ArticleCureus2024
Impact of Artificial Intelligence on Polyp Size and Surveillance Colonoscopy: A Phantom Study.
Article in Cureus, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background Artificial intelligence (AI) is a hot topic in the world of medicine. AI may be useful in identifying and sizing polyps, which influence surveillance intervals. Therefore, we examined polyp size estimation by AI using a survey study. Methods A survey study was performed using a phantom colon model. Eleven videos were produced in the colon phantom using a colonoscope. Gastroenterologists were compared to a new AI system (Argus) for sizing polyps and their impact on surveillance intervals. Results Eleven gastroenterologists completed the survey with a mean age of 51.1 ± 8.1 years and an average of 19.3 ± 10 years of experience. Mean accuracy rates for gastroenterologists were 76% ± 0.1% (range 54-89%) compared to 96% ± 0.05% for Argus. Endoscopists estimated polyp size within ± 1 mm 44 times (36%) versus 9 times (82%) with Argus. Endoscopists' surveillance recommendations were significantly more often inappropriate compared to Argus (34 vs 0). The interval of next colonoscopy was too short for 27 endoscopists (22%) and too long for seven endoscopists (6%). Conclusions AI appears to be more accurate in estimating polyp size than experienced endoscopists. Given the potential impact on surveillance intervals, AI may result in cost savings.
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.