Observational studyThe Journal of international medical research2019
An artificial neural network model for prediction of hypoxemia during sedation for gastrointestinal endoscopy.
Observational study in The Journal of international medical research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it, 23 citations in OpenAlex.
- Artificial intelligence and its clinical application in Anesthesiology: a systematic review.Journal of clinical monitoring and computing · 2024Pooled it
- Ultrasonography and a novel combined prediction model for anticipating hypoxemia during painless gastroscopy.Experimental and therapeutic medicine · 2026Article
- Sedation in Gastrointestinal Endoscopy: From Drug-Centered Protocols to Personalized, Technology-Supported Pathways: A Narrative Review.Journal of clinical medicine · 2026Review
- Article
- Machine Learning Techniques for the Analysis of the Influence of Blood Gasometry Parameters on Acid-Base Homeostasis in Pediatric Patients.Diagnostics (Basel, Switzerland) · 2025Article
- Article
- From static to dynamic: Artificial intelligence revolution in perioperative care through multimodal data fusion and closed-loop optimization.Journal of anesthesia and translational medicine · 2025Review
- Development and validation of a hypoxemia prediction model in middle-aged and elderly outpatients undergoing painless gastroscopy.Scientific reports · 2025Article
- Predicting Hypoxia Using Machine Learning: Systematic Review.JMIR medical informatics · 2024Review
- Res-SE-ConvNet: A Deep Neural Network for Hypoxemia Severity Prediction for Hospital In-Patients Using Photoplethysmograph Signal.IEEE journal of translational engineering in health and medicine · 2022Article
- Article
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 at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThis study was designed to assess clinical predictors of hypoxemia and develop an artificial neural network (ANN) model for prediction of hypoxemia during sedation for gastrointestinal endoscopy examination.
methodsA total of 220 patients were enrolled in this prospective observational study. Data on demographics, chronic concomitant disease information, neck circumference, thyromental distance and anaesthetic dose were collected and statistically analysed.
resultsUnivariate analysis indicated that body mass index (BMI), habitual snoring and neck circumference were associated with hypoxemia. An ANN model was developed with three variables (BMI, habitual snoring and neck circumference). The area under the receiver operating characteristic curve for the ANN model was 0.80.
conclusionsThe ANN model developed here, comprising BMI, habitual snoring and neck circumference, was useful for prediction of hypoxemia during sedation for gastrointestinal endoscopy.
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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.