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使用智能手机摄像头预测贫血和估算血红蛋白浓度。

Prediction of anemia and estimation of hemoglobin concentration using a smartphone camera.

机构信息

Department of Emergency Medicine, Brown University, Providence, Rhode Island, United States of America.

School of Engineering, Brown University, Providence, Rhode Island, United States of America.

出版信息

PLoS One. 2021 Jul 14;16(7):e0253495. doi: 10.1371/journal.pone.0253495. eCollection 2021.

Abstract

Anemia, defined as a low hemoglobin concentration, has a large impact on the health of the world's population. We describe the use of a ubiquitous device, the smartphone, to predict hemoglobin concentration and screen for anemia. This was a prospective convenience sample study conducted in Emergency Department (ED) patients of an academic teaching hospital. In an algorithm derivation phase, images of both conjunctiva were obtained from 142 patients in Phase 1 using a smartphone. A region of interest targeting the palpebral conjunctiva was selected from each image. Image-based parameters were extracted and used in stepwise regression analyses to develop a prediction model of estimated hemoglobin (HBc). In Phase 2, a validation model was constructed using data from 202 new ED patients. The final model based on all 344 patients was tested for accuracy in anemia and transfusion thresholds. Hemoglobin concentration ranged from 4.7 to 19.6 g/dL (mean 12.5). In Phase 1, there was a significant association between HBc and laboratory-predicted hemoglobin (HBl) slope = 1.07 (CI = 0.98-1.15), p<0.001. Accuracy, sensitivity, and specificity of HBc for predicting anemia was 82.9 [79.3, 86.4], 90.7 [87.0, 94.4], and 73.3 [67.1, 79.5], respectively. In Phase 2, accuracy, sensitivity and specificity decreased to 72.6 [71.4, 73.8], 72.8 [71, 74.6], and 72.5 [70.8, 74.1]. Accuracy for low (<7 g/dL) and high (<9 g/dL) transfusion thresholds was 94.4 [93.7, 95] and 86 [85, 86.9] respectively. Error trended with increasing HBl values (slope 0.27 [0.19, 0.36] and intercept -3.14 [-4.21, -2.07] (p<0.001) such that HBc tended to underestimate hemoglobin in higher ranges and overestimate in lower ranges. Higher quality images had a smaller bias trend than lower quality images. When separated by skin tone results were unaffected. A smartphone can be used in screening for anemia and transfusion thresholds. Improvements in image quality and computational corrections can further enhance estimates of hemoglobin.

摘要

贫血是指血红蛋白浓度降低,对全球人口健康有重大影响。我们描述了一种普遍存在的设备——智能手机,如何用于预测血红蛋白浓度和筛查贫血。这是一项前瞻性便利样本研究,在学术教学医院的急诊科患者中进行。在算法推导阶段,第 1 阶段从 142 名患者的智能手机中获得了结膜的图像。从每张图像中选择针对睑结膜的感兴趣区域。提取基于图像的参数,并用于逐步回归分析,以建立估计血红蛋白(HBc)的预测模型。在第 2 阶段,使用 202 名新急诊科患者的数据构建验证模型。在所有 344 名患者的基础上测试最终模型在贫血和输血阈值方面的准确性。血红蛋白浓度范围为 4.7 至 19.6 g/dL(平均 12.5)。在第 1 阶段,HBc 与实验室预测的血红蛋白(HBl)斜率之间存在显著相关性 = 1.07(CI = 0.98-1.15),p<0.001。HBc 预测贫血的准确性、敏感性和特异性分别为 82.9[79.3,86.4]、90.7[87.0,94.4]和 73.3[67.1,79.5]。在第 2 阶段,准确性、敏感性和特异性降低至 72.6[71.4,73.8]、72.8[71,74.6]和 72.5[70.8,74.1]。对于低(<7 g/dL)和高(<9 g/dL)输血阈值的准确性分别为 94.4[93.7,95]和 86[85,86.9]。误差呈随着 HBl 值增加的趋势(斜率为 0.27[0.19,0.36],截距为-3.14[-4.21,-2.07](p<0.001),因此 HBc 倾向于低估高范围内的血红蛋白值,高估低范围内的血红蛋白值。高质量图像的偏差趋势小于低质量图像。当按肤色分开时,结果不受影响。智能手机可用于筛查贫血和输血阈值。提高图像质量和计算校正可以进一步提高血红蛋白估计值。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f94c/8279386/dadb6b278025/pone.0253495.g001.jpg

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