深度学习在心力衰竭中的最新进展和应用: 一个系统性审查
Georgios Petmezas1, Vasileios E Papageorgiou2, Vasileios Vassilikos3
12nd Department of Obstetrics and Gynecology, Medical School, Aristotle University of Thessaloniki, Thessaloniki, Greece; Centre for Research and Technology Hellas, Thessaloniki, Greece.
Computers in biology and medicine
|May 10, 2024
概括
深度学习 (DL) 对改善心力衰竭 (HF) 诊断和个性化治疗有很大的前景. 本综述探讨了DL在高频评估中的应用,以帮助临床决策和改善患者的治疗结果.
科学领域:
- 心脏病学 心脏病学
- 医疗信息学 医疗信息学
- 人工智能的人工智能
背景情况:
- 心力衰竭 (HF) 是一个全球性的健康挑战,需要先进的诊断和管理策略.
- 医疗保健领域深度学习 (DL) 的快速发展需要对其改善高频评估的潜力进行彻底评估.
- 将技术进步与临床实践相结合,对于有效的高频管理至关重要.
研究的目的:
- 系统地审查DL技术在评估心力衰竭 (HF) 的贡献.
- 探索DL在提高诊断准确性,个性化治疗和管理HF患者的并发症方面的潜力.
- 评估DL对心血管疾病 (CVD) 分类和HF检测的影响.
主要方法:
- 在PubMed,Scopus,Web of Science和IEEE Xplore进行了全面的文献搜索.
- 确定了137篇文章,并将其分类为高频评估中DL的五个主要应用领域.
- 选择了使用DL算法进行高频数据分析的研究,包括分类,检测,图像分析和风险评估.
主要成果:
- 深度学习 (DL) 算法在心血管疾病 (CVD) 分类和心力衰竭 (HF) 检测方面显示出显著的潜力.
- DL有效地区分患有和没有HF的个体,并识别诸如心肌病等并发症.
- 该审查强调了DL在改进诊断过程和为HF患者提供个性化治疗计划方面的实用性.
结论:
- 深度学习 (DL) 是心力衰竭 (HF) 管理的关键创新,提高了诊断准确性和个性化护理.
- 建议将DL整合到临床环境中,以提高患者的治疗结果.
- 建议未来的研究方向是进一步优化DL在HF护理中的应用.
相关概念视频
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01:22Heart Failure Drugs: β-Blockers
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01:22Heart Failure Drugs: Diuretics
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