A Smartphone Application Using Artificial Intelligence Is Superior To Subject Self-Reporting When Assessing Stool Form. (14th July 2022)

• Record Type: Journal Article
• Authors: Pimentel, Mark; Mathur, Ruchi; Wang, Jiajing; Chang, Christine; Hosseini, Ava; Fiorentino, Alyson; Rashid, Mohamad; Pichetshote, Nipaporn; Basseri, Benjamin; Treyzon, Leo; Chang, Bianca; Leite, Gabriela; Morales, Walter; Weitsman, Stacy; Kraus, Asaf; Rezaie, Ali
• Is Part Of: American journal of gastroenterology. Volume 117:Number 7(2022)
• Page Start: 1118
• Page End: 1124
• Publication Date: 2022-07-14
• Subjects: Stomach -- Diseases -- Periodicals; Intestines -- Diseases -- Periodicals; Gastroenterology -- Periodicals; Gastrointestinal Diseases -- Periodicals; Electronic journals; Periodicals; 616.33
• DOI: 10.14309/ajg.0000000000001723
• Languages: English
• ISSNs: 0002-9270

Abstract

Abstract : INTRODUCTION: Stool form assessment relies on subjective patient reports using the Bristol Stool Scale (BSS). In a novel smartphone application (app), trained artificial intelligence (AI) characterizes digital images of users' stool. In this study, we evaluate this AI for accuracy in assessing stool characteristics. METHODS: Subjects with diarrhea-predominant irritable bowel syndrome image-captured every stool for 2 weeks using the app, which assessed images for 5 visual characteristics (BSS, consistency, fragmentation, edge fuzziness, and volume). In the validation phase, using 2 expert gastroenterologists as a gold standard, sensitivity, specificity, accuracy, and diagnostic odds ratios of subject-reported vs AI-graded BSS scores were compared. In the implementation phase, agreements between AI-graded and subject-reported daily average BSS scores were determined, and subject BSS and AI stool characteristics scores were correlated with diarrhea-predominant irritable bowel syndrome symptom severity scores. RESULTS: In the validation phase (n = 14), there was good agreement between the 2 experts and AI characterizations for BSS (intraclass correlation coefficients [ICC] = 0.782–0.852), stool consistency (ICC = 0.873–0.890), edge fuzziness (ICC = 0.836–0.839), fragmentation (ICC = 0.837–0.863), and volume (ICC = 0.725–0.851). AI outperformed subjects' self-reports in categorizing daily average BSS scores as constipation, normal, or diarrhea. In the implementation phase (n = 25), the agreement between AI and self-reported BSS scores was moderate (ICC = 0.61). AI stool characterization also correlated better than subject reports with diarrhea severity scores. DISCUSSION: A novel smartphone application can determine BSS and other visual stool characteristics with high accuracy compared with the 2 expert gastroenterologists. Moreover, trained AI was superior to subject self-reporting of BSS. AI assessments could provide more objective outcome measures for stool characterization in gastroenterology.