Deep Learning in Classifying Bowel Obstruction Radiographs

NCT06321614 | Active Not Recruiting

• 1 other identifier: 2022098
• Study Type: observational
• Target: 4,500
• Locations: 1 country
• Sites: 1

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.

Conditions

Digestive System Disease; Polyp of Colon; Bowel Disease

Keywords

Deep learning; Artificial intelligence; Machine learning

Outcome Measures

Primary Outcomes (1)

• Diagnostic and classification performance

  Accuracy, Recall, Precision, F1-score and confusion matrix

  1 week

Secondary Outcomes (1)

• Visualized interpretation of the self-supervised model

  1 week

Study Arms (3)

patients with normal abdominal radiographs

patients with normal abdominal radiographs, which were confirmed by extra imaging examinations and clinical data. The imaging examinations comprised CT, magnetic resonance imaging (MRI) and colonoscopy in the subsequent 72 hours, while clinical data included recent hospital admission information and surgical operation notes.

patients with small bowel obstruction radiographs

patients with small bowel obstruction radiographs, which were confirmed by extra imaging examinations and clinical data. The imaging examinations comprised CT, magnetic resonance imaging (MRI) and colonoscopy in the subsequent 72 hours, while clinical data included recent hospital admission information and surgical operation notes. In terms of location, small-bowel obstruction (SBO) involves the duodenum, jejunum, and ileum

patients with large bowel obstruction radiographs

patients with large bowel obstruction radiographs, which were confirmed by extra imaging examinations and clinical data. The imaging examinations comprised CT, magnetic resonance imaging (MRI) and colonoscopy in the subsequent 72 hours, while clinical data included recent hospital admission information and surgical operation notes. In terms of location, large-bowel obstruction (SBO), involves the cecum, colon, and rectum.

Eligibility Criteria

• Age: 18 Years - 80 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• 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.

You may qualify if:

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

You may not qualify if:

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

Sponsors & Collaborators

• The First Affiliated Hospital of Soochow University: lead

Study Sites (1)

TheFirst Affiliated Hospital of Soochow University

Suzhou, Jiangsu, 215006, China

Location

Related Publications (9)

• 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. 2007 Jan 21;13(3):432-7. doi: 10.3748/wjg.v13.i3.432.

  PMID: 17230614 RESULT

• 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.

  PMID: 30476452 RESULT

• 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):20201407. doi: 10.1259/bjr.20201407. Epub 2021 Apr 27.

  PMID: 33904763 RESULT

• 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.

  PMID: 12481731 RESULT

• 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.

  PMID: 18387377 RESULT

• 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.

  PMID: 23013804 RESULT

• 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: 10.1186/s13244-021-01150-y.

  PMID: 35072813 RESULT

• 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.

  PMID: 36006881 RESULT

• 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.

  PMID: 37040011 RESULT

MeSH Terms

Conditions

Digestive System Diseases; Colonic Polyps; Intestinal Diseases

Condition Hierarchy (Ancestors)

Intestinal Polyps; Polyps; Pathological Conditions, Anatomical; Pathological Conditions, Signs and Symptoms; Gastrointestinal Diseases

Study Officials

• Rui Li, MD

  The First Affiliated Hospital of Soochow University

  STUDY DIRECTOR

Study Design

• Study Type: observational
• Observational Model: CASE CONTROL
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: March 13, 2024
• First Posted: March 20, 2024
• Study Start: December 31, 2022
• Primary Completion: April 30, 2024
• Study Completion: December 31, 2024
• Last Updated: March 20, 2024

Record last verified: 2024-03

Data Sharing

• IPD Sharing: Will not share

Locations

CN (1)