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Deep Learning in Classifying Bowel Obstruction Radiographs

NCT06321614 · Status: ACTIVE_NOT_RECRUITING · Type: OBSERVATIONAL · Enrollment: 4500

Last updated 2024-03-20

No results posted yet for this study

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.

Conditions

  • Digestive System Disease
  • Polyp of Colon
  • Bowel Disease

Sponsors & Collaborators

  • The First Affiliated Hospital of Soochow University

    lead OTHER

Principal Investigators

  • Rui Li, MD · The First Affiliated Hospital of Soochow University

Eligibility

Min Age
18 Years
Max Age
80 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2022-12-31
Primary Completion
2024-04-30
Completion
2024-12-31

Countries

  • China

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT06321614 on ClinicalTrials.gov