深度学习技术用于成像诊断和治疗大动脉动脉瘤
Legang Huang1, Jiankuan Lu1, Ying Xiao1
1Department of Vascular Intervention, Shengli Oilfield Central Hospital, Dongying, China.
Frontiers in cardiovascular medicine
|March 14, 2024
概括
深度学习通过加强查,诊断和治疗计划,显著改善了大动脉动脉瘤 (AA) 管理. 这些人工智能进步承诺更准确和个性化的患者护理,以获得更好的结果.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 心血管外科心血管外科
背景情况:
- 大动脉动脉瘤 (AA) 的诊断和治疗存在复杂的挑战.
- 传统的成像分析可能耗时且主观.
- 深度学习 (DL) 为自动化和准确的分析提供了潜力.
研究的目的:
- 审查深度学习在大动脉动脉瘤成像中的应用.
- 专注于DL在查,诊断,细分,外科辅助和AA的预后预测中的作用.
主要方法:
- 综合文献综述使用DL模型的研究,包括卷积神经网络 (CNN).
- 在AA查,细分,手术规划和预后预测中分析DL应用.
- 评估DL对诊断准确性和治疗结果的影响.
主要成果:
- DL模型在AA查和诊断方面显示出高准确度 (例如,CT扫描上的ResNet).
- 细分技术 (例如U-Net) 提供了用于外科手术规划的精确测量.
- DL有助于手术程序,预测并发症,并准确预测AA进展和预后.
结论:
- 深度学习显示了提高AA诊断和治疗的巨大潜力.
- 这些人工智能进步可以带来更准确,更个性化的患者护理.
- DL整合有望改善大动脉动脉瘤管理的结果.
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