PDF(Pubmed)
Abstract：
BACKGROUND: Diabetes mellitus is the most challenging and fastest-growing global public health concern. Approximately 10.5% of the global adult population is affected by diabetes, and almost half of them are undiagnosed. The growing at-risk population exacerbates the shortage of health resources, with an estimated 10.6% and 6.2% of adults worldwide having impaired glucose tolerance and impaired fasting glycemia, respectively. All current diabetes screening methods are invasive and opportunistic and must be conducted in a hospital or laboratory by trained professionals. At-risk participants might remain undetected for years and miss the precious time window for early intervention to prevent or delay the onset of diabetes and its complications.
OBJECTIVE: We aimed to develop an artificial intelligence solution to recognize elevated blood glucose levels (≥7.8 mmol/L) noninvasively and evaluate diabetic risk based on repeated measurements.
METHODS: This study was conducted at KK Women\'s and Children\'s Hospital in Singapore, and 500 participants were recruited (mean age 38.73, SD 10.61 years; mean BMI 24.4, SD 5.1 kg/m2). The blood glucose levels for most participants were measured before and after consuming 75 g of sugary drinks using both a conventional glucometer (Accu-Chek Performa) and a wrist-worn wearable. The results obtained from the glucometer were used as ground-truth measurements. We performed extensive feature engineering on photoplethysmography (PPG) sensor data and identified features that were sensitive to glucose changes. These selected features were further analyzed using an explainable artificial intelligence approach to understand their contribution to our predictions.
RESULTS: Multiple machine learning models were trained and assessed with 10-fold cross-validation, using participant demographic data and critical features extracted from PPG measurements as predictors. A support vector machine with a radial basis function kernel had the best detection performance, with an average accuracy of 84.7%, a sensitivity of 81.05%, a specificity of 88.3%, a precision of 87.51%, a geometric mean of 84.54%, and F score of 84.03%.
CONCLUSIONS: Our findings suggest that PPG measurements can be used to identify participants with elevated blood glucose measurements and assist in the screening of participants for diabetes risk.
OBJECTIVE: We aimed to develop an artificial intelligence solution to recognize elevated blood glucose levels (≥7.8 mmol/L) noninvasively and evaluate diabetic risk based on repeated measurements.
METHODS: This study was conducted at KK Women\'s and Children\'s Hospital in Singapore, and 500 participants were recruited (mean age 38.73, SD 10.61 years; mean BMI 24.4, SD 5.1 kg/m2). The blood glucose levels for most participants were measured before and after consuming 75 g of sugary drinks using both a conventional glucometer (Accu-Chek Performa) and a wrist-worn wearable. The results obtained from the glucometer were used as ground-truth measurements. We performed extensive feature engineering on photoplethysmography (PPG) sensor data and identified features that were sensitive to glucose changes. These selected features were further analyzed using an explainable artificial intelligence approach to understand their contribution to our predictions.
RESULTS: Multiple machine learning models were trained and assessed with 10-fold cross-validation, using participant demographic data and critical features extracted from PPG measurements as predictors. A support vector machine with a radial basis function kernel had the best detection performance, with an average accuracy of 84.7%, a sensitivity of 81.05%, a specificity of 88.3%, a precision of 87.51%, a geometric mean of 84.54%, and F score of 84.03%.
CONCLUSIONS: Our findings suggest that PPG measurements can be used to identify participants with elevated blood glucose measurements and assist in the screening of participants for diabetes risk.
摘要：
背景：糖尿病是最具挑战性和增长最快的全球公共卫生问题。全球约有10.5%的成年人患有糖尿病。几乎一半的人都没有确诊.日益增加的高危人群加剧了卫生资源的短缺,全球估计有10.6%和6.2%的成年人有糖耐量受损和空腹血糖受损，分别。所有目前的糖尿病筛查方法都是侵入性和机会性的，必须由受过培训的专业人员在医院或实验室进行。有风险的参与者可能多年未被发现，并错过了早期干预以预防或延迟糖尿病及其并发症发作的宝贵时间窗。
目的：我们旨在开发一种人工智能解决方案，以无创性识别升高的血糖水平（≥7.8mmol/L），并基于重复测量评估糖尿病风险。
方法：本研究在新加坡KK妇女儿童医院进行，招募500名参与者(平均年龄38.73,SD10.61岁;平均BMI24.4,SD5.1kg/m2).大多数参与者的血糖水平是在使用常规血糖仪（Accu-ChekPerforma）和手腕穿戴式可穿戴设备食用75g含糖饮料之前和之后进行测量的。从血糖仪获得的结果用作地面实况测量。我们对光电体积描记术（PPG）传感器数据进行了广泛的特征工程，并确定了对葡萄糖变化敏感的特征。使用可解释的人工智能方法进一步分析了这些选定的特征，以了解它们对我们的预测的贡献。
结果：通过10倍交叉验证对多个机器学习模型进行了训练和评估，使用参与者人口统计数据和从PPG测量中提取的关键特征作为预测因子。具有径向基函数核的支持向量机具有最佳的检测性能，平均准确率为84.7%，灵敏度为81.05%，特异性为88.3%，精度为87.51%，几何平均值为84.54%，F评分为84.03%。
结论：我们的研究结果表明，PPG测量可用于识别血糖测量升高的参与者，并协助筛查参与者的糖尿病风险。
目的：我们旨在开发一种人工智能解决方案，以无创性识别升高的血糖水平（≥7.8mmol/L），并基于重复测量评估糖尿病风险。
方法：本研究在新加坡KK妇女儿童医院进行，招募500名参与者(平均年龄38.73,SD10.61岁;平均BMI24.4,SD5.1kg/m2).大多数参与者的血糖水平是在使用常规血糖仪（Accu-ChekPerforma）和手腕穿戴式可穿戴设备食用75g含糖饮料之前和之后进行测量的。从血糖仪获得的结果用作地面实况测量。我们对光电体积描记术（PPG）传感器数据进行了广泛的特征工程，并确定了对葡萄糖变化敏感的特征。使用可解释的人工智能方法进一步分析了这些选定的特征，以了解它们对我们的预测的贡献。
结果：通过10倍交叉验证对多个机器学习模型进行了训练和评估，使用参与者人口统计数据和从PPG测量中提取的关键特征作为预测因子。具有径向基函数核的支持向量机具有最佳的检测性能，平均准确率为84.7%，灵敏度为81.05%，特异性为88.3%，精度为87.51%，几何平均值为84.54%，F评分为84.03%。
结论：我们的研究结果表明，PPG测量可用于识别血糖测量升高的参与者，并协助筛查参与者的糖尿病风险。
