Eri Hoshino1, Kuniyoshi Hayashi2, Mitsuyoshi Suzuki3, Masayuki Obatake4, Kevin Y Urayama2,5, Satoshi Nakano3, Yasuyuki Taura6, Masaki Nio7, Osamu Takahashi2. 1. 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. hoshieri@luke.ac.jp. 2. 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. 3. Department of Pediatrics, Juntendo University Graduate School of Medicine, Tokyo, Japan. 4. Department of Pediatric Surgery, Kochi Medical School, Nankoku, Japan. 5. Department of Social Medicine, National Center for Child Health and Development, Tokyo, Japan. 6. Department of Surgical Oncology, Nagasaki University School of Biomedical Sciences, Nagasaki, Japan. 7. Department of Pediatric Surgery, Tohoku University School of Medicine, Sendai, Japan.
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.
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.
Authors: J B Otte; J de Ville de Goyet; R Reding; V Hausleithner; E Sokal; C Chardot; B Debande Journal: Hepatology Date: 1994-07 Impact factor: 17.425
Authors: Richard A Schreiber; Lisa Masucci; Janusz Kaczorowski; J P Collet; Pamela Lutley; Victor Espinosa; Stirling Bryan Journal: J Med Screen Date: 2014-07-09 Impact factor: 2.136