Formal validation of a deep learning-based automated interpretation system for cardiac structure and function in adult echocardiography

作者：Guijuan Peng, Rongbo Ling, Xiaohua Liu, Qian Liu, Xiaofang Zhong, Yuanyuan Sheng, Yingqi Zheng, Shuyu Luo, Yumei Yang, Xiaoxuan Lin, Keming Tang, Jialan Zheng, Lixin Chen, Dong Ni, Jinfeng Xu, Yingying Liu, Wufeng Xue · 发表于：Quantitative Imaging in Medicine and Surgery · 年份：2025 · DOI：10.21037/qims-24-1852 · 被引用次数：3 · 研究领域：Cardiovascular Function and Risk Factors、Ultrasound Imaging and Elastography、Phonocardiography and Auscultation Techniques

Background: Accurate measurement of cardiac structure and function is the basis of diagnosis of cardiac diseases, but it is time-consuming and empirically-dependent. This study attempted to propose a deep learning (DL) interpretation of cardiac structure and function. Methods: The training dataset consisted of 416 video loops and 892 Doppler images drawn from 141 patients undergoing clinical echocardiography from 2020 to 2021. Two experts labeled these images using the Pair platform. From this, DL algorithms including the Auto-Echo and Auto-Doppler were trained to measure echocardiographic parameters. Subsequently, eight sonographers with different years of echocardiographic experience labeled a validation dataset of 178 new video loops and 391 Doppler images obtained from 60 new patients. One highly trained expert annotated the external validation dataset of 90 two-dimensional (2D) videos and 120 Doppler images. The standard deviation ratio (SD ratio), Bland-Altman analysis, interclass correlation coefficient (ICC), mean absolute deviation (MAD), absolute relative deviation, and correlation analysis were employed to investigate the agreement between DL and human experts. Results: For the structure parameters' measurements including four-chamber dimensions, the SD ratios ranged from 0.70 to 1.02, and the ICCs showed that automated measurements were equivalent or superior to human expert measurements. The correlation coefficients were greater than 0.85 for 83.3% of the paramet...