对于胸部X射线诊断的深度学习:带有或没有人工智能辅助的放射科医生之间的竞争
Lili Guo1, Changsheng Zhou2, Jingxu Xu3
1Department of Radiology, The Affiliated Huaian No. 1 People's Hospital of Nanjing Medical University, Huai'an, 223300, China. guolili163@163.com.
Journal of imaging informatics in medicine
|February 9, 2024
概括
深度学习算法显著提高了放射科医生在胸部X射线解释方面的表现. 人工智能辅助提高了诊断的准确性和效率,有助于检测各种异常.
科学领域:
- 放射学 放射学是一门学科.
- 人工智能的人工智能
- 医疗成像医学成像
背景情况:
- 胸部X射线图是一种常见的诊断工具.
- 对胸部X射线的准确解释对于患者的护理至关重要.
- 放射科医生在有效和准确地诊断广泛的异常方面面临着挑战.
研究的目的:
- 评估深度学习算法对放射科医生在胸部放射诊断中的表现的影响.
- 用人工智能协助评估诊断准确性和效率的改进.
- 为了比较放射科医生的表现,并没有AI支持.
主要方法:
- 开发了一个深度学习算法来检测胸部X射线中的正常发现和13个异常.
- 111名放射科医生 (初级,中级,高级) 解读了100张X射线图,分为两个组:控制组 (没有AI) 和测试组 (使用AI).
- 性能指标包括准确性,错误阳性/负率,分析时间和接收器操作特征曲线 (AUC) 下的面积.
主要成果:
- 在人工智能协助下,放射科医生平均得分显著提高 (619比597,P<0.001),并且花费的时间更少 (1926比3279,P<0.001).
- 人工智能辅助提高了识别正常发现,肺纤维化,心脏扩大,质量,多流和巩固的性能 (更高的AUC).
- 没有人工智能的放射科医生在识别大动脉化,化,腔,结节,质加厚和肋骨骨折方面表现更好.
结论:
- 深度学习方法对放射科医生在解释胸部X射线中的表现产生积极影响.
- 人工智能协助提高了放射科医生在诊断任务中的有效性和效率.
- 这项研究证实了人工智能在改善胸部X射线图解读方面的有益作用.
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