通过计算机断层扫描检测肺栓塞的人工智能的现状 肺血管图:系统性审查
Moustafa Said Taman Ahmed Hassan1, Mahmoud Abdrabo Mahmoud Elhotiby2, Viraj Shah3
1Musgrove Park Hospital, Somerset NHS Foundation Trust, Taunton, UK.
概括
人工智能 (AI) 在CTPA扫描上检测肺栓塞 (PE),提高诊断准确性. 需要进一步验证以解决广泛临床使用的可概括性和偏见.
科学领域:
- 放射学 放射学是一门学科.
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 肺栓塞 (PE) 带来了诊断挑战,当前的标准CTPA压迫医疗保健系统.
- 错过或延迟PE检测的高率强调了需要改进诊断工具的必要性.
研究的目的:
- 系统地审查人工智能模型在CTPA上检测PE的诊断准确性和临床适用性.
- 探索AI在提高PE诊断的诊断可靠性和效率方面的潜力.
主要方法:
- 按照PRISMA指南进行系统审查,搜索EMBASE,MEDLINE,Cochrane,PubMed和谷歌学者.
- 包括12项分析人工智能与CTPA整合用于PE检测的研究,包括341,112张放射图像.
- 识别和分析人工智能模式,包括CNN,细分模型和NLP.
主要成果:
- 卷积神经网络 (CNN) 是最常见的AI模式.
- 像EmbNet这样的AI模型表现出高灵敏度 (88-90.9%) 并减少了假阳性 (每扫描0.45).
结论:
- 人工智能在CTPA上具有显著的PE检测潜力,特别是当与临床数据集成时.
- 挑战包括确保可通用性,减轻偏见和在不同环境中标准化验证.
- 未来的研究应该集中在模型改进,外围栓塞检测和临床实施的外部验证上.
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