Difficult tracheal intubation is the third most common respiratory-related adverse co-morbid episode and can lead to death or brain damage. Since difficult tracheal intubation is less frequent, trainees have fewer opportunities to perform difficult tracheal intubation; this leads to the need to practice with a hyper-realistic intubation simulator. However, conventional simulators are expensive, relatively stiffer than the human airway, and have a lack of diversity in terms of disease variations and anatomic reproducibility. Therefore, we proposed the development of a patient-specific and hyper-realistic difficult tracheal intubation simulator using three-dimensional printing technology and silicone moulding and to test the feasibility of patient-specific and hyper-realistic difficult intubation simulation using 3D phantom for the trainee. This difficult tracheal intubation phantom can provide a realistic simulation experience of managing various difficult tracheal intubation cases to trainees, which could minimise unexpected tissue damage before anaesthesia. To achieve a more realistic simulation, a patient-specific phantom was fabricated to mimic human tissue with realistic mouth opening and accurate difficult airway shape. This has great potential for the medical education and training field.

Summary Objectives The aim of the study was to evaluate the accuracy of a cascaded two-stage convolutional neural network (CNN) model in detecting upper airway (UA) soft tissue landmarks in comparison with the skeletal landmarks on the lateral cephalometric images. Materials and methods The dataset contained 600 lateral cephalograms of adult orthodontic patients, and the ground-truth positions of 16 landmarks (7 skeletal and 9 UA landmarks) were obtained from 500 learning dataset. We trained a UNet with EfficientNetB0 model through the region of interest-centred circular segmentation labelling process. Mean distance errors (MDEs, mm) of the CNN algorithm was compared with those from human examiners. Successful detection rates (SDRs, per cent) assessed within 1–4 mm precision ranges were compared between skeletal and UA landmarks. Results The proposed model achieved MDEs of 0.80 ± 0.55 mm for skeletal landmarks and 1.78 ± 1.21 mm for UA landmarks. The mean SDRs for UA landmarks were 72.22 per cent for 2 mm range, and 92.78 per cent for 4 mm range, contrasted with those for skeletal landmarks amounting to 93.43 and 98.71 per cent, respectively. As compared with mean interexaminer difference, however, this model showed higher detection accuracies for geometrically constructed UA landmarks on the nasopharynx (AD2 and Ss), while lower accuracies for anatomically located UA landmarks on the tongue (Td) and soft palate (Sb and St). Conclusion The proposed CNN model suggests the availability of an automated cephalometric UA assessment to be integrated with dentoskeletal and facial analysis.