An automatic and real-time echocardiography quality scoring system based on deep learning to improve reproducible assessment of left ventricular ejection fraction.

Background: Echocardiography can conveniently, rapidly, and economically evaluate the structure and function of the heart, and has important value in the diagnosis and evaluation of cardiovascular diseases (CVDs). However, echocardiography still exhibits significant variability in image acquisition and diagnosis, with a heavy dependency on the operator's experience. Image quality affects disease diagnosis in the later stage, and even image quality assessment still has variability in human evaluation. This study aimed to develop an automated and real-time quality assessment system using deep learning (DL) techniques while decreasing the measurement error of left ventricular ejection fraction (LVEF).

Methods: This study involved over 5,000 echocardiography datasets from 2,461 participants across 10 medical centers in China to build the model. A 5-point quality scoring system was used to assess the integrity, clarity, and alignment of anatomical structures in each echocardiogram view. Additionally, an innovative DL model was developed to autonomously detect these essential cardiac anatomical structures in real-time, subsequently providing quality score estimations and LVEF. A total of 175 participants from two distinct external medical centers were enrolled for model validation. This dataset was employed to assess the consistency and repeatability of quality score and ejection fraction (EF) measurements, and the assessments made by human experts were compared with those of our model.

Results: The developed model demonstrated exceptional performance, achieving Intersection over Union (IoU) scores exceeding 0.8 for left ventricular (LV) segmentation, a mean average precision when IoU >0.5 (mAP50) of 0.91 for cardiac anatomical structures detection, and a 0.96±0.05 accuracy in view classification. The quality scores assessed by the model closely matched those of human experts, indicating strong agreement. The weighted average precision and weighted average recall scores fell within the range of 0.5 to 0.6. Notably, there was no statistically significant difference in LVEF assessments between human experts and our model (P=0.09), as demonstrated by an intraclass correlation coefficient (ICC) analysis of 0.821, reflecting high-level consistency. When assessing echocardiograms with high-quality scores, the model demonstrated a significantly closer alignment and a higher correlation coefficient with human experts (R=0.90±0.04).

Conclusions: This study demonstrates that artificial intelligence-assisted echocardiography scoring system aligns well with manual quality scoring. Through the supervision of real-time echocardiogram quality, the artificial intelligence model can assist doctors in providing more reproducible and consistent assessments of cardiac function.