影响深度学习性能的因素 检测肺栓塞
Vera Sorin1, Panagiotis Korfiatis1, Steve G Langer1
1Department of Radiology, Mayo Clinic College of Medicine and Science, Mayo Clinic, Rochester, MN, USA.
Journal of the American College of Radiology : JACR
|December 28, 2025
概括
人工智能肺栓塞检测模型的现实世界性能差异很大. 技术因素,如扫描仪类型和成像工件,以及患者的并发症,影响了AI的准确性,需要定制的验证策略.
科学领域:
- 放射学 放射学是一门学科.
- 人工智能在医学中的应用
- 临床决策支持 临床决策支持
背景情况:
- 人工智能模型在临床实践中越来越多地使用.
- 人工智能模型在受控环境之外的通用性是一个问题.
- 对FDA批准的人工智能工具的现实性能评估至关重要.
研究的目的:
- 评估商业AI肺栓塞 (PE) 检测模型的现实世界性能.
- 确定影响AI性能变化的技术,人口和临床因素.
- 为人工智能工具的后期生产监测和部署策略提供信息.
主要方法:
- 对11,144次CT肺血管扫描检查的回顾性分析.
- 商业PE检测模型处理了所有成像数据.
- 从DICOM标题和EHR中提取了技术参数,人口统计和并发症.
- 使用单变量和多变量逻辑回归来识别与性能相关的因素.
主要成果:
- 模型的总体灵敏度为83.5%,正预测值 (PPV) 为90.5%.
- 与特定扫描仪制造商相关的降低灵敏度,增加切片厚度和成像工件.
- 患者的并发症,如心力衰竭和肺高血压显著降低了AI的敏感性.
- 人口统计学因素 (年龄,性别,种族,BMI) 与模型性能没有显著关联.
结论:
- 临床实践中的AI性能受到技术成像参数和患者并发性疾病的影响.
- 了解这些变量对于选择合适的人工智能工具和有效的部署后监控至关重要.
- 需要当地验证框架,以确保在各种医疗保健环境中安全部署人工智能.
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