of imaging artifacts from patient movement and arrhythmias, and longer acquisition and processing times ^[49^]. The method may not be widely used due to its high operator dependence and the need for specialized training for precise image acquisition and interpretation. Furthermore, transesophageal echocardiography (TEE) may be required in certain situations due to inadequate acoustic windows in obese patients or those with post-surgical scarring, which can impair image quality^[50^]. Cost and accessibility remain major obstacles, especially in resource-limited environments where 3D echocardiography equipment and expertise might not be readily available ^[47^].

Fetal echocardiography

Fetal echocardiography is a specialized diagnostic tool that allows the assessment of the fetal heart during pregnancy, typically starting as early as 12–16 weeks of gestation^[51,52^]. It is especially recommended for fetuses with abnormal cardiac ultrasound findings or parents with congenital heart diseases^[52^]. This technique proves invaluable in pregnancies with risk factors such as a family history of CHD, maternal diabetes, or in vitro fertilization. Additionally, maternal infections, such as rubella or Zika virus, and fetal conditions like abnormal nuchal translucency (NT) or non-immune hydrops, are strong indications for fetal echocardiography. A study demonstrated the high diagnostic accuracy of early fetal echocardiography (EFE) in prenatal care, with a sensitivity of 92.9% and a specificity of 100% for identifying major fetal cardiac diseases (FCD). The incorporation of endo-vaginal imaging improved visualization of cardiac structures, with assessment scores increasing from 1.6/9 to 3.5/9 (P = 0.049). Additionally, repeat EFE studies further enhanced diagnostic yield, with scores rising from 5.2/9 to 7.4/9 (P < .0001)^[53^]. This non-invasive imaging modality is instrumental in identifying CHD before birth, enabling early interventions and optimized perinatal care. By utilizing high-frequency ultrasound waves, fetal echocardiography provides detailed views of the cardiac chambers, valves, and great vessels. It can also assess rhythm abnormalities and evaluate the fetal hemodynamic status^[54^]. Fetal echocardiography is particularly effective in detecting structural heart defects, such as hypoplastic left heart syndrome, TGA, and AVSDs^[55–57^]. Furthermore, its role extends beyond structural assessment; it enables the diagnosis of arrhythmias, such as supraventricular tachycardia, and functional anomalies, including fetal heart failure^[58^]. Advances, including the use of Doppler imaging and 3D/4D fetal echocardiography, have significantly enhanced the functional evaluation of fetal hemodynamics. Color Doppler provides insights into blood flow patterns, while spectral Doppler aids in quantifying velocity and pressure gradients^[59^]. 3D/4D fetal echocardiography allows for rapid volumetric data acquisition, enhancing diagnosis and understanding of spatial relationships in CHD^[54^].

A study evaluated 164 fetuses from 152 women with risk factors (such as pregestational diabetes, in vitro fertilization (IVF), increased NT, tricuspid regurgitation, reversed ductus venosus (DV) a-wave, prior child with CHD, and maternal anticonvulsant use) through first-trimester 4D fetal echocardiography, utilizing spatiotemporal image correlation with tomographic ultrasound imaging (STIC-TUI) and color Doppler to identify complex CHD in these high-risk pregnancies. Abnormal STIC-TUI findings were observed in 12% (n = 20), including defects such as atrioventricular canal (n = 9), hypoplastic left heart (n = 2), and persistent truncus arteriosus (n = 1). The sensitivity and specificity of the technique for detecting CHD were 91 and 100%, respectively. While 85% of anomalies were evident in the four-chamber view, others required outflow tract imaging with color Doppler. Thirteen cases had aneuploidy, all leading to first-trimester termination, and among the remaining, seven were confirmed postnatally or at autopsy. This study highlights the feasibility and high diagnostic accuracy of STIC-TUI echocardiography for early detection of CHD, offering critical insights into fetal cardiac health in high-risk pregnancies^[60^]. Moreover, with advancements in fetal telemedicine, fetal tele-echocardiography has also evolved to be a transformative approach in prenatal care, using cost-effective technologies like Zoom and Open Broadcaster Software Studio (OBS Studio), thus providing remote diagnosis and timely management of CHDs^[61^]. A study involving 513 high-risk pregnancies demonstrated the effectiveness of fetal tele-echocardiography in identifying CHDs in 5.3% of cases, with hypoplastic left heart syndrome being the most common anomaly. The service enhances diagnostic accuracy, reduces costs (~$749 per patient), and offers parents comprehensive counseling in a single session^[62^].

A few limitations must be acknowledged: fetal echocardiography is heavily operator-dependent and is limited by gestational age, fetal position, and maternal body habitus. It is challenging to visualize certain cardiac structures, such as the inferior vena cava (IVC) and pulmonary veins, before 12 weeks of gestation^[59^]. Subtle defects, such as coronary artery anomalies, may remain undetected due to the spatial resolution constraints of ultrasound^[63^].

Stress echocardiography

SE is a dynamic diagnostic tool that evaluates myocardial function under stress conditions to detect ischemic and non-ischemic cardiac diseases, assess myocardial viability in patients with LV dysfunction, and stratify cardiovascular risk, thus providing a more comprehensive evaluation than static imaging modalities^[64^]. By comparing resting and post-stress echocardiograms, we can identify regional wall motion abnormalities, pulmonary pressures, valve function, and exercise-induced arrhythmias that might remain undiagnosed under resting conditions ^[65,66^]. Stress can be induced either through physical exercise (e.g., treadmill or bicycle) or pharmacological agents such as dobutamine or adenosine^[67^].

The non-invasive nature of SE and its ability to provide real-time functional data have made SE an indispensable tool for structural and functional assessments and thus for evaluating CHDs^[68^]. Moreover, the integration of Doppler imaging, tissue Doppler imaging (TDI), strain analysis, and speckle-tracking echocardiography (STE) has enabled precise quantification of myocardial function and deformation, especially STE, which can detect subclinical ischemia and strain abnormalities in patients with CHD.

In fetal echocardiography, SE principles help in assessing early ventricular strain patterns, hemodynamic responses, and flow abnormalities, which can guide preterm diagnosis and management of CHD. For example, SE effectively detects shunt dynamics and latent pulmonary hypertension in ASD while assessing shunt changes, repair zone function, and stress-induced arrhythmias in VSD^[69^]. For conditions like TOF, stress echocardiography monitors RV function, outflow gradients, and residual pulmonary regurgitation^[70^]. In coarctation of the aorta, it identifies stress-induced gradients and systemic hypertension^[71^]. For systemic right ventricles, such as those in congenitally corrected transposition of the great arteries (CCTGA), it evaluates systemic ventricular function, valve regurgitation, and outflow gradients^[72^]. In univentricular hearts, particularly post-Fontan physiology, it assesses systemic and pulmonary pressures and cavo-pulmonary flow abnormalities^[73^]. Additionally, stress echocardiography aids in monitoring pulmonary venous gradients and post-repair obstructions in anomalous pulmonary venous return, while in complete atrioventricular canal defects, it evaluates patch function, valve performance, and residual defects^[74^].

Stress echocardiography is highly operator-dependent, requiring expertise for accurate image acquisition and interpretation. Suboptimal imaging in individuals with poor acoustic windows, such as those with significant obesity or prior thoracic surgeries, can affect diagnostic accuracy. Pharmacological stress is contraindicated in patients with arrhythmias, severe AS, or other high-risk conditions.

Transthoracic echocardiography

Transthoracic echocardiography (TTE) serves as the first-line assessment tool for detecting CHDs, as it is an easy-to-use, non-invasive imaging modality^[75^]. It delves deep into chamber dimensions, wall motion, ventricular function, and blood flow dynamics, thus easily detecting septal defects (ASD, VSD, PDA), valvular anomalies, and ventricular hypertrophy^[76–78^]. For better image resolution, Doppler imaging, STE, and three-dimensional echocardiography (3D TTE) have been integrated into TTE, making it the investigation of choice for comprehensive assessment of congenital heart defects and other cardiac abnormalities. Doppler imaging has significantly enhanced the detection of intracardiac shunts, pressure gradients, and valvular regurgitation, while 3D imaging facilitates precise evaluation of valve morphology and function in conditions like Ebstein’s anomaly and complete atrioventricular canal defects^[79^]. STE further complements TTE by enabling strain analysis, which helps detect subclinical myocardial dysfunction in systemic right ventricles (e.g., in CCTGA)^[80,81^].

TTE diagnostic utility can be compromised in cases of poor acoustic windows (like in obese patients, severe lung diseases, or those with prior thoracic surgeries). Moreover, its ability to provide detailed visualization of posterior structures and complex anomalies may be constrained, necessitating complementary imaging modalities like TEE or cardiac MRI^[82,83^].

Transesophageal echocardiography

TEE is a semi-invasive technique that provides improved imaging quality by bypassing thoracic structures and thus providing high-resolution visualization of posterior heart structures, like the left atrium, pulmonary veins, and atrial septum. Due to this property, it has become an integral tool for diagnosing and managing CHDs, especially in neonates and complex congenital conditions (ASD, VSD, valve morphology)^[84^].

TEE has also revolutionized intraoperative monitoring during surgical repairs or catheter-based interventions, providing real-time imaging and hemodynamic assessment, ensuring accurate placement of devices (like septal occluders or prosthetic valves) and minimizing complications^[85,86^]. For example, in ASD or VSD, TEE helps confirm complete closure and assess for residual shunting^[87^]. Similarly, valve repair or replacement procedures evaluate valve morphology, regurgitation, and stenosis. In total anomalous pulmonary venous return (TAPVR) or complex surgeries, such as the Norwood procedure or Fontan operation, intra-op echo aids in assessing pulmonary and systemic venous connections^[88^].

Real-time three-dimensional transesophageal echocardiography (RT 3D TEE) has further enhanced the diagnostic properties of TEE^[89^]. RT 3D TEE provides better spatial orientation and improved anatomical visualization of intricate structures (mitral valve clefts in atrioventricular canal defects, paravalvular leaks, identifying obstructions in anomalous pulmonary venous return, and assessing residual defects or shunt dynamics post-repair)^[90^]. The real-time aspect of 3D TEE also enhances its utility in guiding interventions, such as percutaneous closures or valve implantations^[81,91^].

TEE is limited by its semi-invasive nature, requiring sedation or general anesthesia in pediatric populations, which restricts its routine use in neonates and infants^[92^]. Moreover, probe manipulation can be challenging in patients with altered anatomy or restricted esophageal space, such as in pediatric populations, thereby increasing the risk of esophageal trauma, aspiration, or hemodynamic instability^[93^]. 3D TEE remains constrained in pediatric cases, particularly in children weighing less than 30 kg, due to probe size limitations ^[84^]. In fetal echocardiography, TEE is not applicable, highlighting the reliance on alternative modalities like TTE or advanced imaging techniques, as demonstrated in a study where TTE with contrast showed moderate sensitivity (73%) and high specificity (94%) for detecting right-to-left shunt (RLS) when compared to TEE^[94^].

The strengths and limitations of standard echocardiography in the diagnosis of CHD are outlined in Table 1.

Table 1.

Strengths and limitations of standard echocardiographic modalities.

Standard echocardiography	Strengths	Limitations
2D echocardiography	Evaluate cardiac morphology, contractility, and valve function, and secondary complications (pulmonary artery dilation, RV hypertrophy)[95–97]	Struggles with complex anatomical relationships (e.g., DORV, abnormal pulmonary venous connections), may require MRI/CT for confirmation[98]
M-mode echocardiography	High temporal resolution, tracks ventricular function, wall motion, and chamber size, essential for newborns and infants with CHD[99,100]	Single-plane imaging, cannot assess overall cardiac structure, limited in complex anomalies[101,102]
Doppler echocardiography	Assesses blood flow direction, velocity, and pressure gradients, crucial for detecting valvular stenosis, regurgitation, shunts, and pulmonary artery pressures[103,104]	PW Doppler limited by aliasing at high velocities, CW Doppler cannot pinpoint flow location, affected by transducer settings[105,106]
3D echocardiography	Improved visualization of complex CHD, better spatial understanding of cardiac structures like AVSD and corrected TGA[15,107]	Higher cost, longer image acquisition, requires expertise[28]
Fetal echocardiography	Detects CHD as early as 12-16 weeks, useful in high-risk pregnancies (e.g., maternal diabetes, IVF), high accuracy with STIC-TUI and Doppler[86,87]	Operator-dependent, limited by fetal position, maternal obesity, and ultrasound resolution, difficult to visualize IVC and pulmonary veins before 12 weeks[59].
Stress echocardiography	Assesses ischemia, myocardial viability, detects subclinical strain abnormalities, evaluates shunt dynamics, pulmonary hypertension, and stress-induced arrhythmias in CHD[108]	Requires expertise, suboptimal imaging in obese patients, contraindicated in severe AS, arrhythmias, or high-risk conditions[108]
Transthoracic echocardiography (TTE)	First-line, non-invasive, evaluates chamber dimensions, ventricular function, integrates Doppler, STE, and 3D for better CHD assessment[75]	Poor acoustic windows in obese, lung disease, or post-surgical patients, limited in posterior structures, may require TEE/MRI for detailed evaluation[109,110]
Transesophageal echocardiography (TEE)	High-resolution imaging of posterior structures (LA, pulmonary veins, atrial septum), crucial for CHD diagnosis and intraoperative monitoring. RT 3D TEE enhances spatial visualization and intervention guidance[111,112].	Semi-invasive, requires sedation/anesthesia in pediatrics, risk of esophageal trauma. Limited in children < 30 kg due to probe size. Not applicable in fetal echocardiography[113,114].

Advances in echocardiography for detection of CHDs

AI, ML, and DL in echocardiography

AI plays a pivotal role in enhancing fetal echocardiography by automating and standardizing the visualization of diagnostic sections of the fetal heart. This advancement improves diagnostic accuracy and streamlines clinical applications. AI is a branch of computer science dedicated to creating algorithms that mimic human intelligence by learning, reasoning, and self-correcting. It aims to replicate and enhance human cognitive abilities to perform various tasks effectively^[115^]. ML, a key subset of AI, enables systems to analyze data, recognize patterns in images, and make informed decisions^[115^]. DL, a significant advancement within ML, is widely recognized as a powerful approach.

Fetal Intelligent Navigation Echocardiography (FINE) is a recently developed technology designed to enhance CHD screening. It relies on a four-chamber view as a foundational plane to gather fetal heart volume data and identify intracardiac structures. This approach enables the rapid, intelligent, and standardized generation of nine essential sections for effective diagnosis^[116^]. FINE has proven to be highly effective, achieving a diagnostic section rate of 98–100% in standard fetal echocardiography^[117^]. This impressive result highlights its ability to consistently deliver accurate and reliable diagnostic information. The findings indicated that FINE demonstrated a high success rate in displaying diagnostic sections and identifying key elements for common fetal CHDs. A study introduced a CNN model that automatically identifies fetal heart sections. Trained on 1326 ultrasound grayscale images, the model was able to differentiate five key sections of the fetal heart – normal and abnormal – including the abdominal plane, four-chamber view, LV outflow tract, three-vessel view, and three-vessel trachea. With a remarkable area under the curve of 0.99, the model achieved 95% sensitivity, 96% specificity, and 100% negative predictive value. Its performance closely matched that of clinicians and showed great promise in improving the detection rate of fetal CHD^[118^]. Specific AI tools like EchoNet-Labs and AutoEF utilize DL algorithms to automate image interpretation and segmentation, reducing inter-operator variability and improving diagnostic consistency^[119^]. For instance, EchoNet-Labs has demonstrated the ability to accurately measure left ventricular ejection fraction (LVEF) and detect subtle structural anomalies, such as ASDs and ventricular outflow tract abnormalities^[120^].

AI also excels in predicting outcomes for complex congenital conditions. For example, models trained on large datasets can analyze hemodynamic parameters to forecast the severity of TOF or the risk of complications in coarctation of the aorta. These advancements enhance early detection and enable timely intervention, which are critical for improving patient outcomes.

A study used an algorithm utilizing a fully connected neural network to detect Doppler patterns in fetal echocardiography. This method achieved an average accuracy of 88%, demonstrating its practical value in identifying patients with abnormal rhythms or subtle physiological changes associated with CHD^[121^].

The Deep Generative Adversarial Convolutional Neural Network model leveraged video transfer learning to enhance the classification of fetal ultrasound images for detecting CHDs. This approach achieved an 85% recognition rate in identifying fetal congenital heart disease from ultrasound video clips^[122^]. AI has the potential to minimize observer variability and enhance diagnostic accuracy in cardiovascular imaging, particularly in echocardiography^[123–125^].

DL has recently led to significant advancements in the evolving field of cardiovascular image segmentation^[125^]. Segmentation methods often utilize specific anatomical landmarks, such as the left ventricle apex, to guide contour placement. This improvement ensures precise identification of heart structures, which is critical for detecting congenital abnormalities. By using specific anatomical landmarks, such as the left ventricle apex, heart chambers, valves, and major blood vessels can be accurately outlined. This helps in identifying malformations such as VSD, atrioventricular canal defects, and abnormal valve placements, allowing for early diagnosis and intervention. A newer technique for reliably locating these landmarks incorporates multi-scale deep reinforcement learning^[126,127^]. The ability to reliably locate anatomical landmarks ensures consistent and accurate imaging, which is essential in evaluating the structure and function of the fetal and neonatal heart. By improving landmark detection, it becomes easier to assess heart alignment, detect structural abnormalities, and measure chamber dimensions. This is particularly useful for diagnosing conditions like hypoplastic left heart syndrome and transposition of great arteries, where precise anatomical assessment is crucial for surgical planning. In conditions such as TOF or LV non-compaction, detailed imaging helps in determining the severity of structural defects and guides treatment decisions. Additionally, coronary calcium scoring, though more common in adult cardiology, helps in identifying early signs of vascular abnormalities that may be linked to congenital conditions affecting coronary circulation.

Medical imaging offers exciting opportunities for the application of AI, particularly with the vast amounts of image data available for analysis^[128^]. With large volumes of imaging data, clinicians can compare fetal and pediatric heart scans to recognize subtle abnormalities that might otherwise be missed. The ability to analyze a wide dataset allows for better pattern recognition in CHD, making it easier to differentiate between normal and abnormal heart structures. This is particularly useful for complex cases where congenital defects present with variations in anatomy and function, requiring detailed comparisons across multiple cases^[120^]. For instance, each pixel’s features can be analyzed in various ways, such as detecting edges and patterns like borders, tracking the movement of features as vectors, extracting color information, and identifying shapes or areas with similar characteristics, such as image density^[125^]. By analyzing individual pixel details, fetal heart scans and echocardiograms can provide highly detailed structural information. Detecting edges and borders ensures that septal defects, valve abnormalities, and outflow tract obstructions are clearly visualized. Tracking movement patterns helps in assessing blood flow and identifying functional abnormalities, such as regurgitation in congenital valve defects. This level of detail allows for earlier detection of CHD, even in cases where abnormalities are subtle or progressive over time.

AI has significantly advanced in diagnostic medical imaging, with current computer vision systems capable of performing certain image interpretation tasks at a level comparable to that of expert physicians^[129,130^]. More advanced imaging interpretation ensures greater accuracy in diagnosing congenital heart defects. Automated systems can detect structural and functional heart anomalies with high precision, reducing the risk of observer variability and misdiagnosis. This is particularly beneficial for early screening of fetal heart abnormalities, where small errors in interpretation can lead to delayed or incorrect diagnoses, impacting treatment outcomes. In addition to image interpretation, AI has potential applications across the entire process, including image acquisition, processing, reporting, and planning for follow-up care^[131^].

Algorithmic bias, stemming from the limited representation of diverse patient populations in training datasets, may lead to disparities in diagnostic accuracy across demographic groups^[132^]. Furthermore, the reliance on high-quality annotated datasets for training and validation limits the applicability of these models in resource-constrained settings. Ethical considerations, such as data privacy and security, also pose significant hurdles.

Looking forward, real-time AI-guided echocardiography offers exciting possibilities. This includes dynamic image acquisition optimization and real-time feedback for clinicians during congenital defect evaluations, potentially revolutionizing pediatric care in underserved regions.

Digital signal processing

DSP has played a pivotal role in advancing echocardiography, particularly in the detection of CHDs. DSP techniques, such as adaptive filtering, Fourier transforms, and wavelet analysis, have significantly enhanced the quality of echocardiographic imaging. These methods improve noise reduction, contrast enhancement, and image resolution, facilitating the detailed visualization of complex CHDs like ASDs and ventricular hypertrophy^[133^].

A study demonstrated that advanced DSP techniques improved echocardiographic image resolution by 35%, directly enhancing the detection and quantification of congenital defects. DSP has also contributed to the evolution of 3D and 4D echocardiography, enabling real-time imaging of cardiac structures^[134^]. This is particularly valuable for assessing dynamic processes, such as valve motion or septal wall abnormalities, in CHDs.

High-end DSP systems demand significant computational resources, which may restrict their availability in low-resource settings. Moreover, complex processing algorithms can introduce artifacts under certain conditions, potentially compromising diagnostic accuracy^[135^]. To address the challenges and limitations associated with DSP, hybrid approaches that combine traditional imaging methods with advanced DSP could provide a practical solution. For instance, incorporating DSP enhancements into portable echocardiography systems could extend their utility to remote and underserved regions. Future advancements in DSP for congenital cardiac defect detection include the integration of real-time motion correction algorithms, which could minimize artifacts during dynamic imaging, and the development of lightweight processing techniques for mobile platforms. Such innovations will further democratize access to high-quality echocardiographic diagnostics.

Contrast echocardiogram

Contrast-enhanced ultrasound (CEU) works by using microbubbles that generate acoustic signals through their oscillation within the ultrasound field. These microbubbles are approved for cardiovascular use, mainly to enhance the visualization of the heart’s chambers and the inner heart lining. CEU can also be used off-label to assess blood flow in the heart’s small vessels, helping to diagnose conditions like chest pain due to microvascular problems. During echocardiography, contrast agents can be used to help confirm or rule out specific LV) structural abnormalities. For example, the distinctive spade-shaped appearance of the LV cavity, commonly seen in apical hypertrophic cardiomyopathy, can be more clearly visualized with ultrasound-enhanced agents (UEA)^[136^]. Contrast echocardiography also enables the echocardiographer to measure myocardial thickness with greater accuracy and confidence. In the case of LV non-compaction, using UEA helps distinguish the non-compacted layer from the compacted myocardium more clearly^[137^]. Contrast echocardiography can provide clearer visualization of abnormal anatomical structures and help confirm the presence or absence of extracardiac contrast agent leakage, which is crucial in cases where ventricular free wall rupture is suspected^[138^]. Studies have shown that contrast echocardiography can help lower healthcare costs in two main ways. First, by providing clear diagnoses, it reduces the need for more expensive or invasive tests in patients who initially have unclear echocardiograms. This method has been found to have better sensitivity and specificity for certain heart conditions and should be used when necessary. Second, it boosts efficiency in the lab by shortening scan times, especially for patients with difficult imaging windows, as echocardiographers can spend less time trying to capture the best possible images^[139,140^].

Speckle tracking echocardiography

STE is an advanced imaging modality that enables the quantitative assessment of myocardial deformation by measuring strain. Unlike traditional echocardiographic techniques, such as TDI, which rely on Doppler shift principles and are limited by angle dependency, STE is based on the tracking of natural acoustic markers (speckles) in the myocardium. These speckles are generated by the interaction of ultrasound waves with tissue microstructures and remain relatively stable between consecutive frames, allowing for the assessment of myocardial motion independent of insonation angle^[141^]. This feature makes STE particularly valuable for the evaluation of complex cardiac geometries frequently encountered in CHDs.

As the understanding of myocardial strain patterns has evolved, STE has gained clinical importance for detecting subclinical ventricular dysfunction in CHD patients before overt changes in ejection fraction occur^[142^]. This capability is especially relevant for long-term survivors of CHDs, where myocardial remodeling and progressive deterioration may remain undetected by conventional systolic function assessments^[143^].

In the assessment of patients with systemic right ventricles, such as those with atrial switch transposition of the great arteries and congenitally corrected transposition of the great arteries, STE-derived strain analysis has proven to be a reliable method for detecting early ventricular dysfunction. Studies have demonstrated that reduced right ventricular longitudinal strain (RV S₁) is associated with major adverse cardiovascular events, making it a valuable prognostic biomarker in this population^[144^].

Similarly, in TOF, STE has been extensively used to evaluate both RV and LV myocardial deformation. Longitudinal strain abnormalities have been shown to correlate with reduced exercise capacity and arrhythmic risk, emphasizing the importance of strain imaging in guiding risk stratification and the timing of pulmonary valve replacement^[144^].

For patients with single-ventricle physiology, particularly those who have undergone Fontan palliation, ventricular strain analysis has provided insights into myocardial performance at different stages of palliation. Longitudinal strain reductions in the systemic ventricle have been linked to adverse outcomes, reinforcing the potential role of STE in monitoring disease progression and guiding clinical management^[145^].

In fetal cardiology, STE has been applied to assess myocardial deformation in fetuses with CHDs, offering a potential tool for prenatal risk stratification. Abnormalities in global longitudinal strain and strain rate have been observed in fetuses with structural heart defects, such as coarctation of the aorta and TOF, suggesting that fetal strain imaging could provide a non-invasive method for early diagnosis and postnatal outcome prediction^[145^].

Despite its numerous advantages, STE has several inherent limitations that must be considered when interpreting strain data in CHD patients. One of the primary challenges is the variability in strain measurements across different echocardiographic vendors. Software-specific differences in speckle-tracking algorithms can lead to discrepancies in absolute strain values, making inter-study comparisons difficult^[146^]. Efforts to standardize strain measurement protocols across different platforms are ongoing but have not yet been universally implemented. Another limitation of STE is its dependence on image quality. The accuracy of strain measurements is significantly affected by poor acoustic windows, which are commonly encountered in post-surgical CHD patients with residual scars or implanted prosthetic material. Artifacts caused by suboptimal endocardial tracking can result in inconsistent strain measurements, particularly in complex congenital heart defects with highly distorted ventricular geometries^[147^].

The application of STE in the pediatric and neonatal population remains an area of active investigation. Unlike adult patients, where well-established reference values for strain parameters exist, normative data for pediatric and fetal CHD patients are still being refined. This gap in knowledge has limited the widespread adoption of strain imaging in younger patients, although ongoing research aims to establish robust strain reference ranges for different age groups and CHD subtypes^[146^].

RV strain assessment also poses unique challenges due to the complex shape and load-dependent function of the right ventricle. Unlike the left ventricle, which has a more uniform conical shape, the right ventricle has a crescentic morphology that makes strain analysis more difficult. Variability in RV strain values has been reported, raising concerns about the reproducibility of strain-based risk assessment in certain CHD populations^[147^].

Recent advancements in echocardiographic imaging are addressing many of the limitations associated with STE and expanding its role in the management of CHD. AI and ML algorithms are being developed to enhance the automation of strain analysis, improving both the accuracy and reproducibility of measurements. AI-driven software has shown promise in reducing interobserver variability and increasing the efficiency of clinical workflow, making strain analysis more accessible for routine use^[148^].

The introduction of three-dimensional speckle tracking echocardiography (3D-STE) represents another significant technological leap. Unlike traditional 2D-STE, which is limited to planar assessments, 3D-STE enables a more comprehensive evaluation of myocardial deformation by capturing strain in all three spatial dimensions. This advancement is particularly useful for CHD patients with complex ventricular geometries, where traditional strain measurements may be insufficient for accurate assessment^[148^].

A novel application of STE is blood speckle tracking echocardiography (BSTE), which allows for the visualization of intracardiac flow patterns and diastolic function. By tracking speckles within the blood pool rather than myocardial tissue, BSTE has the potential to provide additional hemodynamic information that complements strain-based myocardial assessments. Early studies suggest that this technique may improve the evaluation of diastolic dysfunction and pressure gradients in CHD patients with abnormal intracardiac flow dynamics^[147^].

In addition to technological advancements, ongoing research is focused on refining strain reference values across different age groups and CHDs. Large-scale multicenter studies are needed to establish normative data that can facilitate the clinical application of STE in both pediatric and adult CHD patients. Future efforts in standardization and validation will be crucial in ensuring the broader integration of STE into routine congenital cardiology practice^[146^].

The summary of these advanced echocardiographic techniques is provided in Table 2.

Table 2.

Strengths and limitations of advanced echocardiographic modalities.

Advanced echocardiography	Strengths	Limitations
AI in echocardiography	High diagnostic accuracy, automation, standardization[149]	Algorithmic bias, and data privacy concerns; are mitigated through diverse dataset training, robust validation protocols, and federated learning approaches[149]
Digital signal processing	Improved image resolution, noise reduction[150]	High computational demand, the potential introduction of motion artifacts and edge distortion under suboptimal imaging conditions[150]
Speckle echocardiography	Angle independent strain assessment, early dysfunction detected[151]	Vendor variability, image quality dependence[151]
Contrast echocardiography	Enhances chamber visualization, reduces the need for more invasive tests[152]	Not suitable for all patients, off-label uses[152]
3D STE	Comprehensive myocardial deformation analysis[153]	High cost, limited availability; ongoing research is exploring cost reduction strategies and wider adoption through portable imaging solutions[153].
Blood speckle echocardiography	Improved diastolic function assessment[154]	Limited validation, requires further research[154]

Conclusion

While traditional echocardiographic methods are generally reliable for diagnosing CHD, they often face challenges in visualizing complex cardiac structures. Even with advancements in imaging techniques, accurate interpretation still requires specialized training. This is where AI and DL are poised to make a lasting impact. AI-driven echocardiography has the potential to standardize image interpretation, minimize human error, and even predict patient outcomes with greater precision. However, challenges remain regarding the risk of algorithmic bias, accessibility, and the need for real-world validation, which must be addressed before AI becomes a routine part of congenital cardiology. Looking ahead, the future of CHD diagnosis and management is promising. By integrating AI, ML, and DSP with traditional echocardiographic techniques, a more refined, automated, and efficient approach to congenital heart imaging could be possible.

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No funding was received for this work.

Author contributions

M.A.H. conceptualized the review and contributed to the literature search and initial manuscript drafting. C.J. assisted in refining the scope of the review, performed a critical appraisal of the included literature, and contributed significantly to manuscript writing. A.Z. conducted extensive background research and helped with referencing and formatting. A.A. participated in literature screening and the synthesis of key thematic sections. H.M. supported manuscript editing and contributed to the development of figures and tables. A.Z. Khan provided an expert review, revised the manuscript for intellectual content, and ensured methodological clarity. A.Z. participated in writing the manuscript and ensured methodological clarity. J.S. participated in writing the manuscript and ensured methodological clarity. A.A. supervised the entire project, provided expert guidance throughout the drafting process, critically reviewed all sections, and approved the final version of the manuscript. All authors read and approved the final manuscript.

Conflicts of interest disclosure

None of the author has any conflict of interest.

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Guarantor

Anum Akbar.

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Not commissioned, externally peer reviewed.

Data availability statement

Data availability is not applicable to this article as no datasets were generated or analyzed.

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