Review Article: Extending the Frontiers of Intestinal Ultrasound Knowledge, Performance and Expansion
Bibliographic record
Abstract
BACKGROUND: Intestinal ultrasonography (IUS) is increasingly utilised for diagnosing and monitoring IBD. Despite its cost-effectiveness, patient tolerance and suitability for serial bedside assessments, broad adoption has been limited by knowledge gaps in evidence, training and standardisation. AIMS: To summarise key knowledge gaps in the assessment of luminal disease activity, postoperative recurrence, complications, pouch-related disorders and the use of IUS in paediatrics, contrast enhancement, elastography, as well as education, training and future applications involving artificial intelligence. METHODS: We conducted a systematic umbrella review, following PRISMA guidelines, to map the current landscape of high-quality evidence and identify gaps in IUS research relevant to IBD. We searched MEDLINE from inception to February 2025 for systematic reviews, meta-analyses and consensus statements. We extracted data from eligible studies on design, outcomes and identified research gaps. Gaps were categorised by insufficient information, bias, inconsistency or lack of relevant data. RESULTS: Sixty of 507 studies met inclusion criteria. Key gaps included lack of validated and standardised IUS activity indices for Crohn's disease and ulcerative colitis, limited evidence for IUS in post-operative recurrence, paediatric populations and perianal or pouch disease. Data on the use of contrast-enhanced ultrasound and elastography were sparse. Small sample sizes, heterogeneous designs and inadequate follow-up limited most studies. Training, competency assessment and integration of artificial intelligence remain underexplored. CONCLUSIONS: Sizable gaps persist in the evidence base for IUS in IBD. Addressing these gaps through robust, multicentre studies and consensus-driven frameworks is essential to optimise the clinical and research utility of IUS in IBD management.
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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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".