Clinical trial NCT06321614: Deep Learning in Classifying Bowel Obstruction Radiographs
Official title: Self-supervised Learning for Classifying Bowel Obstruction on Upright Abdominal Radiography
Study type: OBSERVATIONAL
Overall status: ACTIVE_NOT_RECRUITING
Conditions: Digestive System Disease
Polyp of Colon
Bowel Disease
Keywords: Deep learning
Artificial intelligence
Machine learning
Lead sponsor: The First Affiliated Hospital of Soochow University
Sponsor class: OTHER
First posted: 2024-03-20
Enrollment: 4500
Sex: ALL
Minimum age (years): 18
Maximum age (years): 80
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: CASE_CONTROL
Time perspective: RETROSPECTIVE
Sampling method: PROBABILITY_SAMPLE
Study population: participants received upright abdominal radiographs were included in this study. They can be divided with participants with normal abdominal radiographs, participants with small bowel obstruction, and participants with large bowel obstruction. The study strictly follow the inclusion and exclusion criteria.

Brief 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.


Eligibility criteria: Inclusion Criteria:

1. The hospital imaging system looked for plain abdominal standing films diagnosed as intestinal obstruction or normal between 2022 and 2024
2. Aged 18 to 80 years
3. The main complaint was gastrointestinal symptoms

Exclusion Criteria:

1. Image interference, fuzzy performance, difficult to distinguish
2. Non-gastrointestinal symptoms were the main complaint
3. Supine, prone, or lateral decubitus radiography
4. Paralytic obstruction, closed loop obstruction, et al

Primary outcomes:
measure: Diagnostic and classification performance
timeFrame: 1 week
Secondary outcomes:
measure: Visualized interpretation of the self-supervised model
timeFrame: 1 week
Other outcomes:
Locations:
city: Suzhou
country: China
facility: TheFirst Affiliated Hospital of Soochow University
state: Jiangsu
Study officials:
affiliation: The First Affiliated Hospital of Soochow University
name: Rui Li, MD
role: STUDY_DIRECTOR
References:
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