Pengfei Li, Jiaye Liu, Yang Li, Junhong Su, Zhongren Ma, Wichor M Bramer, Wanlu Cao, Robert A de Man, Maikel P Peppelenbosch, Qiuwei Pan
The global epidemiology of hepatitis E virus infection: A systematic review and meta-analysis
Liver international : official journal of the International Association for the Study of the Liver
purpose machine learning algorithms hypothesized able predict quality colonoscopy luminal images enhance training quality indicators endoscopy methods separate study involving randomized controlled trial capped vs uncapped colonoscopies provided colonoscopy videos study videos analyzed algorithm devised australian institute machine learning image analysis validated focus measure steerable filtersbased metrics sfil used assess luminal visualization quality compared two independent clinician assessments c1 c2 goodman kruskals gamma g measure used assess rank correlation data using ibm spss statistics windows version 250 ibm corp armonk ny results total 500 random colonoscopy video clips extracted analyzed 88 excluded sfil scores matched c1 45 c2 42 cases respectively significant correlation sfil c1 g 0644 p 0005 sfil c2 g 0734 p 0005 conclusion study demonstrates machine learning algorithms recognize quality luminal visualization colonoscopy intend apply future enhance colonoscopy training metric quality assessment