Observational studyColorectal disease : the official journal of the Association of Coloproctology of Great Britain and Ireland2025
AUGUR-AIM: Clinical validation of an artificial intelligence indocyanine green fluorescence angiography expert representer.
Observational study in Colorectal disease : the official journal of the Association of Coloproctology of Great Britain and Ireland, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed.
- Fluorescence-guided surgery in colorectal cancer: current evidence, quantitative advances, and future perspectives.Annals of coloproctology · 2026Review
- Beyond fluorescence intensity: a Minimum acquisition and reporting standard and emerging technologies for colorectal indocyanine green fluorescence angiography studies.Frontiers in surgery · 2026Article
- Technological Advances in Intra-Operative Navigation: Integrating Fluorescence, Extended Reality, and Artificial Intelligence.Journal of clinical medicine · 2025Review
- AUGUR-AI: a real-time artificial intelligence interpreter for indocyanine green fluorescence angiography in colorectal surgery.Surgical endoscopy · 2025Article
- Development of an AI algorithm for early identification of MASLD in the electronic health record.Hepatology communications · 2025Article
- AUGUR-AIM: Clinical validation of an artificial intelligence indocyanine green fluorescence angiography expert representer.Colorectal disease : the official journal of the Association of Coloproctology of Great Britain and Ireland · 2025Observational
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
aimRecent randomized controlled trials and meta-analyses have demonstrated a reduction in the anastomotic leak rate when indocyanine green fluorescence angiography (ICGFA) is used versus when it is not in colorectal resections. We have previously demonstrated that an artificial intelligence (AI) model, AUGUR-AI, can digitally represent in real time where experienced ICGFA users would place their surgical stapler based on their interpretation of the fluorescence imagery. The aim of this study, called AUGUR-AIM, is to validate this method across multiple clinical sites with regard to generalizability, usability and accuracy while generating new algorithms for testing and determining the optimal mode of deployment for the software device.
methodThis is a prospective, observational, multicentre European study involving patients undergoing resectional colorectal surgery with ICGFA as part of their standard clinical care enrolled over a 1-year period. Video recordings of the ICGFA imagery will be computationally analysed both in real time and post hoc by AUGUR-AI, with the operating surgeon blinded to the results, testing developed algorithms iteratively versus the actual surgeon's ICGFA interpretation. AI-based interpretation of the fluorescence signal will be compared with the actual transection site selected by the operating surgeon and usability optimized.
conclusionAUGUR-AIM will validate the use of AUGUR-AI to interpret ICGFA imagery in real time to the level of an expert ICGFA user, building on our previous work to include a larger, more diverse patient and surgeon population. This could allow future progression to develop the AI model into a usable clinical tool that could provide decision support, including to new/infrequent ICGFA users, and documentary support of the decision made by experienced users.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.