Crohn's DiseaseNCT07757217
Artificial Intelligence-Based Classification and Prognostic Prediction in Small Bowel Crohn's Disease
An observational study of Crohn's Disease, run by Shanghai 10th People's Hospital at 1 site in China.
Trial summary
This retrospective observational study aims to develop an artificial intelligence-based system for the precise classification and prognostic prediction of small bowel Crohn's disease. Read more about this study
- Who is it for?
- Any age
- Conditions the study is for
- Crohn's Disease
- What is being tested?
- Artificial Intelligence-Based Multimodal Analysis
- How many people
- 437 participants
- Where
- 1 siteChina
- Study dates
- 1 Jan 2020to 30 Apr 2026
- 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 Any age
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
Study population: The study population consists of patients with small bowel Crohn's disease who were hospitalized in the Department of Gastroenterology at Shanghai Tenth People's Hospital between January 1, 2020, and January 31, 2025. Eligible patients will be identified retrospectively from existing medical records. Patients must have adequate CTE or MRE images and sufficient clinical and follow-up information for multimodal feature extraction and assessment of subsequent treatment response, treatment switching, or Crohn's disease-related intestinal surgery.
Who may be able to join
- Diagnosis of Crohn's disease established according to the European Crohn's and Colitis Organisation criteria based on clinical, endoscopic, radiological, and/or histopathological findings.
- Small bowel involvement confirmed by computed tomography enterography, magnetic resonance enterography, endoscopy, surgery, and/or histopathology.
- Availability of CTE or MRE images obtained before treatment and during follow-up. Follow-up imaging was performed within 6 months after treatment for patients with active disease or within 1 to 2 years for patients in remission.
- Availability of sufficient clinical and follow-up information to determine treatment response, treatment switching, or Crohn's disease-related surgery.
Who cannot join
- Failure to receive regular medical treatment or follow-up.
- Incomplete clinical or laboratory data that prevent assessment of the prespecified variables or clinical outcomes.
- Poor-quality or incomplete CTE or MRE images that prevent reliable image segmentation or evaluation.
- Presence of a malignant tumor.
- Presence of severe comorbidities, including heart failure or other severe organ dysfunction, that may substantially affect clinical outcomes.
Trial locations and contacts
1 site in 1 country.
China1 site
The Tenth People's Hospital of Shanghai
Shanghai, Shanghai Municipality, 201505
About this study
Official title: Establishment and Application of an Artificial Intelligence-Driven Precision Classification and Prognostic Prediction System for Small Bowel Crohn's Disease
This retrospective observational study aims to develop an artificial intelligence-based system for the precise classification and prognostic prediction of small bowel Crohn's disease. The study includes 437 patients with Crohn's disease who were hospitalized at Shanghai Tenth People's Hospital between January 1, 2020, and January 31, 2025.
Clinical information, laboratory results, endoscopic findings, computed tomography enterography or magnetic resonance enterography images, and available pathological and molecular data will be collected from existing medical records. Artificial intelligence-based image segmentation and multimodal analysis will be used to identify and quantify intestinal lesions, strictures, mesenteric changes, fistulas, abscesses, and other disease characteristics. The study will examine whether these features can classify patients more accurately and predict clinical outcomes, including response to medical treatment, treatment failure or switching, and the need for surgery. The resulting system may support individualized assessment and clinical decision-making for patients with small bowel Crohn's disease.
Read the detailed description
Small bowel involvement is common in Crohn's disease and is associated with an increased risk of strictures, penetrating complications, and surgery. Because conventional ileocolonoscopy cannot fully assess most small bowel segments or transmural and extraintestinal abnormalities, computed tomography enterography (CTE) and magnetic resonance enterography (MRE) play important roles in evaluating small bowel Crohn's disease. However, interpretation of these images may vary among observers, and conventional imaging assessment may not fully quantify the complex intestinal and mesenteric features associated with treatment response and disease progression.
This study will use an interactive artificial intelligence-based image segmentation method to identify and quantify small bowel lesions on existing CTE or MRE images. The imaging features of interest include the number and length of affected bowel segments, bowel wall thickness and enhancement, luminal narrowing, prestenotic dilatation, inflammatory or fibrotic characteristics of strictures, creeping fat, comb sign, internal fistulas, and intra-abdominal abscesses. Where available, pathological features from endoscopic biopsy or surgical specimens and molecular features, including tissue RNA expression and cytokine measurements, will also be analyzed.
The imaging features will be integrated with clinical information, including demographic characteristics, disease duration, disease location and behavior, perianal disease, previous Crohn's disease-related surgery, clinical and endoscopic disease activity, nutritional status, laboratory findings, and treatment history. Multimodal data analysis will be used to establish a classification system for small bowel Crohn's disease and to develop a model for predicting subsequent clinical outcomes.
Patients will be categorized according to their clinical course as having an effective response to medical treatment, treatment failure or recurrence requiring a treatment switch, or requiring Crohn's disease-related surgery. Univariable and multivariable analyses will be conducted to identify factors associated with these outcomes. The predictive performance of the resulting model will be evaluated using the area under the receiver operating characteristic curve, sensitivity, and specificity. The study is expected to provide an objective tool for disease classification, risk assessment, and individualized clinical decision-making in patients with small bowel Crohn's disease.
- Conditions
- Crohn's Disease
- Being tested
- Artificial Intelligence-Based Multimodal Analysis
- Keywords
- Small Bowel Crohn Disease, Artificial Intelligence, Machine Learning, Prognostic Prediction, Radiomics
- Medical subject headings
- Crohn Disease
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Artificial Intelligence-Based Multimodal AnalysisOther
Existing CTE or MRE images will be analyzed using interactive artificial intelligence-based image segmentation. Imaging features will be integrated with available clinical, laboratory, endoscopic, pathological, and molecular data to classify small bowel Crohn's disease and predict subsequent clinical outcomes. This retrospective observational study does not assign any treatment or alter routine clinical care.
| Study group | Role | What they receive |
|---|---|---|
| Effective Medical Treatment | Patients with small bowel Crohn's disease who achieved and maintained an effective clinical response to medical treatment without treatment switching or Crohn's disease-related surgery during the prespecified follow-up period. Receives: Other: Artificial Intelligence-Based Multimodal Analysis | |
| Treatment Failure or Switching | Patients with small bowel Crohn's disease who experienced an inadequate response, loss of response, or disease recurrence requiring a switch in medical treatment during the prespecified follow-up period. Receives: Other: Artificial Intelligence-Based Multimodal Analysis | |
| Crohn's Disease-Related Surgery | Patients with small bowel Crohn's disease who underwent Crohn's disease-related intestinal surgery because of disease activity, intestinal stricture or obstruction, penetrating complications, or other Crohn's disease-related indications during the prespecified follow-up period. Receives: Other: Artificial Intelligence-Based Multimodal Analysis |
What is being measured
The primary outcome is the main question the study is designed to answer.
Discriminative Performance of the Artificial Intelligence-Based Multimodal Model for Predicting 12-Month Clinical Outcomes
The area under the receiver operating characteristic curve will be used to evaluate the ability of the artificial intelligence-based multimodal model to predict the patient's clinical outcome. Clinical outcomes will be classified as effective medical treatment, treatment failure or recurrence requiring treatment switching, or Crohn's disease-related intestinal surgery.
Measured: Within 12 months after the index CTE or MRE examination
Secondary outcomes (2)
Sensitivity and Specificity of the Multimodal Prediction Model
Sensitivity and specificity will be calculated to evaluate the ability of the multimodal model to correctly identify patients with each prespecified clinical outcome.
Measured: Within 12 months after the index CTE or MRE examination
Proportion of Patients Requiring Treatment Switching
The proportion of patients who experience an inadequate response, loss of response, or disease recurrence requiring a switch in medical treatment will be determined from medical records.
Measured: Within 12 months after the index CTE or MRE examination
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted11 Aug 2026
- Study start1 Jan 2020
- Primary completion31 Oct 2025
- Study completion30 Apr 2026
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Crohn's 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
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 437 participants (actual)
Sponsor and investigators
- Lead sponsor
- Shanghai 10th People's Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Xiaolei Wang, Shanghai 10th People's Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07757217
- Sponsor's protocol ID
- SHSY-IEC-6.0/25KY58/P01
- Status last verified
- Aug 2026
- First submitted
- 6 Aug 2026
- Last updated
- 11 Aug 2026
- Results posted
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 11 Aug 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Establishment and Application of an Artificial Intelligence-Driven Precision Classification and Prognostic Prediction System for Small Bowel Crohn's Disease
- ClinicalTrials.gov ID
- NCT07757217
- Acronym / sponsor ID
- SHSY-IEC-6.0/25KY58/P01
- Conditions
- Crohn's Disease
- Keywords
- Small Bowel Crohn Disease, Artificial Intelligence, Machine Learning, Prognostic Prediction, Radiomics
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 437 participants (actual)
- Lead sponsor
- Shanghai 10th People's HospitalUniversity, hospital or other
- Study start
- 1 Jan 2020 (actual)
- Primary completion
- 31 Oct 2025 (actual)
- Study completion
- 30 Apr 2026 (actual)
- First posted
- 11 Aug 2026 (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.
This retrospective observational study aims to develop an artificial intelligence-based system for the precise classification and prognostic prediction of small bowel Crohn's disease. The study includes 437 patients with Crohn's disease who were hospitalized at Shanghai Tenth People's Hospital between January 1, 2020, and January 31, 2025.
Clinical information, laboratory results, endoscopic findings, computed tomography enterography or magnetic resonance enterography images, and available pathological and molecular data will be collected from existing medical records. Artificial intelligence-based image segmentation and multimodal analysis will be used to identify and quantify intestinal lesions, strictures, mesenteric changes, fistulas, abscesses, and other disease characteristics. The study will examine whether these features can classify patients more accurately and predict clinical outcomes, including response to medical treatment, treatment failure or switching, and the need for surgery. The resulting system may support individualized assessment and clinical decision-making for patients with small bowel Crohn's disease.
Detailed description
Small bowel involvement is common in Crohn's disease and is associated with an increased risk of strictures, penetrating complications, and surgery. Because conventional ileocolonoscopy cannot fully assess most small bowel segments or transmural and extraintestinal abnormalities, computed tomography enterography (CTE) and magnetic resonance enterography (MRE) play important roles in evaluating small bowel Crohn's disease. However, interpretation of these images may vary among observers, and conventional imaging assessment may not fully quantify the complex intestinal and mesenteric features associated with treatment response and disease progression.
This study will use an interactive artificial intelligence-based image segmentation method to identify and quantify small bowel lesions on existing CTE or MRE images. The imaging features of interest include the number and length of affected bowel segments, bowel wall thickness and enhancement, luminal narrowing, prestenotic dilatation, inflammatory or fibrotic characteristics of strictures, creeping fat, comb sign, internal fistulas, and intra-abdominal abscesses. Where available, pathological features from endoscopic biopsy or surgical specimens and molecular features, including tissue RNA expression and cytokine measurements, will also be analyzed.
The imaging features will be integrated with clinical information, including demographic characteristics, disease duration, disease location and behavior, perianal disease, previous Crohn's disease-related surgery, clinical and endoscopic disease activity, nutritional status, laboratory findings, and treatment history. Multimodal data analysis will be used to establish a classification system for small bowel Crohn's disease and to develop a model for predicting subsequent clinical outcomes.
Patients will be categorized according to their clinical course as having an effective response to medical treatment, treatment failure or recurrence requiring a treatment switch, or requiring Crohn's disease-related surgery. Univariable and multivariable analyses will be conducted to identify factors associated with these outcomes. The predictive performance of the resulting model will be evaluated using the area under the receiver operating characteristic curve, sensitivity, and specificity. The study is expected to provide an objective tool for disease classification, risk assessment, and individualized clinical decision-making in patients with small bowel Crohn's disease.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 3
| Arm | Type | Receives |
|---|---|---|
| Effective Medical Treatment | – | Other: Artificial Intelligence-Based Multimodal AnalysisPatients with small bowel Crohn's disease who achieved and maintained an effective clinical response to medical treatment without treatment switching or Crohn's disease-related surgery during the prespecified follow-up period. |
| Treatment Failure or Switching | – | Other: Artificial Intelligence-Based Multimodal AnalysisPatients with small bowel Crohn's disease who experienced an inadequate response, loss of response, or disease recurrence requiring a switch in medical treatment during the prespecified follow-up period. |
| Crohn's Disease-Related Surgery | – | Other: Artificial Intelligence-Based Multimodal AnalysisPatients with small bowel Crohn's disease who underwent Crohn's disease-related intestinal surgery because of disease activity, intestinal stricture or obstruction, penetrating complications, or other Crohn's disease-related indications during the prespecified follow-up period. |
Who may qualify
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- Study population
- The study population consists of patients with small bowel Crohn's disease who were hospitalized in the Department of Gastroenterology at Shanghai Tenth People's Hospital between January 1, 2020, and January 31, 2025. Eligible patients will be identified retrospectively from existing medical records. Patients must have adequate CTE or MRE images and sufficient clinical and follow-up information for multimodal feature extraction and assessment of subsequent treatment response, treatment switching, or Crohn's disease-related intestinal surgery.
Key inclusion criteria (4 listed)
- Diagnosis of Crohn's disease established according to the European Crohn's and Colitis Organisation criteria based on clinical, endoscopic, radiological, and/or histopathological findings.
- Small bowel involvement confirmed by computed tomography enterography, magnetic resonance enterography, endoscopy, surgery, and/or histopathology.
- Availability of CTE or MRE images obtained before treatment and during follow-up. Follow-up imaging was performed within 6 months after treatment for patients with active disease or within 1 to 2 years for patients in remission.
- Availability of sufficient clinical and follow-up information to determine treatment response, treatment switching, or Crohn's disease-related surgery.
Key exclusion criteria (5 listed)
- Failure to receive regular medical treatment or follow-up.
- Incomplete clinical or laboratory data that prevent assessment of the prespecified variables or clinical outcomes.
- Poor-quality or incomplete CTE or MRE images that prevent reliable image segmentation or evaluation.
- Presence of a malignant tumor.
- Presence of severe comorbidities, including heart failure or other severe organ dysfunction, that may substantially affect clinical outcomes.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Artificial Intelligence-Based Multimodal AnalysisExisting CTE or MRE images will be analyzed using interactive artificial intelligence-based image segmentation. Imaging features will be integrated with available clinical, laboratory, endoscopic, pathological, and molecular data to classify small bowel Crohn's disease and predict subsequent clinical outcomes. This retrospective observational study does not assign any treatment or alter routine clinical care. | Other | Crohn's Disease-Related Surgery, Effective Medical Treatment, Treatment Failure or Switching |
Primary outcome
Discriminative Performance of the Artificial Intelligence-Based Multimodal Model for Predicting 12-Month Clinical Outcomes
Time frame: Within 12 months after the index CTE or MRE examination
The area under the receiver operating characteristic curve will be used to evaluate the ability of the artificial intelligence-based multimodal model to predict the patient's clinical outcome. Clinical outcomes will be classified as effective medical treatment, treatment failure or recurrence requiring treatment switching, or Crohn's disease-related intestinal surgery.
2 secondary outcomes
Sensitivity and Specificity of the Multimodal Prediction Model
Time frame: Within 12 months after the index CTE or MRE examination
Proportion of Patients Requiring Treatment Switching
Time frame: Within 12 months after the index CTE or MRE examination
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 11 Aug 2026.
- 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.
The record lists no contact. Use the official record to reach the sponsor.
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 Crohn's Disease, run by Shanghai 10th People's Hospital 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 4 inclusion and 5 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: finished, as of the sponsor's last update on 11 Aug 2026. 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?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
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?
Discriminative Performance of the Artificial Intelligence-Based Multimodal Model for Predicting 12-Month Clinical Outcomes (time frame: Within 12 months after the index CTE or MRE examination). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
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 11 Aug 2026; AzTrials last refreshed its copy on 30 Oct 2026.