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

Clinical Trial Details — Status: Active, not recruiting

Administrative data

• NCT number: NCT06321614
• Other study ID #: 2022098
• Status: Active, not recruiting
• Start date: December 31, 2022
• Est. completion date: December 31, 2024

Study information

• Verified date: March 2024
• Source: The First Affiliated Hospital of Soochow University
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational

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.

Recruitment information / eligibility

• Status: Active, not recruiting
• Enrollment: 4500
• Est. completion date: December 31, 2024
• Est. primary completion date: April 30, 2024
• Accepts healthy volunteers: No
• Gender: All
• Age group: 18 Years to 80 Years

Eligibility

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

Related Conditions & MeSH terms

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

Locations

Country	Name	City	State
China	TheFirst Affiliated Hospital of Soochow University	Suzhou	Jiangsu

Sponsors (1)

Lead Sponsor	Collaborator
The First Affiliated Hospital of Soochow University

Country where clinical trial is conducted

China, 

References & Publications (9)

Cappell MS, Batke M. Mechanical obstruction of the small bowel and colon. Med Clin North Am. 2008 May;92(3):575-97, viii. doi: 10.1016/j.mcna.2008.01.003. — View Citation

Chen Y, Mancini M, Zhu X, Akata Z. Semi-Supervised and Unsupervised Deep Visual Learning: A Survey. IEEE Trans Pattern Anal Mach Intell. 2024 Mar;46(3):1327-1347. doi: 10.1109/TPAMI.2022.3201576. Epub 2024 Feb 6. — View Citation

Cheng PM, Tran KN, Whang G, Tejura TK. Refining Convolutional Neural Network Detection of Small-Bowel Obstruction in Conventional Radiography. AJR Am J Roentgenol. 2019 Feb;212(2):342-350. doi: 10.2214/AJR.18.20362. Epub 2018 Nov 26. — View Citation

Frager D. Intestinal obstruction role of CT. Gastroenterol Clin North Am. 2002 Sep;31(3):777-99. doi: 10.1016/s0889-8553(02)00026-2. — View Citation

Kim DH, Wit H, Thurston M, Long M, Maskell GF, Strugnell MJ, Shetty D, Smith IM, Hollings NP. An artificial intelligence deep learning model for identification of small bowel obstruction on plain abdominal radiographs. Br J Radiol. 2021 Jun 1;94(1122):202 — View Citation

Li G, Togo R, Ogawa T, Haseyama M. Self-supervised learning for gastritis detection with gastric X-ray images. Int J Comput Assist Radiol Surg. 2023 Oct;18(10):1841-1848. doi: 10.1007/s11548-023-02891-5. Epub 2023 Apr 11. — View Citation

Markogiannakis H, Messaris E, Dardamanis D, Pararas N, Tzertzemelis D, Giannopoulos P, Larentzakis A, Lagoudianakis E, Manouras A, Bramis I. Acute mechanical bowel obstruction: clinical presentation, etiology, management and outcome. World J Gastroenterol — View Citation

ten Broek RP, Strik C, Issa Y, Bleichrodt RP, van Goor H. Adhesiolysis-related morbidity in abdominal surgery. Ann Surg. 2013 Jul;258(1):98-106. doi: 10.1097/SLA.0b013e31826f4969. — View Citation

Vanderbecq Q, Ardon R, De Reviers A, Ruppli C, Dallongeville A, Boulay-Coletta I, D'Assignies G, Zins M. Adhesion-related small bowel obstruction: deep learning for automatic transition-zone detection by CT. Insights Imaging. 2022 Jan 24;13(1):13. doi: 10 — View Citation

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Diagnostic and classification performance	Accuracy, Recall, Precision, F1-score and confusion matrix	1 week
Secondary	Visualized interpretation of the self-supervised model	Grad-CAM and t-SNE to visualize the interpretation of the SSL model	1 week