Deep Learning-Based Automated Echocardiographic Quantification of Left Ventricular Ejection Fraction: A Point-of-Care Solution

Automated echocardiographic diastolic function grading: A hybrid multi-task deep learning and machine learning approach

BACKGROUND

Assessing left ventricular diastolic function (LVDF) with echocardiography as per ASE guidelines is tedious and time-consuming. The study aims to develop a fully automatic approach of this procedure by a lightweight hybrid algorithm combining deep learning (DL) and machine learning (ML).

METHODS

The model features multi-modality input and multi-task output, measuring LV ejection fraction (LVEF), left atrial end-systolic volume (LAESV), and Doppler parameters: mitral E wave velocity (E), A wave velocity (A), mitral annulus e' velocity (e'), and tricuspid regurgitation velocity (TRmax). The algorithm was trained and tested on two internal datasets (862 and 239 echocardiograms) and validated using three external datasets, including EchoNet-Dynamic and CAMUS. The ASE diastolic function decision tree and total probability theory were used to provide diastolic grading probabilities.

RESULTS

The algorithm, named MMnet, demonstrated high accuracy in both test and validation datasets, with Dice coefficients for segmentation between 0.922 and 0.932 and classification accuracies between 0.9977 and 1.0. The mean absolute errors (MAEs) for LVEF and LAESV were 3.7 % and 5.8 ml, respectively, and for LVEF in external validation, MAEs ranged from 4.9 % to 5.6 %. The diastolic function grading accuracy was 0.88 with hard criteria and up to 0.98 with soft criteria which account for the top two probability in total probability theory.

CONCLUSIONS

MMnet can automatically grade ASE diastolic function with high accuracy and efficiency by annotating 2D videos and Doppler images.

Leveraging the Capabilities of AI: Novice Neurology-Trained Operators Performing Cardiac POCUS in Patients with Acute Brain Injury

BACKGROUND

Cardiac point-of-care ultrasound (cPOCUS) can aid in the diagnosis and treatment of cardiac disorders. Such disorders can arise as complications of acute brain injury, but most neurologic intensive care unit (NICU) providers do not receive formal training in cPOCUS. Caption artificial intelligence (AI) uses a novel deep learning (DL) algorithm to guide novice cPOCUS users in obtaining diagnostic-quality cardiac images. The primary objective of this study was to determine how often NICU providers with minimal cPOCUS experience capture quality images using DL-guided cPOCUS as well as the association between DL-guided cPOCUS and change in management and time to formal echocardiograms in the NICU.

METHODS

From September 2020 to November 2021, neurology-trained physician assistants, residents, and fellows used DL software to perform clinically indicated cPOCUS scans in an academic tertiary NICU. Certified echocardiographers evaluated each scan independently to assess the quality of images and global interpretability of left ventricular function, right ventricular function, inferior vena cava size, and presence of pericardial effusion. Descriptive statistics with exact confidence intervals were used to calculate proportions of obtained images that were of adequate quality and that changed management. Time to first adequate cardiac images (either cPOCUS or formal echocardiography) was compared using a similar population from 2018.

RESULTS

In 153 patients, 184 scans were performed for a total of 943 image views. Three certified echocardiographers deemed 63.4% of scans as interpretable for a qualitative assessment of left ventricular size and function, 52.6% of scans as interpretable for right ventricular size and function, 34.8% of scans as interpretable for inferior vena cava size and variability, and 47.2% of scans as interpretable for the presence of pericardial effusion. Thirty-seven percent of screening scans changed management, most commonly adjusting fluid goals (81.2%). Time to first adequate cardiac images decreased significantly from 3.1 to 1.7 days (p < 0.001).

CONCLUSIONS

With DL guidance, neurology providers with minimal to no cPOCUS training were often able to obtain diagnostic-quality cardiac images, which informed management changes and significantly decreased time to cardiac imaging.

Challenges for augmenting intelligence in cardiac imaging

Artificial Intelligence (AI), through deep learning, has brought automation and predictive capabilities to cardiac imaging. However, despite considerable investment, tangible health-care cost reductions remain unproven. Although AI holds promise, there has been insufficient time for both methodological development and prospective clinical trials to establish its advantage over human interpretations in terms of its effect on patient outcomes. Challenges such as data scarcity, privacy issues, and ethical concerns impede optimal AI training. Furthermore, the absence of a unified model for the complex structure and function of the heart and evolving domain knowledge can introduce heuristic biases and influence underlying assumptions in model development. Integrating AI into diverse institutional picture archiving and communication systems and devices also presents a clinical hurdle. This hurdle is further compounded by an absence of high-quality labelled data, difficulty sharing data between institutions, and non-uniform and inadequate gold standards for external validations and comparisons of model performance in real-world settings. Nevertheless, there is a strong push in industry and academia for AI solutions in medical imaging. This Series paper reviews key studies and identifies challenges that require a pragmatic change in the approach for using AI for cardiac imaging, whereby AI is viewed as augmented intelligence to complement, not replace, human judgement. The focus should shift from isolated measurements to integrating non-linear and complex data towards identifying disease phenotypes-emphasising pattern recognition where AI excels. Algorithms should enhance imaging reports, enriching patients' understanding, communication between patients and clinicians, and shared decision making. The emergence of professional standards and guidelines is essential to address these developments and ensure the safe and effective integration of AI in cardiac imaging.

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).

The Promise of Artificial Intelligence-Assisted Point-of-Care Ultrasonography in Perioperative Care

The role of point-of-care ultrasonography in the perioperative setting has expanded rapidly over recent years. Revolutionizing this technology further is integrating artificial intelligence to assist clinicians in optimizing images, identifying anomalies, performing automated measurements and calculations, and facilitating diagnoses. Artificial intelligence can increase point-of-care ultrasonography efficiency and accuracy, making it an even more valuable point-of-care tool. Given this topic's importance and ever-changing landscape, this review discusses the latest trends to serve as an introduction and update in this area.

Artificial Intelligence and Its Role in Diagnosing Heart Failure: A Narrative Review

Heart failure (HF) is prevalent globally. It is a dynamic disease with varying definitions and classifications due to multiple pathophysiologies and etiologies. The diagnosis, clinical staging, and treatment of HF become complex and subjective, impacting patient prognosis and mortality. Technological advancements, like artificial intelligence (AI), have been significant roleplays in medicine and are increasingly used in cardiovascular medicine to transform drug discovery, clinical care, risk prediction, diagnosis, and treatment. Medical and surgical interventions specific to HF patients rely significantly on early identification of HF. Hospitalization and treatment costs for HF are high, with readmissions increasing the burden. AI can help improve diagnostic accuracy by recognizing patterns and using them in multiple areas of HF management. AI has shown promise in offering early detection and precise diagnoses with the help of ECG analysis, advanced cardiac imaging, leveraging biomarkers, and cardiopulmonary stress testing. However, its challenges include data access, model interpretability, ethical concerns, and generalizability across diverse populations. Despite these ongoing efforts to refine AI models, it suggests a promising future for HF diagnosis. After applying exclusion and inclusion criteria, we searched for data available on PubMed, Google Scholar, and the Cochrane Library and found 150 relevant papers. This review focuses on AI's significant contribution to HF diagnosis in recent years, drastically altering HF treatment and outcomes.

Left Ventricular Segmentation, Warping, and Myocardial Registration for Automated Strain Measurement

The left ventricular global longitudinal strain (LVGLS) is a crucial prognostic indicator. However, inconsistencies in measurements due to the speckle tracking algorithm and manual adjustments have hindered its standardization and democratization. To solve this issue, we proposed a fully automated strain measurement by artificial intelligence-assisted LV segmentation contours. The LV segmentation model was trained from echocardiograms of 368 adults (11,125 frames). We compared the registration-like effects of dynamic time warping (DTW) with speckle tracking on a synthetic echocardiographic dataset in experiment-1. In experiment-2, we enrolled 80 patients to compare the DTW method with commercially available software. In experiment-3, we combined the segmentation model and DTW method to create the artificial intelligence (AI)-DTW method, which was then tested on 40 patients with general LV morphology, 20 with dilated cardiomyopathy (DCMP), and 20 with transthyretin-associated cardiac amyloidosis (ATTR-CA), 20 with severe aortic stenosis (AS), and 20 with severe mitral regurgitation (MR). Experiments-1 and -2 revealed that the DTW method is consistent with dedicated software. In experiment-3, the AI-DTW strain method showed comparable results for general LV morphology (bias - 0.137 ± 0.398%), DCMP (- 0.397 ± 0.607%), ATTR-CA (0.095 ± 0.581%), AS (0.334 ± 0.358%), and MR (0.237 ± 0.490%). Moreover, the strain curves showed a high correlation in their characteristics, with R-squared values of 0.8879-0.9452 for those LV morphology in experiment-3. Measuring LVGLS through dynamic warping of segmentation contour is a feasible method compared to traditional tracking techniques. This approach has the potential to decrease the need for manual demarcation and make LVGLS measurements more efficient and user-friendly for daily practice.

Preliminary Evidence of the Use of Generative AI in Health Care Clinical Services: Systematic Narrative Review

BACKGROUND

Generative artificial intelligence tools and applications (GenAI) are being increasingly used in health care. Physicians, specialists, and other providers have started primarily using GenAI as an aid or tool to gather knowledge, provide information, train, or generate suggestive dialogue between physicians and patients or between physicians and patients' families or friends. However, unless the use of GenAI is oriented to be helpful in clinical service encounters that can improve the accuracy of diagnosis, treatment, and patient outcomes, the expected potential will not be achieved. As adoption continues, it is essential to validate the effectiveness of the infusion of GenAI as an intelligent technology in service encounters to understand the gap in actual clinical service use of GenAI.

OBJECTIVE

This study synthesizes preliminary evidence on how GenAI assists, guides, and automates clinical service rendering and encounters in health care The review scope was limited to articles published in peer-reviewed medical journals.

METHODS

We screened and selected 0.38% (161/42,459) of articles published between January 1, 2020, and May 31, 2023, identified from PubMed. We followed the protocols outlined in the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines to select highly relevant studies with at least 1 element on clinical use, evaluation, and validation to provide evidence of GenAI use in clinical services. The articles were classified based on their relevance to clinical service functions or activities using the descriptive and analytical information presented in the articles.

RESULTS

Of 161 articles, 141 (87.6%) reported using GenAI to assist services through knowledge access, collation, and filtering. GenAI was used for disease detection (19/161, 11.8%), diagnosis (14/161, 8.7%), and screening processes (12/161, 7.5%) in the areas of radiology (17/161, 10.6%), cardiology (12/161, 7.5%), gastrointestinal medicine (4/161, 2.5%), and diabetes (6/161, 3.7%). The literature synthesis in this study suggests that GenAI is mainly used for diagnostic processes, improvement of diagnosis accuracy, and screening and diagnostic purposes using knowledge access. Although this solves the problem of knowledge access and may improve diagnostic accuracy, it is oriented toward higher value creation in health care.

CONCLUSIONS

GenAI informs rather than assisting or automating clinical service functions in health care. There is potential in clinical service, but it has yet to be actualized for GenAI. More clinical service-level evidence that GenAI is used to streamline some functions or provides more automated help than only information retrieval is needed. To transform health care as purported, more studies related to GenAI applications must automate and guide human-performed services and keep up with the optimism that forward-thinking health care organizations will take advantage of GenAI.

Automatic measurements of left ventricular volumes and ejection fraction by artificial intelligence: clinical validation in real time and large databases

AIMS

Echocardiography is a cornerstone in cardiac imaging, and left ventricular (LV) ejection fraction (EF) is a key parameter for patient management. Recent advances in artificial intelligence (AI) have enabled fully automatic measurements of LV volumes and EF both during scanning and in stored recordings. The aim of this study was to evaluate the impact of implementing AI measurements on acquisition and processing time and test-retest reproducibility compared with standard clinical workflow, as well as to study the agreement with reference in large internal and external databases.

METHODS AND RESULTS

Fully automatic measurements of LV volumes and EF by a novel AI software were compared with manual measurements in the following clinical scenarios: (i) in real time use during scanning of 50 consecutive patients, (ii) in 40 subjects with repeated echocardiographic examinations and manual measurements by 4 readers, and (iii) in large internal and external research databases of 1881 and 849 subjects, respectively. Real-time AI measurements significantly reduced the total acquisition and processing time by 77% (median 5.3 min, P < 0.001) compared with standard clinical workflow. Test-retest reproducibility of AI measurements was superior in inter-observer scenarios and non-inferior in intra-observer scenarios. AI measurements showed good agreement with reference measurements both in real time and in large research databases.

CONCLUSION

The software reduced the time taken to perform and volumetrically analyse routine echocardiograms without a decrease in accuracy compared with experts.

Value Creation Through Artificial Intelligence and Cardiovascular Imaging: A Scientific Statement From the American Heart Association

Multiple applications for machine learning and artificial intelligence (AI) in cardiovascular imaging are being proposed and developed. However, the processes involved in implementing AI in cardiovascular imaging are highly diverse, varying by imaging modality, patient subtype, features to be extracted and analyzed, and clinical application. This article establishes a framework that defines value from an organizational perspective, followed by value chain analysis to identify the activities in which AI might produce the greatest incremental value creation. The various perspectives that should be considered are highlighted, including clinicians, imagers, hospitals, patients, and payers. Integrating the perspectives of all health care stakeholders is critical for creating value and ensuring the successful deployment of AI tools in a real-world setting. Different AI tools are summarized, along with the unique aspects of AI applications to various cardiac imaging modalities, including cardiac computed tomography, magnetic resonance imaging, and positron emission tomography. AI is applicable and has the potential to add value to cardiovascular imaging at every step along the patient journey, from selecting the more appropriate test to optimizing image acquisition and analysis, interpreting the results for classification and diagnosis, and predicting the risk for major adverse cardiac events.

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.

Enhancing Arrhythmogenic Right Ventricular Cardiomyopathy Detection and Risk Stratification: Insights from Advanced Echocardiographic Techniques

INTRODUCTION

The echocardiographic diagnosis criteria for arrhythmogenic right ventricular cardiomyopathy (ARVC) are highly specific but sensitivity is low, especially in the early stages of the disease. The role of echocardiographic strain in ARVC has not been fully elucidated, although prior studies suggest that it can improve the detection of subtle functional abnormalities. The purposes of the study were to determine whether these advanced measures of right ventricular (RV) dysfunction on echocardiogram, including RV strain, increase diagnostic value for ARVC disease detection and to evaluate the association of echocardiographic parameters with arrhythmic outcomes.

METHODS

The study included 28 patients from the Heart Institute of São Paulo ARVC cohort with a definite diagnosis of ARVC established according to the 2010 Task Force Criteria. All patients were submitted to ECHO's advanced techniques including RV strain, and the parameters were compared to prior conventional visual ECHO and CMR.

RESULTS

In total, 28 patients were enrolled in order to perform ECHO's advanced techniques. A total of 2/28 (7%) patients died due to a cardiovascular cause, 2/28 (7%) underwent heart transplantation, and 14/28 (50%) patients developed sustained ventricular arrhythmic events. Among ECHO's parameters, RV dilatation, measured by RVDd (p = 0.018) and RVOT PSAX (p = 0.044), was significantly associated with arrhythmic outcomes. RV free wall longitudinal strain < 14.35% in absolute value was associated with arrhythmic outcomes (p = 0.033).

CONCLUSION

Our data suggest that ECHO's advanced techniques improve ARVC detection and that abnormal RV strain can be associated with arrhythmic risk stratification. Further studies are necessary to better demonstrate these findings and contribute to risk stratification in ARVC, in addition to other well-known risk markers.

Assessment of left ventricular ejection fraction in artificial intelligence based on left ventricular opacification

Background

Left ventricular opacification (LVO) improves the accuracy of left ventricular ejection fraction (LVEF) by enhancing the visualization of the endocardium. Manual delineation of the endocardium by sonographers has observer variability. Artificial intelligence (AI) has the potential to improve the reproducibility of LVO to assess LVEF.

Objectives

The aim was to develop an AI model and evaluate the feasibility and reproducibility of LVO in the assessment of LVEF.

Methods

This retrospective study included 1305 echocardiography of 797 patients who had LVO at the Department of Ultrasound Medicine, Union Hospital, Huazhong University of Science and Technology from 2013 to 2021. The AI model was developed by 5-fold cross validation. The validation datasets included 50 patients prospectively collected in our center and 42 patients retrospectively collected in the external institution. To evaluate the differences between LV function determined by AI and sonographers, the median absolute error (MAE), spearman correlation coefficient, and intraclass correlation coefficient (ICC) were calculated.

Results

In LVO, the MAE of LVEF between AI and manual measurements was 2.6% in the development cohort, 2.5% in the internal validation cohort, and 2.7% in the external validation cohort. Compared with two-dimensional echocardiography (2DE), the left ventricular (LV) volumes and LVEF of LVO measured by AI correlated significantly with manual measurements. AI model provided excellent reliability for the LV parameters of LVO (ICC > 0.95).

Conclusions

AI-assisted LVO enables more accurate identification of the LV endocardium and reduces observer variability, providing a more reliable way for assessing LV function.

Artificial Intelligence-Powered Left Ventricular Ejection Fraction Analysis Using the LVivoEF Tool for COVID-19 Patients

We sought to prospectively investigate the accuracy of an artificial intelligence (AI)-based tool for left ventricular ejection fraction (LVEF) assessment using a hand-held ultrasound device (HUD) in COVID-19 patients and to examine whether reduced LVEF predicts the composite endpoint of in-hospital death, advanced ventilatory support, shock, myocardial injury, and acute decompensated heart failure. COVID-19 patients were evaluated with a real-time LVEF assessment using an HUD equipped with an AI-based tool vs. assessment by a blinded fellowship-trained echocardiographer. Among 42 patients, those with LVEF < 50% were older with more comorbidities and unfavorable exam characteristics. An excellent correlation was demonstrated between the AI and the echocardiographer LVEF assessment (0.774, p < 0.001). Substantial agreement was demonstrated between the two assessments (kappa = 0.797, p < 0.001). The sensitivity, specificity, PPV, and NPV of the HUD for this threshold were 72.7% 100%, 100%, and 91.2%, respectively. AI-based LVEF < 50% was associated with worse composite endpoints; unadjusted OR = 11.11 (95% CI 2.25-54.94), p = 0.003; adjusted OR = 6.40 (95% CI 1.07-38.09, p = 0.041). An AI-based algorithm incorporated into an HUD can be utilized reliably as a decision support tool for automatic real-time LVEF assessment among COVID-19 patients and may identify patients at risk for unfavorable outcomes. Future larger cohorts should verify the association with outcomes.

The Role of Artificial Intelligence in Echocardiography: A Clinical Update

PURPOSE OF REVIEW

In echocardiography, there has been robust development of artificial intelligence (AI) tools for image recognition, automated measurements, image segmentation, and patient prognostication that has created a monumental shift in the study of AI and machine learning models. However, integrating these measurements into complex disease recognition and therapeutic interventions remains challenging. While the tools have been developed, there is a lack of evidence regarding implementing heterogeneous systems for guiding clinical decision-making and therapeutic action.

RECENT FINDINGS

Newer AI modalities have shown concrete positive data in terms of user-guided image acquisition and processing, precise determination of both basic and advanced quantitative echocardiographic features, and the potential to construct predictive models, all with the possibility of seamless integration into clinical decision support systems. AI in echocardiography is a powerful and ever-growing tool with the potential for revolutionary effects on the practice of cardiology. In this review article, we explore the growth of AI and its applications in echocardiography, along with clinical implications and the associated regulatory, legal, and ethical considerations.

Real-Time Artificial Intelligence-Based Guidance of Echocardiographic Imaging by Novices: Image Quality and Suitability for Diagnostic Interpretation and Quantitative Analysis

BACKGROUND

We aimed to assess in a prospective multicenter study the quality of echocardiographic exams performed by inexperienced users guided by a new artificial intelligence software and evaluate their suitability for diagnostic interpretation of basic cardiac pathology and quantitative analysis of cardiac chamber and function.

METHODS

The software (UltraSight, Ltd) was embedded into a handheld imaging device (Lumify; Philips). Six nurses and 3 medical residents, who underwent minimal training, scanned 240 patients (61±16 years; 63% with cardiac pathology) in 10 standard views. All patients were also scanned by expert sonographers using the same device without artificial intelligence guidance. Studies were reviewed by 5 certified echocardiographers blinded to the imager's identity, who evaluated the ability to assess left and right ventricular size and function, pericardial effusion, valve morphology, and left atrial and inferior vena cava sizes. Finally, apical 4-chamber images of adequate quality, acquired by novices and sonographers in 100 patients, were analyzed to measure left ventricular volumes, ejection fraction, and global longitudinal strain by an expert reader using conventional methodology. Measurements were compared between novices' and experts' images.

RESULTS

Of the 240 studies acquired by novices, 99.2%, 99.6%, 92.9%, and 100% had sufficient quality to assess left ventricular size and function, right ventricular size, and pericardial effusion, respectively. Valve morphology, right ventricular function, and left atrial and inferior vena cava size were visualized in 67% to 98% exams. Images obtained by novices and sonographers yielded concordant diagnostic interpretation in 83% to 96% studies. Quantitative analysis was feasible in 83% images acquired by novices and resulted in high correlations (r≥0.74) and small biases, compared with those obtained by sonographers.

CONCLUSIONS

After minimal training with the real-time guidance software, novice users can acquire images of diagnostic quality approaching that of expert sonographers in most patients. This technology may increase adoption and improve accuracy of point-of-care cardiac ultrasound.

Real-time guidance by deep learning of experienced operators to improve the standardization of echocardiographic acquisitions

Aims

Impaired standardization of echocardiograms may increase inter-operator variability. This study aimed to determine whether the real-time guidance of experienced sonographers by deep learning (DL) could improve the standardization of apical recordings.

Methods and results

Patients (n = 88) in sinus rhythm referred for echocardiography were included. All participants underwent three examinations, whereof two were performed by sonographers and the third by cardiologists. In the first study period (Period 1), the sonographers were instructed to provide echocardiograms for the analyses of the left ventricular function. Subsequently, after brief training, the DL guidance was used in Period 2 by the sonographer performing the second examination. View standardization was quantified retrospectively by a human expert as the primary endpoint and the DL algorithm as the secondary endpoint. All recordings were scored in rotation and tilt both separately and combined and were categorized as standardized or non-standardized. Sonographers using DL guidance had more standardized acquisitions for the combination of rotation and tilt than sonographers without guidance in both periods (all P ≤ 0.05) when evaluated by the human expert and DL [except for the apical two-chamber (A2C) view by DL evaluation]. When rotation and tilt were analysed individually, A2C and apical long-axis rotation and A2C tilt were significantly improved, and the others were numerically improved when evaluated by the echocardiography expert. Furthermore, all, except for A2C rotation, were significantly improved when evaluated by DL (P < 0.01).

Conclusion

Real-time guidance by DL improved the standardization of echocardiographic acquisitions by experienced sonographers. Future studies should evaluate the impact with respect to variability of measurements and when used by less-experienced operators.

ClinicalTrialsgov Identifier

NCT04580095.

Deep Learning-Based Prediction of Right Ventricular Ejection Fraction Using 2D Echocardiograms

BACKGROUND

Evidence has shown the independent prognostic value of right ventricular (RV) function, even in patients with left-sided heart disease. The most widely used imaging technique to measure RV function is echocardiography; however, conventional 2-dimensional (2D) echocardiographic assessment is unable to leverage the same clinical information that 3-dimensional (3D) echocardiography-derived right ventricular ejection fraction (RVEF) can provide.

OBJECTIVES

The authors aimed to implement a deep learning (DL)-based tool to estimate RVEF from 2D echocardiographic videos. In addition, they benchmarked the tool's performance against human expert reading and evaluated the prognostic power of the predicted RVEF values.

METHODS

The authors retrospectively identified 831 patients with RVEF measured by 3D echocardiography. All 2D apical 4-chamber view echocardiographic videos of these patients were retrieved (n = 3,583), and each subject was assigned to either the training or the internal validation set (80:20 ratio). Using the videos, several spatiotemporal convolutional neural networks were trained to predict RVEF. The 3 best-performing networks were combined into an ensemble model, which was further evaluated in an external data set containing 1,493 videos of 365 patients with a median follow-up time of 1.9 years.

RESULTS

The ensemble model predicted RVEF with a mean absolute error of 4.57 percentage points in the internal and 5.54 percentage points in the external validation set. In the latter, the model identified RV dysfunction (defined as RVEF <45%) with an accuracy of 78.4%, which was comparable to an expert reader's visual assessment (77.0%; P = 0.678). The DL-predicted RVEF values were associated with major adverse cardiac events independent of age, sex, and left ventricular systolic function (HR: 0.924 [95% CI: 0.862-0.990]; P = 0.025).

CONCLUSIONS

Using 2D echocardiographic videos alone, the proposed DL-based tool can accurately assess RV function, with similar diagnostic and prognostic power as 3D imaging.

Artificial Intelligence Applications in Space Medicine

INTRODUCTION:During future interplanetary space missions, a number of health conditions may arise, owing to the hostile environment of space and the myriad of stressors experienced by the crew. When managing these conditions, crews will be required to make accurate, timely clinical decisions at a high level of autonomy, as telecommunication delays and increasing distances restrict real-time support from the ground. On Earth, artificial intelligence (AI) has proven successful in healthcare, augmenting expert clinical decision-making or enhancing medical knowledge where it is lacking. Similarly, deploying AI tools in the context of a space mission could improve crew self-reliance and healthcare delivery.METHODS: We conducted a narrative review to discuss existing AI applications that could improve the prevention, recognition, evaluation, and management of the most mission-critical conditions, including psychological and mental health, acute radiation sickness, surgical emergencies, spaceflight-associated neuro-ocular syndrome, infections, and cardiovascular deconditioning.RESULTS: Some examples of the applications we identified include AI chatbots designed to prevent and mitigate psychological and mental health conditions, automated medical imaging analysis, and closed-loop systems for hemodynamic optimization. We also discuss at length gaps in current technologies, as well as the key challenges and limitations of developing and deploying AI for space medicine to inform future research and innovation. Indeed, shifts in patient cohorts, space-induced physiological changes, limited size and breadth of space biomedical datasets, and changes in disease characteristics may render the models invalid when transferred from ground settings into space.Cheung HC, De Louche C, Komorowski M. Artificial intelligence applications in space medicine. Aerosp Med Hum Perform. 2023; 94(8):610-622.

Artificial intelligence in cardiovascular diseases: diagnostic and therapeutic perspectives

Artificial intelligence (AI), the technique of extracting information from complex database using sophisticated computer algorithms, has incorporated itself in medical field. AI techniques have shown the potential to accelerate the progression of diagnosis and treatment of cardiovascular diseases (CVDs), including heart failure, atrial fibrillation, valvular heart disease, hypertrophic cardiomyopathy, congenital heart disease and so on. In clinical scenario, AI have been proved to apply well in CVD diagnosis, enhance effectiveness of auxiliary tools, disease stratification and typing, and outcome prediction. Deeply developed to capture subtle connections from massive amounts of healthcare data, recent AI algorithms are expected to handle even more complex tasks than traditional methods. The aim of this review is to introduce current applications of AI in CVDs, which may allow clinicians who have limited expertise of computer science to better understand the frontier of the subject and put AI algorithms into clinical practice.

Machine learning for the real-time assessment of left ventricular ejection fraction in critically ill patients: a bedside evaluation by novices and experts in echocardiography

BACKGROUND

Machine learning algorithms have recently been developed to enable the automatic and real-time echocardiographic assessment of left ventricular ejection fraction (LVEF) and have not been evaluated in critically ill patients.

METHODS

Real-time LVEF was prospectively measured in 95 ICU patients with a machine learning algorithm installed on a cart-based ultrasound system. Real-time measurements taken by novices (LVEF_Nov) and by experts (LVEF_Exp) were compared with LVEF reference measurements (LVEF_Ref) taken manually by echo experts.

RESULTS

LVEF_Ref ranged from 26 to 80% (mean 54 ± 12%), and the reproducibility of measurements was 9 ± 6%. Thirty patients (32%) had a LVEF_Ref < 50% (left ventricular systolic dysfunction). Real-time LVEF_Exp and LVEF_Nov measurements ranged from 31 to 68% (mean 54 ± 10%) and from 28 to 70% (mean 54 ± 9%), respectively. The reproducibility of measurements was comparable for LVEF_Exp (5 ± 4%) and for LVEF_Nov (6 ± 5%) and significantly better than for reference measurements (p < 0.001). We observed a strong relationship between LVEF_Ref and both real-time LVEF_Exp (r = 0.86, p < 0.001) and LVEF_Nov (r = 0.81, p < 0.001). The average difference (bias) between real time and reference measurements was 0 ± 6% for LVEF_Exp and 0 ± 7% for LVEF_Nov. The sensitivity to detect systolic dysfunction was 70% for real-time LVEF_Exp and 73% for LVEF_Nov. The specificity to detect systolic dysfunction was 98% both for LVEF_Exp and LVEF_Nov.

CONCLUSION

Machine learning-enabled real-time measurements of LVEF were strongly correlated with manual measurements obtained by experts. The accuracy of real-time LVEF measurements was excellent, and the precision was fair. The reproducibility of LVEF measurements was better with the machine learning system. The specificity to detect left ventricular dysfunction was excellent both for experts and for novices, whereas the sensitivity could be improved.

TRIAL REGISTRATION

NCT05336448. Retrospectively registered on April 19, 2022.

Artificial intelligence for the echocardiographic assessment of valvular heart disease

Developments in artificial intelligence (AI) have led to an explosion of studies exploring its application to cardiovascular medicine. Due to the need for training and expertise, one area where AI could be impactful would be in the diagnosis and management of valvular heart disease. This is because AI can be applied to the multitude of data generated from clinical assessments, imaging and biochemical testing during the care of the patient. In the area of valvular heart disease, the focus of AI has been on the echocardiographic assessment and phenotyping of patient populations to identify high-risk groups. AI can assist image acquisition, view identification for review, and segmentation of valve and cardiac structures for automated analysis. Using image recognition algorithms, aortic and mitral valve disease states have been directly detected from the images themselves. Measurements obtained during echocardiographic valvular assessment have been integrated with other clinical data to identify novel aortic valve disease subgroups and describe new predictors of aortic valve disease progression. In the future, AI could integrate echocardiographic parameters with other clinical data for precision medical management of patients with valvular heart disease.

An artificial intelligence approach for predicting cardiotoxicity in breast cancer patients receiving anthracycline

Although anti-cancer therapy-induced cardiotoxicity is known, until now it lacks a reliable risk predictive model of the subsequent cardiotoxicity in breast cancer patients receiving anthracycline therapy. An artificial intelligence (AI) with a machine learning approach has yet to be applied in cardio-oncology. Herein, we aimed to establish a predictive model for differentiating patients at a high risk of developing cardiotoxicity, including cancer therapy-related cardiac dysfunction (CTRCD) and symptomatic heart failure with reduced ejection fraction. This prospective single-center study enrolled patients with newly diagnosed breast cancer who were preparing for anthracycline therapy from 2014 to 2018. We randomized the patients into a 70%/30% split group for ML model training and testing. We used 15 variables, including clinical, chemotherapy, and echocardiographic parameters, to construct a random forest model to predict CTRCD and heart failure with a reduced ejection fraction (HFrEF) during the 3-year follow-up period (median, 30 months). Comparisons of the predictive accuracies among the random forest, logistic regression, support-vector clustering (SVC), LightGBM, K-nearest neighbor (KNN), and multilayer perceptron (MLP) models were also performed. Notably, predicting CTRCD using the MLP model showed the best accuracy compared with the logistic regression, random forest, SVC, LightGBM, and KNN models. The areas under the curves (AUC) of MLP achieved 0.66 with the sensitivity and specificity as 0.86 and 0.53, respectively. Notably, among the features, the use of trastuzumab, hypertension, and anthracycline dose were the major determinants for the development of CTRCD in the logistic regression. Similarly, MLP, logistic regression, and SVM also showed higher AUCs for predicting the development of HFrEF. We also validated the AI prediction model with an additional set of patients developing HFrEF, and MLP presented an AUC of 0.81. Collectively, an AI prediction model is promising for facilitating physicians to predict CTRCD and HFrEF in breast cancer patients receiving anthracycline therapy. Further studies are warranted to evaluate its impact in clinical practice.

Artificial intelligence in echocardiography: Review and limitations including epistemological concerns

BACKGROUND AND PURPOSE

In this review we describe the use of artificial intelligence in the field of echocardiography. Various aspects and terminologies used in artificial intelligence are explained in an easy-to-understand manner and supplemented with illustrations related to echocardiography. Limitations of artificial intelligence, including epistemologic concerns from a philosophical standpoint, are also discussed.

METHODS

A narrative review of relevant papers was conducted.

CONCLUSION

We provide an overview of the usefulness of artificial intelligence in echocardiography and focus on how it can supplement current day-to-day clinical practice in the assessment of various cardiovascular disease entities. On the other hand, there are significant limitations, including epistemological concerns, which need to be kept in perspective.

Artificial Intelligence-Enhanced Echocardiography for Systolic Function Assessment

The accurate assessment of left ventricular systolic function is crucial in the diagnosis and treatment of cardiovascular diseases. Left ventricular ejection fraction (LVEF) and global longitudinal strain (GLS) are the most critical indexes of cardiac systolic function. Echocardiography has become the mainstay of cardiac imaging for measuring LVEF and GLS because it is non-invasive, radiation-free, and allows for bedside operation and real-time processing. However, the human assessment of cardiac function depends on the sonographer’s experience, and despite their years of training, inter-observer variability exists. In addition, GLS requires post-processing, which is time consuming and shows variability across different devices. Researchers have turned to artificial intelligence (AI) to address these challenges. The powerful learning capabilities of AI enable feature extraction, which helps to achieve accurate identification of cardiac structures and reliable estimation of the ventricular volume and myocardial motion. Hence, the automatic output of systolic function indexes can be achieved based on echocardiographic images. This review attempts to thoroughly explain the latest progress of AI in assessing left ventricular systolic function and differential diagnosis of heart diseases by echocardiography and discusses the challenges and promises of this new field.

Artificial Intelligence-Enhanced Echocardiography for Systolic Function Assessment

The accurate assessment of left ventricular systolic function is crucial in the diagnosis and treatment of cardiovascular diseases. Left ventricular ejection fraction (LVEF) and global longitudinal strain (GLS) are the most critical indexes of cardiac systolic function. Echocardiography has become the mainstay of cardiac imaging for measuring LVEF and GLS because it is non-invasive, radiation-free, and allows for bedside operation and real-time processing. However, the human assessment of cardiac function depends on the sonographer's experience, and despite their years of training, inter-observer variability exists. In addition, GLS requires post-processing, which is time consuming and shows variability across different devices. Researchers have turned to artificial intelligence (AI) to address these challenges. The powerful learning capabilities of AI enable feature extraction, which helps to achieve accurate identification of cardiac structures and reliable estimation of the ventricular volume and myocardial motion. Hence, the automatic output of systolic function indexes can be achieved based on echocardiographic images. This review attempts to thoroughly explain the latest progress of AI in assessing left ventricular systolic function and differential diagnosis of heart diseases by echocardiography and discusses the challenges and promises of this new field.

Machine learning algorithm using publicly available echo database for simplified “visual estimation” of left ventricular ejection fraction

BACKGROUND

Left ventricular ejection fraction calculation automation typically requires complex algorithms and is dependent of optimal visualization and tracing of endocardial borders. This significantly limits usability in bedside clinical applications, where ultrasound automation is needed most.

AIM

To create a simple deep learning (DL) regression-type algorithm to visually estimate left ventricular (LV) ejection fraction (EF) from a public database of actual patient echo examinations and compare results to echocardiography laboratory EF calculations.

METHODS

A simple DL architecture previously proven to perform well on ultrasound image analysis, VGG16, was utilized as a base architecture running within a long short term memory algorithm for sequential image (video) analysis. After obtaining permission to use the Stanford EchoNet-Dynamic database, researchers randomly removed approximately 15% of the approximately 10036 echo apical 4-chamber videos for later performance testing. All database echo examinations were read as part of comprehensive echocardiography study performance and were coupled with EF, end systolic and diastolic volumes, key frames and coordinates for LV endocardial tracing in csv file. To better reflect point-of-care ultrasound (POCUS) clinical settings and time pressure, the algorithm was trained on echo video correlated with calculated ejection fraction without incorporating additional volume, measurement and coordinate data. Seventy percent of the original data was used for algorithm training and 15% for validation during training. The previously randomly separated 15% (1263 echo videos) was used for algorithm performance testing after training completion. Given the inherent variability of echo EF measurement and field standards for evaluating algorithm accuracy, mean absolute error (MAE) and root mean square error (RMSE) calculations were made on algorithm EF results compared to Echo Lab calculated EF. Bland-Atlman calculation was also performed. MAE for skilled echocardiographers has been established to range from 4% to 5%.

RESULTS

The DL algorithm visually estimated EF had a MAE of 8.08% (95%CI 7.60 to 8.55) suggesting good performance compared to highly skill humans. The RMSE was 11.98 and correlation of 0.348.

CONCLUSION

This experimental simplified DL algorithm showed promise and proved reasonably accurate at visually estimating LV EF from short real time echo video clips. Less burdensome than complex DL approaches used for EF calculation, such an approach may be more optimal for POCUS settings once improved upon by future research and development.

Assessment and validation of a novel fast fully automated artificial intelligence left ventricular ejection fraction quantification software

BACKGROUND

Quantification of left ventricular ejection fraction (LVEF) by transthoracic echocardiography (TTE) is operator-dependent, time-consuming, and error-prone. LVivoEF by DIA is a new artificial intelligence (AI) software, which displays the tracking of endocardial borders and rapidly quantifies LVEF. We sought to assess the accuracy of LVivoEF compared to cardiac magnetic resonance imaging (cMRI) as the reference standard and to compare LVivoEF to the standard-of-care physician-measured LVEF (MD-EF) including studies with ultrasound enhancing agents (UEAs).

METHODS

In 273 consecutive patients, we compared MD-EF and AI-derived LVEF to cMRI. AI-derived LVEF was obtained from a non-UEA four-chamber view without manual correction. Thirty-one patients were excluded: 25 had interval interventions or incomplete TTE or cMRI studies and six had uninterpretable non-UEA apical views.

RESULTS

In the 242 subjects, the correlation between AI and cMRI was r = .890, similar to MD-EF and cMRI with r = .891 (p = 0.48). Of the 126 studies performed with UEAs, the correlation of AI using the unenhanced four-chamber view was r = .89, similar to MD-EF with r = .90. In the 116 unenhanced studies, AI correlation was r = .87, similar to MD-EF with r = .84. From Bland-Altman analysis, LVivoEF underreported the LVEF with a bias of 3.63 ± 7.40% EF points compared to cMRI while MD-EF to cMRI had a bias of .33 ± 7.52% (p = 0.80).

CONCLUSIONS

Compared to cMRI, LVivoEF can accurately quantify LVEF from a standard apical four-chamber view without manual correction. Thus, LVivoEF has the ability to improve and expedite LVEF quantification.

Machine Learning for Clinical Decision-Making: Challenges and Opportunities in Cardiovascular Imaging

The use of machine learning (ML) approaches to target clinical problems is called to revolutionize clinical decision-making in cardiology. The success of these tools is dependent on the understanding of the intrinsic processes being used during the conventional pathway by which clinicians make decisions. In a parallelism with this pathway, ML can have an impact at four levels: for data acquisition, predominantly by extracting standardized, high-quality information with the smallest possible learning curve; for feature extraction, by discharging healthcare practitioners from performing tedious measurements on raw data; for interpretation, by digesting complex, heterogeneous data in order to augment the understanding of the patient status; and for decision support, by leveraging the previous steps to predict clinical outcomes, response to treatment or to recommend a specific intervention. This paper discusses the state-of-the-art, as well as the current clinical status and challenges associated with the two later tasks of interpretation and decision support, together with the challenges related to the learning process, the auditability/traceability, the system infrastructure and the integration within clinical processes in cardiovascular imaging.

Artificial intelligence in echocardiography: detection, functional evaluation, and disease diagnosis

Ultrasound is one of the most important examinations for clinical diagnosis of cardiovascular diseases. The speed of image movements driven by the frequency of the beating heart is faster than that of other organs. This particularity of echocardiography poses a challenge for sonographers to diagnose accurately. However, artificial intelligence for detection, functional evaluation, and disease diagnosis has gradually become an alternative for accurate diagnosis and treatment using echocardiography. This work discusses the current application of artificial intelligence in echocardiography technology, its limitations, and future development directions.