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机会性检测2型糖尿病使用深度学习从前额胸部X射线图
Ayis Pyrros1,2, Stephen M Borstelmann3, Ramana Mantravadi4
1Duly Health and Care, Department of Radiology, Downers Grove, IL, USA. ayis@uic.edu.
Nature communications
|July 7, 2023
概括
深度学习模型可以使用胸部X射线和电子健康记录来检测2型糖尿病 (T2D). 这种人工智能方法对早期T2D查和诊断充满希望.
科学领域:
- 人工智能在医学中的应用
- 放射学和成像学 放射学和成像学
- 内分泌学和代谢性疾病.
背景情况:
- 电子健康记录 (EHR) 和深度学习 (DL) 能够预测疾病.
- 门诊性胸部放射 (CXR) 经常在临床实践中使用.
研究的目的:
- 通过整合胸部X射线和EHR数据,研究DL模型在检测2型糖尿病 (T2D) 中的有效性.
- 通过使用现有的成像和临床信息,评估模型在识别T2D风险方面的表现.
主要方法:
- 使用来自160,244名患者的271,065个CXR的大数据集开发了一个DL模型.
- 该模型对T2D的预测能力在9,943个CXR的前数据集上进行了评估.
- 在一个独立的机构进行了外部验证,以确认可通用性.
主要成果:
- 在DL模型中,在初级队列中检测T2D的ROC AUC为0.84,T2D患病率为16%.
- 该算法确定了1381例 (14%) 的病例可疑为T2D.
- 外部验证显示ROC AUC为0.77,5%的患者随后被诊断为T2D.
结论:
- 集CXR和EHR数据的DL模型可以有效地检测T2D.
- 可解释的AI确定了与高预测性相关的脂肪度,这表明CXRs对T2D查的潜力.
- 这种方法为在常规临床工作流程中进行增强的非侵入性T2D查提供了一个新的途径.
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