Hoshino Eri, Hayashi Kuniyoshi, Suzuki Mitsuyoshi, Obatake Masayuki, Urayama Kevin Y, Nakano Satoshi, Taura Yasuyuki, Nio Masaki, Takahashi Osamu
Center for Clinical Epidemiology, Center for Clinical Academia, St Luke's International University, 5th Floor, Tsukiji 3-6-2, Chuo-ku, Tokyo, 104-0045, Japan.
Department of Pediatrics, Juntendo University Graduate School of Medicine, Tokyo, Japan.
Pediatr Surg Int. 2017 Oct;33(10):1115-1121. doi: 10.1007/s00383-017-4146-8. Epub 2017 Aug 17.
The stool color card has been the primary tool for identifying acholic stools in infants with biliary atresia (BA), in several countries. However, BA stools are not always acholic, as obliteration of the bile duct occurs gradually. This study aims to introduce Baby Poop (Baby unchi in Japanese), a free iPhone application, employing a detection algorithm to capture subtle differences in colors, even with non-acholic BA stools.
The application is designed for use by caregivers of infants aged approximately 2 weeks-1 month. Baseline analysis to determine optimal color parameters predicting BA stools was performed using logistic regression (n = 50). Pattern recognition and machine learning processes were performed using 30 BA and 34 non-BA images. Additional 5 BA and 35 non-BA pictures were used to test accuracy.
Hue, saturation, and value (HSV) were the preferred parameter for BA stool identification. A sensitivity and specificity were 100% (95% confidence interval 0.48-1.00 and 0.90-1.00, respectively) even among a collection of visually non-acholic, i.e., pigmented BA stools and relatively pale-colored non-BA stools.
Results suggest that an iPhone mobile application integrated with a detection algorithm is an effective and convenient modality for early detection of BA, and potentially for other related diseases.
在一些国家，大便颜色卡一直是识别胆道闭锁（BA）婴儿无胆汁粪便的主要工具。然而，由于胆管闭塞是逐渐发生的，BA患儿的粪便并不总是无胆汁的。本研究旨在推出一款免费的iPhone应用程序“宝宝便便”（日语为“宝宝大便”），该应用程序采用检测算法，即使对于非无胆汁的BA粪便，也能捕捉到颜色上的细微差异。
该应用程序供年龄约2周 - 1个月婴儿的护理人员使用。使用逻辑回归（n = 50）进行基线分析，以确定预测BA粪便的最佳颜色参数。使用30张BA和34张非BA图像进行模式识别和机器学习过程。另外5张BA和35张非BA图片用于测试准确性。
色调、饱和度和明度（HSV）是识别BA粪便的首选参数。即使在视觉上非无胆汁的粪便集合中，即有色素的BA粪便和颜色相对较浅的非BA粪便中，灵敏度和特异性分别为100%（95%置信区间分别为0.48 - 1.00和0.90 - 1.00）。
结果表明，集成检测算法的iPhone移动应用程序是早期检测BA以及潜在检测其他相关疾病的有效且便捷的方式。
