Systematic Review: Ultrasound Goes Echo—Decarbonising Inflammatory Bowel Disease Care Through Intestinal Ultrasound
Bibliographic record
Abstract
ABSTRACT Background Inflammatory bowel disease (IBD) is a chronic, resource‐intensive condition requiring repeated diagnostic assessments. Healthcare contributes ~5% of global greenhouse gas emissions, and key diagnostic tools in IBD—gastrointestinal (GI) endoscopy, computed tomography (CT) and magnetic resonance imaging (MRI)—are associated with substantial environmental impacts. The environmental burden of these diagnostic pathways, however, remains underappreciated. Aim To systematically assess the carbon footprint and environmental impact of diagnostic imaging modalities commonly used in IBD, with particular focus on intestinal ultrasound (IUS) as a sustainable, low‐carbon alternative. Methods A systematic review was conducted according to PRISMA 2020 guidelines. PubMed, Scopus and Embase were searched from inception to May 2025 for studies reporting the environmental impact of diagnostic modalities relevant to IBD care (GI endoscopy, CT, MRI and IUS). Studies providing quantitative or qualitative data on carbon footprint, energy consumption, waste generation or sustainability metrics were included. Data were synthesised narratively. Results Thirty‐one studies were included. GI endoscopy generates approximately 7.8–56.4 kg CO 2 ‐equivalent per procedure, largely driven by transportation, energy use and disposables. CT carries a carbon footprint of 7–10 kg CO 2 e per procedure in direct life cycle assessments, while broader institutional and modelling estimates extend this to ~20 kg CO 2 e depending on throughput, protocol and energy sources. MRI is substantially more energy‐intensive, ranging from 17–22 kg CO 2 e per scan in most studies, and up to 200–300 kg CO 2 e for high‐field (3T) systems when full life cycle impacts are included. In contrast, IUS produces only 0.5–1.5 kg CO 2 e per scan, with minimal energy demand and negligible waste. IUS enables point‐of‐care assessments, reducing patient travel and associated emissions. Conclusion GI endoscopy, CT and MRI are indispensable in IBD care but carry considerable environmental costs. The broader adoption of IUS offers a clinically effective, low‐carbon alternative that can contribute to more sustainable IBD management, aligning with planetary health goals. Trial Registration PROSPERO International Prospective Register of Systematic Reviews: CRD420251088016
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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 | 0.007 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| 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".