离线CNN框架的诊断准确度,使用多视图胸部X射线查14种同时发生的传染性和非传染性疾病
Latika Giri1, Pradeep Raj Regmi2, Ghanshyam Gurung2
1Department of Radiology, Kathmandu University School of Medical Sciences, Dhulikhel 45200, Nepal.
Diagnostics (Basel, Switzerland)
|January 10, 2026
概括
一个深度学习的人工智能 (AI) 系统准确地检测成人胸部X射线上的胸部异常. 这种在各种数据集上训练的AI工具在支持放射科医生方面表现有前途,特别是在资源有限的环境中.
科学领域:
- 医疗成像医学成像
- 人工智能在医学中的应用
- 放射学 放射学是一门学科.
背景情况:
- 胸部X射线图是全球重要的诊断工具,但由于放射科医生短缺,特别是低收入和中等收入国家 (LMICs) 面临挑战.
- 解释胸部X射线图是耗时的,并且在大量的临床环境中容易出现错误.
- 现有的AI系统可能缺乏对本地,现实世界的医疗图像的概括性.
研究的目的:
- 评估深度学习卷积神经网络 (CNN) 的诊断准确性,用于检测成人胸部X射线影像中的胸部异常.
- 通过混合数据集 (公共和本地) 和其在线模式中的实用性来评估CNN的表现.
- 确定在临床环境中部署此类AI工具的可行性.
主要方法:
- 一个CNN在公共数据集上进行了预训练,并在尼泊尔三级医院的当地数据集上进行了微调,包括正面 (PA/AP) 和侧面视图.
- 数据集包括急诊,ICU和门诊机构,有三个放射科医生对14种胸部病理的注释.
- 在一个测试集 (N=522) 上使用AUC,灵敏度,特异性,mAP和报告时间评估性能,并对PACS集成和离线功能进行测试.
主要成果:
- 在14个异常中,CNN的AUC总值为0.86,具有68%的灵敏度,99%的特异性和0.93 mAP.
- 该系统在PA,AP和横向视图上表现出有效性,包括来自紧急和ICU设置的质量差的图像.
- 部署测试证实了无的PACS集成和成功的离线功能.
结论:
- 开发的CNN可靠地检测成年人胸部X射线中的关键胸部发现,跨越各种临床环境.
- 人工智能对图像质量的稳定性和整合多个视图的能力表明它有可能成为放射学分拣和诊断的有价值工具.
- 该系统的离线能力提高了其在资源有限的环境中的适用性.
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