Generation and application of a convolutional neural networks algorithm in evaluating stool consistency in diapers

Abstract

AbstractAimThe aim of the study was to develop a deep convolutional neural networks (CNNs) algorithm for automated assessment of stool consistency from diaper photographs and test its performance under real‐world conditions.MethodsDiaper photographs were enrolled via a mobile phone application. The stool consistency was assessed independently according to the Brussels Infant and Toddler Stool Scale (BITSS) by paediatricians. These images were randomised into a training data set and a test data set. After training and testing, the new algorithm was used under real‐world conditions by parents.ResultsThere was an overall agreement of 92.9% between paediatricians and the CNN‐generated algorithm. Post hoc classification into the validated 4 categories of the BITSS yielded an agreement of 95.4%. Spearman correlation analysis across the ranking of 7 BITSS photographs and validated 4 categories showed a significant correlation of rho = 0.93 (95% CI, 0.92, 0.94; p < 0.001) and rho = 0.92 (95% CI, 0.90, 0.93; p < 0.001), respectively. The real‐world application yielded further insights into changes in stool consistency between age categories and mode of feeding.ConclusionThe new CNN‐based algorithm is able to reliably identify stool consistency from diaper photographs and may support the communication between parents and paediatricians.