Delft University of Technology
Ribbers, Tamara (author).
A tool to predict and communicate facial recognition after orthognathic surgeries.
Degree: 2020, Delft University of Technology
A problem at the VUmc hospital in Amsterdam is that some patients become unrecognizable after the surgery. It is hard for surgeons to explain during the consultation how and how much the patients will change due to the surgery. Partly, because the surgeons cannot predict if the patient will become unrecognizable. Therefore, they tell them that there is a possibility they change drastically, and with this information, the patients do not know what they can expect. Even if the outcome of the surgery is optimal according to aesthetic and functional guidelines, the mismatching expectations with the outcomes can dissatisfy the patient. Some patients cannot get used to their new changes and do not recover emotionally, resulting in mental problems. Therefore, there is a need for a tool in the consultation room to predict changes in facial recognition per patient and which helps the surgeon to communicate this to the patient. The changes in facial recognition were noticed at the VUmc, but there was no proof that the orthognathic surgeries actually influenced facial recognition, because no research in the topic was done before this project. This project proves that orthognathic surgeries can influence facial recognition which creates a new field of research. The research in this project shows that not all patients become unrecognizable, it depends on the specific face of the patient and the type of surgery the patient undergoes. Facial recognition is complex since it depends on who is trying to recognize the patient, how familiar the patient is to this person, and a persons ability to recognize someone can differ per day and is therefore variable. Therefore, a test has been done to analyse the influence of orthognathic surgeries on facial recognition within a familiar group. Testing a larger dataset would be too time-consuming to test among humans and, therefore, a landmark-based computer recognition method has been used to analyse a dataset of 75 pre- and post-surgical patient pictures. Both tests show that some regions of the face influence facial recognition more than others, as well as specific landmark movements which simulate orthognathic surgeries. Also, facial recognition depends on the face of the patient. Therefore, a concept of a tailored tool was created, called Fraos, showing the predictable changes in facial recognition per patient and helping the surgeon to communicate this to the patient. It also supports the patient in emotionally recovering from the facial changes.
Advisors/Committee Members: Goossens, R.H.M. (graduation committee), Song, Y. (mentor), Delft University of Technology (degree granting institution).
Subjects/Keywords: Orthognathic surgery; Facial recognition; Prediction; Communication; VUmc; Tool
to Zotero / EndNote / Reference
APA (6th Edition):
Ribbers, T. (. (2020). A tool to predict and communicate facial recognition after orthognathic surgeries. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:585df940-f776-454e-9db1-9848662c8621
Chicago Manual of Style (16th Edition):
Ribbers, Tamara (author). “A tool to predict and communicate facial recognition after orthognathic surgeries.” 2020. Masters Thesis, Delft University of Technology. Accessed October 20, 2020.
MLA Handbook (7th Edition):
Ribbers, Tamara (author). “A tool to predict and communicate facial recognition after orthognathic surgeries.” 2020. Web. 20 Oct 2020.
Ribbers T(. A tool to predict and communicate facial recognition after orthognathic surgeries. [Internet] [Masters thesis]. Delft University of Technology; 2020. [cited 2020 Oct 20].
Available from: http://resolver.tudelft.nl/uuid:585df940-f776-454e-9db1-9848662c8621.
Council of Science Editors:
Ribbers T(. A tool to predict and communicate facial recognition after orthognathic surgeries. [Masters Thesis]. Delft University of Technology; 2020. Available from: http://resolver.tudelft.nl/uuid:585df940-f776-454e-9db1-9848662c8621