Real-time face keypoint detection for pre-anesthetic assessment with optimized YOLO11 model based on DeBiFormer

Pre-anesthetic assessment involves a series of evaluations for airway management. However, the lack of quantitative analysis may limit its reliability. In this study, we propose an optimized YOLO11-Pose framework with a DeBiFormer module for automated, image-based pre-anesthetic assessment.

The proposed model performs single-stage inference to localize clinically relevant facial and hand keypoints associated with mouth opening, thyromental distance, and neck mobility. The framework was evaluated on a dataset collected from Ruijin Hospital under controlled acquisition conditions.

Experimental results demonstrated high detection performance. Measurement-based evaluations, including Bland-Altman analysis, pixel-level error, and normalized mean error (NME), indicated reliable agreement with reference annotations. In addition, Mallampati classification was evaluated to assess clinical applicability. The model achieved an accuracy of 77.34% in the four-class setting and improved performance in a clinically motivated binary setting, with an accuracy of 83.99% and a quadratic weighted kappa of 0.65, indicating substantial agreement.

These results suggest that the proposed method provides robust and clinically meaningful assessment of airway characteristics and may support automated pre-anesthetic evaluation in clinical practice.