深度学习检测内出血在国家电磁放射学计划和对解释时间的影响
Andrew James Del Gaizo1, Thomas F Osborne1, Troy Shahoumian1
1From the VA National Teleradiology Program, 795 Willow Rd, Bldg 3342, Menlo Park, CA 94025 (A.J.D.G., R.S.); VA Palo Alto Health Care System, Palo Alto, Calif (T.F.O.); Department of Radiology, Stanford University School of Medicine, Stanford, Calif (T.F.O.); and VA Health Solutions, Patient Care Services, Washington, DC (T.S.).
Radiology. Artificial intelligence
|July 17, 2024
概括
一个用于检测内出血 (ICH) 的人工智能 (AI) 工具显示中度准确性,但增加了放射科医生读取时间. 系统的低效率可能会超过人工智能的好处在高容量,低流行程度的设置.
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:
- 急性内出血 (ICH) 需要快速诊断.
- 远程放射实践面临着越来越多的工作负载.
- 人工智能临床决策支持工具正在开发中,以帮助诊断.
研究的目的:
- 评估AI解决方案对急性ICH检测的诊断性能.
- 量化人工智能对放射科医生的读数时间和系统效率的影响.
- 为了评估人工智能的实用性在一个大型的teleradiology设置.
主要方法:
- 追溯分析了61,704次没有对比度的头部CT检查.
- 放射科医生读取时间和系统效率在AI实施前后的比较.
- 人工智能性能指标的计算:灵敏度,特异性,准确性,PPV.
主要成果:
- 人工智能灵敏度:75.6%,特异性:92.1%,准确性:91.7%,PPV:21.1%. 这是一个很好的例子.
- 人工智能错误标记的ICH案件增加了翻译时间,平均为1分15秒.
- 需要放射科医生覆盖ICH检测的病例也显示读取时间增加.
结论:
- 人工智能解决方案在ICH检测方面表现出适度的诊断性能.
- 错误标记案件的阅读时间增加表明系统潜在的低效率.
- 在大容量,低流行度的远程放射环境中,人工智能的好处可能有限.
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