Detection of facial landmarks by a convolutional neural network in patients with oral and maxillofacial disease.

Facial nerve dysfunction is common in patients with Bell's palsy, trauma, tumour, or iatrogenic injuries. Imaging assessment is the most convenient method for patients and their treating physician. With developments in artificial intelligence (AI), manual work will be replaced. In this study, a database of facial images of patients with oral and maxillofacial diseases was set up to develop a facial nerve functional assessment system based on AI. This database was then used to evaluate the accuracy of a state-of-the-art algorithm for facial landmark detection named 'HRNet'. Utilizing this database and with appropriate human intervention, HRNet was used in facial annotation. The accuracy of annotations was evaluated through the normalized mean error. A total of 912 images were collected from 300 people; 546 of these images had abnormal features including defects, swelling, scars, or facial paralysis. The accuracy for the abnormal group was lower than that for the normal group before and after training, but improvements in accuracy were identified in both groups post-training. In conclusion, this new database demonstrates the ability of HRNet to localize facial landmarks in patients with oral and maxillofacial diseases. More images for training should be added to this database to diversify it in the future.