Left ventricular volumes and ejection fraction quantification using an automated three-dimensional adaptive analytic echocardiographic algorithm in pediatric population

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

Cardiovascular imaging has achieved a crucial role in the management of cardiovascular diseases. In this field, echocardiography advantages include wide availability, portability, and affordability, at a relatively low cost. However, echocardiographic assessment requires highly trained operators, and implies high observer variability, as compared with the other cardiac imaging modalities. Hence, artificial intelligence might be extremely helpful. From the point-of-view of the peripheral "Spoke" Hospital potential user ("the other side of the coin"), artificial intelligence development appears very slow in the clinical arena. Many limitations are still present, and require full involvement, cooperation, and coordination of professional operators into Hub-and-Spoke network.

Heart Model A.I. Three-Dimensional Echocardiographic Evaluation of Left Ventricular Function and Parameter Setting

Objective

This study aims to explore the feasibility of HeartModel ^A.I. (HM) three-dimensional echocardiography (3DE) to assess left ventricular function and discover suitable border parameter settings.

Methods

A total of 113 patients that underwent echocardiography in our hospital were eligible for inclusion. The HM 3DE (HM method) and conventional 3DE (3D method) were used to analyze echocardiography images. The HM was set to different border settings (end-diastolic [ED] and end-systolic [ES] settings) to assess different left ventricular systolic function parameters including left ventricular end diastolic volume (LVEDV), left ventricular end systolic volume (LVESV), and left ventricular ejection fraction (LVEF), and left ventricular diastolic function parameters including maximal left atrium volume (LAV_MAX). All of these parameters were evaluated using the HM method and then compared with the 3D method.

Results

The differences in LVEDV, LVESV, and LVEF measured with different HM border settings were statistically significant (P<0.05) and were strongly correlated with the 3D method. For LVEF, the reading using the HM method with ED and ES = 70 and 30 showed the best agreement with the 3D method, and the difference in the readings was not statistically significant (P > 0.05). For LVEDV and LVESV, the reading using the HM method with ED and ES = 40 and 20 showed the best agreement with the 3D method, but the difference in the readings was statistically significant (P < 0.05). The measurements taken using the HM method were more reproducible than those taken using the 3D method (P<0.05). The measurement time when using the HM method was significantly less than the 3D method (P<0.05). In terms of LAV_MAX, the correlation between the HM and 3D methods was strong, but the requirements for agreement were not satisfied.

Conclusion

Evaluation of the left ventricular function using HM 3DE is feasible, saves time, and is reproducible. To assess the left ventricular function, the border parameter setting of ED and ES = 70 and 30 provided the best fit for the Chinese population.

Utilization of Artificial Intelligence in Echocardiography

Echocardiography has a central role in the diagnosis and management of cardiovascular disease. Precise and reliable echocardiographic assessment is required for clinical decision-making. Even if the development of new technologies (3-dimentional echocardiography, speckle-tracking, semi-automated analysis, etc.), the final decision on analysis is strongly dependent on operator experience. Diagnostic errors are a major unresolved problem. Moreover, not only can cardiologists differ from one another in image interpretation, but also the same observer may come to different findings when a reading is repeated. Daily high workloads in clinical practice may lead to this error, and all cardiologists require precise perception in this field. Artificial intelligence (AI) has the potential to improve analysis and interpretation of medical images to a new stage compared with previous algorithms. From our comprehensive review, we believe AI has the potential to improve accuracy of diagnosis, clinical management, and patient care. Although there are several concerns about the required large dataset and "black box" algorithm, AI can provide satisfactory results in this field. In the future, it will be necessary for cardiologists to adapt their daily practice to incorporate AI in this new stage of echocardiography.