Observational studyAnaesthesia2026
Pre-operative three-dimensional face scans for predicting difficult facemask ventilation: a prospective development study.
Observational study in Anaesthesia, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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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
1 citing paper in PubMed.
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionFacemask ventilation is a key airway management skill but predicting difficulty can be challenging. Pre-operative three-dimensional face scanning may have diagnostic value. We aimed to identify interpretable facial shape features and to quantify their value for predicting difficult facemask ventilation.
methodsIn this prospective observational single-centre study, pre-operative three-dimensional face scans were obtained, and a structured airway assessment was performed on patients undergoing ear, nose and throat or maxillofacial surgery. The primary outcome was difficult facemask ventilation documented as an alert in the patient health record. After postprocessing, three-dimensional face scans were fitted to an established, non-clinical facial model to identify interpretable shape coefficients. The area under the receiver operating characteristic (AUROC) curve for the DIFFMASK score was calculated before and after enrichment with three facial shape features and the added diagnostic value was assessed using likelihood ratios.
resultsData from 398 patients were analysed. The optimism-corrected AUROC was 0.73 (95%CI 0.65-0.80) for the DIFFMASK score and 0.74 (95%CI 0.66-0.82) for selected facial shape features. Enrichment of the DIFFMASK score with three facial shape features improved goodness of model fit (p = 0.002) and achieved an optimism-corrected AUROC of 0.76 (95%CI 0.68-0.82). Generated face meshes with superimposed colour mapping revealed that morphological features of the nose, lower mandible, neck region and facial convexity were most predictive of difficult facemask ventilation. DISCUSSION: Pre-operative three-dimensional face scans predicted difficult facemask ventilation at least as well as the DIFFMASK score. Integrating the features of three selected facial shapes enriched the DIFFMASK score and improved its diagnostic value. Digital phenotyping can complement traditional clinical assessment.
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