Machine Learning Supports Automated Digital Image Scoring of Stool Consistency in Diapers

Abstract

Background/aims: Accurate stool consistency classification of non-toilet-trained children remains challenging. This study evaluated the feasibility of automated classification of stool consistencies from diaper photos using machine learning (ML).

Methods: In total, 2687 usable smartphone photos of diapers with stool from 96 children younger than 24 months were obtained after independent ethical study approval. Stool consistency was assessed from each photo according to the original 7 types of the Brussels Infant and Toddler Stool Scale independently by study participants and 2 researchers. A health care professional assigned a final score in case of scoring disagreement between the researchers. A proof-of-concept ML model was built upon this collected photo database, using transfer learning to re-train the classification layer of a pretrained deep convolutional neural network model. The model was built on random training (n = 2478) and test (n = 209) subsets.

Results: Agreements between study participants and both researchers were 58.0% and 48.5%, respectively, and between researchers 77.5% (assessable n = 2366). The model classified 60.3% of the test photos in exact agreement with the final score. With respect to the 4-class grouping of the 7 Brussels Infant and Toddler Stool Scale types, the agreement between model-based and researcher classification was 77.0%.

Conclusion: The automated and objective scoring of stool consistency from diaper photos by the ML model shows robust agreement with human raters and overcomes limitations of other methods relying on caregiver reporting. Integrated with a smartphone application, this new framework for photo database construction and ML classification has numerous potential applications in clinical studies and home assessment.