Application of Echocardiography in Anaesthesia: From Preoperative Risk Assessment to Postoperative Care.
Cureus
Echocardiography has carved out a fundamental niche in anaesthesiology, revolutionizing the monitoring and management of cardiac function during surgery. Clinical practice has changed from simple 2D and 3D echocardiography to more sophisticated applications such as incorporating artificial intelligence. Echocardiography provides detailed real-time information about cardiac anatomy and function, helping anaesthesiologists make better decisions regarding tailoring anesthetic interventions and optimizing patient outcomes. From optimizing hemodynamic management in patients with severe aortic stenosis to fine-tuning fluid and vasopressor therapy in patients with right heart dysfunction, echocardiography has improved the care provided in the perioperative period. These applications permit the demonstration of not only technical advantages that could accrue from echocardiography but are also a part of individualized care to improve the outcomes of patients. The challenges in integrating echocardiography with anaesthesia include operator dependency, a steep learning curve in acquiring echocardiographic skills, and limitations due to patient factors and technological limitations, which lead to poor echocardiographic performance. Additionally, transoesophageal echocardiography (TEE) is an invasive procedure with several potential risks that must be considered cautiously. Continuing education, certification recommendations, and skill development are prerequisites for this echocardiography tool to remain robust and reliable in anaesthesiology. Technological innovation, especially in improving 3D imaging and integration with artificial intelligence, is where a very bright future lies ahead for echocardiography. It would further accelerate the process of echocardiographic evaluation and improve diagnostic accuracy. All these would turn out to be more person-centered for each patient. Anaesthesiologists must, therefore, pace themselves with such developments so these can be appropriately applied in the clinics. In summary, echocardiography became so integrally ingrained into anaesthesia that it propelled the specialty with essential tools anaesthesiologists use to manage patients for optimum outcomes. Its application has difficulties and limitations, but continued professional development and development of echocardiographic technology will make sure that its benefits are maximized. Quickly, echocardiography is becoming central to anaesthesiology's role in optimizing patient care and surgical success as we move into the application of evermore sophisticated echocardiographic techniques.
10.7759/cureus.69559
Deep learning for transesophageal echocardiography view classification.
Scientific reports
Transesophageal echocardiography (TEE) imaging is a vital tool used in the evaluation of complex cardiac pathology and the management of cardiac surgery patients. A key limitation to the application of deep learning strategies to intraoperative and intraprocedural TEE data is the complexity and unstructured nature of these images. In the present study, we developed a deep learning-based, multi-category TEE view classification model that can be used to add structure to intraoperative and intraprocedural TEE imaging data. More specifically, we trained a convolutional neural network (CNN) to predict standardized TEE views using labeled intraoperative and intraprocedural TEE videos from Cedars-Sinai Medical Center (CSMC). We externally validated our model on intraoperative TEE videos from Stanford University Medical Center (SUMC). Accuracy of our model was high across all labeled views. The highest performance was achieved for the Trans-Gastric Left Ventricular Short Axis View (area under the receiver operating curve [AUC] = 0.971 at CSMC, 0.957 at SUMC), the Mid-Esophageal Long Axis View (AUC = 0.954 at CSMC, 0.905 at SUMC), the Mid-Esophageal Aortic Valve Short Axis View (AUC = 0.946 at CSMC, 0.898 at SUMC), and the Mid-Esophageal 4-Chamber View (AUC = 0.939 at CSMC, 0.902 at SUMC). Ultimately, we demonstrate that our deep learning model can accurately classify standardized TEE views, which will facilitate further downstream deep learning analyses for intraoperative and intraprocedural TEE imaging.
10.1038/s41598-023-50735-8
Overview and Clinical Applications of Artificial Intelligence and Machine Learning in Cardiac Anesthesiology.
Journal of cardiothoracic and vascular anesthesia
Artificial intelligence- (AI) and machine learning (ML)-based applications are becoming increasingly pervasive in the healthcare setting. This has in turn challenged clinicians, hospital administrators, and health policymakers to understand such technologies and develop frameworks for safe and sustained clinical implementation. Within cardiac anesthesiology, challenges and opportunities for AI/ML to support patient care are presented by the vast amounts of electronic health data, which are collected rapidly, interpreted, and acted upon within the periprocedural area. To address such challenges and opportunities, in this article, the authors review 3 recent applications relevant to cardiac anesthesiology, including depth of anesthesia monitoring, operating room resource optimization, and transthoracic/transesophageal echocardiography, as conceptual examples to explore strengths and limitations of AI/ML within healthcare, and characterize this evolving landscape. Through reviewing such applications, the authors introduce basic AI/ML concepts and methodologies, as well as practical considerations and ethical concerns for initiating and maintaining safe clinical implementation of AI/ML-based algorithms for cardiac anesthesia patient care.
10.1053/j.jvca.2024.02.004
Continuous monitoring of left ventricular function in postoperative intensive care patients using artificial intelligence and transesophageal echocardiography.
Intensive care medicine experimental
BACKGROUND:Continuous monitoring of mitral annular plane systolic excursion (MAPSE) using transesophageal echocardiography (TEE) may improve the evaluation of left ventricular (LV) function in postoperative intensive care patients. We aimed to assess the utility of continuous monitoring of LV function using TEE and artificial intelligence (autoMAPSE) in postoperative intensive care patients. METHODS:In this prospective observational study, we monitored 50 postoperative intensive care patients for 120 min immediately after cardiac surgery. We recorded a set of two-chamber and four-chamber TEE images every five minutes. We defined monitoring feasibility as how often the same wall from the same patient could be reassessed, and categorized monitoring feasibility as excellent if the same LV wall could be reassessed in ≥ 90% of the total recordings. To compare autoMAPSE with manual measurements, we rapidly recorded three sets of repeated images to assess precision (least significant change), bias, and limits of agreement (LOA). To assess the ability to identify changes (trending ability), we compared changes in autoMAPSE with the changes in manual measurements in images obtained during the initiation of cardiopulmonary bypass as well as before and after surgery. RESULTS:Monitoring feasibility was excellent in most patients (88%). Compared with manual measurements, autoMAPSE was more precise (least significant change 2.2 vs 3.1 mm, P < 0.001), had low bias (0.4 mm), and acceptable agreement (LOA - 2.7 to 3.5 mm). AutoMAPSE had excellent trending ability, as its measurements changed in the same direction as manual measurements (concordance rate 96%). CONCLUSION:Continuous monitoring of LV function was feasible using autoMAPSE. Compared with manual measurements, autoMAPSE had excellent trending ability, low bias, acceptable agreement, and was more precise.
10.1186/s40635-024-00640-9
Automatic assessment of left ventricular function for hemodynamic monitoring using artificial intelligence and transesophageal echocardiography.
Journal of clinical monitoring and computing
We have developed a method to automatically assess LV function by measuring mitral annular plane systolic excursion (MAPSE) using artificial intelligence and transesophageal echocardiography (autoMAPSE). Our aim was to evaluate autoMAPSE as an automatic tool for rapid and quantitative assessment of LV function in critical care patients. In this retrospective study, we studied 40 critical care patients immediately after cardiac surgery. First, we recorded a set of echocardiographic data, consisting of three consecutive beats of midesophageal two- and four-chamber views. We then altered the patient's hemodynamics by positioning them in anti-Trendelenburg and repeated the recordings. We measured MAPSE manually and used autoMAPSE in all available heartbeats and in four LV walls. To assess the agreement with manual measurements, we used a modified Bland-Altman analysis. To assess the precision of each method, we calculated the least significant change (LSC). Finally, to assess trending ability, we calculated the concordance rates using a four-quadrant plot. We found that autoMAPSE measured MAPSE in almost every set of two- and four-chamber views (feasibility 95%). It took less than a second to measure and average MAPSE over three heartbeats. AutoMAPSE had a low bias (0.4 mm) and acceptable limits of agreement (- 3.7 to 4.5 mm). AutoMAPSE was more precise than manual measurements if it averaged more heartbeats. AutoMAPSE had acceptable trending ability (concordance rate 81%) during hemodynamic alterations. In conclusion, autoMAPSE is feasible as an automatic tool for rapid and quantitative assessment of LV function, indicating its potential for hemodynamic monitoring.
10.1007/s10877-023-01118-x
Artificial intelligence in detecting left atrial appendage thrombus by transthoracic echocardiography and clinical features: the Left Atrial Thrombus on Transoesophageal Echocardiography (LATTEE) registry.
European heart journal
AIMS:Transoesophageal echocardiography (TOE) is often performed before catheter ablation or cardioversion to rule out the presence of left atrial appendage thrombus (LAT) in patients on chronic oral anticoagulation (OAC), despite associated discomfort. A machine learning model [LAT-artificial intelligence (AI)] was developed to predict the presence of LAT based on clinical and transthoracic echocardiography (TTE) features. METHODS AND RESULTS:Data from a 13-site prospective registry of patients who underwent TOE before cardioversion or catheter ablation were used. LAT-AI was trained to predict LAT using data from 12 sites (n = 2827) and tested externally in patients on chronic OAC from two sites (n = 1284). Areas under the receiver operating characteristic curve (AUC) of LAT-AI were compared with that of left ventricular ejection fraction (LVEF) and CHA2DS2-VASc score. A decision threshold allowing for a 99% negative predictive value was defined in the development cohort. A protocol where TOE in patients on chronic OAC is performed depending on the LAT-AI score was validated in the external cohort. In the external testing cohort, LAT was found in 5.5% of patients. LAT-AI achieved an AUC of 0.85 [95% confidence interval (CI): 0.82-0.89], outperforming LVEF (0.81, 95% CI 0.76-0.86, P < .0001) and CHA2DS2-VASc score (0.69, 95% CI: 0.63-0.7, P < .0001) in the entire external cohort. Based on the proposed protocol, 40% of patients on chronic OAC from the external cohort would safely avoid TOE. CONCLUSION:LAT-AI allows accurate prediction of LAT. A LAT-AI-based protocol could be used to guide the decision to perform TOE despite chronic OAC.
10.1093/eurheartj/ehad431
Artificial Intelligence for Dynamic Echocardiographic Tricuspid Valve Analysis: A New Tool in Echocardiography.
Fatima Huma,Mahmood Feroze,Sehgal Sankalp,Belani Kiran,Sharkey Aidan,Chaudhary Omar,Baribeau Yanick,Matyal Robina,Khabbaz Kamal R
Journal of cardiothoracic and vascular anesthesia
There has been a resurgence of interest in the structure and function of the tricuspid valve (TV) with the established prognostic impact of functional tricuspid regurgitation. Current 3-dimensional transesophageal echocardiography prototype software is limited to exploration of the mitral and aortic valves exclusively. Thus, newer analytical software is required for dynamic geometric analysis of the TV morphology for remodeling. This article presents a preliminary experience with novel artificial intelligence-based semiautomated software for TV analysis. The software offers high correlation to surgical inspection by its ability to analyze morphology and dynamics of the valve throughout the cardiac cycle. In addition, it allows higher reproducibility of data analysis and reduces interobserver variability with minimal need for manual intervention. Integration of interactivity through preprocedural placement of specific devices of different sizes and shapes in the mitral and aortic positions facilitates prognostic evaluation of surgical and interventional procedures.
10.1053/j.jvca.2020.04.056
Transesophageal echocardiography: Revolutionizing perioperative cardiac care.
Biomolecules & biomedicine
Cardiovascular diseases (CVDs) are a major challenge in global health. Despite significant advances in treatment and management, the incidence and mortality rates of CVDs have been rising in recent years, particularly in the United States. With continuous advancements in medical technology, perioperative transesophageal echocardiography (TEE) has become a key technology in cardiac surgery, enhancing surgical success rates and patient safety. The application of TEE spans preoperative planning, intraoperative monitoring, and postoperative evaluation, especially in complex procedures such as mitral valve repair and aortic valve replacement, where it plays an indispensable role. Simultaneously, the introduction of artificial intelligence (AI) brings new prospects for TEE image analysis and diagnostic support, significantly improving diagnostic accuracy and real-time decision-making capabilities. However, the application of TEE technology faces challenges such as high costs, uneven technological diffusion, and the high skill requirements for medical personnel. Therefore, establishing standardized training protocols and strengthening multidisciplinary collaboration are crucial. This paper reviews the application of TEE in cardiac surgery and its path toward educational and practical standardization from a global perspective, emphasizing its importance in improving the postoperative quality of life for patients and exploring future directions in technological innovation and educational optimization.
10.17305/bb.2024.10847