Prospective Intestinal Ultrasound Study for Artificial Intelligence Algorithm Development (PSIUSAD)

Prospective Study for Intestinal Ultrasound Algorithm Development

This observational study will use intestinal ultrasound images to help develop computer programs that may make it easier to assess bowel inflammation.

The study will include adults who are healthy or who are having an intestinal ultrasound as part of care for inflammatory bowel disease, such as Crohn disease or ulcerative colitis. Researchers want to find out whether a computer program can measure bowel wall thickness from ultrasound images as accurately as experienced ultrasound clinicians. They will also explore whether the program can identify other changes in the bowel.

Participants will have an intestinal ultrasound as part of their usual care. People who agree to take part will allow the research team to use de-identified ultrasound images and a small amount of related health information. No medication, experimental treatment, or additional ultrasound procedure will be given for this study. The computer program will not be used to make decisions about a participant's care, and participants will not receive individual results from the program.

Study Overview

• Status: Not yet recruiting
• Conditions: Inflammatory Bowel Disease (IBD)
• Intervention / Treatment: Other: De-identified Intestinal Ultrasound Data Collection

Detailed Description

Inflammatory bowel disease, including Crohn disease and ulcerative colitis, requires reliable assessment of intestinal inflammation to support clinical monitoring and treatment decisions. Intestinal ultrasound is a non-invasive, point-of-care imaging method that can assess bowel inflammation without radiation exposure. However, its interpretation may vary by operator experience and local scanning practice.

This prospective, multicentre, observational study will collect de-identified intestinal ultrasound images, cine loops, and limited associated metadata from adults aged 18 years and older. Eligible participants may be healthy individuals with no known inflammatory bowel disease or individuals undergoing intestinal ultrasound for inflammatory bowel disease screening, monitoring, or another clinical reason. Intestinal ultrasound examinations will be performed as part of routine clinical care. The study does not add a treatment intervention, investigational drug, experimental device, or clinically directed diagnostic procedure.

Following informed consent, ultrasound images and cine loops generated during the routine examination will be exported in de-identified DICOM format. Limited information relevant to artificial intelligence development and validation, such as age, sex, self-reported race, pregnancy status, eligibility confirmation, and relevant scan-related or pre-existing clinical information, may also be recorded. Direct personal identifiers and personal health information will be removed before the data are transferred outside the clinical site.

The de-identified data will be reviewed for appropriate de-identification and formatting before secure transfer to Dova Health Intelligence Inc. The data will be annotated and used to develop, train, tune, and test computer vision algorithms designed to identify bowel structures and measure bowel wall thickness on intestinal ultrasound. The study will also assess the feasibility of developing algorithms to evaluate bowel wall layer stratification, luminal diameter, bowel wall flow, bowel wall scarring, gastrointestinal motility, and mesenteric fat proliferation.

The primary objective is to evaluate whether the computer vision algorithm prototype can measure bowel wall thickness with performance comparable to manual measurements by experienced sonographers. Secondary objectives include evaluating the quality, diversity, and usability of the collected ultrasound data for artificial intelligence development and assessing the feasibility of measuring additional intestinal ultrasound features.

The algorithms developed in this study are investigational and will not be used in routine clinical care during the study. No participant-level algorithm findings will be returned to participants, and participation is not expected to provide a direct clinical benefit. The primary foreseeable risk is a loss of confidentiality; this risk is mitigated through de-identification at the study site, verification of de-identification before transfer, secure file transfer, controlled access, and secure cloud-based storage. The study plans to enroll up to 95 participants internationally, including up to 30 participants at the British Columbia site.

• Study Type: Observational
• Enrollment (Estimated): 95

Contacts and Locations

• Study Contact: Name: Cristian A Massaro, MSc; Phone Number: 587-223-5248; Email: iusresearch@ibdcentrebc.ca

Study Locations

• Canada
  • British Columbia
    • Vancouver, British Columbia, Canada, V6Z 2L2
      • IBD Centre of BC
      • Contact: Cristian A Massaro, MSc; Phone Number: 587-223-5248; Email: iusresearch@ibdcentrebc.ca
      • Principal Investigator: Matthew J Smyth, MD

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: Yes

• Sampling Method: Non-Probability Sample

Study Population

The study population will consist of adults aged 18 years and older who are either healthy or undergoing intestinal ultrasound for inflammatory bowel disease screening, monitoring, or another clinical reason. Participants will be recruited through the IBD Centre of BC and other participating study sites. The study may include individuals with inflammatory bowel disease and healthy volunteers. The study aims to include a diverse participant population to support evaluation of artificial intelligence algorithm performance across different participant characteristics.

Description

Inclusion Criteria:

• Adults aged 18 years or older.
• Individuals who are either healthy with no known finding of inflammatory bowel disease before the ultrasound examination or undergoing intestinal ultrasound for inflammatory bowel disease screening, monitoring, or another clinical reason.
• Individuals who are able and willing to provide informed consent for the collection and use of de-identified study data.

Exclusion Criteria:

• Individuals who do not meet the study demographic requirements.
• Individuals unable or unwilling to provide informed consent.
• Pregnant individuals.
• Individuals with health conditions managed using drugs or medical-device intervention, or with unmanaged conditions or conditions requiring monitoring, that could affect study eligibility or the ultrasound assessment.
• Individuals who have recently taken contrast-enhancing compounds.

Study Plan

How is the study designed?

Number of groups / cohorts

1

Cohorts and Interventions

Group / Cohort

Intervention / Treatment

Intestinal Ultrasound Participants; This cohort includes adults aged 18 years and older who are either healthy or undergoing intestinal ultrasound for inflammatory bowel disease screening, monitoring, or another clinical reason. Participants will undergo intestinal ultrasound as part of routine clinical care. With informed consent, de-identified ultrasound images and cine loops, along with limited associated information such as age, sex, self-reported race, and relevant scan-related details, will be collected for artificial intelligence algorithm development. No study drug, experimental treatment, or experimental device will be administered. The artificial intelligence algorithms will not be used to guide participant care or provide individual diagnostic results during the study.

Other: De-identified Intestinal Ultrasound Data Collection; Intestinal ultrasound images and cine loops will be collected from adults undergoing ultrasound as part of routine clinical care. Following informed consent, the images and cine loops will be de-identified and used for artificial intelligence algorithm development, training, tuning, and testing. The study does not assign participants to receive an intervention, and the ultrasound is not performed solely for research purposes. The artificial intelligence algorithms developed using the data will not be used to guide participant care or provide individual diagnostic results during the study.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Artificial Intelligence-Generated Bowel Wall Thickness Measurement Agreement	Agreement between bowel wall thickness measurements generated by the artificial intelligence computer-vision algorithm and manual bowel wall thickness measurements performed by experienced sonographers using de-identified intestinal ultrasound images and cine loops. Agreement will be assessed using the pre-specified performance metric in the statistical analysis plan.	Day 1, after completion of the intestinal ultrasound examination

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Bowel Wall Layer Stratification Feasibility	Proportion of de-identified intestinal ultrasound images and cine loops for which the artificial intelligence algorithm generates an interpretable bowel wall layer stratification.	Day 1, after completion of the intestinal ultrasound examination
Luminal Diameter Measurement Feasibility	Proportion of de-identified intestinal ultrasound images and cine loops for which the artificial intelligence algorithm generates an interpretable luminal diameter measurement.	Day 1, after completion of the intestinal ultrasound examination
Bowel Wall Flow Assessment Feasibility	Proportion of de-identified intestinal ultrasound images and cine loops for which the artificial intelligence algorithm generates an interpretable bowel wall flow assessment.	Day 1, after completion of the intestinal ultrasound examination
Bowel Wall Scarring Assessment Feasibility	Proportion of de-identified intestinal ultrasound images and cine loops for which the artificial intelligence algorithm generates an interpretable bowel wall scarring assessment.	Day 1, after completion of the intestinal ultrasound examination
Gastrointestinal Motility Assessment Feasibility	Proportion of de-identified intestinal ultrasound images and cine loops for which the artificial intelligence algorithm generates an interpretable gastrointestinal motility assessment.	Day 1, after completion of the intestinal ultrasound examination
Mesenteric Fat Proliferation Assessment Feasibility	Proportion of de-identified intestinal ultrasound images and cine loops for which the artificial intelligence algorithm generates an interpretable assessment of mesenteric fat proliferation.	Day 1, after completion of the intestinal ultrasound examination
Proportion of Cine Loops Suitable for AI Development	Percentage of collected de-identified cine loops that meet all pre-specified completeness, image-quality, and required-metadata criteria for artificial intelligence algorithm training, tuning, and testing.	At completion of dataset preparation

Collaborators and Investigators

• Sponsor: University of British Columbia
• Collaborators: IBD Centre of BC; Providence Health Care Ventures; Dova Health Intelligence Inc
• Investigators: Principal Investigator: Matthew J Smyth, MD, University of British Columbia, IBD Centre of BC

Publications and helpful links

General Publications

• Novak KL, Nylund K, Maaser C, Petersen F, Kucharzik T, Lu C, Allocca M, Maconi G, de Voogd F, Christensen B, Vaughan R, Palmela C, Carter D, Wilkens R. Expert Consensus on Optimal Acquisition and Development of the International Bowel Ultrasound Segmental Activity Score [IBUS-SAS]: A Reliability and Inter-rater Variability Study on Intestinal Ultrasonography in Crohn's Disease. J Crohns Colitis. 2021 Apr 6;15(4):609-616. doi: 10.1093/ecco-jcc/jjaa216.
• Goodsall TM, Nguyen TM, Parker CE, Ma C, Andrews JM, Jairath V, Bryant RV. Systematic Review: Gastrointestinal Ultrasound Scoring Indices for Inflammatory Bowel Disease. J Crohns Colitis. 2021 Jan 13;15(1):125-142. doi: 10.1093/ecco-jcc/jjaa129.
• Jauregui-Amezaga A, Rimola J. Role of Intestinal Ultrasound in the Management of Patients with Inflammatory Bowel Disease. Life (Basel). 2021 Jun 23;11(7):603. doi: 10.3390/life11070603.
• Frias-Gomes C, Torres J, Palmela C. Intestinal Ultrasound in Inflammatory Bowel Disease: A Valuable and Increasingly Important Tool. GE Port J Gastroenterol. 2021 Nov 23;29(4):223-239. doi: 10.1159/000520212. eCollection 2022 Jul.

Study record dates

Study Major Dates

• Study Start (Estimated): September 15, 2026
• Primary Completion (Estimated): February 1, 2027
• Study Completion (Estimated): February 1, 2027

Study Registration Dates

• First Submitted: September 2, 2026
• First Submitted That Met QC Criteria: September 9, 2026
• First Posted (Actual): September 10, 2026

Study Record Updates

• Last Update Posted (Actual): September 10, 2026
• Last Update Submitted That Met QC Criteria: September 9, 2026
• Last Verified: September 1, 2026

More Information

Terms related to this study

• Keywords: Ultrasound; Ulcerative Colitis; Inflammatory Bowel Disease; Artificial Intelligence; Crohn Disease; Machine Learning; Intestinal Ultrasound; Bowel Wall Thickness
• Additional Relevant MeSH Terms: Intestinal Diseases; Digestive System Diseases; Gastrointestinal Diseases; Colonic Diseases; Gastroenteritis; Colitis; Colitis, Ulcerative; Crohn Disease; Inflammatory Bowel Diseases
• Other Study ID Numbers: H26-00188

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO
• IPD Plan Description: Individual participant-level data will not be made available to external researchers. The study will collect de-identified intestinal ultrasound images, cine loops, and limited associated information for use by Dova Health Intelligence Inc. in quality assessment, annotation, artificial intelligence algorithm training, tuning, testing, and related future algorithm-development activities described in the study protocol. Data will not be made publicly available or shared outside the authorized study collaborators.

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No