Deep Learning in Classifying Bowel Obstruction Radiographs

Digestive System Disease Clinical Trial

Official title: Self-supervised Learning for Classifying Bowel Obstruction on Upright Abdominal Radiography

Status Active, not recruiting

Clinical Trial Summary

Background: Accurate labeling of obstruction site on upright abdominal radiograph is a challenging task. The lack of ground truth leads to poor performance on supervised learning models. To address this issue, self-supervised learning (SSL) is proposed to classify normal, small bowel obstruction (SBO), and large bowel obstruction (LBO) radiographs using a few confirmed samples. Methods: A few number of confirmed and a large number of unlabeled radiographs were categorized based on the ground truth. The SSL model was firstly trained on the unlabeled radiographs, and then fine-tuned on the confirmed radiographs. ResNet50 and VGG16 were used for the embedded base encoders, whose weights and parameters were adjusted during training process. Furthermore, it was tested on an independent dataset, compared with supervised learning models and human interpreters. Finally, the t-SNE and Grad-CAM were used to visualize the model's interpretation.

Clinical Trial Description

n/a

Related Conditions & MeSH terms

• Bowel Disease
• Colonic Polyps
• Digestive System Disease
• Digestive System Diseases
• Gastrointestinal Diseases
• Intestinal Obstruction
• Polyp of Colon

• NCT number: NCT06321614
• Study type: Observational
• Source: The First Affiliated Hospital of Soochow University
• Status: Active, not recruiting
• Start date: December 31, 2022
• Completion date: December 31, 2024