Abstract
Background
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.
Methods
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.
Results
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.
Conclusions
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.
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Acknowledgements
We wish to thank Dr. Yusuke Yamane, Ms. Saeko Hishinuma, and Dr. Saeko Hirai for their professional advice. We also wish to thank Takashi Taguchi and Shinsuke Ito at UNLOG K.K. (Tokyo) for their substantial cooperation in developing Baby Poop. In addition, we wish to acknowledge the biliary atresia patients’ community (BA no kodomowo mamorukai) for their generous support of this project.
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All authors in this study declared that they do not have anything to disclose regarding conflict of interest.
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Funding organization does not have any role in design and conduct of the study; collection, management, analysis, and interpretation of the data; and preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.
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This work was supported by Japan Society for the Promotion of Science KAKENHI Grant number 16K19177.
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Hoshino, E., Hayashi, K., Suzuki, M. et al. An iPhone application using a novel stool color detection algorithm for biliary atresia screening. Pediatr Surg Int 33, 1115–1121 (2017). https://doi.org/10.1007/s00383-017-4146-8
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DOI: https://doi.org/10.1007/s00383-017-4146-8