This review provides an overview of the current clinical applications of artificial intelligence (AI) throughout the echocardiographic workflow, including automated view recognition, image quality assessment, cardiac phase identification, chamber segmentation, left ventricular ejection fraction estimation, strain analysis, and prediction of hemodynamic and disease-related parameters.
Abstract
Echocardiography is one of the most commonly used diagnostic methods in cardiovascular diseases because it is non-invasive, widely available, and capable of providing real-time assessment of function and cardiac structure. Despite these advantages, conventional echocardiography is often limited by operator dependence, interobserver inconstancy, the time-intensive nature of manual acquisition and measurement. Recent advances in artificial intelligence (AI), particularly deep learning, have created new opportunities to automate multiple steps of the echocardiographic workflow, starting from image acquisition and view classification to chamber segmentation, functional quantification, and hemodynamic estimation. This review provides an overview of the current clinical applications of artificial intelligence (AI) throughout the echocardiographic workflow. It focuses on key areas where AI has been applied, including automated view recognition, image quality assessment, cardiac phase identification, chamber segmentation, left ventricular ejection fraction estimation, strain analysis, and prediction of hemodynamic and disease-related parameters. Major challenges limiting wider clinical implementation were also highlighted in this review, such as insufficient external validation, dependence on image quality, differences between ultrasound vendors and patient populations, limited model interpretability, and lack of clear evidence demonstrating improved patient outcomes. Several AI-based tools, particularly those for automated view classification and chamber quantification, are becoming increasingly integrated into routine clinical practice, but many more advanced applications are possible, for which research is ongoing. The successful adoption of AI in echocardiography will depend not only on continued improvements in algorithm performance, but also on rigorous clinical validation, smooth integration into existing workflows, transparent reporting of model development and evaluation, and evidence that these technologies provide meaningful benefits for patient care.
Echocardiography remains the cornerstone of cardiovascular imaging. However, traditional workflows including manual acquisition, sequential measurement, and expert interpretation face challenges from increased clinical demand, workforce shortage, and the physical burden of repetitive scanning. Artificial intelligence (AI) has begun to address these issues, transitioning from proof-of-concept to prospective clinical evaluations. Recent evidence suggests that AI integration reduces examination time and automates measurements, enabling more comprehensive data collection while mitigating sonographer fatigue and improving image quality. The sonographer's role is accordingly evolving from conventional measurement to active verification. AI applications in echocardiography now extend beyond ejection fraction to integrated assessments of myocardial texture and Doppler hemodynamics. New model architectures incorporate both structural and functional evaluations, reflecting clinical reasoning of the expert. These methods are being applied to valvular heart disease, cardiomyopathy, and pericardial disorders. Clinical implementation of AI in echocardiography requires more than high accuracy. Current evidence is limited by reliance on single-center studies, inconsistent performance across platforms, and the potential for automation bias in high-volume settings. This review evaluates current evidence, identifies existing gaps, and outlines the requirements for responsible clinical implementation of AI.
R. Heo, Seung-Ah Lee, Hyuk-Jae Chang· Journal of Cardiovascular Im...· 0 citations
Hypertrophic cardiomyopathy (HCM) is a common and highly heterogeneous inherited cardiomyopathy characterized by complex clinical phenotypes and diverse disease trajectories, posing significant challenges for early diagnosis, precise phenotypic classification, and risk stratification. Owing to its noninvasive nature, repeatability, and wide availability, echocardiography remains the cornerstone imaging modality for the diagnosis and longitudinal management of HCM. However, conventional echocardiographic analysis relies heavily on operator expertise and is limited in its ability to comprehensively extract latent structural, functional, and tissue-level information embedded within imaging data. In recent years, artificial intelligence (AI), particularly deep learning, has undergone rapid development in automated echocardiographic analysis, enabling a paradigm shift from traditional morphology-based assessment toward data-driven intelligent decision-support platforms. This review systematically categorizes AI-based methods in echocardiography according to the complexity of data processing, ranging from single-frame structural and texture analysis to spatiotemporal modeling of cardiac function, multi-view representation learning, and ultimately multimodal integration incorporating diverse clinical data sources. We further summarize the clinical applications of these AI-based methods in HCM, including diagnosis and differential diagnosis, phenotype characterization, and risk prediction. In addition, current challenges are discussed, including limited interpretability, data heterogeneity, and insufficient large-scale clinical validation, and future research directions are proposed. Overall, AI-empowered echocardiography holds substantial promise for advancing precision diagnosis, risk stratification, and personalized management of HCM, facilitating a transition toward more intelligent and individualized cardiovascular care.
Unknown authors· Frontiers in Cardiovascular...· 0 citations
One of the primary causes of death worldwide is still heart disease. Although echocardiography is a commonly used method for identifying cardiovascular diseases, precise interpretation of echocardiogram pictures necessitates specialist medical knowledge. In order to overcome this difficulty, this paper presents a deep learning-based method for automatically classifying heart conditions from echocardiography data using the EfficientNetB0 architecture. For medical picture analysis, EfficientNetB0 offers a lightweight yet effective solution thanks to its compound scaling technique, which balances network depth, width, and resolution. In order to lessen the need for human interpretation, the model is trained to automatically extract intricate and distinctive features from echocardiographic images. EfficientNetB0 is especially well-suited for real-time clinical use since it guarantees great accuracy at a cheap computing cost by utilizing its efficiency and good generalization potential. This strategy seeks to assist healthcare providers in enhancing diagnostic accessibility, consistency, and efficiency. The suggested approach has the potential to improve cardiovascular disease prognosis and early detection, thereby increasing the scalability of sophisticated diagnostic capabilities in a variety of healthcare settings.
Taha Tahseen, Afshan Fatima· International Journal of Eng...· 0 citations
The role of AI-enhanced cardiovascular ultrasound in the transition from descriptive imaging toward predictive and personalized medicine is examined, with AI-enhanced cardiovascular ultrasound poised to become a central tool of precision cardiology.
Ancuța Elena Țupu, Simona Steliana Tudor, C. Dumitru et al.· Journal of Clinical Medicine· 0 citations
PURPOSE OF REVIEW
Assessment of left ventricular diastolic function remains one of the most challenging aspects of echocardiography. Artificial intelligence (AI) has emerged as a transformative tool capable of automating data acquisition, analysis, and interpretation. This review summarizes recent advances in the use of AI to facilitate diastolic function assessment.
RECENT FINDINGS
An increasing number of studies have shown the potential for AI-based models to equal or exceed expert-guideline approaches for evaluating diastolic function while improving reproducibility and workflow. Recent trends include the use of more deep learning techniques, reliance on fewer input variables, validation with relevant clinical outcomes, and shift in diastolic function classification from a categorical grading system to a more continuous probabilistic score.
SUMMARY
Current guideline-based approaches integrate multiple Doppler, structural, and hemodynamic variables to classify diastolic function. Although these algorithms have improved standardization, they remain limited by interobserver variability, discordant parameters, indeterminate classifications, incomplete datasets, and reduced applicability in complex clinical settings. Machine learning and deep learning approaches can integrate multidimensional echocardiographic features, electrocardiographic signals, and clinical variables to identify latent physiologic patterns beyond conventional rule-based algorithms. Future work will focus on addressing limitations of AI including explainability, generalizability, regulatory considerations, and integration into clinical workflows.
T. Tsang, Darwin Yeung· Current Opinion in Cardiolog...· 0 citations
Echocardiography is the cornerstone for risk stratification, diagnosis, and monitoring of cancer therapy–related cardiac dysfunction (CTRCD)(1). Artificial intelligence (AI)–guided echocardiography has shown high accuracy and reliability in diverse cardiac populations and may reduce variability while improving workflow efficiency(2, 3). However, this technology has not yet been validated in a dedicated cohort of patients with cancer.
To evaluate the accuracy and reliability of AI-guided echocardiography in assessing left ventricular ejection fraction (LVEF) and additional parameters, compared with conventional echocardiography, in a cardio-oncology population.
This study included patients identified retrospectively from a cardio-oncology registry. Studies, that had already been analysed manually by expert sonographers and reported using AGFA PACS system, were uploaded to the US2.ai platform for automated analysis. The primary outcome was the level of agreement (LoA) between AI-guided and standard echocardiography for LVEF. Secondary outcomes included LoA for additional echocardiographic parameters and LoA between AI- LVEF and 3D LVEF. The performance of the deep learning (DL) algorithm in identifying LVEF <50% was evaluated using the area under the receiver operating characteristic curve (ROC-AUC). Subgroup analyses were performed in predefined populations clinically relevant in cardio-oncology.
A total of 282 patients were included. Mean age was 60 ± 16 years, and 61% were women. Breast cancer was the most frequent malignancy (30.5%), followed by haematological malignancies (16.7%) and gastrointestinal tumours (10.6%). Manual median 2D LVEF was 60% (IQR: 55-64) and AI-derived LVEF was 59.2% (IQR: 53-64) showing good agreement and correlation (bias: −0.138, SD: 5.38, 95% LoA: −10.7 to 10.4, ICC: 0.791, 95% CI: 0.742–0.831, Spearman ρ: 0.718,), Table 1. Comparison between 3D echocardiography LVEF and AI-derived 2D LVEF showed similar agreement with narrower limits (bias: −0.13, 95% LoA: −9.51 to 9.26). The DL algorithm accurately identified LVEF <50% (ROC-AUC: 0.918, 95% CI: 0.875–0.961), Figure 1. Subgroup analyses demonstrated consistent agreement in patients with breast cancer, body mass index >30, prior radiotherapy and pericardial effusion.
In a large real-world cardio-oncology cohort, AI-guided echocardiography demonstrated strong agreement with conventional echocardiography for LVEF assessment and high accuracy for detecting clinically relevant LV dysfunction. Performance was consistent across key subgroups, supporting the feasibility, reliability, and potential clinical value of integrating DL-based analysis into routine cardio-oncology echocardiographic workflows.Agreement between manual and AI-echo AUC-ROC curve for LVEF<50%
M. Andres, V. Maharajan, M. C. Llamedo et al.· European Heart Journal, Supp...· 0 citations