用于心脏成像的深度学习:专注于心肌疾病,叙述性审查
Theodoros Tsampras1, Theodora Karamanidou2, Giorgos Papanastasiou2
11st Cardiology Department, Hippokration Hospital, Athens, Greece.
概括
计算心脏病学中的深度学习 (DL) 提高了使用医学成像的心血管疾病诊断. DL算法改进了心肌组织细分和放射性分析,以便更早检测和个性化治疗.
科学领域:
- 计算心脏病学计算心脏病学
- 医学成像分析分析 医学成像分析
- 医疗保健中的人工智能
背景情况:
- 心血管疾病需要精确的诊断和管理.
- 计算技术,特别是成像和信息学,正在彻底改变心脏病学.
- 在无症状患者中早期发现疾病对于改善结果至关重要.
研究的目的:
- 审查心血管医学成像中的深度学习 (DL) 应用的最新情况.
- 专注于DL用于自动细分,放射性特征表型和疾病预测.
- 讨论将DL模型整合到临床实践中的挑战.
主要方法:
- 对心血管成像中DL的当前文献的综述 (CT,CMR,心声学,SPECT).
- 专注于自动细分和放射性分析等技术.
- 探索DL用于疾病表型和预测.
主要成果:
- DL算法使心肌组织的有效和一致的自动细分成为可能.
- 使用DL进行放射性分析可以提高疾病检测,患者分层和监测.
- 放射性生物标志物在区分心肌病理方面具有很高的准确性.
结论:
- 心血管成像中的DL应用为早期疾病检测和个性化治疗提供了显著的潜力.
- 自动细分和放射性表型是进步的关键领域.
- 需要解决临床实践中DL模型的整合挑战.
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