-
Nature Medicine May 2024The development of robust artificial intelligence models for echocardiography has been limited by the availability of annotated clinical data. Here, to address this...
The development of robust artificial intelligence models for echocardiography has been limited by the availability of annotated clinical data. Here, to address this challenge and improve the performance of cardiac imaging models, we developed EchoCLIP, a vision-language foundation model for echocardiography, that learns the relationship between cardiac ultrasound images and the interpretations of expert cardiologists across a wide range of patients and indications for imaging. After training on 1,032,975 cardiac ultrasound videos and corresponding expert text, EchoCLIP performs well on a diverse range of benchmarks for cardiac image interpretation, despite not having been explicitly trained for individual interpretation tasks. EchoCLIP can assess cardiac function (mean absolute error of 7.1% when predicting left ventricular ejection fraction in an external validation dataset) and identify implanted intracardiac devices (area under the curve (AUC) of 0.84, 0.92 and 0.97 for pacemakers, percutaneous mitral valve repair and artificial aortic valves, respectively). We also developed a long-context variant (EchoCLIP-R) using a custom tokenizer based on common echocardiography concepts. EchoCLIP-R accurately identified unique patients across multiple videos (AUC of 0.86), identified clinical transitions such as heart transplants (AUC of 0.79) and cardiac surgery (AUC 0.77) and enabled robust image-to-text search (mean cross-modal retrieval rank in the top 1% of candidate text reports). These capabilities represent a substantial step toward understanding and applying foundation models in cardiovascular imaging for preliminary interpretation of echocardiographic findings.
Topics: Humans; Echocardiography; Image Interpretation, Computer-Assisted; Artificial Intelligence
PubMed: 38689062
DOI: 10.1038/s41591-024-02959-y -
Journal of Cardiothoracic and Vascular... Oct 2023Cardiogenic shock causes hypoperfusion within the microcirculation, leading to impaired oxygen delivery, cell death, and progression of multiple organ failure.... (Review)
Review
Cardiogenic shock causes hypoperfusion within the microcirculation, leading to impaired oxygen delivery, cell death, and progression of multiple organ failure. Mechanical circulatory support (MCS) is the last line of treatment for cardiac failure. The goal of MCS is to ensure end-organ perfusion by maintaining perfusion pressure and total blood flow. However, machine-blood interactions and the nonobvious translation of global macrohemodynamics into the microcirculation suggest that the use of MCS may not necessarily be associated with improved capillary flow. With the use of hand-held vital microscopes, it is possible to assess the microcirculation at the bedside. The paucity of literature on the use of microcirculatory assessment suggests the need for an in-depth look into microcirculatory assessment within the context of MCS. The purpose of this review is to discuss the possible interactions between MCS and microcirculation, as well as to describe the research conducted in this area. Regarding sublingual microcirculation, 3 types of MCS will be discussed: venoarterial extracorporeal membrane oxygenation, intra-aortic balloon counterpulsation, and microaxial flow pumps (Impella).
Topics: Humans; Microcirculation; Mouth Floor; Shock, Cardiogenic; Heart Failure; Hemodynamics; Heart-Assist Devices; Intra-Aortic Balloon Pumping
PubMed: 37330330
DOI: 10.1053/j.jvca.2023.05.028 -
European Heart Journal. Cardiovascular... Sep 2023Heart failure demographics have evolved in past decades with the development of improved diagnostics, therapies, and prevention. Cardiac magnetic resonance (CMR) has... (Review)
Review
Heart failure demographics have evolved in past decades with the development of improved diagnostics, therapies, and prevention. Cardiac magnetic resonance (CMR) has developed in a similar timeframe to become the gold-standard non-invasive imaging modality for characterizing diseases causing heart failure. CMR techniques to assess cardiac morphology and function have progressed since their first use in the 1980s. Increasingly efficient acquisition protocols generate high spatial and temporal resolution images in less time. This has enabled new methods of characterizing cardiac systolic and diastolic function such as strain analysis, exercise real-time cine imaging and four-dimensional flow. A key strength of CMR is its ability to non-invasively interrogate the myocardial tissue composition. Gadolinium contrast agents revolutionized non-invasive cardiac imaging with the late gadolinium enhancement technique. Further advances enabled quantitative parametric mapping to increase sensitivity at detecting diffuse pathology. Novel methods such as diffusion tensor imaging and artificial intelligence-enhanced image generation are on the horizon. Magnetic resonance spectroscopy (MRS) provides a window into the molecular environment of the myocardium. Phosphorus (31P) spectroscopy can inform the status of cardiac energetics in health and disease. Proton (1H) spectroscopy complements this by measuring creatine and intramyocardial lipids. Hyperpolarized carbon (13C) spectroscopy is a novel method that could further our understanding of dynamic cardiac metabolism. CMR of other organs such as the lungs may add further depth into phenotypes of heart failure. The vast capabilities of CMR should be deployed and interpreted in context of current heart failure challenges.
Topics: Humans; Artificial Intelligence; Contrast Media; Diffusion Tensor Imaging; Gadolinium; Heart Failure; Magnetic Resonance Imaging; Magnetic Resonance Imaging, Cine; Myocardium; Predictive Value of Tests
PubMed: 37267310
DOI: 10.1093/ehjci/jead124 -
Diagnostics (Basel, Switzerland) Jul 2023In recent years, there has been a significant surge in discussions surrounding artificial intelligence (AI), along with a corresponding increase in its practical... (Review)
Review
In recent years, there has been a significant surge in discussions surrounding artificial intelligence (AI), along with a corresponding increase in its practical applications in various facets of everyday life, including the medical industry. Notably, even in the highly specialized realm of neurosurgery, AI has been utilized for differential diagnosis, pre-operative evaluation, and improving surgical precision. Many of these applications have begun to mitigate risks of intraoperative and postoperative complications and post-operative care. This article aims to present an overview of the principal published papers on the significant themes of tumor, spine, epilepsy, and vascular issues, wherein AI has been applied to assess its potential applications within neurosurgery. The method involved identifying high-cited seminal papers using PubMed and Google Scholar, conducting a comprehensive review of various study types, and summarizing machine learning applications to enhance understanding among clinicians for future utilization. Recent studies demonstrate that machine learning (ML) holds significant potential in neuro-oncological care, spine surgery, epilepsy management, and other neurosurgical applications. ML techniques have proven effective in tumor identification, surgical outcomes prediction, seizure outcome prediction, aneurysm prediction, and more, highlighting its broad impact and potential in improving patient management and outcomes in neurosurgery. This review will encompass the current state of research, as well as predictions for the future of AI within neurosurgery.
PubMed: 37510174
DOI: 10.3390/diagnostics13142429 -
Sensors (Basel, Switzerland) Aug 2023Smart wearable devices enable personalized at-home healthcare by unobtrusively collecting patient health data and facilitating the development of intelligent platforms... (Review)
Review
Smart wearable devices enable personalized at-home healthcare by unobtrusively collecting patient health data and facilitating the development of intelligent platforms to support patient care and management. The accurate analysis of data obtained from wearable devices is crucial for interpreting and contextualizing health data and facilitating the reliable diagnosis and management of critical and chronic diseases. The combination of edge computing and artificial intelligence has provided real-time, time-critical, and privacy-preserving data analysis solutions. However, based on the envisioned service, evaluating the additive value of edge intelligence to the overall architecture is essential before implementation. This article aims to comprehensively analyze the current state of the art on smart health infrastructures implementing wearable and AI technologies at the far edge to support patients with chronic heart failure (CHF). In particular, we highlight the contribution of edge intelligence in supporting the integration of wearable devices into IoT-aware technology infrastructures that provide services for patient diagnosis and management. We also offer an in-depth analysis of open challenges and provide potential solutions to facilitate the integration of wearable devices with edge AI solutions to provide innovative technological infrastructures and interactive services for patients and doctors.
Topics: Humans; Artificial Intelligence; Awareness; Chronic Disease; Heart Failure; Wearable Electronic Devices
PubMed: 37571678
DOI: 10.3390/s23156896 -
Journal of Imaging Oct 2023Chest radiography (CXR) is the most frequently performed radiological test worldwide because of its wide availability, non-invasive nature, and low cost. The ability of... (Review)
Review
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.
PubMed: 37998083
DOI: 10.3390/jimaging9110236 -
Journal of Electrocardiology 2024An elderly man with severe chronic obstructive pulmonary disease and a history of complete heart block with pacemaker placement was found to have pacemaker lead...
An elderly man with severe chronic obstructive pulmonary disease and a history of complete heart block with pacemaker placement was found to have pacemaker lead infection and required device extraction. He had a standard dual chamber pacemaker in place, however the ECG obtained showed paced QRS complexes with presence of R wave in lead V1 and QS in lead I suggestive of left ventricular pacing. Additional imaging with CT scan obtained for confirmation revealed that the heart was displaced to the left posterior hemithorax secondary to pulmonary disease. Due to significant posterolateral rotation of the heart, a right ventricular paced rhythm can demonstrate Q/S waves in the lateral leads (I, aVL, V5-6) and R waves in the right precordial leads (V1-3). This can be misdiagnosed as a left ventricular paced rhythm.
Topics: Male; Humans; Aged; Cardiac Resynchronization Therapy; Electrocardiography; Heart Ventricles; Pacemaker, Artificial; Tomography, X-Ray Computed; Cardiac Pacing, Artificial
PubMed: 38118295
DOI: 10.1016/j.jelectrocard.2023.11.013 -
CJC Pediatric and Congenital Heart... Feb 2024The field of fetal cardiology has evolved significantly in recent years. This review focuses on specific advances in fetal cardiac imaging and intervention that are... (Review)
Review
The field of fetal cardiology has evolved significantly in recent years. This review focuses on specific advances in fetal cardiac imaging and intervention that are increasingly used in clinical practice. On the imaging frontier, updated screening guidelines and artificial intelligence hold promise for improving prenatal detection of congenital heart disease. Advances in ultrasound technology and magnetic resonance imaging techniques have enabled greater diagnostic and prognostic accuracy of fetal heart disease from the first to third trimesters, and maternal hyperoxygenation can offer additional physiological insights. Fetal cardiac therapy has also seen great progress, with advances in transplacental pharmacologic treatments, infusions of enzyme replacement therapy, and fetal surgery for select rare and severe conditions.
PubMed: 38544880
DOI: 10.1016/j.cjcpc.2023.10.012 -
Frontiers in Cardiovascular Medicine 2023This study aimed to determine the fit of two small-sized (pediatric and infant) continuous-flow total artificial heart pumps (CFTAHs) in congenital heart surgery...
BACKGROUND
This study aimed to determine the fit of two small-sized (pediatric and infant) continuous-flow total artificial heart pumps (CFTAHs) in congenital heart surgery patients.
METHODS
This study was approved by Cleveland Clinic Institutional Review Board. Pediatric cardiac surgery patients ( = 40) were evaluated for anatomical and virtual device fitting (3D-printed models of pediatric [P-CFTAH] and infant [I-CFTAH] models). The virtual sub-study consisted of analysis of preoperative thoracic radiographs and computed tomography ( = 3; 4.2, 5.3, and 10.2 kg) imaging data.
RESULTS
P-CFTAH pump fit in 21 out of 40 patients (fit group, 52.5%) but did not fit in 19 patients (non-fit group, 47.5%). I-CFTAH pump fit all of the 33 patients evaluated. There were critical differences due to dimensional variation ( < 0.0001) for the P-CFTAH, such as body weight (BW), height (Ht), and body surface area (BSA). The cutoff values were: BW: 5.71 kg, Ht: 59.0 cm, BSA: 0.31 m. These cutoff values were additionally confirmed to be optimal by CT imaging.
CONCLUSIONS
This study demonstrated the range of proper fit for the P-CFTAH and I-CFTAH in congenital heart disease patients. These data suggest the feasibility of both devices for fit in the small-patient population.
PubMed: 37529709
DOI: 10.3389/fcvm.2023.1193800 -
Oman Medical Journal Sep 2023Coronary artery calcium (CAC) scoring improves traditional risk factor-based coronary heart disease (CHD) risk stratification. Here, the contribution of CAC scoring to a... (Review)
Review
Coronary artery calcium (CAC) scoring improves traditional risk factor-based coronary heart disease (CHD) risk stratification. Here, the contribution of CAC scoring to a traditional 10-year CHD risk prediction scores and new artificial intelligence methods used to automate CAC scoring were reviewed. Research shows that traditional risk factors tend to overestimate or underestimate the actual risk of CHD, meaning that including CAC score in the risk stratification has potential to reduce over- and undertreatment. The automated CAC scoring methods are shown to be accurate and significantly more time-effective than the commonly used semi-automated method.
PubMed: 38053612
DOI: 10.5001/omj.2023.73