在中国西北偏远地区开发和应用基于深度学习的结核病诊断辅助系统
Pahatijiang Nijiati1,2, Saimaitikari Abudoubari1,2, Yimin Zimin1
1Xinjiang Key Laboratory of Artificial Intelligence Assisted Imaging Diagnose, The First People's Hospital of Kashi Prefecture, Kashi, China.
Scientific reports
|November 1, 2025
概括
深度学习系统通过提高诊断准确性和效率,改善了中国喀什地区的结核病查. 这种由人工智能驱动的工具显著减少了初级医疗保健机构的误诊和遗漏病例.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 公共卫生 公共卫生
背景情况:
- 结核病 (TB) 的发病率在中国西北的喀什高.
- 传统的结核病查方法面临诸如低效率和高误诊率等挑战,原因是初级保健资源有限.
研究的目的:
- 开发和部署基于深度学习的结核病查诊断辅助系统.
- 解决初级卫生保健机构的局限性,提高结核病检测效率和准确性.
主要方法:
- 收集了10,897张胸部X射线图像用于培训 (10,002) 和测试 (895).
- 训练了一种TB-UNET模型并将其集成到诊断辅助系统中.
- 在卡什加地区的12个县和178个乡镇医院部署了该系统.
主要成果:
- 放射科医生的结核病诊断灵敏度增加了11.8% (74.5%),准确度增加了2.8% (90.0%).
- 平均图像阅读时间从38.83秒下降到15.93秒.
- 该系统改善了初级医疗机构的信息化.
结论:
- 深度学习系统显著提高了结核病查效率,并减少了偏远地区的诊断错误.
- 该系统为服务不足地区的传染病查提供了一个实用和可复制的模型.
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