title: PoopMD, a Mobile Health Application, Accurately Identifies Infant Acholic Stools.
abstract: Biliary atresia (BA) is the leading cause of pediatric end-stage liver disease in the United States. Education of parents in the perinatal period with stool cards depicting acholic and normal stools has been associated with improved time-to-diagnosis and survival in BA. PoopMD is a mobile application that utilizes a smartphone's camera and color recognition software to analyze an infant's stool and determine if additional follow-up is indicated. PoopMD was developed using custom HTML5/CSS3 and wrapped to work on iOS and Android platforms. In order to define the gold standard regarding stool color, seven pediatricians were asked to review 45 photographs of infant stool and rate them as acholic, normal, or indeterminate. Samples for which 6+ pediatricians demonstrated agreement defined the gold standard, and only these samples were included in the analysis. Accuracy of PoopMD was assessed using an iPhone 5s with incandescent lighting. Variability in analysis of stool photographs as acholic versus normal with intermediate rating weighted as 50% agreement (kappa) was compared between three laypeople and one expert user. Variability in output was also assessed between an iPhone 5s and a Samsung Galaxy S4, as well as between incandescent lighting and compact fluorescent lighting. Six-plus pediatricians agreed on 27 normal and 7 acholic photographs; no photographs were defined as indeterminate. The sensitivity was 7/7 (100%). The specificity was 24/27 (89%) with 3/27 labeled as indeterminate; no photos of normal stool were labeled as acholic. The Laplace-smoothed positive likelihood ratio was 6.44 (95% CI 2.52 to 16.48) and the negative likelihood ratio was 0.13 (95% CI 0.02 to 0.83). kappauser was 0.68, kappaphone was 0.88, and kappalight was 0.81. Therefore, in this pilot study, PoopMD accurately differentiates acholic from normal color with substantial agreement across users, and almost perfect agreement across two popular smartphones and ambient light settings. PoopMD may be a valuable tool to help parents identify acholic stools in the perinatal period, and provide guidance as to whether additional evaluation with their pediatrician is indicated. PoopMD may improve outcomes for children with BA.
authors.affiliation: Department of Pediatrics, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America.
authors.forename: Amy
authors.initials: A
authors.lastname: Franciscovich
authors.affiliation: Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland, United States of America.
authors.forename: Dhananjay
authors.initials: D
authors.lastname: Vaidya
authors.affiliation: HCB Health, Austin, Texas, United States of America.
authors.forename: Joe
authors.initials: J
authors.lastname: Doyle
authors.affiliation: HCB Health, Austin, Texas, United States of America.
authors.forename: Josh
authors.initials: J
authors.lastname: Bolinger
authors.affiliation: Office of Technology Transfer, Johns Hopkins University, Baltimore, Maryland, United States of America.
authors.forename: Montserrat
authors.initials: M
authors.lastname: Capdevila
authors.affiliation: HCB Health, Austin, Texas, United States of America.
authors.forename: Marcus
authors.initials: M
authors.lastname: Rice
authors.affiliation: HCB Health, Austin, Texas, United States of America.
authors.forename: Leslie
authors.initials: L
authors.lastname: Hancock
authors.affiliation: HCB Health, Austin, Texas, United States of America.
authors.forename: Tanya
authors.initials: T
authors.lastname: Mahr
authors.affiliation: Department of Pediatrics, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America.
authors.forename: Douglas B
authors.initials: DB
authors.lastname: Mogul
mesh_terms: D001656:Biliary Atresia / Q000175:diagnosis*; D040421:Cell Phone; D005243:Feces; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted; D007223:Infant; D008297:Male; D063731:Mobile Applications; D011237:Predictive Value of Tests