Systolic function assessment is essential in children with congenital heart disease. Traditional methods of echocardiographic left ventricular ejection fraction (LVEF) estimation might overestimate systolic function compared to the gold standard of cardiac magnetic resonance imaging (CMR), especially in Tetralogy of Fallot (TOF). Deep learning technologies such as EchoNet-Dynamic offer more consistent cardiac evaluations and can potentially accurately predict LVEF using echocardiographic videos. The EchoNet-Dynamic/EchoNet-Peds models predict LVEF using echocardiograms with expert-measured LVEF as the ground truth. Using a transfer learning approach, we fine-tuned this model to predict LVEF with CMR-derived LVEF as ground truth and TOF echocardiograms as input images. For echocardiograms in the PSAX view, the model predicted CMR LVEF with an R2 of 0.79 and an MAE of 4.41. For the A4C view, the model predicted CMR LVEF with an R2 of 0.53 and an MAE of 6.4. Plotted ROC curves indicate that both tuned models differentiated well between normal and reduced LVEF. This study shows the potential of Convolutional Neural Network (CNN) models in transforming the field of cardiac imaging interpretation via a hybrid approach using the CMR labels and echocardiogram videos offering advancements over conventional methods.

Artificial intelligence (AI) has shown transformative potential in various medical fields, including diagnostic imaging. Recent advances in AI-driven technologies have opened new avenues for improving echocardiographic practices. AI algorithms enhance the image quality, automate measurements, and assist in the diagnosis of cardiovascular diseases. These technologies reduce manual errors, increase consistency, and match the diagnostic performances of experienced echocardiographers. AI in tele-echocardiography offers significant benefits, particularly in rural and remote regions in Japan, where healthcare provider shortages and geographic isolation hinder access to advanced medical care. AI enhances accessibility, provides real-time remote analyses, supports continuous monitoring, and improves the quality and efficiency of remotely delivered cardiac care. However, addressing challenges related to data security, transparency, integration into clinical workflows, and ethical considerations is essential for the successful implementation of AI in echocardiography. On overcoming these challenges, AI will be able to revolutionize echocardiography and ensure timely and effective cardiac care for all patients in the future.

The artificial intelligence (AI) technology in automated measurements has seen remarkable advancements across various vendors, thereby offering new opportunities in echocardiography. Fully automated software particularly has the potential to elevate the analysis and the interpretation of medical images to a new level compared to previous algorithms. Tasks that traditionally required significant time, such as ventricular and atrial volume measurements and Doppler tracing, can now be performed swiftly through AI's automated phase setting and waveform tracing capabilities. The benefits of AI-driven systems include high-precision and reliable measurements, significant time savings, and enhanced workflow efficiency. By automating routine tasks, AI can reduce the burden on clinicians, allowing them to gather additional information, perform additional tests, and improve patient care. While many studies confirm the accuracy and the reproducibility of AI-driven techniques, it is crucial for clinicians to verify AI-generated measurements and ensure high-quality imaging and Doppler waveforms to fully take advantage of the benefits from these technologies. This review discusses the current state of AI-driven automated measurements in echocardiography, their impact on clinical practice, and the strategies required for the effective integration of AI into clinical workflows.

Accurate identification of pulmonary arterial hypertension (PAH) in primary care and rural areas can be a challenging task. However, recent advancements in computer vision offer the potential for automated systems to detect PAH from echocardiography.

Our aim was to develop a precise and efficient diagnostic model for PAH tailored to the unique requirements of intelligent diagnosis, especially in challenging locales like high-altitude regions.

We proposed the Chamber Attention Network (CAN) for PAH identification from echocardiographic images, trained on a dataset comprising 13,912 individual subjects. A convolutional neural network (CNN) for view classification was used to select the clinically relevant apical four chamber (A4C) and parasternal long axis (PLAX) views for PAH diagnosis. To assess the importance of different heart chambers in PAH diagnosis, we developed a novel Chamber Attention Module.

The experimental results demonstrated that: 1) The substantial correspondence between our obtained chamber attention vector and clinical expertise suggested that our model was highly interpretable, potentially uncovering diagnostic insights overlooked by the clinical community. 2) The proposed CAN model exhibited superior image-level accuracy and faster convergence on the internal validation dataset compared to the other four models. Furthermore, our CAN model outperformed the others on the external test dataset, with image-level accuracies of 82.53% and 83.32% for A4C and PLAX, respectively. 3) Implementation of the voting strategy notably enhanced the positive predictive value (PPV) and negative predictive value (NPV) of individual-level classification results, enhancing the reliability of our classification outcomes.

These findings indicate that CAN is a feasible technique for AI-assisted PAH diagnosis, providing new insights into cardiac structural changes observed in echocardiography.

Atrial fibrillation (AF) is a prevalent cardiac arrhythmia characterized by irregular atrial electrical activity, posing significant challenges to patient management and healthcare systems worldwide. Non-rheumatic AF, distinct from AF due to rheumatic heart disease, encompasses a spectrum of etiologies, including hypertension, coronary artery disease, and structural heart abnormalities. This review examines the latest advancements in managing non-rheumatic AF, encompassing diagnostic approaches, pharmacological therapies, and innovative non-pharmacological interventions. Diagnostic strategies ranging from traditional electrocardiography to advanced imaging modalities are explored alongside emerging biomarkers and wearable technologies facilitating early detection and management. Pharmacological management options, including novel anticoagulants and rhythm control agents, are evaluated in light of current guidelines and recent clinical trials. Non-pharmacological interventions, such as catheter ablation and device-based therapies, are discussed regarding their evolving techniques and outcomes. Special considerations for diverse patient populations, including elderly individuals and athletes, are addressed, emphasizing personalized approaches to optimize therapeutic outcomes. The review concludes with insights into future directions for AF management, highlighting promising avenues in gene therapy, regenerative medicine, and precision medicine approaches. By synthesizing recent research findings and clinical innovations, this review provides a comprehensive overview of the dynamic landscape of non-rheumatic AF management, offering insights for clinicians, researchers, and healthcare stakeholders.

The quantification of left ventricular ejection fraction (LVEF) has important clinical utility in the assessment of cardiac function and is vital for the diagnosis of cardiovascular diseases. A transthoracic echocardiogram serves as the most commonly used tool for LVEF assessment for several reasons, including, its noninvasive nature, great safety profile, real-time image processing ability, portability, and cost-effectiveness. However, transthoracic echocardiogram is highly dependent on the clinical skill of the sonographer and interpreting physician. Moreover, even amongst well-trained clinicians, significant interobserver variability exists in the quantification of LVEF. In search of possible solutions, the usage of artificial intelligence (AI) has been increasingly tested in the clinical setting. While AI-derived ejection fraction is in the preliminary stages of development, it has shown promise in its ability to rapidly quantify LVEF, decrease variability, increase accuracy, and utilize higher-order processing capabilities. This review will delineate the latest advancements of AI in evaluating LVEF through echocardiography and explore the challenges and future trajectory of this emerging domain.

Since the 2009 publication of the stress echocardiography expert consensus of the European Association of Echocardiography, and after the 2016 advice of the American Society of Echocardiography-European Association of Cardiovascular Imaging for applications beyond coronary artery disease, new information has become available regarding stress echo. Until recently, the assessment of regional wall motion abnormality was the only universally practiced step of stress echo. In the state-of-the-art ABCDE protocol, regional wall motion abnormality remains the main step A, but at the same time, regional perfusion using ultrasound-contrast agents may be assessed. Diastolic function and pulmonary B-lines are assessed in step B; left ventricular contractile and preload reserve with volumetric echocardiography in step C; Doppler-based coronary flow velocity reserve in the left anterior descending coronary artery in step D; and ECG-based heart rate reserve in non-imaging step E. These five biomarkers converge, conceptually and methodologically, in the ABCDE protocol allowing comprehensive risk stratification of the vulnerable patient with chronic coronary syndromes. The present document summarizes current practice guidelines recommendations and training requirements and harmonizes the clinical guidelines of the European Society of Cardiology in many diverse cardiac conditions, from chronic coronary syndromes to valvular heart disease. The continuous refinement of imaging technology and the diffusion of ultrasound-contrast agents improve image quality, feasibility, and reader accuracy in assessing wall motion and perfusion, left ventricular volumes, and coronary flow velocity. Carotid imaging detects pre-obstructive atherosclerosis and improves risk prediction similarly to coronary atherosclerosis. The revolutionary impact of artificial intelligence on echocardiographic image acquisition and analysis makes stress echo more operator-independent and objective. Stress echo has unique features of low cost, versatility, and universal availability. It does not need ionizing radiation exposure and has near-zero carbon dioxide emissions. Stress echo is a convenient and sustainable choice for functional testing within and beyond coronary artery disease.

The integration of artificial intelligence (AI) into clinical management of aortic stenosis (AS) has redefined our approach to the assessment and management of this heterogenous valvular heart disease (VHD). While the large-scale early detection of valvular conditions is limited by socioeconomic constraints, AI offers a cost-effective alternative solution for screening by utilizing conventional tools, including electrocardiograms and community-level auscultations, thereby facilitating early detection, prevention, and treatment of AS. Furthermore, AI sheds light on the varied nature of AS, once considered a uniform condition, allowing for more nuanced, data-driven risk assessments and treatment plans. This presents an opportunity to re-evaluate the complexity of AS and to refine treatment using data-driven risk stratification beyond traditional guidelines. AI can be used to support treatment decisions including device selection, procedural techniques, and follow-up surveillance of transcatheter aortic valve replacement (TAVR) in a reproducible manner. While recognizing notable AI achievements, it is important to remember that AI applications in AS still require collaboration with human expertise due to potential limitations such as its susceptibility to bias, and the critical nature of healthcare. This synergy underpins our optimistic view of AI's promising role in the AS clinical pathway.

DUS measurements for popliteal artery aneurysms (PAAs) specifically can be time-consuming, error-prone, and operator-dependent. To eliminate this subjectivity and provide efficient segmentation, we applied artificial intelligence (AI) to accurately delineate inner and outer lumen on DUS. DUS images were selected from a cohort of patients with PAAs from a multi-institutional platform. Encord is an easy-to-use, readily available online AI platform that was used to segment both the inner lumen and outer lumen of the PAA on DUS images. A model trained on 20 images and tested on 80 images had a mean Average Precision of 0.85 for the outer polygon and 0.23 for the inner polygon. The outer polygon had a higher recall score than precision score at 0.90 and 0.85, respectively. The inner polygon had a score of 0.25 for both precision and recall. The outer polygon false-negative rate was the lowest in images with the least amount of blur. This study demonstrates the feasibility of using the widely available Encord AI platform to identify standard features of PAAs that are critical for operative decision making.

In addition to the traditional clinical risk factors, an increasing amount of imaging biomarkers have shown value for cardiovascular risk prediction. Clinical and imaging data are captured from a variety of data sources during multiple patient encounters and are often analyzed independently. Initial studies showed that fusion of both clinical and imaging features results in superior prognostic performance compared with traditional scores. There are different approaches to fusion modeling, combining multiple data resources to optimize predictions, each with its own advantages and disadvantages. However, manual extraction of clinical and imaging data is time and labor intensive and often not feasible in clinical practice. An automated approach for clinical and imaging data extraction is highly desirable. Convolutional neural networks and natural language processing can be utilized for the extraction of electronic medical record data, imaging studies, and free-text data. This review outlines the current status of cardiovascular risk prediction and fusion modeling; and in addition gives an overview of different artificial intelligence approaches to automatically extract data from images and electronic medical records for this purpose.

Understanding exercise physiology as it relates to adult congenital heart disease (ACHD) can be complex. Here we review fundamental physiologic principles and provide a framework for application to the unique ACHD patient population.

ACHD exercise participation has changed dramatically in the last 50 years. A modern approach focuses on exercise principles and individual anatomic and physiologic considerations. With an evolving better understanding of ACHD exercise physiology, we can strategize plans for patients to participate in dynamic and static exercises. Newly developed technologies including wearable devices provide additive information for ACHD providers for further assessment and monitoring. Preparation and assessment for ACHD patients prior to exercise require a thoughtful, personalized approach. Exercise prescriptions can be formulated to adequately meet the needs of our patients.

Current noninvasive estimation of right atrial pressure (RAP) by inferior vena cava (IVC) measurement during echocardiography may have significant inter-rater variability due to different levels of observers' experience. Therefore, there is a need to develop new approaches to decrease the variability of IVC analysis and RAP estimation. This study aims to develop a fully automated artificial intelligence (AI)-based system for automated IVC analysis and RAP estimation. We presented a multi-stage AI system to identify the IVC view, select good quality images, delineate the IVC region and quantify its thickness, enabling temporal tracking of its diameter and collapsibility changes. The automated system was trained and tested on expert manual IVC and RAP reference measurements obtained from 255 patients during routine clinical workflow. The performance was evaluated using Pearson correlation and Bland-Altman analysis for IVC values, as well as macro accuracy and chi-square test for RAP values. Our results show an excellent agreement (r=0.96) between automatically computed versus manually measured IVC values, and Bland-Altman analysis showed a small bias of [Formula: see text]0.33 mm. Further, there is an excellent agreement ([Formula: see text]) between automatically estimated versus manually derived RAP values with a macro accuracy of 0.85. The proposed AI-based system accurately quantified IVC diameter, collapsibility index, both are used for RAP estimation. This automated system could serve as a paradigm to perform IVC analysis in routine echocardiography and support various cardiac diagnostic applications.

Zoonotic diseases, transmitted between humans and animals, pose a substantial threat to global public health. In recent years, artificial intelligence (AI) has emerged as a transformative tool in the fight against diseases. This comprehensive review discusses the innovative applications of AI in the management of zoonotic diseases, including disease prediction, early diagnosis, drug development, and future prospects. AI-driven predictive models leverage extensive datasets to predict disease outbreaks and transmission patterns, thereby facilitating proactive public health responses. Early diagnosis benefits from AI-powered diagnostic tools that expedite pathogen identification and containment. Furthermore, AI technologies have accelerated drug discovery by identifying potential drug targets and optimizing candidate drugs. This review addresses these advancements, while also examining the promising future of AI in zoonotic disease control. We emphasize the pivotal role of AI in revolutionizing our approach to managing zoonotic diseases and highlight its potential to safeguard the health of both humans and animals on a global scale.

Prenatal ultrasonography is the most crucial imaging modality during pregnancy. However, problems such as high fetal mobility, excessive maternal abdominal wall thickness, and inter-observer variability limit the development of traditional ultrasound in clinical applications. The combination of artificial intelligence (AI) and obstetric ultrasound may help optimize fetal ultrasound examination by shortening the examination time, reducing the physician's workload, and improving diagnostic accuracy. AI has been successfully applied to automatic fetal ultrasound standard plane detection, biometric parameter measurement, and disease diagnosis to facilitate conventional imaging approaches. In this review, we attempt to thoroughly review the applications and advantages of AI in prenatal fetal ultrasound and discuss the challenges and promises of this new field.

To improve monitoring of cardiac function during major surgery and intensive care, we have developed a method for fully automatic estimation of mitral annular plane systolic excursion (auto-MAPSE) using deep learning in transoesophageal echocardiography (TOE). The aim of this study was a clinical validation of auto-MAPSE in patients with heart disease.

TOE recordings were collected from 185 consecutive patients without selection on image quality. Deep-learning-based auto-MAPSE was trained and optimized from 105 patient recordings. We assessed auto-MAPSE feasibility, and agreement and inter-rater reliability with manual reference in 80 patients with and without electrocardiogram (ECG) tracings. Mean processing time for auto-MAPSE was 0.3 s per cardiac cycle/view. Overall feasibility was >90% for manual MAPSE and ECG-enabled auto-MAPSE and 82% for ECG-disabled auto-MAPSE. Feasibility in at least two walls was ≥95% for all methods. Compared with manual reference, bias [95% limits of agreement (LoA)] was -0.5 [-4.0, 3.1] mm for ECG-enabled auto-MAPSE and -0.2 [-4.2, 3.6] mm for ECG-disabled auto-MAPSE. Intra-class correlation coefficient (ICC) for consistency was 0.90 and 0.88, respectively. Manual inter-observer bias [95% LoA] was -0.9 [-4.7, 3.0] mm, and ICC was 0.86.

Auto-MAPSE was fast and highly feasible. Inter-rater reliability between auto-MAPSE and manual reference was good. Agreement between auto-MAPSE and manual reference did not differ from manual inter-observer agreement. As the principal advantages of deep-learning-based assessment are speed and reproducibility, auto-MAPSE has the potential to improve real-time monitoring of left ventricular function. This should be investigated in relevant clinical settings.

Looking at the extremely large amount of literature, as summarized in two recent reviews on applications of Artificial Intelligence in Cardiology, both in the adult and pediatric age groups, published in the Journal of Clinical Medicine [...].

Echocardiography is the most frequently used imaging modality in cardiology. However, its acquisition is affected by inter-observer variability and largely dependent on the operator's experience. In this context, artificial intelligence techniques could reduce these variabilities and provide a user independent system. In recent years, machine learning (ML) algorithms have been used in echocardiography to automate echocardiographic acquisition. This review focuses on the state-of-the-art studies that use ML to automate tasks regarding the acquisition of echocardiograms, including quality assessment (QA), recognition of cardiac views and assisted probe guidance during the scanning process. The results indicate that performance of automated acquisition was overall good, but most studies lack variability in their datasets. From our comprehensive review, we believe automated acquisition has the potential not only to improve accuracy of diagnosis, but also help novice operators build expertise and facilitate point of care healthcare in medically underserved areas.

Technical advances in artificial intelligence (AI) in cardiac imaging are rapidly improving the reproducibility of this approach and the possibility to reduce time necessary to generate a report. In cardiac computed tomography angiography (CCTA) the main application of AI in clinical practice is focused on detection of stenosis, characterization of coronary plaques, and detection of myocardial ischemia. In cardiac magnetic resonance (CMR) the application of AI is focused on post-processing and particularly on the segmentation of cardiac chambers during late gadolinium enhancement. In echocardiography, the application of AI is focused on segmentation of cardiac chambers and is helpful for valvular function and wall motion abnormalities. The common thread represented by all of these techniques aims to shorten the time of interpretation without loss of information compared to the standard approach. In this review we provide an overview of AI applications in multimodality cardiac imaging.

With the prevalence of digitalization in all aspects of modern society, health assessment is becoming digital too. Taking advantage of the most recent technological advances and approaching medicine from an interdisciplinary perspective has allowed for important progress in healthcare services. Digital health technologies and biotelemetry devices have been more extensively employed for preventing, detecting, diagnosing, monitoring, and predicting the evolution of various diseases, without requiring wires, invasive procedures, or face-to-face interaction with medical personnel. This paper aims to review the concepts correlated to digital health, classify and describe biotelemetry devices, and present the potential of digitalization for remote health assessment, the transition to personalized medicine, and the streamlining of clinical trials.

Artificial intelligence has the potential to revolutionize modern society in all its aspects. Encouraged by the variety and vast amount of data that can be gathered from patients (e.g., medical images, text, and electronic health records), researchers have recently increased their interest in developing AI solutions for clinical care. Moreover, a diverse repertoire of methods can be chosen towards creating performant models for use in medical applications, ranging from disease prediction, diagnosis, and prognosis to opting for the most appropriate treatment for an individual patient. In this respect, the present paper aims to review the advancements reported at the convergence of AI and clinical care. Thus, this work presents AI clinical applications in a comprehensive manner, discussing the recent literature studies classified according to medical specialties. In addition, the challenges and limitations hindering AI integration in the clinical setting are further pointed out.

Multisystem inflammatory syndrome in children (MIS-C) is one of the most significant sequela of coronavirus disease 2019 (COVID-19) in children. Emerging literature has described myocardial dysfunction in MIS-C patients using traditional and two-dimensional speckle tracking echocardiography in the acute phase. However, data regarding persistence of subclinical myocardial injury after recovery is limited. We aimed to detect these changes with deformation imaging, hypothesizing that left ventricular global longitudinal (GLS) and circumferential strain (GCS) would remain impaired in the chronic phase despite normalization of ventricular function parameters assessed by two-dimensional echocardiography. A retrospective, single-institution review of 22 patients with MIS-C was performed. Fractional shortening, GLS, and GCS, along with regional longitudinal (RLS) and circumferential strain (RCS) were compared across the acute, subacute, and chronic timepoints (presentation, 14-42, and > 42 days, respectively). Mean GLS improved from - 18.4% in the acute phase to - 20.1% in the chronic phase (p = 0.4). Mean GCS improved from - 19.4% in the acute phase to - 23.5% in the chronic phase (p = 0.03). RCS and RLS were impaired in the acute phase and showed a trend towards recovery by the chronic phase, with the exception of the basal anterolateral segment. In our longitudinal study of MIS-C patients, GLS and GCS were lower in the acute phase, corroborating with left ventricular dysfunction by traditional measures. Additionally, as function globally recovers, GLS and GCS also normalize. However, some regional segments continue to have decreased strain values which may be an important subclinical marker for future adverse events.