对人工智能算法的性能评估,用于腹腔大动脉动脉瘤的机会性查
Danling Chen1, Mark Krycia1, Jerome Avondo2
1Yale University, Department of Radiology and Biomedical Imaging, New Haven, CT, USA.
Clinical imaging
|June 25, 2025
概括
人工智能 (AI) 可以改善在急诊室CT扫描中偶然发现的腹腔大动脉动脉瘤 (AAA) 的检测. 这项研究表明,人工智能增强了AAA检测7.4%,识别了以前未经记录的病例.
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:
- 腹腔大动脉瘤 (AAA) 经常在CT扫描中偶然检测到.
- 人工智能 (AI) 算法可以自动测量大动脉光量以检测AAA.
- 有限的研究已经评估了AI工具在大型临床环境中的性能.
研究的目的:
- 评估商业人工智能算法对偶然的AAA进行机会性选.
- 在紧急情况下评估AI在非优化的CT成像上的性能.
主要方法:
- 追溯分析了来自急诊室的4023个腹部/骨盆CT检查.
- 使用人工智能分析CT图像和自然语言处理 (NLP) 审查放射学报告.
- 鉴定了用于放射科医生审查的差异 (AI阳性,NLP阴性).
主要成果:
- 在3955个负面报告中,AI标记了16个潜在的AAA差异.
- 二次审查证实了16个差异中的5个 (31%) 是以前未经记录的AAA.
- 人工智能协助增加了AAA检测率7.4%.
结论:
- 人工智能算法显示出增强CT上偶然AAA检测的潜力.
- 人工智能在大量的急救部门工作流程中尤其有益.
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