Artificial intelligence applied to cardiovascular imaging, a critical focus on echocardiography: The point-of-view from "the other side of the coin"

Accuracy and Efficacy of Artificial Intelligence-Derived Automatic Measurements of Transthoracic Echocardiography in Routine Clinical Practice

Background: Transthoracic echocardiography (TTE) is the gold standard modality for evaluating cardiac morphology, function, and hemodynamics in clinical practice. While artificial intelligence (AI) is expected to contribute to improved accuracy and is being applied clinically, its impact on daily clinical practice has not been fully evaluated. Methods: We retrospectively examined 30 consecutive patients who underwent AI-equipped TTE at a single institution. All patients underwent manual and automatic measurements of TTE parameters using the AI-equipped TTE. Measurements were performed by three sonographers with varying experience levels: beginner, intermediate, and expert. Results: A comparison between the manual and automatic measurements assessed by the experts showed extremely high agreement in the left ventricular (LV) filling velocities (E wave: r = 0.998, A wave: r = 0.996; both p < 0.001). The automated measurements of LV end-diastolic and end-systolic diameters were slightly smaller (-2.41 mm and -1.19 mm) than the manual measurements, although without significant differences, and both methods showing high agreement (r = 0.942 and 0.977, both p < 0.001). However, LV wall thickness showed low agreement between the automated and manual measurements (septum: r = 0.670, posterior: r = 0.561; both p < 0.01), with automated measurements tending to be larger. Regarding interobserver variabilities, statistically significant agreement was observed among the measurements of expert, intermediate, and beginner sonographers for all the measurements. In terms of measurement time, automatic measurement significantly reduced measurement time compared to manual measurement (p < 0.001). Conclusions: This preliminary study confirms the accuracy and efficacy of AI-equipped TTE in routine clinical practice. A multicenter study with a larger sample size is warranted.

From ejection fraction, to myocardial strain, and myocardial work in echocardiography: Clinical impact and controversies

Enhancing an echocardiographic tool, aimed to detect even subtle left ventricular (LV) systolic function abnormalities, capable of obtaining both early diagnosis and risk prediction of heart disease, represents an ambitious, attractive, and arduous purpose in the modern era of cardiovascular imaging. Ideally, that tool should be simple, reliable, and reproducible, in order to be concretely applied in routine clinical practice. Importantly, that technique should be physiologically plausible and useful both at the population-level, as well as in the individual subject. For a long time, LV ejection fraction (EF) has been considered the first-line parameter for assessing LV global systolic function, strictly related to the prognosis, at least in some settings. However, LV EF limitations are well-known, even though frequently overemphasized, including its load-dependency. Therefore, myocardial strain techniques have been proposed, deemed able to disclose even subtle early LV function anomalies. Nevertheless, many disadvantages of myocardial strain have been reported as well. More recently, myocardial work (MW) analysis has been introduced as a new echocardiographic tool for the evaluation of LV global systolic function, attempting to overcome EF and strain disadvantages. However, MW has shown many limits as well. Notwithstanding, LV EF still remains a landmark functional classification marker for heart failure and cardiac oncology, allowing reliable fast reassessment of LV function changes during patient management, in order to guide treatment in individual cases as well. Notably, global longitudinal strain and MW parameters seem to show better meaningful results at the population-level, but controversial clinical impact, major limitations, wide cut-offs spread and overlap, when the single value needs to be applied to the single case. Taking into account the recent literature-based evidence, the scope of the present narrative critical review is trying to delineate the different types of information given by the described LV global systolic function parameters, both at the population-level and in the individual case, in order to trace a comparative analysis of advantages and limitations in clinical practice.

Current role and future perspectives of artificial intelligence in echocardiography

Echocardiography is an essential tool in diagnostic cardiology and is fundamental to clinical care. Artificial intelligence (AI) can help health care providers serving as a valuable diagnostic tool for physicians in the field of echocardiography specially on the automation of measurements and interpretation of results. In addition, it can help expand the capabilities of research and discover alternative pathways in medical management specially on prognostication. In this review article, we describe the current role and future perspectives of AI in echocardiography.

The Role of Artificial Intelligence in Echocardiography

Echocardiography is an integral part of the diagnosis and management of cardiovascular disease. The use and application of artificial intelligence (AI) is a rapidly expanding field in medicine to improve consistency and reduce interobserver variability. AI can be successfully applied to echocardiography in addressing variance during image acquisition and interpretation. Furthermore, AI and machine learning can aid in the diagnosis and management of cardiovascular disease. In the realm of echocardiography, accurate interpretation is largely dependent on the subjective knowledge of the operator. Echocardiography is burdened by the high dependence on the level of experience of the operator, to a greater extent than other imaging modalities like computed tomography, nuclear imaging, and magnetic resonance imaging. AI technologies offer new opportunities for echocardiography to produce accurate, automated, and more consistent interpretations. This review discusses machine learning as a subfield within AI in relation to image interpretation and how machine learning can improve the diagnostic performance of echocardiography. This review also explores the published literature outlining the value of AI and its potential to improve patient care.

Machine Learning for Energy Systems Optimization

This editorial overviews the contents of the Special Issue “Machine Learning for Energy Systems 2021” and review the trends in machine learning (ML) techniques for energy system (ES) optimization [...]