从视网膜血管参数的糖尿病病自动检测结合使用人工智能的临床变量在2型糖尿病患者的2型糖尿病患者
Shaomin Shi1,2, Ling Gao2, Juan Zhang1
1Department of Nephrology, Zhongnan Hospital of Wuhan University, 169 Donghu Road, Wuhan, 430071, Hubei, China.
BMC medical informatics and decision making
|October 31, 2023
概括
一个新的AI算法使用视网膜图像和临床数据来检测2型糖尿病患者的糖尿病病 (DKD). 这种非侵入性方法对早期的DKD查和诊断有希望.
科学领域:
- 眼科医生 眼科 眼科
- 腎臟病學 (nephrology) 是一種醫學專業.
- 人工智能的人工智能
- 医学诊断 医学诊断 医学诊断
背景情况:
- 糖尿病病 (DKD) 是末期病的主要原因.
- 目前的DKD检测方法 (白血病,eGFR) 是侵入性的和不理想的.
- 视网膜血管变化与DKD有关,这表明非侵入性查的潜力.
研究的目的:
- 开发一种新的人工智能 (AI) 算法,用于检测糖尿病病 (DKD).
- 利用视网膜血管参数和易于获取的临床数据来检测2型糖尿病患者的DKD.
- 改善DKD的早期和准确诊断.
主要方法:
- 通过使用515名2型糖尿病患者的数据开发了一种机器学习算法 (MLA).
- 患者被随机分配到训练 (70%) 和测试 (30%) 组.
- 采用随机森林分类器,并对绩效评估进行验证.
主要成果:
- 随机森林MLA在测试的分类器中表现最佳.
- 经过验证的MLA获得了84.5%的准确性,灵敏性和特异性.
- 该模型报告F1得分为0.845和AUC为0.914.
结论:
- 成功开发了一种基于人工智能的新型 DKD 诊断算法.
- 视网膜血管参数,结合临床数据,可以帮助进行DKD查.
- 这种方法为DKD检测提供了一个有希望的非侵入性工具.
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