超声波中的深度学习:应用,挑战和未来前景
Yong Zhang1,2, Yao Hou1, Tingting Qiu2
1College of Biomedical Engineering, Sichuan University, Chengdu, China.
Frontiers in oncology
|January 28, 2026
概括
人工智能,特别是深度学习 (DL),增强脏超声波用于脏疾病分析. 在细分和诊断等任务中,DL提高了准确性和效率,尽管数据和集成方面的挑战仍然存在.
科学领域:
- 腎臟病學 (nephrology) 是一種醫學專業.
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 病是全球主要的健康问题,经常导致末期病.
- 脏超声波是评估脏结构和病理学的关键非侵入性成像技术.
- 目前,观察者间的变异性限制了性超声波的诊断准确性.
研究的目的:
- 系统地审查深度学习 (DL) 在性超声波中用于病分析的应用.
- 评估DL模型的性能,包括卷积神经网络 (CNN) 和变压器,在关键的超声任务中.
- 确定脏超声波中DL的当前挑战和未来方向.
主要方法:
- 对脏超声波中的深度学习应用程序的系统审查.
- 对细分,体积测量,功能预测和疾病诊断的DL模型的评估.
- 对模型性能指标的分析和限制的识别.
主要成果:
- 深度学习显著提高了分析病的准确性和效率,包括慢性病 (CKD).
- DL模型在自动化细分和疾病诊断等任务方面表现有前途.
- 在各种DL架构中,包括CNN和变压器,都注意到了性能改进.
结论:
- 深度学习提供了一个强大的工具,可以提高脏超声波工作流程的客观性和自动化.
- 需要解决数据质量,模型解释性,概括和临床整合等挑战.
- 未来的进步需要将DL与多式联络数据,大型模型,联合学习和可解释的AI集成为个性化的超声波.
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