一个全面的大脑计算机断层扫描深度学习模型对放射科医生检测准确性的影响
Quinlan D Buchlak1,2,3, Cyril H M Tang4, Jarrel C Y Seah4,5
1Annalise.ai, Sydney, NSW, Australia. quinlan.buchlak1@my.nd.edu.au.
European radiology
|August 22, 2023
概括
一个深度学习模型显著提高了放射科医生在解释无对比电脑断层扫描 (NCCTB) 扫描中的精度. 这种人工智能协助增强了异常的检测和减少了阅读时间,显示了改善患者护理的潜力.
科学领域:
- 医疗成像中的人工智能
- 放射学和诊断成像 放射学和诊断成像
- 机器学习在医疗保健中的应用
背景情况:
- 大脑非对比计算断层扫描 (NCCTB) 对于检测内病理至关重要.
- 对NCCTB扫描的解释可能容易出现错误,影响诊断准确度.
- 机器学习 (ML) 具有增强放射学临床决策的潜力.
研究的目的:
- 评估深度学习 (DL) 模型在协助放射科医生进行NCCTB解释方面的性能.
- 为了比较放射科医生的诊断准确度,有和没有DL模型的帮助.
- 评估DL辅助对放射科医生的解释时间的影响.
主要方法:
- 一个DL模型被训练在一个大数据集 (212,484扫描) 的NCCTB.
- 32名放射科医生审查了2848个NCCTB扫描,无论是用DL模型辅助还是没有DL模型辅助.
- 通过使用接收器运行特征曲线 (AUC) 下的面积和马修斯相关系数 (MCC) 与黄金标准对比来评估性能.
主要成果:
- 在144个发现中,DL模型实现了0.93的平均AUC.
- 在DL模型的帮助下,放射科医生的表现显著改善 (平均AUC为0.79与0.73无助).
- DL辅助导致91个发现的AUC显著改善,阅读时间显著减少.
结论:
- 一个全面的深度学习系统显著提高了放射科医生在检测NCCTB扫描中广泛异常的准确性.
- DL模型表现出强大的独立性能,并提高了放射科医生的解释效率.
- 这项技术有可能减少诊断错误,提高工作流程效率,并促进及时的患者护理.
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