Artificial Intelligence and Anorectal Manometry: Automatic Detection and Differentiation of Anorectal Motility Patterns-A Proof-of-Concept Study

• Type: Article in International Scientific Journal
• Date: 2023

Authors

Saraiva, MM (Author) Other; Pouca, MV (Author) Other; Ribeiro, T (Author) Other; Afonso, J (Author) Other; Cardoso, H (Author) Other; Sousa, P (Author) FEUP; Ferreira, J (Author) FEUP; Macedo G (Author) FMUP; Froehner, I (Author) Other

Journal

Title: Clinical and Translational Gastroenterology

Vol. 14

ISSN: 2155-384X

Publisher: Springer Nature

Indexing

ISI Web of Knowledge - 7 Citations

Scopus - 8 Citations

Other information

Authenticus ID: P-00Z-8GR

DOI: 10.14309/ctg.0000000000000555

Abstract (EN): INTRODUCTION: Anorectal manometry (ARM) is the gold standard for the evaluation of anorectal functional disorders, prevalent in the population. Nevertheless, the accessibility to this examination 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 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, support vector machines, random forests, 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 examinations were used in this study. After fine-tuning, the xGB model presented an overall accuracy (84.6% +/- 2.9%), similar to that of random forests (82.7% +/- 4.8%) and support vector machines (81.0% +/- 8.0%) and higher that of k-nearest neighbors (74.4% +/- 3.8%). The xGB models showed the highest discriminating performance between OD and FI, withanarea under the curve of 0.939. DISCUSSION: The tested machine learning algorithms, particularly the xGBmodel, 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 6