Related Experiment Video
Updated: Jul 1, 2025
Electroacupuncture Combined with Chinese Medicine Ironing Therapy for Functional Constipation
Published on: July 5, 2024
Artificial intelligence-powered early identification of refractory constipation in children
Yi-Hsuan Huang1, Ruixuan Wan2, Yan Yang3
1Department of Gastroenterology, Children's Hospital of Nanjing Medical University, Nanjing, China.
Insights
Machine learning accurately identifies children with refractory constipation using colon size measurements. Early detection via barium enema and ML models can improve management and quality of life.
Area of Science:
- Pediatric Gastroenterology
- Medical Imaging Analysis
- Machine Learning in Healthcare
Background:
- Refractory constipation in children leads to severe symptoms and reduced quality of life, persisting into adulthood.
- Early identification of refractory constipation is crucial for effective management.
- Understanding colonic anatomical differences in children with constipation types is essential.
Purpose of the Study:
- To characterize colonic anatomy in children with functional constipation, refractory constipation, and no constipation.
- To develop a supervised machine learning model for early identification of refractory constipation in children.
Main Methods:
- Retrospective study analyzing patient characteristics and standardized colon size (SCS) ratios from barium enema (BE) in three groups: functional constipation (n=77), refractory constipation (n=63), and non-constipation (n=65).
- Statistical analyses to identify significant differences in colonic dimensions.
- Development of a supervised machine learning model using SCS ratios for classification.
Main Results:
- Significant differences were observed in rectum diameter, sigmoid diameter, descending colon diameter, transverse colon diameter, and rectosigmoid length across the three groups.
- A linear support vector machine model, using five SCS ratios (sigmoid colon, descending colon, transverse colon, rectum, and rectosigmoid), achieved 81% accuracy in classification.
- The model's 95% confidence interval for accuracy was 79.17% to 83.19%.
Conclusions:
- A supervised machine learning strategy demonstrated 81% accuracy in distinguishing children with refractory constipation.
- Combining barium enema imaging with a machine learning model offers practical utility for guiding the management of pediatric refractory constipation.
- This approach holds significant potential for improving clinical decision-making in pediatric constipation cases.
Background:
Children experiencing refractory constipation, resistant to conventional pharmacological approaches, develop severe symptoms that persist into adulthood, leading to a substantial decline in their quality of life. Early identification of refractory constipation may improve their management. We aimed to describe the characteristics of colonic anatomy in children with different types of constipation and develop a supervised machine-learning model for early identification.
Methods:
In this retrospective study, patient characteristics and standardized colon size (SCS) ratios by barium enema (BE) were studied in patients with functional constipation (n=77), refractory constipation (n=63), and non-constipation (n=65). Statistical analyses were performed and a supervised machine learning (ML) model was developed based on these data for the classification of the three groups.
Results:
Significant differences in rectum diameter, sigmoid diameter, descending diameter, transverse diameter, and rectosigmoid length were found in the three groups. A linear support vector machine was utilized to build the early detection model. Using five features (SCS ratios of sigmoid colon, descending colon, transverse colon, rectum, and rectosigmoid), the model demonstrated an accuracy of 81% [95% confidence interval (CI): 79.17% to 83.19%].
Conclusions:
The application of using a supervised ML strategy obtained an accuracy of 81% in distinguishing children with refractory constipation. The combination of BE and ML model can be used for practical implications, which is important for guiding management in children with refractory constipation.
More Related Videos
05:45Diagnosis of Hirschsprung's Disease by Immunostaining Rectal Suction Biopsies for Calretinin, S100 Protein and Protein Gene Product 9.5
Published on: April 26, 2019
11:29Measuring the Functional Abilities of Children Aged 3-6 Years Old with Observational Methods and Computer Tools
Published on: June 20, 2020
Related Concept Videos
01:21Drugs for Treatment of Constipation-Predominant IBS
01:25Assessment of the Rectum and Anus
Rectal Inspection
Begin by inspecting the perianal and anal areas for color, texture, rashes,...
01:26Imaging Studies III: Gastrointestinal Motility Studies and Virtual Colonoscopy
Radionuclide Testing
Radionuclide testing is a sophisticated medical technique for assessing gastrointestinal motility. It focuses on gastric emptying and colonic transit time. Radioactive markers track the movement of food through the digestive system, providing insights into gastrointestinal disorders.
In gastric emptying studies, a meal's liquid and...
01:30Irritable Bowel Syndrome II: Clinical Features and Diagnostic Evaluation
Irritable Bowel Syndrome (IBS) is classified into subtypes based on the predominant bowel habits as determined by the Bristol Stool Form Scale (BSFS). The subtypes are:
01:17Drugs for Treatment of Diarrhea-Predominant IBS
Two specific drugs used in the treatment are alosetron (Lotronex) and eluxadoline (Viberzi). Alosetron, a 5-HT3 antagonist, works by slowing the movement of stools in the gut, reducing bowel...