Next-Generation Point of Care Cardiac Imaging in Critical Care: Leveraging AI for Acquisition, Interpretation, Training and Standardization for Improved Outcomes

In the field of intensive care cardiovascular medicine, prompt and accurate assessment of cardiac function is essential for guiding management in critically ill patients. Cardiac point-of-care ultrasound (POCUS) and bedside echocardiography are vital for diagnosing shock etiologies, evaluating ventricular dysfunction, gauging volume status, and understanding complex cardiopulmonary interactions. However, persistent challenges remain in the form of operator dependency, variability in image quality, lack of standardization across platforms, and limited integration of imaging data with other ICU clinical parameters. Recent studies have shown promise for artificial intelligence (AI) tools to enhance these workflows, ranging from automated image acquisition and quality control, to interpretation and advanced hemodynamic evaluation, yet robust validation, seamless integration, and translation to bedside impact are still lacking. As the field evolves, there is a pressing need for multidisciplinary research that bridges technical innovation with real-world critical care applicability.This Research Topic aims to showcase the latest advances and future directions in leveraging AI for cardiac POCUS and echocardiography in the intensive care unit. The primary goal is to explore how AI can improve every stage of the imaging workflow, including acquisition guidance, automated interpretation, and integration with multimodal clinical data, to deliver actionable hemodynamic assessments and enhance patient outcomes. Of special interest are approaches that address high-acuity conditions such as sepsis cardiomyopathy, mixed shock, acute right heart failure, and peri-procedural hemodynamic instability. Key objectives include evaluating robustness across diverse hardware and settings, external validation, understanding the risks of bias and fairness, and practical strategies for implementation, clinician oversight, and training in ICU environments. While POCUS and echocardiography remain the primary focus given their bedside accessibility and central role in acute ICU decision-making, we also welcome contributions applying AI to cardiac MRI, CT, and PET where these modalities inform critical care management.The scope of this Research Topic encompasses studies and perspectives that drive innovation in AI-enabled cardiac imaging specifically for critically ill populations, while excluding work unrelated to cardiovascular care in the ICU. We encourage submission of original research and reviews on both technical and clinical aspects, including but not limited to the following themes:AI-guided image acquisition and real-time view quality assuranceAutomated interpretation and quantification of cardiac function (LV/RV function, strain, cardiac output, congestion) across POCUS, echocardiography, MRI, CT, and PETMultimodal integration of imaging with bedside clinical and physiologic data for predictive and decision-support algorithmsExternal validation and robustness across devices, operators, and patient populationsClinical implementation, bias mitigation, clinician oversight, and training in ICU workflows Topic editors: Venu Velagapudi, Krzysztof Laudanski, Dennis Tighe, Phillip Kim, Christopher Erb Source: https://www.frontiersin.org/research-topics/79347/next-generation-point-of-care-cardiac-imaging-in-critical-care-leveraging-ai-for-acquisition-interpretation-training-and-standardization-for-improved-outcomes