Enhanced Facial Landmarks Detection for Patients with Repaired Cleft Lip and Palate

Cleft lip and palate (CLP) is a congenital condition causing deformities in the oral and labial tissues. Post-surgery, patients often experience residual issues like facial asymmetry, and speech disorders. Tracking points in the orofacial area using a facial landmark detector (FLD) contributes to the assessment of speech development and movement impairments. However, off-the-shelf FLDs fail at delineating the lips of patients with repaired CLP. To address this need, our study introduces the CLP-Trans strategy, a domain transfer solution that employs tailor-made affine transformations to modify facial images sourced from publicly available datasets, which constitute our source domain, whereas images of patients with repaired CLP form our target domain. We aim to reduce distribution disparities between the source and target domains for FLD by simulating common outcomes of CLP repair surgery. The system utilizes a deep convolutional neural network (CNN) to learn from transformed images, therefore, preserving the privacy and facilitating the reproducibility of the findings. The strategy achieves statistically significant improvements in the normalized mean square error (NMSE), reducing it from 2.417 to 2.086 (i.e., 13.7% error reduction) by using the proposed strategy when evaluating images of patients with CLP.