Theoretically, artificial intelligence can provide an accurate automatic solution to measure right ventricular (RV) ejection fraction (RVEF) from cardiovascular magnetic resonance (CMR) images, ...despite the complex RV geometry. However, in our recent study, commercially available deep learning (DL) algorithms for RVEF quantification performed poorly in some patients. The current study was designed to test the hypothesis that quantification of RV function could be improved in these patients by using more diverse CMR datasets in addition to domain-specific quantitative performance evaluation metrics during the cross-validation phase of DL algorithm development.
We identified 100 patients from our prior study who had the largest differences between manually measured and automated RVEF values. Automated RVEF measurements were performed using the original version of the algorithm (DL1), an updated version (DL2) developed from a dataset that included a wider range of RV pathology and validated using multiple domain-specific quantitative performance evaluation metrics, and conventional methodology performed by a core laboratory (CORE). Each of the DL-RVEF approaches was compared against CORE-RVEF reference values using linear regression and Bland-Altman analyses. Additionally, RVEF values were classified into 3 categories: ≤ 35%, 35-50%, and ≥ 50%. Agreement between RVEF classifications made by the DL approaches and the CORE measurements was tested.
CORE-RVEF and DL-RVEFs were obtained in all patients (feasibility of 100%). DL2-RVEF correlated with CORE-RVEF better than DL1-RVEF (r = 0.87 vs. r = 0.42), with narrower limits of agreement. As a result, DL2 algorithm also showed increasing accuracy from 0.53 to 0.80 for categorizing RV function.
The use of a new DL algorithm cross-validated on a dataset with a wide range of RV pathology using multiple domain-specific metrics resulted in a considerable improvement in the accuracy of automated RVEF measurements. This improvement was demonstrated in patients whose images were the most challenging and resulted in the largest RVEF errors. These findings underscore the critical importance of this strategy in the development of DL approaches for automated CMR measurements.
The Supply and Demand of the Cardiovascular Workforce Narang, Akhil, MD; Sinha, Shashank S., MD; Rajagopalan, Bharath, MBBS ...
Journal of the American College of Cardiology,
10/2016, Volume:
68, Issue:
15
Journal Article
Peer reviewed
Open access
Abstract As the burden of cardiovascular disease in the United States continues to increase, uncertainty remains on how well-equipped the cardiovascular workforce is to meet the challenges that lie ...ahead. In a time when health care is rapidly shifting, numerous factors affect the supply and demand of the cardiovascular workforce. This Council Commentary critically examines several factors that influence the cardiovascular workforce. These include current workforce demographics and projections, evolving health care and practice environments, and the increasing burden of cardiovascular disease. Finally, we propose 3 strategies to optimize the workforce. These focus on cardiovascular disease prevention, the effective utilization of the cardiovascular care team, and alterations to the training pathway for cardiologists.
We present the novel use of a deep learning–derived technology trained on the skilled hand movements of cardiac sonographers that guides novice users to acquire high-quality bedside cardiac ...ultrasound images. We illustrate its use at the point of care through a series of patient encounters in the COVID-19 intensive care unit. (Level of Difficulty: Beginner.)
Display omitted
Transcatheter mitral valve-in-valve replacement (TMVR) offers a less invasive strategy for managing bioprosthetic mitral valve dysfunction. TMVR positioning is challenging in the setting of a ...radiolucent bioprosthetic sewing ring. We present 2 cases demonstrating the roles of fluoroscopy and echocardiography in guiding TMVR placement within bioprostheses with radiolucent sewing rings. (Level of Difficulty: Intermediate.)
Display omitted
Transcatheter mitral valve-in-valve replacement (TMVR) offers a less invasive strategy for managing bioprosthetic mitral valve dysfunction…
PDF
