Research on the Whole Process of AI Intelligent Management System for the Diagnosis and Treatment of Inflammatory Bowel Diseases

May 11, 2026 updated by: Chung-Hsin Chang, Taichung Veterans General Hospital

Inflammatory bowel disease (IBD), including Crohn's disease (CD) and ulcerative colitis (UC), is a chronic immune-mediated disorder requiring long-term management. Clinically, IBD may involve recurrent intestinal inflammation, ulcer formation, and complications such as strictures and fistulas. The etiology of IBD is associated with immune dysregulation, gut microbiome imbalance, and genetic susceptibility. Its clinical manifestations are heterogeneous; early symptoms such as abdominal pain, diarrhea, weight loss, hematochezia, or anemia often resemble gastroenteritis, irritable bowel syndrome, or infectious enterocolitis, leading to misdiagnosis and delayed diagnosis. According to international studies, the interval between initial symptom onset and confirmed diagnosis can range from several months to years, during which untreated disease progression increases the risks of hospitalization, surgery, bowel strictures, and fistulizing complications, resulting in significant impacts on patient quality of life.

This study adopts a retrospective design, analyzing our hospital's electronic medical record data from 2023 to 2025.The objective is to evaluate the performance and feasibility of an artificial intelligence (AI) model-developed and incorporating natural language processing (NLP) and phenotypic recognition algorithms-in supporting early identification and diagnosis of IBD. The model has been validated in multiple European healthcare systems and is capable of recognizing high-risk phenotypic clusters from large-scale structured and unstructured medical data. This study represents the first application of this AI technology in the Taiwanese IBD population. All data processing will occur within a de-identified and secure computing environment to ensure data privacy and information security.

The study will compare AI-generated diagnostic suggestions derived from medical records with actual clinical diagnoses to assess consistency and accuracy. The model's performance across different clinical characteristics, disease severity levels, and stages of illness will also be examined. In addition, statistical metrics such as precision and recall will be used to generate PRC curves for determining the optimal diagnostic threshold. The outcomes of this study are expected to validate the potential of AI technology in facilitating early recognition, accelerating diagnosis, and supporting clinical decision-making for IBD. The findings will provide essential data for developing localized AI models for IBD, ultimately enhancing diagnostic efficiency, shortening the diagnostic timeline, and improving long-term patient outcomes and quality of life.

Objective 1：To retrospectively analyze the clinical characteristics and diagnostic pathways of patients with IBD (CD/UC).

Objective 2：To evaluate the performance of the AI model in identifying and providing diagnostic suggestions for high-risk IBD cases.

Objective 3：To compare the accuracy and consistency between AI-generated diagnostic suggestions and actual clinical diagnoses.

Study Overview

Status

Not yet recruiting

Conditions

Study Type

Observational

Enrollment (Estimated)

4500

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Locations

  • Taiwan
    • Xitun Dist.
      • Taichung, Xitun Dist., Taiwan, 407219
        • Taichung Veterans General Hospital
        • Contact:

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Adult
  • Older Adult

Accepts Healthy Volunteers

No

Sampling Method

Non-Probability Sample

Study Population

IBD and non-IBD Patients

Description

Inclusion Criteria:

Inclusion criteria for the IBD group:

Patients diagnosed with IBD (K50.00 to K51.919) within the specified time interval.

Inclusion criteria for the non-IBD group:

Patients never diagnosed with IBD (K50.00 to K51.919) within the specified time interval.

Exclusion Criteria:

Patients not within the specified time interval Deceased patients

Exclusion criteria for the non-IBD group:

Patients not within the specified time interval Deceased patients Patients with fewer than 5 hospital visits

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
developing localized AI models for IBD
Time Frame: No direct participant involvement; retrospective chart review of medical records from 2023 to 2025 only.
The findings will provide essential data for developing localized AI models for IBD, ultimately enhancing diagnostic efficiency, shortening the diagnostic timeline, and improving long-term patient outcomes and quality of life.
No direct participant involvement; retrospective chart review of medical records from 2023 to 2025 only.

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Taichung Veterans General Hospital

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Estimated)

June 1, 2026

Primary Completion (Estimated)

December 1, 2027

Study Completion (Estimated)

December 31, 2027

Study Registration Dates

First Submitted

May 11, 2026

First Submitted That Met QC Criteria

May 11, 2026

First Posted (Actual)

May 15, 2026

Study Record Updates

Last Update Posted (Actual)

May 15, 2026

Last Update Submitted That Met QC Criteria

May 11, 2026

Last Verified

May 1, 2026

More Information

Terms related to this study

Additional Relevant MeSH Terms

Other Study ID Numbers

  • CE26157A

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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