Deep-learning model associating lateral cervical radiographic features with Cormack-Lehane grade 3 or 4 glottic view

Unanticipated difficult laryngoscopy is associated with serious airway-related complications. We aimed to develop and test a convolutional neural network-based deep-learning model that uses lateral cervical spine radiographs to predict Cormack-Lehane grade 3 or 4 direct laryngoscopy views of the glottis. We analysed the radiographs of 5939 thyroid surgery patients at our hospital, 253 (4%) of whom had grade 3 or 4 glottic views. We used 10 randomly sampled datasets to train a model. We compared the new model with six similar models (VGG, ResNet, Xception, ResNext, DenseNet and SENet). The Brier score (95%CI) of the new model, 0.023 (0.021-0.025), was lower ('better') than the other models: VGG, 0.034 (0.034-0.035); ResNet, 0.033 (0.033-0.035); Xception, 0.032 (0.031-0.033); ResNext, 0.033 (0.032-0.033); DenseNet, 0.030 (0.029-0.032); SENet, 0.031 (0.029-0.032), all p < 0.001. We calculated mean (95%CI) of the new model for: R² , 0.428 (0.388-0.468); mean squared error, 0.023 (0.021-0.025); mean absolute error, 0.048 (0.046-0.049); balanced accuracy, 0.713 (0.684-0.742); and area under the receiver operating characteristic curve, 0.965 (0.962-0.969). Radiographic features around the hyoid bone, pharynx and cervical spine were associated with grade 3 and 4 glottic views.