使用人工智能在感染性内心炎中通过食道心声扫描自动检测植被
Daniel Pinilla-García1, Luis Llamas-Fernández2,3, Carmen Olmos4
1Hospital Clínico Universitario de Valladolid, Cardiology, Valladolid, Spain. pinilla.garcia.daniel@gmail.com.
Journal of imaging informatics in medicine
|October 31, 2025
概括
一个人工智能模型准确地检测到穿越食道心声图像 (TEE) 中的植被,用于诊断感染性内心炎 (IE). 这种工具有助于非专家心脏病专家更快,更可靠地进行IE诊断.
科学领域:
- 心脏病学 心脏病学
- 人工智能的人工智能
- 医疗成像医学成像
背景情况:
- 感染性内心炎 (IE) 诊断依赖于检测植被,通常通过透食管心声学 (TEE) 可视化.
- 目前对植被TEE图像的手动分析受到基本测量和运营商间显著变化的限制.
- 需要客观和可重复的方法来改善IE诊断.
研究的目的:
- 开发和评估基于人工智能的模型,用于在TEE图像中自动检测和诊断植被.
- 为了比较不同的人工智能架构 (YOLO和DETR) 对此任务的性能.
- 评估AI在IE诊断中协助非专家心脏病学家的潜力.
主要方法:
- 一项追溯观察性研究,涉及7家医院的329名IE患者.
- 在TEE图像上训练使用YOLO和DETR架构的AI模型来检测植被.
- 在层面 (检测) 和患者层面 (诊断) 评估AI模型的性能.
主要成果:
- 人工智能模型表现出强大的诊断能力,AUROC为0.91 (PPV=0.81,TPR=0.83).
- 框架级植被检测取得了有希望的结果 (PPV=0.83,TPR=0.75).
- 无论是YOLO还是DETR架构都在检测植被和诊断IE方面表现出很高的性能.
结论:
- 开发的AI算法有效地检测植被,并从TEE图像中识别IE患者.
- 这种人工智能工具有可能促进和加速IE诊断,特别是对于非专家心脏病学家来说.
- 与传统方法相比,人工智能驱动的分析提供了更高的准确性和更少的可变性.
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