一个AI深度学习算法用于在紧急情况下在超低剂量的CT上检测肺结节:一个读者研究.
Inge A H van den Berk1, Colin Jacobs2, Maadrika M N P Kanglie3,4
1Department of Radiology and Nuclear Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands. i.a.vandenberk@amsterdamumc.nl.
European radiology experimental
|November 20, 2024
概括
一个人工智能 (AI) 算法显著提高了对肺结节的检测,需要在超低剂量计算机断层扫描 (ULDCT) 扫描上进行随访. 然而,这也导致了虚假阳性病例的大幅增加,特别是在严重异常的患者中.
科学领域:
- 放射学和医学成像学 医学成像学
- 医疗保健中的人工智能
- 肺部医学 肺部医学
背景情况:
- 超低剂量计算机断层扫描 (ULDCT) 越来越多地用于急诊室 (ED) 怀疑肺部疾病.
- 在OPTIMACT试验中,研究了AI在ULDCT上检测肺结节的实用性.
- 准确检测偶然的肺结节对于早期诊断和管理至关重要.
研究的目的:
- 评估人工智能 (AI) 算法在ULDCT上检测肺结节的附加值.
- 为了比较人工智能检测肺结节的检测率与ED环境中的标准放射科医生解释.
- 在使用人工智能检测结节时,评估真正和假正结果之间的权衡.
主要方法:
- 从OPTIMACT试验中接受ULDCT的870名患者的回顾性分析.
- 由ED放射学家进行前性读取,然后使用人工智能深度学习算法进行后期分析来检测结节 (≥6毫米).
- 由三个胸部放射科医生进行独立审查,以确定前性检测到的结节和AI标记的真正积极参考标准.
主要成果:
- 人工智能检测到需要随访的真正肺结节的数量是潜在ED放射科医生报告的5.8倍 (104对18).
- 使用人工智能导致错误阳性结果增加了42.9倍 (1,758比41).
- 观察到每次ULDCT的中位数为1AI标志,错误阳性主要在患有重大异常的患者中发现.
结论:
- 人工智能显著提高了在紧急情况下在ULDCT上检测偶然的肺结节的性能.
- 检测率的增加伴随着更高的虚假阳性率的大量权衡.
- 人工智能可能有助于早期检测肺癌,但由于虚假阳性增加,需要仔细考虑其对工作流程的影响.
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