机器学习和人工智能的进步在肺栓塞的放射检测中
Maneeshaa Mohanarajan1, Prachi P Salunke2, Ali Arif3
1Integrated Urgent Care, London Ambulance Service, London, GBR.
Cureus
|March 3, 2025
概括
机器学习和人工智能在改善使用CTPA扫描的肺栓塞 (PE) 诊断方面表现有前途. 这些人工智能工具可以匹配或超过放射科医生的准确性,潜在地减少错过诊断和改善患者的治疗结果.
科学领域:
- 医疗成像医学成像
- 人工智能在医学中的应用
- 放射学 放射学是一门学科.
背景情况:
- 肺栓塞 (PE) 诊断具有挑战性,依赖于临床评估,D-二次测试和成像.
- 计算机断层扫描肺血管图 (CTPA) 是黄金标准,但可能会受到阅读器变化的影响,导致错过诊断.
- 需要更快,更精确的诊断策略来减少PE相关的发病率和死亡率.
研究的目的:
- 通过CTPA审查机器学习 (ML) 和人工智能 (AI) 在通过CTPA诊断PE方面的有效性.
- 评估人工智能工具在提高诊断准确性和减少遗漏PE病例方面的潜力.
- 探索人工智能的未来整合在PE管理的临床实践中.
主要方法:
- 审查现有的关于AI和ML在CTPAPE检测中的应用研究.
- 对人工智能工具性能与放射科医生的解释进行比较分析.
- 评估AI识别微妙PE发现的能力.
主要成果:
- 人工智能和计算机辅助设计 (CAD) 工具的灵敏度和特异性与放射科医生在CTPA上识别PE时的灵敏度和特异性相当或超过.
- 几种人工智能工具在检测轻微PE方面显示出潜力,这表明错过病例的显著减少.
- 人工智能有望帮助临床医生更有效地诊断PE.
结论:
- 人工智能和机器学习工具显示出显著的潜力,可以提高使用CTPA进行PE诊断的准确性和效率.
- 进一步开发复杂的人工智能工具和大规模临床试验是临床整合所必需的.
- 制定道德AI应用指南对于其在治疗PE患者中的广泛采用至关重要.
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