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人工智能作为前列腺成像报告和数据系统类别3的诊断辅助工具:回顾性多中心队列研究的结果

Kexin Wang¹, Zhangli Xing², Zixuan Kong³

¹School of Basic Medical Sciences, Capital Medical University, Beijing, 100069, China.

Abdominal radiology (New York)

|September 22, 2023

概括

人工智能 (AI) 提高了前列腺PI-RADS 3病例的放射科医生的诊断准确度. 人工智能还提高了诊断效率和对解释这些具有挑战性的MRI扫描的信心.

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:

前列腺癌的诊断严重依赖于磁共振成像 (MRI).
前列腺成像报告和数据系统 (PI-RADS) 将病变分类,PI-RADS 3需要仔细解释.
人工智能 (AI) 具有增加医学成像诊断能力的潜力.

研究的目的:

评估AI对放射科医生诊断前列腺多参数MRI (mpMRI) PI-RADS 3病变的诊断性能的影响.
为了比较诊断效果,阅读时间和信心水平,有和没有人工智能辅助.

主要方法:

一项涉及16名放射科医生的多中心研究,解释了87例前列腺mpMRI病例,最初被归类为PI-RADS 3.
这些病例包括28种临床显著的癌症 (csPCa) 和59种非csPCa.
诊断表现,阅读时间和信心在与AI和没有AI的读数之间进行了比较.

主要成果:

在87例中,AI在65例中改变了诊断,提高了特异性和准确性 (P < 0.001).
人工智能正确降级了41个非csPCa案例,并升级了20个csPCa案例.
人工智能显著减少了阅读时间 (P < 0.001) 并增加了诊断信心.

结论:

人工智能显著提高了放射科医生的诊断准确性,用于PI-RADS 3前列腺MRI解释.
人工智能提高了诊断效率和信心,有助于更好地管理患者.
人工智能被证明是前列腺癌诊断中的一个有价值的工具.

关键词:

深度学习是一种深度学习.磁共振成像技术磁共振成像技术前列腺成像报告和数据系统前列腺新生体前列腺瘤.

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