ArticleBMJ open2025
Artificial intelligence for difficult airway assessment: a protocol for a systematic review with meta-analysis.
Article in BMJ open, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07609147 (Ultrasound Measurement of Skin to Epiglottis Distance for Prediction of Difficult Airway in Adult Patients), which is not on this map. Cited by 3 papers, 1 of them a synthesis that pooled it.
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.
Ultrasound Measurement of Skin to Epiglottis Distance for Prediction of Difficult Airway in Adult Patients
Who cites it
3 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
- Artificial Intelligence in Airway Management: Current Evidence and Future Perspectives.Cureus · 2026Review
- Artificial Intelligence for Predicting Difficult Airways: A Review.Journal of clinical medicine · 2025Review
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
introductionIdentifying difficult airways and avoiding unanticipated difficult airways through difficult airway assessment are crucial for patient safety prior to airway management. Therefore, accurately predicting difficult airways through airway assessment is a fundamental and significant technique in airway management by clinicians. Artificial intelligence (AI) is a rapidly evolving science with greater data processing ability than humans. AI, given its ever-expanding applications in medical diagnosis and disease prediction, has been employed to predict cases with difficult airways. Nevertheless, the diagnostic performance of AI algorithms for difficult airway assessment remains unclear due to the small sample sizes, insufficient image acquisition standards and poor predictive accuracies. Consequently, this study aims to formulate a protocol for a systematic review and meta-analysis to ascertain the diagnostic value of AI in assessing difficult airways. METHODS AND ANALYSIS: English-language databases (Cochrane Library, Web of Science, PubMed, Ovid Medline and Embase), Chinese electronic databases (China National Knowledge Infrastructure, VIP and Wanfang ] and clinical trial registry databases will be searched from their inception until January 2025 to identify clinical trials of AI for difficult airway assessment. Sensitivities, specificities, areas under the receiver operating characteristic curve, diagnostic likelihood ratios and diagnostic ORs with 95% CIs will be presented as indicators of AI's diagnostic accuracy in assessing difficult airways. Depending on the level of statistical heterogeneity evaluated by the I-square test, the fixed-effects or random-effects model will be employed. The risk of bias will be evaluated using the Quality Assessment of Diagnostic Accuracy Studies 2 tool. Furthermore, the quality of evidence concerning the outcomes will be assessed based on the Grading of Recommendations Assessment, Development and Evaluation criteria for diagnostic tests. Heterogeneity will be investigated through sensitivity, meta-regression and subgroup analyses. Additionally, Deeks' funnel plot asymmetry test will be used to detect publication bias. ETHICS AND DISSEMINATION: Ethical approval is not required for this systematic review protocol. The results will be disseminated through peer-reviewed publications. PROSPERO REGISTRATION NUMBER: CRD42023462926.
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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.