The Role of Artificial Intelligence in Echocardiography

AI-Augmented Point of Care Ultrasound in Intensive Care Unit Patients: Can Novices Perform a "Basic Echo" to Estimate Left Ventricular Ejection Fraction in This Acute-Care Setting?

Background: Echocardiography is crucial to understanding cardiac function in the Intensive Care Unit (ICU), often by measuring the left ventricular ejection fraction (LVEF). Traditionally, measures of LVEF are completed as part of a comprehensive examination by an expert sonographer or cardiologist, but front-line practitioners increasingly perform focused point-of-care estimates of LVEF while managing life-threatening illness. The two main echocardiographic windows used to grossly estimate LVEF are parasternal and apical windows. Artificial intelligence (AI) algorithms have recently been developed to assist non-experts in obtaining and interpreting point-of-care ultrasound (POCUS) echo images. We tested the feasibility, accuracy and reliability of novice users estimating LVEF using POCUS-AI echo. Methods: A total of 30 novice users (most never holding an ultrasound probe before) received 2 h of instruction, then scanned ICU patients (10 patients, 80 scans) using the Exo Iris POCUS probe with AI guidance tool. They were permitted up to 5 min to attempt parasternal long axis (PLAX) and apical 4 chamber (A4C) views. AI-reported LVEF results from these scans were compared to gold-standard LVEF obtained by an expert echo sonographer. To further assess accuracy, this sonographer also scanned another 65 patients using Exo Iris POCUS-AI vs. conventional protocol. Results: Novices obtained images sufficient to estimate LVEF in 96% of patients in <5 min. Novices obtained PLAX views significantly faster than A4C (1.5 min vs. 2.3 min). Inter-rater reliability of LVEF estimation was very high (ICC 0.88-0.94) whether images were obtained by novices or experts. In n = 65 patients, POCUS-AI LVEF was highly specific for a decreased LVEF ≤ 40% (SP = 90% for PLAX) but only moderately sensitive (SN = 56-70%). Conclusions: Estimating cardiac LVEF from AI-enhanced POCUS is highly feasible even for novices in ICU settings, particularly using the PLAX view. POCUS-AI LVEF results were highly consistent whether performed by novice or expert. When AI detected a decreased LVEF, it was highly accurate, although a normal LVEF reported by POCUS-AI was not necessarily reassuring. This POCUS-AI tool could be clinically useful to rapidly confirm a suspected low LVEF in an ICU patient. Further improvements to sensitivity for low LVEF are needed.

Machine Learning-Based Prediction Models for Healthcare Outcomes in Patients Participating in Cardiac Rehabilitation: A Systematic Review

PURPOSE

Cardiac rehabilitation (CR) has been proven to reduce mortality and morbidity in patients with cardiovascular disease. Machine learning (ML) techniques are increasingly used to predict healthcare outcomes in various fields of medicine including CR. This systemic review aims to perform critical appraisal of existing ML-based prognosis predictive model within CR and identify key research gaps in this area.

REVIEW METHODS

A systematic literature search was conducted in Scopus, PubMed, Web of Science, and Google Scholar from the inception of each database to January 28, 2024. The data extracted included clinical features, predicted outcomes, model development, and validation as well as model performance metrics. Included studies underwent quality assessments using the IJMEDI and Prediction Model Risk of Bias Assessment Tool checklist.

SUMMARY

A total of 22 ML-based clinical models from 7 studies across multiple phases of CR were included. Most models were developed using smaller patient cohorts from 41 to 227, with one exception involving 2280 patients. The prediction objectives ranged from patient intention to initiate CR to graduate from outpatient CR along with interval physiological and psychological progression in CR. The best-performing ML models reported area under the receiver operating characteristics curve between 0.82 and 0.91, with sensitivity from 0.77 to 0.95, indicating good prediction capabilities. However, none of them underwent calibration or external validation. Most studies raised concerns about bias. Readiness of these models for implementation into practice is questionable. External validation of existing models and development of new models with robust methodology based on larger populations and targeting diverse clinical outcomes in CR are needed.

Potential role of targeted echocardiography as a screening test for select diagnoses in the paediatric population: bicuspid aortic valve and left ventricular hypertrophy

OBJECTIVE

We explore the role of targeted echocardiography as a screening tool for bicuspid aortic valve and left ventricular hypertrophy, specifically assessing the risk of missing significant cardiac findings that would otherwise be identified by comprehensive echocardiograms.

METHOD

Children < 18 years at initial echocardiogram for indications of "family history of bicuspid aortic valve" and "left ventricular hypertrophy on electrocardiogram" were queried. Cardiology clinic notes and complete echocardiogram reports were reviewed for additional background histories and incidental findings. Follow-up clinic visits, if any, and management for those with incidental findings were reviewed.

RESULTS

Bicuspid aortic valve group included 138 patients, 71 (51%) males and mean age at comprehensive echo was 8.4 ± 4.8 years. Bicuspid aortic valve was found in 3.6%, incidental findings were found in 15 (11%), and follow-up was recommended in 4 (2.8%). Left ventricular hypertrophy group included 70 patients, 58 (83%) males and mean age at echo 10.9 ± 4.7 years. Left ventricular hypertrophy was found in 2.8%, incidental findings were found in 9 (13%), and follow-up was recommended in 2 (2.8%).None of the follow-up group developed symptoms or required cardiac medications, exercise restrictions, or catheter or surgical-based interventions, except for one case of mild aortic root dilation who was restricted from heavy weightlifting.

CONCLUSION

The risk of missing clinically important findings with targeted echocardiography that would have been identified with comprehensive echocardiography is extremely low for screening indications of isolated left ventricular hypertrophy on electrocardiogram or family history of bicuspid aortic valve, suggesting that targeted echocardiography could be an effective screening tool.

Overcoming barriers in the use of artificial intelligence in point of care ultrasound

Point-of-care ultrasound is a portable, low-cost imaging technology focused on answering specific clinical questions in real time. Artificial intelligence amplifies its capabilities by aiding clinicians in the acquisition and interpretation of the images; however, there are growing concerns on its effectiveness and trustworthiness. Here, we address key issues such as population bias, explainability and training of artificial intelligence in this field and propose approaches to ensure clinical effectiveness.

The Year in Perioperative Echocardiography: Selected Highlights from 2024

This article is the ninth of an annual series reviewing the research highlights of the year pertaining to the subspecialty of perioperative echocardiography for the Journal of Cardiothoracic and Vascular Anesthesia. The authors thank the editor-in-chief, Dr. Kaplan, and the editorial board for the opportunity to continue this series. In most cases, these will be research articles that are targeted at the perioperative echocardiography diagnosis and treatment of patients after cardiothoracic surgery; but in some cases, these articles will target the use of perioperative echocardiography in general.

The Artificial Intelligence-Enhanced Echocardiographic Detection of Congenital Heart Defects in the Fetus: A Mini-Review

Artificial intelligence (AI) is rapidly gaining attention in radiology and cardiology for accurately diagnosing structural heart disease. In this review paper, we first outline the technical background of AI and echocardiography and then present an array of clinical applications, including image quality control, cardiac function measurements, defect detection, and classifications. Collectively, we answer how integrating AI technologies and echocardiography can help improve the detection of congenital heart defects. Particularly, the superior sensitivity of AI-based congenital heart defect (CHD) detection in the fetus (>90%) allows it to be potentially translated into the clinical workflow as an effective screening tool in an obstetric setting. However, the current AI technologies still have many limitations, and more technological developments are required to enable these AI technologies to reach their full potential. Also, integrating diagnostic AI technologies into the clinical workflow should resolve ethical concerns. Otherwise, deploying diagnostic AI may not address low-resource populations' healthcare access disadvantages. Instead, it will further exacerbate the access disparities. We envision that, through the combination of tele-echocardiography and AI, low-resource medical facilities may gain access to the effective detection of CHD at the prenatal stage.

The Role of Artificial Intelligence in the Prediction, Diagnosis, and Management of Cardiovascular Diseases: A Narrative Review

Cardiovascular diseases (CVDs) remain the leading global cause of mortality, and a high prevalence of cardiac conditions, including premature deaths, have increased from decades until today. However, early detection and management of these conditions are challenging, given their complexity, the scale of affected populations, the dynamic nature of the disease process, and the treatment approach. The transformative potential is being brought by Artificial Intelligence (AI), specifically machine learning (ML) and deep learning technologies, to analyze massive datasets, improve diagnostic accuracy, and optimize treatment strategy. The recent advancements in such AI-based frameworks as the personalization of decision-making support systems for customized medicine automated image assessments drastically increase the precision and efficiency of healthcare professionals. However, implementing AI is widely clogged with obstacles, including regulatory, privacy, and validation across populations. Additionally, despite the desire to incorporate AI into clinical routines, there is no shortage of concern about interoperability and clinician acceptance of the system. Despite these challenges, further research and development are essential for overcoming these hurdles. This review explores the use of AI in cardiovascular care, its limitations for current use, and future integration toward better patient outcomes.

Applications of Artificial Intelligence for the Prediction and Diagnosis of Cancer Therapy-Related Cardiac Dysfunction in Oncology Patients

Cardiovascular diseases and cancer are the leading causes of morbidity and mortality in modern society. Expanding cancer therapies that have improved prognosis may also be associated with cardiotoxicity, and extended life span after survivorship is associated with the increasing prevalence of cardiovascular disease. As such, the field of cardio-oncology has been rapidly expanding, with an aim to identify cardiotoxicity and cardiac disease early in a patient who is receiving treatment for cancer or is in survivorship. Artificial intelligence is revolutionizing modern medicine with its ability to identify cardiac disease early. This article comprehensively reviews applications of artificial intelligence specifically applied to electrocardiograms, echocardiography, cardiac magnetic resonance imaging, and nuclear imaging to predict cardiac toxicity in the setting of cancer therapies, with a view to reduce early complications and cardiac side effects from cancer therapies such as chemotherapy, radiation therapy, or immunotherapy.

Revolutionizing Cardiology: The Role of Artificial Intelligence in Echocardiography

Background: Artificial intelligence (AI) in echocardiography represents a transformative advancement in cardiology, addressing longstanding challenges in cardiac diagnostics. Echocardiography has traditionally been limited by operator-dependent variability and subjective interpretation, which impact diagnostic reliability. This study evaluates the role of AI, particularly machine learning (ML), in enhancing the accuracy and consistency of echocardiographic image analysis and its potential to complement clinical expertise. Methods: A comprehensive review of existing literature was conducted to analyze the integration of AI into echocardiography. Key AI functionalities, such as image acquisition, standard view classification, cardiac chamber segmentation, structural quantification, and functional assessment, were assessed. Comparisons with traditional imaging modalities like computed tomography (CT), nuclear imaging, and magnetic resonance imaging (MRI) were also explored. Results: AI algorithms demonstrated expert-level accuracy in diagnosing conditions such as cardiomyopathies while reducing operator variability and enhancing diagnostic consistency. The application of ML was particularly effective in automating image analysis and minimizing human error, addressing the limitations of subjective operator expertise. Conclusions: The integration of AI into echocardiography marks a pivotal shift in cardiovascular diagnostics, offering enhanced accuracy, consistency, and reliability. By addressing operator variability and improving diagnostic performance, AI has the potential to elevate patient care and herald a new era in cardiology.

New ultrasound techniques. Present and future

The present study highlights the advances in ultrasound, especially regarding its clinical applications to critically ill patients. Artificial intelligence (AI) is crucial in automating image interpretation, improving accuracy and efficiency. Software has been developed to make it easier to perform accurate bedside ultrasound examinations, even by professionals lacking prior experience, with automatic image optimization. In addition, some applications identify cardiac structures, perform planimetry of the Doppler wave, and measure the size of vessels, which is especially useful in hemodynamic monitoring and continuous recording. The "strain" and "strain rate" parameters evaluate ventricular function, while "auto strain" automates its calculation from bedside images. These advances, and the automatic determination of ventricular volume, make ultrasound monitoring more precise and faster. The next step is continuous monitoring using gel devices attached to the skin.

Multimodal Fusion Artificial Intelligence Model to Predict Risk for MACE and Myocarditis in Cancer Patients Receiving Immune Checkpoint Inhibitor Therapy

Background

Immune checkpoint inhibitor (ICI) therapy has dramatically improved the prognosis for some cancers but can be associated with myocarditis, adverse cardiovascular events, and mortality.

Objectives

The aim of this study was to develop an artificial intelligence (AI) model to predict the increased likelihood for the development of ICI-related myocarditis and adverse cardiovascular events.

Methods

Cancer patients treated with ICI at a tertiary institution from 2011 to 2022 were reviewed. Baseline characteristics, laboratory values, electrocardiograms, and cardiovascular clinical outcomes were extracted. A composite outcome of ICI-related myocarditis and major adverse cardiovascular events (transient ischemic attack/stroke, new diagnosis of heart failure, myocardial infarction, and cardiac death) was used to develop a multimodal joint fusion AI model by combining baseline tabular data with electrocardiogram (ECG) in a single end-to-end model. ECG data were parsed using 1-D convolution and tubular data using multilayer perceptron.

Results

Of 2,258 cancer patients who had ICI therapy and troponin measurement (mean age 68.5 ± 11.5 years, 59.7% male), the composite of cardiovascular clinical adverse events, including ICI-related myocarditis and major adverse cardiovascular events, occurred in 264 (11.7%) unique patients, with 428 events overall (including 59 [3%] ICI-related myocarditis events and 59 [3%] cardiac deaths). The proposed joint fusion model outperformed individual ECG and baseline electronic medical record data and laboratory value models with an area under the operating characteristics curve of 0.72 (0.64 true positive rate and 0.98 negative predictive value).

Conclusion

A multimodal fusion AI model to predict myocarditis and adverse cardiovascular events in cancer patients starting ICI therapy had good prognostic performance. It may have clinical utility in identifying at-risk patients who may benefit from closer surveillance.

Mitral Regurgitation Evaluation in Modern Echocardiography: Bridging Standard Techniques and Advanced Tools for Enhanced Assessment

Mitral regurgitation (MR) is one of the most common valvular heart diseases worldwide. Echocardiography remains the first line and most effective imaging modality for the diagnosis of mitral valve (MV) pathology and quantitative assessment of MR. The advent of three-dimensional echocardiography has significantly enhanced the evaluation of MV anatomy and function. Furthermore, recent advancements in cardiovascular imaging software have emerged as step-forward tools, providing a powerful support for acquisition, analysis, and interpretation of cardiac ultrasound images in the context of MR. This review aims to provide an overview of the contemporary workflow for echocardiographic assessment of MR, encompassing standard echocardiographic techniques and the integration of semiautomated and automated ultrasound solutions. These novel approaches include advancements in segmentation, phenotyping, morphological quantification, functional grading, and chamber quantification.

Research priorities for the study of atrial fibrillation during acute and critical illness: recommendations from the Symposium on Atrial Fibrillation in Acute and Critical Care

Atrial fibrillation (AF) is a common arrhythmia encountered in acute and critical illness and is associated with poor short and long-term outcomes. Given the consequences of developing AF, research into prevention, prediction and treatment of this arrhythmia in the critically ill are of great potential benefit, however, study of AF in critically ill patients faces unique challenges, leading to a sparse evidence base to guide management in this population. Major obstacles to the study of AF in acute and critical illness include absence of a common definition, challenges in designing studies that capture complex etiology and assess causality, lack of a clear outcome set, difficulites in recruitment in acute environments with respect to timing, consent, and workflow, and failure to embed studies into clinical care platforms and capitalize on emerging technologies. Collaborative effort by researchers, clinicians, and stakeholders should be undertaken to address these challenges, both through interdisciplinary cooperation for the optimization of research efficiency and advocacy to advance the understanding of this common and complex arrhythmia, resulting in improved patient care and outcomes. The Symposium on Atrial Fibrillation in Acute and Critical Care was convened to address some of these challenges and propose potential solutions.

Use of artificial intelligence-guided echocardiography to detect cardiac dysfunction and heart valve disease in rural and remote areas: Rationale and design of the AGILE-echo trial

BACKGROUND

Transthoracic echocardiography (TTE) is essential in the diagnosis of cardiovascular diseases (CVD), including but not limited to heart failure (HF) and heart valve disease (HVD). However, its dependence on expert acquisition means that its accessibility in rural areas may be limited, leading to delayed management decisions and potential missed diagnoses. Artificial intelligence-guided (AI)-TTE offers a solution by permitting non-expert image acquisition. The impact of AI-TTE on the timing of diagnosis and early initiation of cardioprotection is undefined.

METHODS

AGILE-Echo (use of Artificial intelligence-Guided echocardiography to assIst cardiovascuLar patient managEment) is a randomized-controlled trial conducted in 5 rural and remote areas around Australia. Adults with CV risk factors and exercise intolerance, or concerns regarding HVD are randomized into AI-TTE or usual care (UC). AI-TTE participants may have a cardiovascular problem excluded, identified (leading to AI-guided interventions) or unresolved (leading to conventional TTE). UC participants undergo usual management, including referral for standard TTE. The primary endpoint is a composite of HVD or HF diagnosis at 12-months. Subgroup analysis, stratified based on age range and sex, will be conducted. All statistical analyses will be conducted using R.

RESULTS

Of the first 157 participants, 78 have been randomized into AI-TTE (median age 68 [IQR 17]) and 79 to UC (median age 65 [IQR 17], P = .034). HVD was the primary concern in 37 participants (23.6%) while 84.7% (n = 133) experienced exercise intolerance. The overall 10-year HF incidence risk was 13.4% and 20.0% (P = .089) for UC and AI-TTE arm respectively. Atrial remodeling, left ventricular remodeling and valvular regurgitation were the most common findings. Thirty-three patients (42.3%) showed no abnormalities.

CONCLUSIONS

This randomized-controlled trial of AI-TTE will provide proof-of-concept for the role of AI-TTE in identifying pre-symptomatic HF or HVD when access to TTE is limited. Additionally, this could promote the usage of AI-TTE in rural or remote areas, ultimately improving health and quality of life of community dwelling adults with risks, signs or symptoms of cardiac dysfunction.

The Emerging and Important Role of Artificial Intelligence in Cardiac Surgery

Artificial Intelligence (AI) has greatly affected our everyday lives and holds great promise to change the landscape of medicine. AI is particularly positioned to improve care for the increasingly complex patients undergoing cardiac surgery using the immense amount of data generated in the course of their care. When deployed, AI can be used to analyze this information at the patient's bedside more expediently and accurately, all while providing new insights. This review summarizes the current applications of AI in cardiac surgery from the vantage point of a patient's journey. Applications of AI include preoperative risk assessment, intraoperative planning, postoperative patient care, and outpatient telemonitoring, encompassing the spectrum of cardiac surgical care. Offloading of administrative processes and enhanced experience with information gathering also represent a unique and under-represented avenue for future use of AI. As clinicians, understanding the nomenclature and applications of AI is important to contextualize issues, to ensure problem-driven solutions, and for clinical benefit. Precision medicine, and thus clinically relevant AI, remains dependent on data curation and warehousing to gather insights from large multicentre repositories while treating privacy with the utmost importance. AI tasks should not be siloed but rather holistically integrated into clinical workflow to retain context and relevance. As cardiac surgeons, AI allows us to look forward to a bright future of more efficient use of our clinical expertise toward high-level decision making and technical prowess.

The use of echocardiography in the management of shock in critical care: a prospective, multi-centre, observational study

PURPOSE

Echocardiography is recommended as a first-line tool in the assessment of patients with shock. The current provision of echocardiography in critical care is poorly defined. The aims of this work were to evaluate the utilisation of echocardiography in patients presenting to critical care with shock, its impact on decision making, and adherence to governance guidelines.

METHODS

We conducted a prospective, multi-centre, observational study in 178 critical care units across the United Kingdom (UK) and Crown Dependencies, led by the UK's Trainee Research in Intensive Care Network. Consecutive adult patients (≥ 18 years) admitted with shock were followed up for 72 h to ascertain whether they received an echocardiogram, the nature of any scan performed, and its effect on critical treatment decision making.

RESULTS

1015 patients with shock were included. An echocardiogram was performed on 545 (54%) patients within 72 h and 436 (43%) within 24 h of admission. Most scans were performed by the critical care team (n = 314, 58%). Echocardiography was reported to either reduce diagnostic uncertainty or change management in 291 (54%) cases. Patients with obstructive or cardiogenic shock had their management altered numerically more often by echocardiography (n = 15 [75%] and n = 100 [58%] respectively). Twenty-five percent of echocardiograms performed adhered to current national governance and image storage guidance.

CONCLUSION

Use of echocardiography in the assessment of patients with shock remains heterogenous. When echocardiography is used, it improves diagnostic certainty or changes management in most patients. Future research should explore barriers to increasing use of echocardiography in assessing patients presenting with shock.

The Role of Cardiovascular Imaging in the Diagnosis of Athlete's Heart: Navigating the Shades of Grey

Athlete's heart (AH) represents the heart's remarkable ability to adapt structurally and functionally to prolonged and intensive athletic training. Characterized by increased left ventricular (LV) wall thickness, enlarged cardiac chambers, and augmented cardiac mass, AH typically maintains or enhances systolic and diastolic functions. Despite the positive health implications, these adaptations can obscure the difference between benign physiological changes and early manifestations of cardiac pathologies such as dilated cardiomyopathy (DCM), hypertrophic cardiomyopathy (HCM), and arrhythmogenic cardiomyopathy (ACM). This article reviews the imaging characteristics of AH across various modalities, emphasizing echocardiography, cardiac magnetic resonance (CMR), and cardiac computed tomography as primary tools for evaluating cardiac function and distinguishing physiological adaptations from pathological conditions. The findings highlight the need for precise diagnostic criteria and advanced imaging techniques to ensure accurate differentiation, preventing misdiagnosis and its associated risks, such as sudden cardiac death (SCD). Understanding these adaptations and employing the appropriate imaging methods are crucial for athletes' effective management and health optimization.

A multi-task deep learning approach for real-time view classification and quality assessment of echocardiographic images

High-quality standard views in two-dimensional echocardiography are essential for accurate cardiovascular disease diagnosis and treatment decisions. However, the quality of echocardiographic images is highly dependent on the practitioner's experience. Ensuring timely quality control of echocardiographic images in the clinical setting remains a significant challenge. In this study, we aimed to propose new quality assessment criteria and develop a multi-task deep learning model for real-time multi-view classification and image quality assessment (six standard views and "others"). A total of 170,311 echocardiographic images collected between 2015 and 2022 were utilized to develop and evaluate the model. On the test set, the model achieved an overall classification accuracy of 97.8% (95%CI 97.7-98.0) and a mean absolute error of 6.54 (95%CI 6.43-6.66). A single-frame inference time of 2.8 ms was achieved, meeting real-time requirements. We also analyzed pre-stored images from three distinct groups of echocardiographers (junior, senior, and expert) to evaluate the clinical feasibility of the model. Our multi-task model can provide objective, reproducible, and clinically significant view quality assessment results for echocardiographic images, potentially optimizing the clinical image acquisition process and improving AI-assisted diagnosis accuracy.

Application of Echocardiography in Anaesthesia: From Preoperative Risk Assessment to Postoperative Care

Echocardiography has carved out a fundamental niche in anaesthesiology, revolutionizing the monitoring and management of cardiac function during surgery. Clinical practice has changed from simple 2D and 3D echocardiography to more sophisticated applications such as incorporating artificial intelligence. Echocardiography provides detailed real-time information about cardiac anatomy and function, helping anaesthesiologists make better decisions regarding tailoring anesthetic interventions and optimizing patient outcomes. From optimizing hemodynamic management in patients with severe aortic stenosis to fine-tuning fluid and vasopressor therapy in patients with right heart dysfunction, echocardiography has improved the care provided in the perioperative period. These applications permit the demonstration of not only technical advantages that could accrue from echocardiography but are also a part of individualized care to improve the outcomes of patients. The challenges in integrating echocardiography with anaesthesia include operator dependency, a steep learning curve in acquiring echocardiographic skills, and limitations due to patient factors and technological limitations, which lead to poor echocardiographic performance. Additionally, transoesophageal echocardiography (TEE) is an invasive procedure with several potential risks that must be considered cautiously. Continuing education, certification recommendations, and skill development are prerequisites for this echocardiography tool to remain robust and reliable in anaesthesiology. Technological innovation, especially in improving 3D imaging and integration with artificial intelligence, is where a very bright future lies ahead for echocardiography. It would further accelerate the process of echocardiographic evaluation and improve diagnostic accuracy. All these would turn out to be more person-centered for each patient. Anaesthesiologists must, therefore, pace themselves with such developments so these can be appropriately applied in the clinics. In summary, echocardiography became so integrally ingrained into anaesthesia that it propelled the specialty with essential tools anaesthesiologists use to manage patients for optimum outcomes. Its application has difficulties and limitations, but continued professional development and development of echocardiographic technology will make sure that its benefits are maximized. Quickly, echocardiography is becoming central to anaesthesiology's role in optimizing patient care and surgical success as we move into the application of evermore sophisticated echocardiographic techniques.

Assessment of an Artificial Intelligence Tool for Estimating Left Ventricular Ejection Fraction in Echocardiograms from Apical and Parasternal Long-Axis Views

This work aims to evaluate the performance of a new artificial intelligence tool (ExoAI) to compute the left ventricular ejection fraction (LVEF) in echocardiograms of the apical and parasternal long axis (PLAX) views. We retrospectively gathered echocardiograms from 441 individual patients (70% male, age: 67.3 ± 15.3, weight: 87.7 ± 25.4, BMI: 29.5 ± 7.4) and computed the ejection fraction in each echocardiogram using the ExoAI algorithm. We compared its performance against the ejection fraction from the clinical report. ExoAI achieved a root mean squared error of 7.58% in A2C, 7.45% in A4C, and 7.29% in PLAX, and correlations of 0.79, 0.75, and 0.89, respectively. As for the detection of low EF values (EF < 50%), ExoAI achieved an accuracy of 83% in A2C, 80% in A4C, and 91% in PLAX. Our results suggest that ExoAI effectively estimates the LVEF and it is an effective tool for estimating abnormal ejection fraction values (EF < 50%). Importantly, the PLAX view allows for the estimation of the ejection fraction when it is not feasible to acquire apical views (e.g., in ICU settings where it is not possible to move the patient to obtain an apical scan).

Revolutionizing Cardiac Imaging: A Scoping Review of Artificial Intelligence in Echocardiography, CTA, and Cardiac MRI

BACKGROUND AND INTRODUCTION

Cardiac imaging is crucial for diagnosing heart disorders. Methods like X-rays, ultrasounds, CT scans, and MRIs provide detailed anatomical and functional heart images. AI can enhance these imaging techniques with its advanced learning capabilities.

METHOD

In this scoping review, following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) Guidelines, we searched PubMed, Scopus, Web of Science, and Google Scholar using related keywords on 16 April 2024. From 3679 articles, we first screened titles and abstracts based on the initial inclusion criteria and then screened the full texts. The authors made the final selections collaboratively.

RESULT

The PRISMA chart shows that 3516 articles were initially selected for evaluation after removing duplicates. Upon reviewing titles, abstracts, and quality, 24 articles were deemed eligible for the review. The findings indicate that AI enhances image quality, speeds up imaging processes, and reduces radiation exposure with sensitivity and specificity comparable to or exceeding those of qualified radiologists or cardiologists. Further research is needed to assess AI's applicability in various types of cardiac imaging, especially in rural hospitals where access to medical doctors is limited.

CONCLUSIONS

AI improves image quality, reduces human errors and radiation exposure, and can predict cardiac events with acceptable sensitivity and specificity.

Diagnostic Modalities in Heart Failure: A Narrative Review

Heart failure (HF) can present acutely or progress over time. It can lead to morbidity and mortality affecting 6.5 million Americans over the age of 20. The HF type is described according to the ejection fraction classification, defined as the percentage of blood volume that exits the left ventricle after myocardial contraction, undergoing ejection into the circulation, also called stroke volume, and is proportional to the ejection fraction. Cardiac catheterization is an invasive procedure to evaluate coronary artery disease leading to HF. Several biomarkers are being studied that could lead to early detection of HF and better symptom management. Testing for various biomarkers in the patient's blood is instrumental in confirming the diagnosis and elucidating the etiology of HF. There are various biomarkers elevated in response to increased myocardial stress and volume overload, including B-type natriuretic peptide (BNP) and its N-terminal prohormone BNP. We explored online libraries such as PubMed, Google Scholar, and Cochrane to find relevant articles. Our narrative review aims to extensively shed light on diagnostic modalities and novel techniques for diagnosing HF.

The Role of Multimodality Imaging in Cardiomyopathy

PURPOSE OF REVIEW

There has been increasing use of multimodality imaging in the evaluation of cardiomyopathies.

RECENT FINDINGS

Echocardiography, cardiac magnetic resonance (CMR), cardiac nuclear imaging, and cardiac computed tomography (CCT) play an important role in the diagnosis, risk stratification, and management of patients with cardiomyopathies. Echocardiography is essential in the initial assessment of suspected cardiomyopathy, but a multimodality approach can improve diagnostics and management. CMR allows for accurate measurement of volumes and function, and can easily detect unique pathologic structures. In addition, contrast imaging and parametric mapping enable the characterization of tissue features such as scar, edema, infiltration, and deposition. In non-ischemic cardiomyopathies, metabolic and molecular nuclear imaging is used to diagnose rare but life-threatening conditions such amyloidosis and sarcoidosis. There is an expanding use of CCT for planning electrophysiology procedures such as cardioversion, ablations, and device placement. Furthermore, CCT can evaluate for complications associated with advanced heart failure therapies such as cardiac transplant and mechanical support devices. Innovations in multimodality cardiac imaging should lead to increased volumes and better outcomes.

Revolutionizing Cardiology through Artificial Intelligence-Big Data from Proactive Prevention to Precise Diagnostics and Cutting-Edge Treatment-A Comprehensive Review of the Past 5 Years

BACKGROUND

Artificial intelligence (AI) can radically change almost every aspect of the human experience. In the medical field, there are numerous applications of AI and subsequently, in a relatively short time, significant progress has been made. Cardiology is not immune to this trend, this fact being supported by the exponential increase in the number of publications in which the algorithms play an important role in data analysis, pattern discovery, identification of anomalies, and therapeutic decision making. Furthermore, with technological development, there have appeared new models of machine learning (ML) and deep learning (DP) that are capable of exploring various applications of AI in cardiology, including areas such as prevention, cardiovascular imaging, electrophysiology, interventional cardiology, and many others. In this sense, the present article aims to provide a general vision of the current state of AI use in cardiology.

RESULTS

We identified and included a subset of 200 papers directly relevant to the current research covering a wide range of applications. Thus, this paper presents AI applications in cardiovascular imaging, arithmology, clinical or emergency cardiology, cardiovascular prevention, and interventional procedures in a summarized manner. Recent studies from the highly scientific literature demonstrate the feasibility and advantages of using AI in different branches of cardiology.

CONCLUSIONS

The integration of AI in cardiology offers promising perspectives for increasing accuracy by decreasing the error rate and increasing efficiency in cardiovascular practice. From predicting the risk of sudden death or the ability to respond to cardiac resynchronization therapy to the diagnosis of pulmonary embolism or the early detection of valvular diseases, AI algorithms have shown their potential to mitigate human error and provide feasible solutions. At the same time, limits imposed by the small samples studied are highlighted alongside the challenges presented by ethical implementation; these relate to legal implications regarding responsibility and decision making processes, ensuring patient confidentiality and data security. All these constitute future research directions that will allow the integration of AI in the progress of cardiology.

Fiberscope-Based Measurement of Coaptation Height for Intraoperative Assessment of Mitral Valve Repair

BACKGROUND

Restoring adequate coaptation height is a key principle of mitral valve (MV) repair. This study aimed to evaluate the utility of fiberscope (FS) technology to assess MV coaptation height for intraoperative use.

METHODS

Ex-vivo testing was performed on five adult porcine hearts. The left atrium (LA) was resected, and the left ventricle (LV) was pressurized retrograde to 27 ± 1mm Hg. An endoscope was inserted into the LV apex, centered under the MV orifice. An FS system (Milliscope II camera, LED light source, and 0.7 mm diameter × 15 cm long) 90° semirigid scope with 1.2 mm focal length) was mounted above the MV annulus in a custom alignment and measuring fixture. Three blinded measurements were taken at two locations on each MV, A2 and P2 segment, from the top of coaptation to the leaflet edge identified by the FS. Accurate positioning was verified using the LV endoscope. A control (metal rod of similar thickness) was used for comparison, with coaptation height recorded when the control was seen via the endoscope.

RESULTS

Coaptation heights were similar for the control and FS methods across all hearts at A2 (11.6 ± 2.6 mm control vs 11.8 ± 2.2 mm FS) and P2 (13.3 ± 2.6 mm control vs 13.4 ± 2.9 mm FS) segments, with similar measurement variability (control SD 0.1-1.0 mm; FS SD 0.1-0.9 mm). One outlier was excluded from analysis (n = 19/20). The maximum absolute difference and percent error between measurement methods were less than 1.1 mm (median [IQR], 0.6 [0.3-0.9] mm) and less than 14% (4.1 [2.2-7.6]%).

CONCLUSIONS

Utilization of a miniaturized FS enabled precise and accurate quantification of MV coaptation. This technique is promising for evaluating post-repair valve competence and coaptation height.

Echocardiography in the neonatal unit: current status and future prospects

INTRODUCTION

Traditionally echocardiography was used by pediatric cardiologists to diagnose congenital heart defects in neonates. Formalized neonatal hemodynamic fellowships have been established where neonatologists acquire advanced echocardiographic skills to gain anatomical, physiological, and hemodynamic information in real time and utilize this information in making a timely and accurate physiology-based clinical decision.

AREA COVERED

Differences between a comprehensive formal structural echocardiography, neonatologist performed targeted echocardiography and limited assessment on point-of-care-ultrasonography for specific indications have been covered. This article is focused at providing a comprehensive review of the status of echocardiography in the neonatal units, recent advancements and its future prospects in the neonatal intensive care units.

EXPERT OPINION

Comprehensive guidelines providing the scope of practice, a framework for training, and robust clinical governance process for the neonatologist performed targeted echocardiography have been established. In the last decade, echocardiography has emerged as essential vital bedside diagnostic tool in providing high-quality care to the sick infants in the neonatal units, and it has proved to improve the outcomes in neonates. It is now being considered as a modern hemodynamic monitoring tool. Advances in technology, machine learning, and application of artificial intelligence in applications of echocardiography seem promising adjunct tools for rapid assessment in emergency situations.

Accuracy and Efficacy of Artificial Intelligence-Derived Automatic Measurements of Transthoracic Echocardiography in Routine Clinical Practice

Background: Transthoracic echocardiography (TTE) is the gold standard modality for evaluating cardiac morphology, function, and hemodynamics in clinical practice. While artificial intelligence (AI) is expected to contribute to improved accuracy and is being applied clinically, its impact on daily clinical practice has not been fully evaluated. Methods: We retrospectively examined 30 consecutive patients who underwent AI-equipped TTE at a single institution. All patients underwent manual and automatic measurements of TTE parameters using the AI-equipped TTE. Measurements were performed by three sonographers with varying experience levels: beginner, intermediate, and expert. Results: A comparison between the manual and automatic measurements assessed by the experts showed extremely high agreement in the left ventricular (LV) filling velocities (E wave: r = 0.998, A wave: r = 0.996; both p < 0.001). The automated measurements of LV end-diastolic and end-systolic diameters were slightly smaller (-2.41 mm and -1.19 mm) than the manual measurements, although without significant differences, and both methods showing high agreement (r = 0.942 and 0.977, both p < 0.001). However, LV wall thickness showed low agreement between the automated and manual measurements (septum: r = 0.670, posterior: r = 0.561; both p < 0.01), with automated measurements tending to be larger. Regarding interobserver variabilities, statistically significant agreement was observed among the measurements of expert, intermediate, and beginner sonographers for all the measurements. In terms of measurement time, automatic measurement significantly reduced measurement time compared to manual measurement (p < 0.001). Conclusions: This preliminary study confirms the accuracy and efficacy of AI-equipped TTE in routine clinical practice. A multicenter study with a larger sample size is warranted.

Unlocking the potential of artificial intelligence in sports cardiology: does it have a role in evaluating athlete's heart?

The integration of artificial intelligence (AI) technologies is evolving in different fields of cardiology and in particular in sports cardiology. Artificial intelligence offers significant opportunities to enhance risk assessment, diagnosis, treatment planning, and monitoring of athletes. This article explores the application of AI in various aspects of sports cardiology, including imaging techniques, genetic testing, and wearable devices. The use of machine learning and deep neural networks enables improved analysis and interpretation of complex datasets. However, ethical and legal dilemmas must be addressed, including informed consent, algorithmic fairness, data privacy, and intellectual property issues. The integration of AI technologies should complement the expertise of physicians, allowing for a balanced approach that optimizes patient care and outcomes. Ongoing research and collaborations are vital to harness the full potential of AI in sports cardiology and advance our management of cardiovascular health in athletes.

Rapid genomic sequencing for genetic disease diagnosis and therapy in intensive care units: a review

Single locus (Mendelian) diseases are a leading cause of childhood hospitalization, intensive care unit (ICU) admission, mortality, and healthcare cost. Rapid genome sequencing (RGS), ultra-rapid genome sequencing (URGS), and rapid exome sequencing (RES) are diagnostic tests for genetic diseases for ICU patients. In 44 studies of children in ICUs with diseases of unknown etiology, 37% received a genetic diagnosis, 26% had consequent changes in management, and net healthcare costs were reduced by $14,265 per child tested by URGS, RGS, or RES. URGS outperformed RGS and RES with faster time to diagnosis, and higher rate of diagnosis and clinical utility. Diagnostic and clinical outcomes will improve as methods evolve, costs decrease, and testing is implemented within precision medicine delivery systems attuned to ICU needs. URGS, RGS, and RES are currently performed in <5% of the ~200,000 children likely to benefit annually due to lack of payor coverage, inadequate reimbursement, hospital policies, hospitalist unfamiliarity, under-recognition of possible genetic diseases, and current formatting as tests rather than as a rapid precision medicine delivery system. The gap between actual and optimal outcomes in children in ICUs is currently increasing since expanded use of URGS, RGS, and RES lags growth in those likely to benefit through new therapies. There is sufficient evidence to conclude that URGS, RGS, or RES should be considered in all children with diseases of uncertain etiology at ICU admission. Minimally, diagnostic URGS, RGS, or RES should be ordered early during admissions of critically ill infants and children with suspected genetic diseases.

Strengths-weaknesses-opportunities-threats analysis of artificial intelligence in anesthesiology and perioperative medicine

The use of artificial intelligence (AI) and machine learning (ML) in anesthesiology and perioperative medicine is quickly becoming a mainstay of clinical practice. Anesthesiology is a data-rich medical specialty that integrates multitudes of patient-specific information. Perioperative medicine is ripe for applications of AI and ML to facilitate data synthesis for precision medicine and predictive assessments. Examples of emergent AI models include those that assist in assessing depth and modulating control of anesthetic delivery, event and risk prediction, ultrasound guidance, pain management, and operating room logistics. AI and ML support analyzing integrated perioperative data at scale and can assess patterns to deliver optimal patient-specific care. By exploring the benefits and limitations of this technology, we provide a basis of considerations for evaluating the adoption of AI models into various anesthesiology workflows. This analysis of AI and ML in anesthesiology and perioperative medicine explores the current landscape to understand better the strengths, weaknesses, opportunities, and threats (SWOT) these tools offer.

Closing the Last Mile Gap in Access to Multimodality Imaging in Rural Settings: Design of the Imaging Core of the Risk Underlying Rural Areas Longitudinal Study

Achieving optimal cardiovascular health in rural populations can be challenging for several reasons including decreased access to care with limited availability of imaging modalities, specialist physicians, and other important health care team members. Therefore, innovative solutions are needed to optimize health care and address cardiovascular health disparities in rural areas. Mobile examination units can bring imaging technology to underserved or remote communities with limited access to health care services. Mobile examination units can be equipped with a wide array of assessment tools and multiple imaging modalities such as computed tomography scanning and echocardiography. The detailed structural assessment of cardiovascular and lung pathology, as well as the detection of extracardiac pathology afforded by computed tomography imaging combined with the functional and hemodynamic assessments acquired by echocardiography, yield deep phenotyping of heart and lung disease for populations historically underrepresented in epidemiological studies. Moreover, by bringing the mobile examination unit to local communities, innovative approaches are now possible including engagement with local professionals to perform these imaging assessments, thereby augmenting local expertise and experience. However, several challenges exist before mobile examination unit-based examinations can be effectively integrated into the rural health care setting including standardizing acquisition protocols, maintaining consistent image quality, and addressing ethical and privacy considerations. Herein, we discuss the potential importance of cardiac multimodality imaging to improve cardiovascular health in rural regions, outline the emerging experience in this field, highlight important current challenges, and offer solutions based on our experience in the RURAL (Risk Underlying Rural Areas Longitudinal) cohort study.

Exercise Stress Echocardiography in Athletes: Applications, Methodology, and Challenges

This comprehensive review explores the role of exercise stress echocardiography (ESE) in assessing cardiovascular health in athletes. Athletes often exhibit cardiovascular adaptations because of rigorous physical training, making the differentiation between physiological changes and potential pathological conditions challenging. ESE is a crucial diagnostic tool, offering detailed insights into an athlete's cardiac function, reserve, and possible arrhythmias. This review highlights the methodology of ESE, emphasizing its significance in detecting exercise-induced anomalies and its application in distinguishing between athlete's heart and other cardiovascular diseases. Recent advancements, such as LV global longitudinal strain (GLS) and myocardial work (MW), are introduced as innovative tools for the early detection of latent cardiac dysfunctions. However, the use of ESE also subsumes limitations and possible pitfalls, particularly in interpretation and potential false results, as explained in this article.

The Relationship between Pulmonary Artery Pressure and Mortality in Type 2 Diabetes: A Fremantle Diabetes Study Phase II and National Echocardiographic Database of Australia Data Linkage Study

An elevated estimated right ventricular systolic pressure (eRVSP) identified on echocardiography is present in one-third of individuals with type 2 diabetes, but its prognostic significance is unknown. To assess the relationship between eRVSP and mortality, prospective data from 1732 participants in the Fremantle Diabetes Study Phase II were linked with the National Echocardiographic Database of Australia. Of this cohort, 416 (mean age 70.6 years, 47.4% males) had an eRVSP measured and 381 (91.4%) had previously confirmed type 2 diabetes. Receiver- operating characteristic analysis of the relationship between eRVSP and all-cause mortality was conducted. Survival analyses were performed for participants with type 2 diabetes diagnosed before first measured eRVSP (n = 349). Cox regression identified clinical and echocardiographic associates of all-cause mortality. There were 141 deaths (40.4%) during 2348 person-years (mean ± SD 6.7 ± 4.0 years) of follow-up. In unadjusted Kaplan-Meier analysis, mortality rose with higher eRVSP (log-rank test, p < 0.001). In unadjusted pairwise comparisons, eRVSP >30 to 35, >35 to 40, and >40 mmHg had significantly increased mortality compared with eRVSP ≤ 30 mmHg (p = 0.025, p = 0.001, p < 0.001, respectively). There were 50 deaths in 173 individuals (29.1%) with eRVSP ≤ 30 mmHg, and 91 in 177 (51.4%) with eRVSP > 30 mmHg (log-rank test, p < 0.001). In adjusted models including age, Aboriginal descent, Charlson Comorbidity Index ≥ 3 and left heart disease, eRVSP > 30 mmHg predicted a two-fold higher all-cause mortality versus ≤ 30 mmHg. An eRVSP > 30 mmHg predicts increased all-cause mortality in type 2 diabetes. Where available, eRVSP could inform type 2 diabetes outcome models.

The Echocardiographic Evaluation of the Right Heart: Current and Future Advances

PURPOSE OF REVIEW

To discuss physiologic and methodologic advances in the echocardiographic assessment of right heart (RH) function, including the emergence of artificial intelligence (AI) and point-of-care ultrasound.

RECENT FINDINGS

Recent studies have highlighted the prognostic value of right ventricular (RV) longitudinal strain, RV end-systolic dimensions, and right atrial (RA) size and function in pulmonary hypertension and heart failure. While RA pressure is a central marker of right heart diastolic function, the recent emphasis on venous excess imaging (VExUS) has provided granularity to the systemic consequences of RH failure. Several methodological advances are also changing the landscape of RH imaging including post-processing 3D software to delineate the non-longitudinal (radial, anteroposterior, and circumferential) components of RV function, as well as AI segmentation- and non-segmentation-based quantification. Together with recent guidelines and advances in AI technology, the field is shifting from specific RV functional metrics to integrated RH disease-specific phenotypes. A modern echocardiographic evaluation of RH function should focus on the entire cardiopulmonary venous unit-from the venous to the pulmonary arterial system. Together, a multi-parametric approach, guided by physiology and AI algorithms, will help define novel integrated RH profiles for improved disease detection and monitoring.

Artificial Intelligence-Based Prediction of Cardiovascular Diseases from Chest Radiography

Chest radiography (CXR) is the most frequently performed radiological test worldwide because of its wide availability, non-invasive nature, and low cost. The ability of CXR to diagnose cardiovascular diseases, give insight into cardiac function, and predict cardiovascular events is often underutilized, not clearly understood, and affected by inter- and intra-observer variability. Therefore, more sophisticated tests are generally needed to assess cardiovascular diseases. Considering the sustained increase in the incidence of cardiovascular diseases, it is critical to find accessible, fast, and reproducible tests to help diagnose these frequent conditions. The expanded focus on the application of artificial intelligence (AI) with respect to diagnostic cardiovascular imaging has also been applied to CXR, with several publications suggesting that AI models can be trained to detect cardiovascular conditions by identifying features in the CXR. Multiple models have been developed to predict mortality, cardiovascular morphology and function, coronary artery disease, valvular heart diseases, aortic diseases, arrhythmias, pulmonary hypertension, and heart failure. The available evidence demonstrates that the use of AI-based tools applied to CXR for the diagnosis of cardiovascular conditions and prognostication has the potential to transform clinical care. AI-analyzed CXRs could be utilized in the future as a complimentary, easy-to-apply technology to improve diagnosis and risk stratification for cardiovascular diseases. Such advances will likely help better target more advanced investigations, which may reduce the burden of testing in some cases, as well as better identify higher-risk patients who would benefit from earlier, dedicated, and comprehensive cardiovascular evaluation.

Embracing AI: The Imperative Tool for Echo Labs to Stay Ahead of the Curve

Advancements in artificial intelligence (AI) have rapidly transformed various sectors, and the field of echocardiography is no exception. AI-driven technologies hold immense potential to revolutionize echo labs' diagnostic capabilities and improve patient care. This paper explores the importance for echo labs to embrace AI and stay ahead of the curve in harnessing its power. Our manuscript provides an overview of the growing impact of AI on medical imaging, specifically echocardiography. It highlights how AI-driven algorithms can enhance image quality, automate measurements, and accurately diagnose cardiovascular diseases. Additionally, we emphasize the importance of training echo lab professionals in AI implementation to optimize its integration into routine clinical practice. By embracing AI, echo labs can overcome challenges such as workload burden and diagnostic accuracy variability, improving efficiency and patient outcomes. This paper highlights the need for collaboration between echocardiography laboratory experts, AI researchers, and industry stakeholders to drive innovation and establish standardized protocols for implementing AI in echocardiography. In conclusion, this article emphasizes the importance of AI adoption in echocardiography labs, urging practitioners to proactively integrate AI technologies into their workflow and take advantage of their present opportunities. Embracing AI is not just a choice but an imperative for echo labs to maintain their leadership and excel in delivering state-of-the-art cardiac care in the era of advanced medical technologies.

Artificial intelligence in clinical medicine: catalyzing a sustainable global healthcare paradigm

As the demand for quality healthcare increases, healthcare systems worldwide are grappling with time constraints and excessive workloads, which can compromise the quality of patient care. Artificial intelligence (AI) has emerged as a powerful tool in clinical medicine, revolutionizing various aspects of patient care and medical research. The integration of AI in clinical medicine has not only improved diagnostic accuracy and treatment outcomes, but also contributed to more efficient healthcare delivery, reduced costs, and facilitated better patient experiences. This review article provides an extensive overview of AI applications in history taking, clinical examination, imaging, therapeutics, prognosis and research. Furthermore, it highlights the critical role AI has played in transforming healthcare in developing nations.

Role of artificial intelligence in cardiology

Artificial intelligence (AI) is the process of having a computational program that can perform tasks of human intelligence by mimicking human thought processes. AI is a rapidly evolving transdisciplinary field which integrates many elements to develop algorithms that aim to simulate human intuition, decision-making, and object recognition. The overarching aims of AI in cardiovascular medicine are threefold: To optimize patient care, improve efficiency, and improve clinical outcomes. In cardiology, there has been a growth in the potential sources of new patient data, as well as advances in investigations and therapies, which position the field well to uniquely benefit from AI. In this editorial, we highlight some of the main research priorities currently and where the next steps are heading us.

Efficacy of Artificial Intelligence Software in the Automated Analysis of Left Ventricular Function in Echocardiography in Central Vietnam

Background

In recent years, there has been a significant focus on the development of artificial intelligence (AI) applications in healthcare. However, current scientific evidence is still not convincing enough for the general public and the medical community to widely adopt AI in clinical practice.

Objective

We conducted this study to investigate the correlation between left ventricular function indices assessed by AI and those evaluated by physicians.

Methods

This cross-sectional descriptive study was conducted on 136 patients who attended and received treatment at Hue University of Medicine and Pharmacy Hospital from April 2022 to June 2023. Using QLAB version 15, Philips Healthcare.

Results

The AI software accurately identified 98.5% of the echocardiographic cine-loops. However, there were about 1.5% of cine-loops that the software failed to recognize. The sensitivity of Ejection Fraction (EF) calculated by AI was 73.3%, specificity was 81.3%, and accuracy stood at 78.6%. A strong positive correlation was observed between EF measured by AI and that assessed by physicians, r = 0.701, p < 0.01. The sensitivity of Global Longitudinal Strain (GLS) calculated by AI was 42.1%, specificity was 84.8%, and accuracy was 67.6%. A moderate positive correlation was found between GLS measured by AI and physician's assessment, r = 0.460, p < 0.01.

Conclusion

The use of AI software for evaluating left ventricular function through ejection fraction and global longitudinal strain is rapid and yields results comparable to cardiologists' echocardiographic assessments. The AI-powered software holds a promising and feasible future in clinical practice.