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ARTIFICIAL INTELLIGENCE AND ANORECTAL MANOMETRY: AUTOMATIC DETECTION AND DIFFERENTIATION OF ANORECTAL MOTILITY PATTERNS - A PROOF OF CONCEPT STUDY

Tiago Ribeiro 1, Miguel Mascarenhas-Saraiva 1, Maria Vila Pouca 2, João Afonso 1, Pedro Cardoso 1, Francisco Mendes 1, Miguel Martins 1, Hélder Cardoso 1, Pedro Sousa 3, João Ferreira 2, Ilario Froehner 4, Guilherme Macedo 1

Affiliations

1 University Hospital Center of São João, Porto, Portugal

2 Faculdade de Engenharia da Universidade do Porto, Porto, Portugal

3 INEGI – Institute of Science and Innovation in Mechanical and Industrial Engineering, Porto, Portugal

4 Pelvia – Gastrointestinal Motility and Continence, Curitiba, Brazil

Summary

AI Generated

A pilot study developed and validated machine learning algorithms to automatically differentiate fecal incontinence from obstructed defecation patterns in anorectal manometry data, with the gradient boosting model achieving 84.6% accuracy.

Four machine learning models were tested on 827 anorectal manometry exams (493 obstructed defecation, 334 fecal incontinence) from a Brazilian motility clinic between 2015 and 2021.
The gradient boosting model showed the highest discriminating performance with 84.6% overall accuracy and an area under the curve of 0.939, outperforming random forests, support vector machines, and k-nearest neighbors models.
For obstructed defecation classification, the gradient boosting model achieved 83.6% precision and 92.4% sensitivity; for fecal incontinence, it achieved 87.4% precision and 73.6% sensitivity.
The authors suggest that subsequent development of these tools may optimize access to anorectal manometry studies, which are currently limited by accessibility and complexity of data analysis.
This material is relevant to gastroenterologists and motility specialists interested in artificial intelligence applications for anorectal functional disorder diagnosis.

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This summary was generated by an AI large language model based on the content transcript. It is for informational purposes only and should not be considered a substitute for clinical judgment. Always rely on your professional expertise and the full clinical context when making clinical decisions.

Abstract

Introduction

Anorectal disorders, causing fecal incontinence or evacuation dysfunction are common, affecting up to 5% of the general population (1). Most of these disorders result from an intricate combination of disturbances, including neurologic and pelvic floor musculature dysfunction, and have a significant impact in the quality of life of patients (2,3). Anorectal manometry (ARM) is the gold standard for the evaluation of anorectal functional disorders. Nevertheless, the accessibility to this exam is limited, and the complexity of data analysis and report is a significant drawback (4).

Aims & Methods

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.
We included ARM exams performed at a large gastrointestinal motility clinic in Brazil (Pelvia – Gastrointestinal Motility and Continence, Paraná, Brazil) between 2015 and 2021. All ARM procedures were performed using water-perfused probes with eight radial transducers with a distal latex balloon. 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, 493 of patients diagnosed with obstructed defecation (OD), while the remaining 334 presented FI. The performance marks of the different tuned models, including the precision, sensitivity and the F1-score, is summarized on table 1. 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.

Model	Class	Precision (mean % ± SD)	Sensitivity (mean % ± SD)	F1-score (mean % ± SD)
KNN (K-Nearest Neighbors)	OD	79.6 ± 2.7	76.6 ± 5.4	78.0 ± 3.7
	FI	68.0 ± 5.1	72.0 ± 3.7	69.8 ± 3.6
SVM (Support Vector Machines)	OD	85.0 ± 6.1	82.2 ± 8.8	83.6 ± 7.4
	FI	75.6 ± 10.2	79.4 ± 8.0	77.6 ± 8.9
RF (Random Forests)	OD	81.8 ± 1.6	90.0 ± 7.3	85.6 ± 4.4
	FI	84.0 ± 10.5	71.6 ± 1.3	76.8 ± 4.7
xGB (Gradient Boosts)	OD	83.6 ± 4.3	92.4 ± 3.6	87.6 ± 2.2
	FI	87.4 ± 4.8	73.6 ± 8.2	79.6 ± 4.6

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 significantly impact on the management of patients with anorectal functional diseases.

References

1. Whitehead WE, Wald A, Diamant NE, et al. Functional disorders of the anus and rectum. Gut 1999;45 Suppl 2:Ii55-9.
2. Maeda Y, Vaizey CJ, Hollington P, et al. Physiological, psychological and behavioural characteristics of men and women with faecal incontinence. Colorectal Dis 2009;11:927-32.
3. Remes-Troche JM, Rao SS. Defecation disorders: neuromuscular aspects and treatment. Curr Gastroenterol Rep 2006;8:291-9.
4. Brown HW, Guan W, Schmuhl NB, et al. If We Don't Ask, They Won't Tell: Screening for Urinary and Fecal Incontinence by Primary Care Providers. J Am Board Fam Med 2018;31:774-782.

Conference

UEG Week Copenhagen 2023

Topics

Colorectal

Submission format

Abstract

Session

PP 06 Other lower GI disorders (Posters)

Citation

United European Gastroenterology Journal 2023; 11 (Supplement 8)

Published

2023

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GOING DEEP: THE EFFECT OF ULTRA-PROCESSED FOODS ON THE SMALL INTESTINE, A PILOT STUDY ON ILEOSTOMY PATIENTS

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