In Clinical and translational gastroenterology
BACKGROUND : Anorectal manometry (ARM) is the gold standard for the evaluation of anorectal functional disorders, prevalent in the population. Nevertheless, the accessibility to this exam is limited, and the complexity of data analysis and report is a significant drawback. This pilot study aimed to develop and validate an artificial intelligence (AI) model to automatically differentiate motility patterns of fecal incontinence (FI) from obstructed defecation (OD) using ARM data.
METHODS : We developed and tested multiple machine learning algorithms for the automatic interpretation of ARM data. Four models were tested: k-nearest neighbors (KNN), support vector machines (SVM), random forests (RF) and gradient boosting (xGB). These models were trained using a stratified 5-fold strategy. Their performance was assessed after fine-tuning of each model's hyperparameters, using 90% of data for training and 10% of data for testing.
RESULTS : A total of 827 ARM exams were used in this study. After fine-tuning, the xGB model presented an overall accuracy (84.6% ± 2.9%), similar to that of RF (82.7% ± 4.8%) and SVM (81.0% ± 8.0%), and higher that of KNN (74.4% ± 3.8%). The xGB models showed the highest discriminating performance between OD and FI, with an area under the curve of 0.939.
CONCLUSION : The tested machine learning algorithms, particularly the xGB model, accurately differentiated between FI and OD manometric patterns. Subsequent development of these tools may optimize the access to ARM studies, which may have a significant impact on the management of patients with anorectal functional diseases.
Saraiva Miguel Mascarenhas, Pouca Maria Vila, Ribeiro Tiago, Afonso João, Cardoso Hélder, Sousa Pedro, Ferreira João, Macedo Guilherme, Junior Ilario Froehner
2022-Dec-15
