人工智能的临床价值在协助小放射科医生进行甲状腺超声波:来自实际临床实践的多中心前性研究
Dong Xu1,2,3,4, Lin Sui1,2,3,4, Chunquan Zhang5
1Department of Diagnostic Ultrasound Imaging & Interventional Therapy, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, 310022, China.
BMC medicine
|July 11, 2024
概括
甲状腺结节的新两级 (2e) 诊断标准有效选低风险病例. 人工智能 (AI) 的表现与高级放射科医生相当,提高了初级医生的诊断准确度.
科学领域:
- 医学成像和诊断 医学成像和诊断
- 医疗保健中的人工智能
- 瘤学和内分泌学
背景情况:
- 甲状腺结节需要准确的诊断标准来区分良性和恶性病例,尽量减少不必要的侵入性手术.
- 当前的诊断方法可能资源密集,需要更高效的查工具.
- 将人工智能 (AI) 整合到甲状腺结节分析的临床决策中是一个新兴的研究领域.
研究的目的:
- 提出和验证用于甲状腺结节分析的临床应用的两级 (2e) 诊断标准.
- 评估人工智能 (AI) 在前性环境中协助甲状腺结节的临床决策的有效性.
- 将AI,经验丰富的放射科医生和AI辅助放射科医生的诊断性能与拟议的标准进行比较.
主要方法:
- 一项前性研究涉及1036名患者和3个医疗中心的2296个甲状腺结节.
- 开发和应用双级 (2e) 诊断标准,以仲裁委员会和病原学作为验证标准.
- 对人工智能系统,放射科医生和人工智能辅助放射科医生的诊断性能 (灵敏度,特异性,精度,AUC) 的评估.
主要成果:
- 人工智能系统实现了高诊断性能,在与全球2e诊断标准对比时,AUC为0.901.
- 人工智能性能与高级放射科医生相当,优于初级放射科医生 (P < 0.05).
- 人工智能协助显著提高了初级放射科医生的诊断能力,使其与高级放射科医生相提并论.
结论:
- 建议的2e诊断标准在临床上适用,并与现实世界的评估保持一致.
- 人工智能系统表现出强大的诊断性能,与高级放射科医生相美,并显著帮助初级放射科医生.
- 这种人工智能驱动的方法有可能减少临床实践中不必要的侵入性诊断程序.
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