Our study investigates the efficacy of HeartModelAI in assessing echocardiographic indices in patients with DCM. It demonstrates that the HeartModelAI algorithm serves as a dependable assessment tool in this context. It exhibits a strong correlation in evaluating LV function with minimal variability between discrete measurements. Fig. 4 depicts HeartModelAI models.

HeartModelAI illustrations

Accurate assessment of cardiac function is pivotal for diagnosing and monitoring patients with DCM. Various cardiovascular imaging modalities have been introduced for this purpose, with CMR imaging as the gold standard [12]. However, its widespread application is hindered by challenges such as accessibility, the requirement for highly trained experts, and high costs [8]. TTE has emerged as the preferred initial assessment method due to its safety, relative simplicity, and efficiency [5]. Nonetheless, conventional 2D echocardiography suffers from inherent limitations, such as reliance on geometric assumptions [6]. The advent of 3D TTE in the early 2000s has enhanced diagnostic accuracy by overcoming the need for anatomical assumptions in cardiac function measurements [4, 13]. However, it remains time-consuming, demands significant expertise, and offers lower temporal resolution.

HeartModelAI is an artificial intelligence-integrated software that employs an adaptive analytic algorithm, drawing from a vast library of echocardiography images, to assess endocardial and epicardial borders and measure echocardiographic indices [14]. With the HeartModelAI, the software automates border assessment with a single button press, thus eliminating the time and expertise required for manual endocardial border delineation while still allowing for manual correction of regional or global contours [15, 16].

Previous studies have noted that 3D TTE tends to underestimate LVESV and LVEDV compared to CMR in numerous instances [17, 18]. Our findings align with these trends, as Tsang et al. [14] reported HeartModelAI underestimating LVEDV while yielding similar results for LVESV. Similarly, Levy et al. [2] and Tamborini [1] reported underestimations in LVESV and LVEDV, although comparable results were observed for LVEF. Volpato et al. [5] also reported underestimations in LVESV and LVEDV, coupled with an overestimated LVEF. Furthermore, Barletta et al. [19] reported excellent correlation of automated HeartModelAI and CMR. Consistent with prior research, our study demonstrates underestimations in LVESVI and LVEDVI and an overestimation of LVEF with HeartModelAI compared to CMR, despite excellent correlations.

Regarding assessing left ventricular mass and left ventricular mass index, Volpato et al. [5] found no significant difference between HeartModelAI and CMR. Our study observed an overestimation of both parameters compared to CMR measurements, with limited correlations. This discrepancy could be attributed to the low spatial resolution resulting from challenges in definitive endocardial and epicardial border delineation, particularly in dilated LV with difficulty distinguishing compact myocardium and LV trabeculae.

Regarding the need for manual contour correction, Tamborini et al. [1] reported favorable performance of HeartModelAI in DCM patients, albeit often requiring contour correction. In Chen Ke-Pan et al. [6] study, contour correction was necessary in 42% of DCM patients while leading to favorable results. In our study, contour correction had to be done in 26% of cases. Although HeartModelAI is quite good at automating the assessment process, the need for manual adjustments in a sizable subset does add time costs. Indeed, contour correction is an important means of increasing measurement accuracy, which is necessary in some patients, as pointed out by previous studies. For example, Ke-Pan et al. [6] reported significant improvements in diagnostic accuracy with manual correction, while our research found the improvements generally favorable but not statistically significant across the board. The major benefit of using automated 3DE is that it reduces both the time and expertise necessary for accurate assessment. To some extent, the need for manual correction in almost a quarter of cases potentially limits the time-saving capability, especially in a busy clinical setting. Further development in the algorithm of HeartModelAI, reducing the need for manual adjustment, is helpful in fully realizing the intended efficiency benefits of this technology.

Evaluation of the accuracy of HeartModelAI in patients with LV dysfunction has been mixed, but it holds significant potential. Beitner et al. [20] evaluated the agreement of 3DE with CMR imaging to estimate left ventricular volumes and LVEF in post-MI patients. The ICC for the LVEF was quite good between 3DE and CMR, indicating that HeartModelAI could be a viable option in the field. However, for LVESV and LVEDV, the strength of association was comparatively weaker, with ICC values of 0.44 and 0.28, respectively, indicative of moderate and fair agreement. Automated 3DE showed good concordance with CMR measurement in a study by Wang et al. [21] for 53 patients with hypertrophic cardiomyopathy (HCM). While the initial correlations of the two modalities, before contour corrections, were suboptimal, the incorporation of manual contour adjustments significantly improved the agreement, indicating the crucial role of manual refinement in enhancing diagnostic accuracy. Once these discrepancies were corrected, the study confirmed that automated 3DE would work feasibly and reliably compared to manual 3DE in this patient population. Naser et al. [13] further evaluated performance in patients with NYHA I-III status LV dysfunction. In this study, 38 DCM patients were compared to 2DE. In the 166 subjects involved, no significant difference between 2DE and 3DE regarding LVEF and LV volume analyses could be found, reinforcing the reliability of HeartModelAI. These findings further support HeartModelAI clinical value as a reliable alternative for evaluating left ventricular function.

Consistent with prior studies, our research demonstrated excellent inter- and intra-observer correlation coefficients with less than 10% variability, affirming the reliability and reproducibility of HeartModelAI in DCM patients.

Our study’s novelty lies in evaluating automated 3D TTE in DCM patients. Low EF and dilated heart chambers, characteristic of DCM, have previously been reported to affect the diagnostic capability of 3D TTE. Therefore, validating HeartModelAI for DCM assessment could significantly enhance clinical care by improving diagnosis and evaluation in these patients.

While our study contributes to the limited body of research specifically assessing HeartModelAI 3D TTE algorithm accuracy in DCM patients and utilizes adjusted values for BSA, several limitations must be acknowledged. Small sample size remains a primary constraint. Additionally, the use of high-quality images acquired by echocardiography experts with over 10 years of experience restricts the generalizability of our findings. Furthermore, exclusion criteria such as arrhythmia and pacemakers limit the applicability of HeartModelAI in real-world scenarios. The use of multiple-beat breath-hold image acquisition could introduce artifacts, impacting results. The predominantly male participant cohort may also influence observations. Reproducibility assessment solely on saved images rather than test-retest analysis incorporating new image acquisition poses a limitation. Despite implementing wide sector minimization to improve temporal resolution, increased wide sector data acquisition in dilated cardiomyopathy patients may affect HeartModelAI capability. More comprehensive multicenter studies including more patients, with image acquisition by more echocardiography experts, could yield more decisive results.