Background: Echocardiography is a frequently used imaging modality requiring extensive training to master. In order to develop curriculums and teaching material fully favouring students learning within echocardiography, this study aims to investigate students' experiences of learning echocardiography, focusing on that which is perceived as the main challenges as well as what might aid learning within the area. The findings could serve as a foundation in the development of new teaching material or curriculums.
Methods: A qualitative study was performed with data gathered through two audio-recorded focus group interviews with four third year students from the biomedical laboratory programme at Malmö University in each group. Data was analysed by manifest content analysis.
Results: Findings were clustered into two categories reflecting the main findings in the text - practical skills and bridging the theory-practice-gap. Students expressed that main challenges when initially learning echocardiography were the projections and handling the probe as well as connecting ultrasound physics and measurements to practical application. Things that aided their learning were immediate feedback, "playing" with the ultrasound machine, video lectures, the possibility to swiftly alternate between practice and theory as well as the learning by their mistakes in a risk-free environment.
Conclusions: This study shows the main challenges when initially learning echocardiography and what might be helpful during the learning process. These findings may be useful when developing curriculums or new teaching material within echocardiography. One suggestion might be to develop digital resources such as virtual laboratories (vLABs).
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http://dx.doi.org/10.1186/s12909-019-1656-1 | DOI Listing |
Eur Heart J Digit Health
January 2025
Department of Cardiovascular Surgery of Zhongshan Hospital, Fudan University, Shanghai 200032, China.
Aims: Accurate heart function estimation is vital for detecting and monitoring cardiovascular diseases. While two-dimensional echocardiography (2DE) is widely accessible and used, it requires specialized training, is prone to inter-observer variability, and lacks comprehensive three-dimensional (3D) information. We introduce CardiacField, a computational echocardiography system using a 2DE probe for precise, automated left ventricular (LV) and right ventricular (RV) ejection fraction (EF) estimations, which is especially easy to use for non-cardiovascular healthcare practitioners.
View Article and Find Full Text PDFEur Heart J Digit Health
January 2025
Department of Cardiovascular Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA.
Aims: Aortic stenosis (AS) is a common and progressive disease, which, if left untreated, results in increased morbidity and mortality. Monitoring and follow-up care can be challenging due to significant variability in disease progression. This study aimed to develop machine learning models to predict the risks of disease progression and mortality in patients with mild AS.
View Article and Find Full Text PDFEBioMedicine
January 2025
CONNECT-AI Research Center, Yonsei University College of Medicine, Seoul, Republic of Korea; Ontact Health Inc., Seoul, Republic of Korea; Division of Cardiology, Severance Cardiovascular Hospital, Yonsei University College of Medicine, Yonsei University Health System, Seoul, Republic of Korea.
Background: Transthoracic echocardiography (TTE) is the primary modality for diagnosing aortic stenosis (AS), yet it requires skilled operators and can be resource-intensive. We developed and validated an artificial intelligence (AI)-based system for evaluating AS that is effective in both resource-limited and advanced settings.
Methods: We created a dual-pathway AI system for AS evaluation using a nationwide echocardiographic dataset (developmental dataset, n = 8427): 1) a deep learning (DL)-based AS continuum assessment algorithm using limited 2D TTE videos, and 2) automating conventional AS evaluation.
Digit Health
January 2025
Department of Internal Medicine, Seoul National University Hospital, Seoul, Republic of Korea.
Objective: Although the evaluation of left ventricular ejection fraction (LVEF) in patients with atrial fibrillation (AF) or atrial flutter (AFL) is crucial for appropriate medical management, the prediction of reduced LVEF (<50%) with AF/AFL electrocardiograms (ECGs) lacks evidence. This study aimed to investigate deep-learning approaches to predict reduced LVEF (<50%) in patients with AF/AFL ECGs and easily obtainable clinical information.
Methods: Patients with 12-lead ECGs of AF/AFL and echocardiography were divided into those with LVEF <50% and ≥50%.
J Cardiol Cases
January 2025
Department of Cardiology and Geriatrics, Kochi Medical School, Kochi University, Kochi, Japan.
Unlabelled: Scarring of the left atrial (LA) wall from atrial ablation (AF) leads to the development of stiff LA syndrome. Multiple ablation treatments have been considered to be associated with the development of LA calcification (LAC). We report a case of wild-type transthyretin cardiac amyloidosis (CA) who presented with worsening heart failure due to stiff LA syndrome despite the condition after initial ablation for AF.
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