在AI检测的偶发性肺栓塞中,患者症状和CT形态的相关性
Selim Abed1, Lucas Brandstetter1, Klaus Hergan1
1General Hospital Salzburg, Müllnerhauptstrasse 48, 5020 Salzburg, Austria.
Diagnostics (Basel, Switzerland)
|July 12, 2025
概括
人工智能 (AI) 可以检测放射科医生错过的偶然肺栓塞 (IPEs). 然而,大多数人工智能检测到的IEE在临床上是无声的,需要仔细整合人工智能发现与临床数据,以进行适当的患者管理.
科学领域:
- 放射学 放射学是指放射学
- 人工智能的人工智能
- 肺栓塞是因为肺栓塞.
背景情况:
- 偶发性肺栓塞 (IPE) 通常无症状,导致诊断不足和治疗不足.
- 人工智能 (AI) 在协助放射科医生进行IPE检测方面表现有前途.
研究的目的:
- 评估AI检测到的IPEs的临床和放射学意义.
- 以AI检测到的IEE与血栓负担,右心应变和临床症状相关联.
主要方法:
- 使用人工智能算法对13,603张CT扫描进行了回顾性分析.
- 自然语言处理 (NLP) 用于识别放射科医生报告的IEE.
- 评分IPEs和分析临床参数和6个月的结果.
主要成果:
- 国际卫生组织确定了41例IPE病例,61%为瘤患者.
- 大多数IPE是细分的,没有右心应变,90%的患者无症状.
- 在瘤患者中发生了四例死亡; 一名未经治疗的患者患有复发性肺栓塞.
结论:
- 人工智能有效地检测出在初步审查中错过的IPE.
- 大多数AI检测到的IEE在临床上是无声的.
- 将AI与临床和形态数据相结合,对于适当的管理和避免过度治疗至关重要.
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