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An iPhone application using a novel stool color detection algorithm for biliary atresia screening
Eri Hoshino1, Kuniyoshi Hayashi2, Mitsuyoshi Suzuki3
1Center for Clinical Epidemiology, Center for Clinical Academia, St Luke's International University, 5th Floor, Tsukiji 3-6-2, Chuo-ku, Tokyo, 104-0045, Japan. hoshieri@luke.ac.jp.
Pediatric Surgery International
|August 19, 2017
Summary
A new iPhone app, Baby Poop, uses a detection algorithm to identify biliary atresia (BA) in infants by analyzing stool color, even when stools are not fully acholic. This tool offers a convenient method for early disease detection.
Area of Science:
- Pediatrics
- Medical Imaging
- Mobile Health
Background:
- The traditional stool color card for detecting biliary atresia (BA) has limitations as BA stools are not always acholic.
- Gradual bile duct obliteration in BA can result in stools with varying pigmentation, challenging traditional diagnostic methods.
Purpose of the Study:
- To introduce the "Baby Poop" (Baby unchi) iPhone application for early detection of biliary atresia (BA).
- To develop a mobile health tool utilizing a detection algorithm to identify BA even with non-acholic infant stools.
Main Methods:
- The study involved caregivers of infants aged 2 weeks to 1 month.
- Logistic regression (n=50) determined optimal color parameters for BA stool prediction.
- Machine learning algorithms analyzed 30 BA and 34 non-BA images, with 5 BA and 35 non-BA images used for accuracy testing.
Main Results:
- Hue, saturation, and value (HSV) color parameters were most effective for BA stool identification.
- The application achieved 100% sensitivity and specificity in detecting BA stools, including visually non-acholic and pale non-BA stools.
Conclusions:
- An iPhone application with a detection algorithm is an effective and convenient tool for the early detection of biliary atresia (BA).
- This mobile health approach may also aid in diagnosing other related infant diseases.
