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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.
Insights
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.
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.
