利用标准面部照片通过三维图形建模和混合人工智能预测择期手术患者困难喉镜暴露。

文献信息

中文摘要

背景： 意外困难喉镜暴露仍然是麻醉实践中危及生命的并发症的主要原因之一。传统的气道评估测试（如Mallampati分级、颏甲距离）本质上是主观的，并且诊断准确性有限。我们旨在开发一种混合人工智能模型，利用深度学习和基于图的建模从标准化面部照片中提取三维几何比例，以预测困难喉镜暴露。
方法： 在这项前瞻性观察性研究中，连续纳入计划进行择期手术的患者。术前，从八个投影角度获取标准化面部照片。图像不是基于原始像素进行分析，而是在使用对比度受限自适应直方图均衡化（CLAHE）进行局部光照归一化后，结合MediaPipe Face Mesh（468个标志点）和姿态估计（33个骨骼关键点），转换为三维解剖图结构。临床相关的解剖指标，包括下颌后缩、覆盖、颈部粗大和气道可见性，从这些坐标中数学推导出来，并与常规收集的临床和人体测量变量结合，作为结构化模型输入。Cormack-Lehane（CL）分级作为参考标准（I-II级：非困难喉镜暴露；III-IV级：困难喉镜暴露）。结构化数据集用于训练XGBoost、随机森林和包含多头注意力的表格Transformer模型。为防止数据泄露，合成少数类过采样技术（SMOTE）增强和缩放仅应用于训练集。
结果： 分析了198名患者的术前图像和解剖比例。在推导的指标中，张口比例和口咽可见性指数显示出最高的标志点获取成功率，在198名患者中有193名（97.5%）成功提取。所提出的混合人工智能（AI）模型在困难喉镜暴露预测中实现了80.0%的测试集准确率、85.7%的灵敏度（召回率）和0.808的曲线下面积（AUC）。
结论： 与依赖原始像素处理的传统卷积神经网络（CNN）不同，所提出的模型结合了标准化像素空间测量和归一化几何比例。这种基于标志点的表示可能降低在标准化成像条件下对某些采集相关变异的敏感性。这些发现支持基于标志点、几何驱动的方法用于客观术前气道风险评估的可行性。然而，在常规临床实施之前，需要进行外部验证并与标准化床旁气道评估进行直接比较。

英文摘要

BACKGROUND: Unexpected difficult laryngoscopy remains one of the leading causes of life-threatening complications in anesthetic practice. Conventional airway assessment tests (e.g., Mallampati classification, thyromental distance) are inherently subjective and demonstrate limited diagnostic accuracy. We aimed to develop a hybrid artificial intelligence model that extracts three-dimensional geometric ratios from standardized facial photographs using deep learning and graph-based modeling to predict difficult laryngoscopy.
METHODS: In this prospective observational study, consecutive patients scheduled for elective surgery were enrolled. Preoperatively, standardized facial photographs were obtained from eight projection angles. Instead of raw pixel-based image analysis, the images were transformed into three-dimensional anatomical graph structures following local illumination normalization using Contrast Limited Adaptive Histogram Equalization (CLAHE), combined with MediaPipe Face Mesh (468 landmark points) and Pose Estimation (33 skeletal key points). Clinically relevant anatomical indices, including Retrognathia, Overjet, Neck Bulk, and Airway Visibility, were mathematically derived from these coordinates and combined with routinely collected clinical and anthropometric variables as structured model inputs. Cormack-Lehane (CL) grading was used as the reference standard (Grades I-II: non-difficult laryngoscopy; Grades III-IV: difficult laryngoscopy). The structured dataset was used to train XGBoost, Random Forest, and Tabular Transformer models incorporating multi-head attention. To prevent data leakage, Synthetic Minority Over-sampling Technique (SMOTE) augmentation and scaling were applied exclusively to the training set.
RESULTS: Preoperative images and anatomical ratios of 198 patients were analyzed. Among the derived indices, the Mouth Opening Ratio and Oropharyngeal Visibility Index showed the highest landmark acquisition success, with extraction in 193 of 198 patients (97.5%). The proposed hybrid artificial intelligence (AI) model achieved a test-set accuracy of 80.0%, sensitivity (recall) of 85.7%, and an area under the curve (AUC) of 0.808 for difficult laryngoscopy prediction.
CONCLUSIONS: Unlike conventional convolutional neural networks (CNNs) relying on raw pixel processing, the proposed model combines standardized pixel-space measurements and normalized geometric ratios. This landmark-based representation may reduce sensitivity to some acquisition-related variability under standardized imaging conditions. These findings support the feasibility of a landmark-based, geometry-driven approach for objective preoperative airway risk assessment. However, external validation and direct comparison with standardized bedside airway assessment are required before routine clinical implementation.