Inflammatory Bowel Disease (Crohn's Disease and Ulcerative Colitis)NCT07590271
Research on the Whole Process of AI Intelligent Management System for the Diagnosis and Treatment of Inflammatory Bowel Diseases
An observational study of Inflammatory Bowel Disease (Crohn's Disease and Ulcerative Colitis) and Crohn's Disease (CD), run by Taichung Veterans General Hospital at 1 site in Taiwan.
Trial summary
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. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Inflammatory Bowel Disease (Crohn's Disease and Ulcerative Colitis), Crohn's Disease (CD), Ulcerative Colitis (UC)
- How many people
- 4,500 participants
- Where
- 1 siteTaiwan
- Study dates
- 1 Jun 2026to 31 Dec 2027
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
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 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: IBD and non-IBD Patients
Who may be able to join
- Patients diagnosed with IBD (K50.00 to K51.919) within the specified time interval.
- Patients never diagnosed with IBD (K50.00 to K51.919) within the specified time interval.
Who cannot join
- Patients not within the specified time interval Deceased patients
- Patients not within the specified time interval Deceased patients Patients with fewer than 5 hospital visits
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
Taiwan1 site
Taichung Veterans General Hospital
Taichung, Xitun Dist., 407219
Chung Hsin Chang, 886-4-23592525 ext. 3302, chunghsin@vghtc.gov.tw
About this study
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.
Read more
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.
- Conditions
- Inflammatory Bowel Disease (Crohn's Disease and Ulcerative Colitis), Crohn's Disease (CD), Ulcerative Colitis (UC), Artificial Intelligence (AI), Natural Language Processing (NLP)
- Medical subject headings
- Inflammatory Bowel Diseases, Colitis, Ulcerative, Crohn Disease
What is being measured
The primary outcome is the main question the study is designed to answer.
developing localized AI models 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.
Measured: No direct participant involvement; retrospective chart review of medical records from 2023 to 2025 only.
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted15 May 2026
- Study start1 Jun 2026 (estimated)
- Primary completion1 Dec 2027 (estimated)
- Study completion31 Dec 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (4)Papers the sponsor has linked to this study.
- Other trials for Inflammatory Bowel Disease (Crohn's Disease and Ulcerative Colitis)Studies of the same condition, open and under way.
- More trials run by Taichung Veterans General HospitalThe sponsor's other registered studies.
- 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
- Taichung Veterans General Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Chung-Hsin Chang, Taichung Veterans General Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07590271
- Sponsor's protocol ID
- CE26157A
- Status last verified
- May 2026
- First submitted
- 11 May 2026
- Last updated
- 15 May 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Publications
Wang HH, Wang YH, Liang CW, Li YC. Assessment of Deep Learning Using Nonimaging Information and Sequential Medical Records to Develop a Prediction Model for Nonmelanoma Skin Cancer. JAMA Dermatol. 2019 Nov 1;155(11):1277-1283. doi: 10.1001/jamadermatol.2019.2335.
PubMed 31483437Nguyen PA, Syed-Abdul S, Iqbal U, Hsu MH, Huang CL, Li HC, Clinciu DL, Jian WS, Li YC. A probabilistic model for reducing medication errors. PLoS One. 2013 Dec 3;8(12):e82401. doi: 10.1371/journal.pone.0082401. eCollection 2013.
PubMed 24312659Le Berre C, Ricciuto A, Peyrin-Biroulet L, Turner D. Evolving Short- and Long-Term Goals of Management of Inflammatory Bowel Diseases: Getting It Right, Making It Last. Gastroenterology. 2022 Apr;162(5):1424-1438. doi: 10.1053/j.gastro.2021.09.076. Epub 2022 Jan 4.
PubMed 34995529Turner D, Ricciuto A, Lewis A, D'Amico F, Dhaliwal J, Griffiths AM, Bettenworth D, Sandborn WJ, Sands BE, Reinisch W, Scholmerich J, Bemelman W, Danese S, Mary JY, Rubin D, Colombel JF, Peyrin-Biroulet L, Dotan I, Abreu MT, Dignass A; International Organization for the Study of IBD. STRIDE-II: An Update on the Selecting Therapeutic Targets in Inflammatory Bowel Disease (STRIDE) Initiative of the International Organization for the Study of IBD (IOIBD): Determining Therapeutic Goals for Treat-to-Target strategies in IBD. Gastroenterology. 2021 Apr;160(5):1570-1583. doi: 10.1053/j.gastro.2020.12.031. Epub 2021 Feb 19.
PubMed 33359090
Study at a glance
From the registry record, last updated by the sponsor on 15 May 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Research on the Whole Process of AI Intelligent Management System for the Diagnosis and Treatment of Inflammatory Bowel Diseases
- ClinicalTrials.gov ID
- NCT07590271
- Acronym / sponsor ID
- CE26157A
- Conditions
- Inflammatory Bowel Disease (Crohn's Disease and Ulcerative Colitis), Crohn's Disease (CD), Ulcerative Colitis (UC), Artificial Intelligence (AI), Natural Language Processing (NLP)
- Study type
- Observational
- Recruitment status
- Not yet recruiting
- Enrolment
- 4,500 participants (estimated)
- Lead sponsor
- Taichung Veterans General HospitalUniversity, hospital or other
- Study start
- 1 Jun 2026 (estimated)
- Primary completion
- 1 Dec 2027 (estimated)
- Study completion
- 31 Dec 2027 (estimated)
- First posted
- 15 May 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.
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 design
- Observational model
- Case-control
- Time perspective
- Retrospective
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- IBD and non-IBD Patients
Key inclusion criteria (2 listed)
- Patients diagnosed with IBD (K50.00 to K51.919) within the specified time interval.
- Patients never diagnosed with IBD (K50.00 to K51.919) within the specified time interval.
Key exclusion criteria (2 listed)
- Patients not within the specified time interval Deceased patients
- Patients not within the specified time interval Deceased patients Patients with fewer than 5 hospital visits
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
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.
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 15 May 2026.
- Results publication: PubMed 31483437Wang HH, Wang YH, Liang CW, Li YC. Assessment of Deep Learning Using Nonimaging Information and Sequential Medical Records to Develop a Prediction Model for Nonmelanoma Skin Cancer. JAMA Dermatol. 2019 Nov 1;155(11):1277-1283. doi: 10.1001/jamadermatol.2019.2335.
- Results publication: PubMed 24312659Nguyen PA, Syed-Abdul S, Iqbal U, Hsu MH, Huang CL, Li HC, Clinciu DL, Jian WS, Li YC. A probabilistic model for reducing medication errors. PLoS One. 2013 Dec 3;8(12):e82401. doi: 10.1371/journal.pone.0082401. eCollection 2013.
- Results publication: PubMed 34995529Le Berre C, Ricciuto A, Peyrin-Biroulet L, Turner D. Evolving Short- and Long-Term Goals of Management of Inflammatory Bowel Diseases: Getting It Right, Making It Last. Gastroenterology. 2022 Apr;162(5):1424-1438. doi: 10.1053/j.gastro.2021.09.076. Epub 2022 Jan 4.
- Results publication: PubMed 33359090Turner D, Ricciuto A, Lewis A, D'Amico F, Dhaliwal J, Griffiths AM, Bettenworth D, Sandborn WJ, Sands BE, Reinisch W, Scholmerich J, Bemelman W, Danese S, Mary JY, Rubin D, Colombel JF, Peyrin-Biroulet L, Dotan I, Abreu MT, Dignass A; International Organization for the Study of IBD. STRIDE-II: An Update on the Selecting Therapeutic Targets in Inflammatory Bowel Disease (STRIDE) Initiative of the International Organization for the Study of IBD (IOIBD): Determining Therapeutic Goals for Treat-to-Target strategies in IBD. Gastroenterology. 2021 Apr;160(5):1570-1583. doi: 10.1053/j.gastro.2020.12.031. Epub 2021 Feb 19.
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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.
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 Inflammatory Bowel Disease (Crohn's Disease and Ulcerative Colitis) and Crohn's Disease (CD), run by Taichung Veterans General Hospital at 1 site in Taiwan.
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 2 inclusion and 2 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: opening soon, as of the sponsor's last update on 15 May 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 Taiwan. 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?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Each site lists its own contact: choose the nearest one under Trial locations. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
developing localized AI models for IBD (time frame: No direct participant involvement; retrospective chart review of medical records from 2023 to 2025 only.). 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 NCT07590271, which also lists 4 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 15 May 2026.