Taylor James A, Stout James W, de Greef Lilian, Goel Mayank, Patel Shwetak, Chung Esther K, Koduri Aruna, McMahon Shawn, Dickerson Jane, Simpson Elizabeth A, Larson Eric C
Department of Pediatrics, University of Washington, Seattle, Washington;
Department of Pediatrics, University of Washington, Seattle, Washington.
Pediatrics. 2017 Sep;140(3). doi: 10.1542/peds.2017-0312.
The assessment of jaundice in outpatient neonates is problematic. Visual assessment is inaccurate, and more exact methodologies are cumbersome and/or expensive. Our goal in this study was to assess the accuracy of a technology based on the analysis of digital images of newborns obtained using a smartphone application called BiliCam.
Paired BiliCam images and total serum bilirubin (TSB) levels were obtained in a diverse sample of newborns (<7 days old) at 7 sites across the United States. By using specialized software, data on color values in the images ("features") were extracted. Machine learning and regression analysis techniques were used to identify features for inclusion in models to predict an estimated bilirubin level for each newborn. The correlation between estimated bilirubin levels and TSB levels was calculated. In addition, the sensitivity and specificity of the estimated bilirubin levels in identifying newborns with high TSB levels were calculated by using 2 recommended decision rules for jaundice screening.
Estimated bilirubin levels were calculated and compared with TSB levels in a diverse sample of 530 newborns (20.8% African American, 26.3% Hispanic, and 21.2% Asian American). The overall correlation was 0.91, and correlations among white, African American, Hispanic, and Asian American newborns were 0.92, 0.90, 0.91, and 0.88, respectively. The sensitivities of BiliCam in identifying newborns with high TSB levels were 84.6% and 100%, respectively, by using 2 decision rules; specificities were 75.1% and 76.4%, respectively.
BiliCam provided accurate estimates of TSB values, demonstrating that an inexpensive technology that uses commodity smartphones could be used to effectively screen newborns for jaundice.
门诊新生儿黄疸的评估存在问题。视觉评估不准确，而更精确的方法又繁琐且/或昂贵。我们在本研究中的目标是评估一种基于对使用名为BiliCam的智能手机应用程序获取的新生儿数字图像进行分析的技术的准确性。
在美国7个地点对不同样本的新生儿（小于7日龄）获取配对的BiliCam图像和总血清胆红素（TSB）水平。通过使用专门软件，提取图像中的颜色值数据（“特征”）。使用机器学习和回归分析技术来识别纳入模型的特征，以预测每个新生儿的估计胆红素水平。计算估计胆红素水平与TSB水平之间的相关性。此外，通过使用2条推荐的黄疸筛查决策规则，计算估计胆红素水平在识别高TSB水平新生儿方面的敏感性和特异性。
在530名新生儿的不同样本（20.8%为非裔美国人，26.3%为西班牙裔，21.2%为亚裔美国人）中计算了估计胆红素水平并与TSB水平进行比较。总体相关性为0.91，白种人、非裔美国人、西班牙裔和亚裔美国新生儿之间的相关性分别为0.92、0.90、0.91和0.88。通过使用2条决策规则，BiliCam在识别高TSB水平新生儿方面的敏感性分别为84.6%和100%；特异性分别为75.1%和76.4%。
BiliCam能够准确估计TSB值，表明一种使用普通智能手机的廉价技术可有效用于新生儿黄疸筛查。
