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

NCT07590271 | Not Yet Recruiting

• 1 other identifier: CE26157A
• Study Type: observational
• Target: 4,500
• Locations: 1 country
• Sites: 1

Brief 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. 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.

Conditions

Crohn's Disease (CD); Ulcerative Colitis (UC); Artificial Intelligence (AI); Natural Language Processing (NLP); Inflammatory Bowel Disease (Crohn's Disease and Ulcerative Colitis)

Outcome Measures

Primary Outcomes (1)

• 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.

  No direct participant involvement; retrospective chart review of medical records from 2023 to 2025 only.

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

IBD and non-IBD Patients

You may qualify if:

• 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.

You may not qualify if:

• 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

Sponsors & Collaborators

• Taichung Veterans General Hospital: lead

Study Sites (1)

Taichung Veterans General Hospital

Taichung, Xitun Dist., 407219, Taiwan

Location

Related Publications (4)

• 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.

  PMID: 31483437 RESULT

• Nguyen 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.

  PMID: 24312659 RESULT

• Le 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.

  PMID: 34995529 RESULT

• Turner 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.

  PMID: 33359090 RESULT

MeSH Terms

Conditions

Inflammatory Bowel Diseases; Colitis, Ulcerative; Crohn Disease

Condition Hierarchy (Ancestors)

Gastroenteritis; Gastrointestinal Diseases; Digestive System Diseases; Intestinal Diseases; Colitis; Colonic Diseases

Study Design

• Study Type: observational
• Observational Model: CASE CONTROL
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Attending Physician

Study Record Dates

• First Submitted: May 11, 2026
• First Posted: May 15, 2026
• Study Start (Estimated): June 1, 2026
• Primary Completion (Estimated): December 1, 2027
• Study Completion (Estimated): December 31, 2027
• Last Updated: May 15, 2026

Record last verified: 2026-05

Locations

TW (1)