人工智能在儿科超声心动图中的应用：探索可解释人工智能与联邦学习中的挑战、机遇及临床应用
Artificial Intelligence in Pediatric Echocardiography: Exploring Challenges, Opportunities, and Clinical Applications with Explainable AI and Federated Learning
AI总结：
本研究综述了AI在儿科超声心动图中的挑战与机遇，强调联邦学习与可解释AI的协同作用，并通过临床用例展示其在视图识别、疾病分类、结构分割和功能评估中的应用。
摘要：
儿科心脏病涵盖了广泛的先天性和获得性疾病。更复杂的先天性畸形需要差异化且多模态的决策过程，通常将超声心动图作为核心成像方法。人工智能（AI）通过促进儿科超声心动图数据的自动解读，为临床医生提供了可观的前景。然而，将AI技术应用于儿科超声心动图分析面临诸多挑战，如公共数据可用性有限、数据隐私以及AI模型透明度问题。近期，研究人员聚焦于颠覆性技术，如联邦学习（FL）和可解释人工智能（XAI），以改进自动诊断和决策支持工作流程。本研究全面概述了AI在儿科超声心动图中的局限性与机遇，强调了XAI与FL的协同工作流程及作用，识别了研究空白，并探讨了潜在的未来发展方向。此外，三个相关的临床用例展示了XAI和FL的功能，重点关注（i）视图识别，（ii）疾病分类，（iii）心脏结构分割，以及（iv）心脏功能的定量评估。
英文摘要：
Pediatric heart diseases present a broad spectrum of congenital and acquired diseases. More complex congenital malformations require a differentiated and multimodal decision-making process, usually including echocardiography as a central imaging method. Artificial intelligence (AI) offers considerable promise for clinicians by facilitating automated interpretation of pediatric echocardiography data. However, adapting AI technologies for pediatric echocardiography analysis has challenges such as limited public data availability, data privacy, and AI model transparency. Recently, researchers have focused on disruptive technologies, such as federated learning (FL) and explainable AI (XAI), to improve automatic diagnostic and decision support workflows. This study offers a comprehensive overview of the limitations and opportunities of AI in pediatric echocardiography, emphasizing the synergistic workflow and role of XAI and FL, identifying research gaps, and exploring potential future developments. Additionally, three relevant clinical use cases demonstrate the functionality of XAI and FL with a focus on (i) view recognition, (ii) disease classification, (iii) segmentation of cardiac structures, and (iv) quantitative assessment of cardiac function.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
