胎儿超声心动图的帧级视图与图像质量标注工具：与电子表格标注的比较
A Frame-Level View and Image-Quality Annotation Tool for Fetal Echocardiography: Comparison with Spreadsheet Annotation
Mohamed Azzam, Ziyang Xu, Quinlan Couch, Zoe Keese, Andrea Cadlo, Christine Sedze-DeWolf, Micaiah Uhrmacher, Madelyn Graff 等 16 位作者
AI 速览：作者开发了面向胎儿超声心动图的帧级心脏视图与图像质量标注工具CAT，并与电子表格标注进行对比，报告其减少数据录入错误并提高标注效率。
Objective: To develop the Cardiologist Annotation Tool (CAT) for frame-level cardiac view and image-quality annotation in fetal echocardiography and compare it with spreadsheet-based annotation. Methods: CAT provides cine playback, frame navigation, and keyboard-based labeling without manual frame-range entry. Experts annotated 25 studies using both methods, and spreadsheet annotations were audited for predefined data-entry errors. In a randomized crossover study, six medical students annotated two normal studies, one with each method, against an expert reference. Results: Among 1,746 spreadsheet-annotated files, 6.2% contained major or critical data-entry errors, more frequently in congenital heart disease than normal studies (10.9% vs 3.4%). CAT yielded more annotated view segments per clip (3.6 vs 1.5). Rates of labeled and correctly labeled frames per minute were 1.77 and 1.68 times those with spreadsheets, respectively (both P = 0.031), with gains in all six students. No detectable difference in view-label accuracy was observed. Conclusion: CAT eliminates manual frame-range entry and enables finer-grained, structured annotation of fetal echocardiography, providing a practical approach to preparing datasets for AI research.