AimHypertension and diabetes mellitus (DM) are major causes of morbidity and mortality, with growing burdens in low‐income countries where they are underdiagnosed and undertreated. Advances in machine learning may provide opportunities to enhance diagnostics in settings with limited medical infrastructure.Materials and MethodsA non‐interventional study was conducted to develop and validate a machine learning algorithm to estimate cardiovascular clinical and laboratory parameters. At two sites in Kenya, digital retinal fundus photographs were collected alongside blood pressure (BP), laboratory measures and medical history. The performance of machine learning models, originally trained using data from the UK Biobank, were evaluated for their ability to estimate BP, glycated haemoglobin, estimated glomerular filtration rate and diagnoses from fundus images.ResultsIn total, 301 participants were enrolled. Compared with the UK Biobank population used for algorithm development, participants from Kenya were younger and would probably report Black/African ethnicity, with a higher body mass index and prevalence of DM and hypertension. The mean absolute error was comparable or slightly greater for systolic BP, diastolic BP, glycated haemoglobin and estimated glomerular filtration rate. The model trained to identify DM had an area under the receiver operating curve of 0.762 (0.818 in the UK Biobank) and the hypertension model had an area under the receiver operating curve of 0.765 (0.738 in the UK Biobank).ConclusionsIn a Kenyan population, machine learning models estimated cardiovascular parameters with comparable or slightly lower accuracy than in the population where they were trained, suggesting model recalibration may be appropriate. This study represents an incremental step toward leveraging machine learning to make early cardiovascular screening more accessible, particularly in resource‐limited settings.

中文翻译：

---

从视网膜眼底照片预测心血管危险因素：肯尼亚前瞻性非干预性研究中深度学习算法的验证

目的高血压和糖尿病 (DM) 是发病和死亡的主要原因，在低收入国家，这些疾病的诊断和治疗不足，负担日益沉重。机器学习的进步可能为在医疗基础设施有限的情况下增强诊断提供机会。材料和方法进行了一项非干预性研究，以开发和验证机器学习算法来估计心血管临床和实验室参数。在肯尼亚的两个地点，收集了数字视网膜眼底照片以及血压 (BP)、实验室测量结果和病史。机器学习模型最初使用英国生物银行的数据进行训练，评估其估计血压、糖化血红蛋白、估计肾小球滤过率和眼底图像诊断的能力。结果总共招募了 301 名参与者。与用于算法开发的英国生物库人群相比，来自肯尼亚的参与者更年轻，并且可能报告黑人/非洲种族，具有更高的体重指数以及糖尿病和高血压的患病率。收缩压、舒张压、糖化血红蛋白和估计肾小球滤过率的平均绝对误差相当或稍大。经过训练以识别 DM 的模型的接受者操作曲线下面积为 0.762（英国生物库中为 0.818），高血压模型的接受者操作曲线下面积为 0.765（英国生物库中为 0.738）。结论在肯尼亚人群中，机器学习模型估计的心血管参数的准确度与接受训练的人群相当或略低，这表明模型重新校准可能是合适的。这项研究代表了利用机器学习使早期心血管筛查更容易进行的渐进一步，特别是在资源有限的环境中。