Digestive System DiseaseNCT06321614
Deep Learning in Classifying Bowel Obstruction Radiographs
An observational study of Digestive System Disease and Polyp of Colon, run by The First Affiliated Hospital of Soochow University at 1 site in China.
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. Read more about this study
- Who is it for?
- Ages 18 to 80 years
- Conditions the study is for
- Digestive System Disease, Polyp of Colon, Bowel Disease
- How many people
- 4,500 participants
- Where
- 1 siteChina
- Study dates
- 31 Dec 2022to 31 Dec 2024
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 80 years
- All sexes
- No healthy volunteers
- Adult, Older adult
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.
Who may be able to join
- 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
Who cannot join
- 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
Trial locations and contacts
1 site in 1 country.
China1 site
TheFirst Affiliated Hospital of Soochow University
Suzhou, Jiangsu, 215006
About this study
Official title: Self-supervised Learning for Classifying Bowel Obstruction on Upright Abdominal Radiography
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
- Medical subject headings
- Digestive System Diseases, Colonic Polyps, Intestinal Diseases
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| 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. |
What is being measured
The primary outcome is the main question the study is designed to answer.
Diagnostic and classification performance
Accuracy, Recall, Precision, F1-score and confusion matrix
Measured: 1 week
Secondary outcomes (1)
Visualized interpretation of the self-supervised model
Grad-CAM and t-SNE to visualize the interpretation of the SSL model
Measured: 1 week
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted20 Mar 2024
- Study start31 Dec 2022
- Primary completion30 Apr 2024 (estimated)
- Study completion31 Dec 2024 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (9)Papers the sponsor has linked to this study.
- Other trials for Digestive System DiseaseStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Case-control
- Time perspective
- Retrospective
- Enrolment
- 4,500 participants (estimated)
Sponsor and investigators
- Lead sponsor
- The First Affiliated Hospital of Soochow University
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Rui Li, MD, Study director, The First Affiliated Hospital of Soochow University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06321614
- Sponsor's protocol ID
- 2022098
- Status last verified
- Mar 2024
- First submitted
- 13 Mar 2024
- Last updated
- 20 Mar 2024
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
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.
PubMed 17230614Cheng 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.
PubMed 30476452Kim 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.
PubMed 33904763Frager D. Intestinal obstruction role of CT. Gastroenterol Clin North Am. 2002 Sep;31(3):777-99. doi: 10.1016/s0889-8553(02)00026-2.
PubMed 12481731Cappell 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.
PubMed 18387377ten 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.
PubMed 23013804Vanderbecq 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.
PubMed 35072813Chen 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.
PubMed 36006881Li 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.
PubMed 37040011
Study at a glance
From the registry record, last updated by the sponsor on 20 Mar 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Self-supervised Learning for Classifying Bowel Obstruction on Upright Abdominal Radiography
- ClinicalTrials.gov ID
- NCT06321614
- Acronym / sponsor ID
- 2022098
- Conditions
- Digestive System Disease, Polyp of Colon, Bowel Disease
- Keywords
- Deep learning, Artificial intelligence, Machine learning
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 4,500 participants (estimated)
- Lead sponsor
- The First Affiliated Hospital of Soochow UniversityUniversity, hospital or other
- Study start
- 31 Dec 2022 (actual)
- Primary completion
- 30 Apr 2024 (estimated)
- Study completion
- 31 Dec 2024 (estimated)
- First posted
- 20 Mar 2024 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Case-control
- Time perspective
- Retrospective
- Number of arms
- 3
| Arm | Type | Receives |
|---|---|---|
| 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. |
Who may qualify
- Ages
- 18 to 80 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- 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.
Key inclusion criteria (3 listed)
- 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
Key exclusion criteria (4 listed)
- 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
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Diagnostic and classification performance
Time frame: 1 week
Accuracy, Recall, Precision, F1-score and confusion matrix
1 secondary outcome
Visualized interpretation of the self-supervised model
Time frame: 1 week
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 20 Mar 2024.
- Results publication: PubMed 17230614Markogiannakis 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.
- Results publication: PubMed 30476452Cheng 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.
- Results publication: PubMed 33904763Kim 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.
- Results publication: PubMed 12481731Frager D. Intestinal obstruction role of CT. Gastroenterol Clin North Am. 2002 Sep;31(3):777-99. doi: 10.1016/s0889-8553(02)00026-2.
- Results publication: PubMed 18387377Cappell 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.
- Results publication: PubMed 23013804ten 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.
- Results publication: PubMed 35072813Vanderbecq 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.
- Results publication: PubMed 36006881Chen 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.
- Results publication: PubMed 37040011Li 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.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Rui Li, MD study director
The First Affiliated Hospital of Soochow University
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Digestive System Disease and Polyp of Colon, run by The First Affiliated Hospital of Soochow University at 1 site in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 4 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 20 Mar 2024. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Diagnostic and classification performance (time frame: 1 week). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT06321614, which also lists 9 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 20 Mar 2024.