Article

From Theoretical Models to Practical Deployment: A Perspective and Case Study of Opportunities and Challenges in AI-driven Cardiac Auscultation Research for Low-Income Settings

2023-12-29

Abstract excerpt

This article includes a literature review and a case study of artificial intelligence (AI) heart murmur detection models to analyse the opportunities and challenges in deploying AI in cardiovascular healthcare in low- or medium-income countries (LMICs). This study has two parallel components: The literature review assesses the capacity of AI to aid in addressing the observed disparity in healthcare between high- a...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 9b05a2e2-44c4-582f-9e41-96a5ae6af696
DOI: 10.1101/2023.12.26.23300539

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

From Theoretical Models to Practical Deployment: A Perspective and Case Study of Opportunities and Challenges in AI-driven Cardiac Auscultation Research for Low-Income SettingsDOI 10.1101/2023.12.26.23300539Read

Heart Murmur Detection in Phonocardiogram Data Leveraging Data Augmentation and Artificial IntelligenceSemantic proximity Read

RED-RHD (Rice Early Detection – Rheumatic Heart Disease): AI-Based Adaptive Multi-Regional System for Early Detection and Murmur Classification of Rheumatic Heart DiseaseSemantic proximity Read

Artificial Intelligence Driven Software Systems for Cardiovascular Disease Detection Using Physiological Signals: A Systematic ReviewSemantic proximity Read

Multimodal Artificial Intelligence Approaches for the Diagnosis and Understanding of Heart Failure: A Contemporary ReviewSemantic proximity Read

<i>TARGET-AI</i> : a foundational approach for the targeted deployment of artificial intelligence electrocardiography in the electronic health recordSemantic proximity Read

Heart murmur detection from phonocardiogram recordings: The George B. Moody PhysioNet Challenge 2022Semantic proximity Read

Advances in Interpretation of Electrocardiogram by Artificial IntelligenceSemantic proximity Read

Artificial Intelligence-Enabled Electrocardiogram for Elevated Left Ventricular Filling PressureSemantic proximity Read

Phenotypic Selectivity of Artificial Intelligence-enhanced Electrocardiography in Cardiovascular Diagnosis and Risk PredictionSemantic proximity Read

ARTIFICIAL INTELLIGENCE IN AFRICAN CARDIOVASCULAR CARE: OPPORTUNITIES, CHALLENGES, AND PATHWAYS TO IMPROVED OUTCOMESSemantic proximity Read

Machine Learning, Deep Learning, and Hybrid Approaches for ECG-Based Cardiac Arrhythmia Detection: Techniques, Challenges, and Future PerspectivesSemantic proximity Read

Ensemble Post-hoc Explainable AI for Multilead ECG: Identifying Disease-Relevant Features in Single-Lead InterpretationsSemantic proximity Read