对深度学习模型的观察者绩效评估,用于多标签分类活跃肺结核肺部区域智能表现
James Devasia1, Hridayanand Goswami2, Subitha Lakshminarayanan1
1Preventive Medicine, Jawaharlal Institute of Postgraduate Medical Education and Research, Puducherry, IND.
Cureus
|October 11, 2023
概括
这项研究表明,EfficientNet B4深度学习模型与专家临床医生在胸部X射线上检测结核病方面强烈一致. 这种人工智能工具可以帮助在资源有限的环境中选患者.
科学领域:
- 医疗成像中的人工智能
- 深度学习用于放射学分析.
- 结核病的诊断方法 结核病的诊断方法
背景情况:
- 胸部X射线 (CXR) 对于结核病查至关重要,但其准确性有限.
- 深度学习 (DL) 模型,如EfficientNet B4,提供CXRs的自动分析.
- 本研究评估了DL模型在识别结核病相关的肺部异常方面的表现.
研究的目的:
- 评估预先训练的EfficientNet B4模型对活性结核病检测的临床实用性.
- 评估模型识别肺部区域特定异常的能力.
- 在结核病特定环境中,将模型的性能与熟练的临床医生进行比较.
主要方法:
- 一个EfficientNet B4模型通过转移学习与特定的架构修改进行了调整.
- 一位放射科医生确定了CXRs上肺部区域智能的异常的基本真相.
- 一位专家临床医生独立审查了CXR,并将其评估与使用卡帕系数的DL模型的预测进行了比较.
主要成果:
- 在EfficientNet B4模型和临床医生之间观察到强烈的一致性 (Kappa ≥0.81),包括肺胸部,中位移,肺气,纤维化,化,多流和腔腔等多种异常.
- 在空洞,中移位,体积损失和肺缩方面发现了实质性的协议 (Kappa = 0.61-0.80).
- 该模型在图像智能和肺部区域智能评估中,在各种类别中显示出中等到几乎完美的一致性.
结论:
- EfficientNet B4模型显示,在检测肺部区域智能和图像智能结核病的表现方面,与人类读者有很强的共识.
- 这种人工智能工具对于在资源有限的初级保健机构中对结核病的查和分类具有重要的临床实用性.
- 该模型可以帮助医疗保健系统过度负担的临床医生更有效地诊断结核病.
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