AIMC Topic: Echocardiography

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A multimodal multipath AI system for assessing PAH after VSD correction on echocardiography and chest radiography images.

Scientific reports
Developing a novel artificial intelligence (AI) system that can automatically detect pulmonary arterial hypertension (PAH) after correcting the ventricular septal defect (VSD) and to help clinicians make reasonable treatment plans. We analyzed data f...

Evidence-based Management of Heart Failure in the Systemic Right Ventricle.

Current cardiology reports
PURPOSE OF REVIEW: Explore the clinical progression, diagnostic challenges, and evolving treatments of systemic right ventricular (SRV) failure, highlighting key gaps and advances.

Interpretable Machine Learning Model for Pulmonary Hypertension Risk Prediction: Retrospective Cohort Study.

JMIR medical informatics
BACKGROUND: Pulmonary hypertension (PH) is a progressive disorder characterized by elevated pulmonary artery pressure and increased pulmonary vascular resistance, ultimately leading to right heart failure. Early detection is critical for improving pa...

Artificial Intelligence Automation of Echocardiographic Measurements.

Journal of the American College of Cardiology
BACKGROUND: Accurate measurement of echocardiographic parameters is crucial for the diagnosis of cardiovascular disease and tracking of change over time; however, manual assessment requires time-consuming effort and can be imprecise. Artificial intel...

Left ventricular ejection fraction assessment: artificial intelligence compared with echocardiography expert and cardiac magnetic resonance measurements.

Polish archives of internal medicine
INTRODUCTION:  Cardiac magnetic resonance (CMR) is the gold standard for assessing left ventricular ejection fraction (LVEF). Artificial intelligence (AI)-based echocardiographic analysis is increasingly utilized in clinical practice.

Radiomics early assessment of post chemotherapy cardiotoxicity in cancer patients using 2D echocardiography imaging an interpretable machine learning study.

Scientific reports
Cardiotoxicity is the loss of the heart muscle's ability to contract effectively, often due to chemotherapy or radiation therapy. This study uses interpretable machine learning to predict post-chemotherapy cardiotoxicity using radiomics features extr...

Enhancing cardiac function assessment: Developing and validating a domain adaptive framework for automating the segmentation of echocardiogram videos.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
BACKGROUND: Accurate segmentation of echocardiographic images is essential for assessing cardiac function, particularly in calculating key metrics such as ejection fraction. However, challenges such as domain discrepancy, noisy data, anatomical varia...

Enhancing cardiac disease detection via a fusion of machine learning and medical imaging.

Scientific reports
Cardiovascular illnesses continue to be a predominant cause of mortality globally, underscoring the necessity for prompt and precise diagnosis to mitigate consequences and healthcare expenditures. This work presents a complete hybrid methodology that...

A cascade approach for the early detection and localization of myocardial infarction in 2D-echocardiography.

Medical engineering & physics
Myocardial infarction (MI) detection and localization through echocardiography are crucial for effective patient management. However, current diagnostic approaches rely heavily on visual assessment, which can be subjective. In this work we developed ...

Artificial Intelligence-Enabled Point-of-Care Echocardiography: Bringing Precision Imaging to the Bedside.

Current atherosclerosis reports
PURPOSE OF REVIEW: The integration of artificial intelligence (AI) with point-of-care ultrasound (POCUS) is transforming cardiovascular diagnostics by enhancing image acquisition, interpretation, and workflow efficiency. These advancements hold promi...