Deciphering Visual Evaluation on Reconstructive Surgery Outcomes Using an Eye-tracking Platform and Machine Learning Techniques

Jeon Lee Lab

Human interactions begin with unconscious evaluation of the visual characteristics of one another, centered around the face. We automatically and almost immediately assess familiarity and attractiveness. When encountering a person who had craniofacial reconstructive surgery, we make near-instantaneous evaluation of the presence of facial deformity, the outcome of which modifies our initial emotional responses and social behavior toward that person.

Infants born with craniofacial differences (such as cleft lip or craniosynostosis) often undergo craniofacial reconstruction in an attempt to restore ‘normal’ appearance.  However, little is known about how we recognize 'normal and how laypersons assess craniofacial differences. Eye-tracking technology may provide the proxy necessary to evaluate whether reconstructive surgery for craniofacial craniosynostosis or cleft lip has achieved the ultimate goal of surgery, that is, reconstructing a face perceived as normal during social interaction.

In this study, we aim to combine eye-tracking technology with machine learning techniques in order to decipher visual evaluation on reconstructive surgery outcomes. An eye-tracking platform will be used to collect subjects’ gazing patterns and durations over pre- and post-surgery images. Then this ‘human’ behavior driven features, rather than mathematical derived features, will be used to train the machine learners.

For more information about this project email Jeon Lee.