ArticleiGIE : innovation, investigation and insights2024
Impact of a machine learning-based prediction model on annual surveillance endoscopy costs for detecting gastric cancer.
Article in iGIE : innovation, investigation and insights, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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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
1 citing paper in PubMed.
- Global trends in artificial intelligence applications for gastric cancer prediction, treatment, and management: a topic modeling and bibliometric analysis.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background and Aims: In this study, we assessed our machine learning (ML)-based model's impact on reducing annual surveillance endoscopy costs for detecting gastric cancer (GC). Methods: We analyzed 1099 patients with chronic gastritis undergoing annual EGD and randomly divided them into training and test sets (4:1). Using gradient-boosting decision trees and incorporating patient characteristics, we developed the ML model. In the test sets, we compared the EGD number needed to screen (NNS) for 1 GC, cost, and GC detection rate across different risk stratification strategies. Results: The ML-selected high-risk cohort demonstrated low NNS values, low total cost, low cost per 1 GC, and high GC detection rates compared with alternative risk stratification approaches, including operative link for gastric atrophy assessment and operative link for gastric intestinal metaplasia assessment. Conclusions: Our ML model holds promise in reducing endoscopy surveillance costs while maintaining a robust GC detection rate.
Identifiers
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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.