论文标题
基于智能手机的测试和预测模型，可快速，非侵入性和对眼部和心血管并发症的护理点监测与糖尿病有关
Smartphone-Based Test and Predictive Models for Rapid, Non-Invasive, and Point-of-Care Monitoring of Ocular and Cardiovascular Complications Related to Diabetes
论文作者
论文摘要
最有影响力的糖尿病并发症之一是糖尿病性视网膜病，工人阶级成年人失明的主要原因以及心血管疾病，这是全球死亡的主要原因。这项研究描述了改进基于机器学习的筛查的发展。首先，通过回顾性分析各种风险因素（快速，非侵入性获得）对心血管风险的影响而开发了一个随机的森林模型。接下来，开发了一个深度学习模型，用于通过修改和重新训练的InceptionV3图像分类模型从视网膜眼镜图像预测糖尿病性视网膜病变。通过在视网膜图像中自动分割血管来简化输入。转移学习的技术使该模型能够利用目标设备上的现有基础架构，这意味着更通用的部署，尤其是在低资源设置中有用。这些型号已集成到基于智能手机的设备中，并结合了廉价的3D打印视网膜成像附件。精度得分以及接收器操作特征曲线，学习曲线和其他量表都有希望。该测试便宜得多，更快，可以连续监测两种破坏性糖尿病并发症。它具有替代诊断糖尿病性视网膜病和心血管风险的手动方法的潜力，这些方法只有医疗专业人员从护理点远离耗时且昂贵的过程，并通过更快，更便宜，更便宜，更安全的糖尿病并发症来预防不可逆的失明和与心脏病相关的并发症。同样，跟踪糖尿病的心血管和眼部并发症可以改善对其他糖尿病并发症的检测，从而在全球范围内更早，更有效地治疗。
Among the most impactful diabetic complications are diabetic retinopathy, the leading cause of blindness among working class adults, and cardiovascular disease, the leading cause of death worldwide. This study describes the development of improved machine learning based screening of these conditions. First, a random forest model was developed by retrospectively analyzing the influence of various risk factors (obtained quickly and non-invasively) on cardiovascular risk. Next, a deep-learning model was developed for prediction of diabetic retinopathy from retinal fundus images by a modified and re-trained InceptionV3 image classification model. The input was simplified by automatically segmenting the blood vessels in the retinal image. The technique of transfer learning enables the model to capitalize on existing infrastructure on the target device, meaning more versatile deployment, especially helpful in low-resource settings. The models were integrated into a smartphone-based device, combined with an inexpensive 3D-printed retinal imaging attachment. Accuracy scores, as well as the receiver operating characteristic curve, the learning curve, and other gauges, were promising. This test is much cheaper and faster, enabling continuous monitoring for two damaging complications of diabetes. It has the potential to replace the manual methods of diagnosing both diabetic retinopathy and cardiovascular risk, which are time consuming and costly processes only done by medical professionals away from the point of care, and to prevent irreversible blindness and heart-related complications through faster, cheaper, and safer monitoring of diabetic complications. As well, tracking of cardiovascular and ocular complications of diabetes can enable improved detection of other diabetic complications, leading to earlier and more efficient treatment on a global scale.
