School of Computer Science and Software Engineering, Southwest Petroleum University, Chengdu, 610500, China.
Ultrasound in Cardiac Electrophysiology and Biomechanics Key Laboratory of Sichuan Province, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, 32# W. Sec 2, 1st Ring Rd., Chengdu, 610072, China.
Sci Rep. 2024 Sep 3;14(1):20484. doi: 10.1038/s41598-024-71530-z.
High-quality standard views in two-dimensional echocardiography are essential for accurate cardiovascular disease diagnosis and treatment decisions. However, the quality of echocardiographic images is highly dependent on the practitioner's experience. Ensuring timely quality control of echocardiographic images in the clinical setting remains a significant challenge. In this study, we aimed to propose new quality assessment criteria and develop a multi-task deep learning model for real-time multi-view classification and image quality assessment (six standard views and "others"). A total of 170,311 echocardiographic images collected between 2015 and 2022 were utilized to develop and evaluate the model. On the test set, the model achieved an overall classification accuracy of 97.8% (95%CI 97.7-98.0) and a mean absolute error of 6.54 (95%CI 6.43-6.66). A single-frame inference time of 2.8 ms was achieved, meeting real-time requirements. We also analyzed pre-stored images from three distinct groups of echocardiographers (junior, senior, and expert) to evaluate the clinical feasibility of the model. Our multi-task model can provide objective, reproducible, and clinically significant view quality assessment results for echocardiographic images, potentially optimizing the clinical image acquisition process and improving AI-assisted diagnosis accuracy.
高质量的二维超声心动图标准视图对于准确的心血管疾病诊断和治疗决策至关重要。然而，超声心动图图像的质量高度依赖于从业者的经验。在临床环境中确保及时进行超声心动图图像的质量控制仍然是一个重大挑战。在这项研究中，我们旨在提出新的质量评估标准，并开发一个用于实时多视图分类和图像质量评估（六个标准视图和“其他”视图）的多任务深度学习模型。共使用了 2015 年至 2022 年间收集的 170311 张超声心动图图像来开发和评估该模型。在测试集中，该模型的整体分类准确率为 97.8%（95%CI 97.7-98.0），平均绝对误差为 6.54（95%CI 6.43-6.66）。单次推断时间为 2.8 毫秒，满足实时要求。我们还分析了来自三组不同超声心动图医师（初级、高级和专家）的预存储图像，以评估该模型的临床可行性。我们的多任务模型可以为超声心动图图像提供客观、可重复和具有临床意义的视图质量评估结果，有可能优化临床图像采集过程并提高 AI 辅助诊断的准确性。
