Integrating Intestinal Ultrasound to Clinical Trials in Patients With Crohn’s Disease: Opportunities and Challenges
Bibliographic record
Abstract
This narrative review summarizes the current knowledge on using intestinal ultrasonography (IUS) to evaluate disease activity in patients with Crohn's disease (CD) and explores its potential role in clinical trials. Current trial endpoints and their limitations are discussed, highlighting the need for more patient-centric approaches, including increased use of magnetic resonance enterography (MRE) and IUS. Intestinal ultrasonography offers several advantages: it is noninvasive, requires no sedation, bowel preparation, or exposure to ionizing radiation, and enables real-time assessment of disease activity. It also demonstrates high sensitivity and specificity for detecting transmural inflammation and complications such as strictures, abscesses, and fistulas. Compared with cross-sectional imaging modalities like MRE and computed tomography, IUS is more patient-friendly, cost-effective, and suitable for point-of-care examination. However, challenges remain, including the lack of a universally accepted disease activity scoring system for MRE or IUS, despite the development and validation of several scoring tools. Key unmet needs include standardization of image acquisition and reporting, adequate training of healthcare professionals, improved access to equipment, and reimbursement pathways. Intestinal ultrasonography is increasingly being integrated into clinical trials to assess transmural inflammatory changes in CD, with IUS-based measures of transmural remission or response showing promise as potential endpoints. Although its advantages are clear, addressing these unmet needs is essential to broaden the adoption of IUS in both clinical trials and routine clinical practice.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.103 | 0.234 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".