These findings may offer further insight to clinicians, educators, and caregivers into the social challenges faced by cleft individuals.
We collected and assessed 3dMDface images of 115 consecutive patients who underwent primary repair between 2017 and 2019. 3dMD images were taken preoperatively, immediately postoperatively and at a first and second follow-up interval, occurring at an average year of 0.6 and 5.3 years, respectively.
SHAPE reads in a patient’s 3D photogram, automatically places a set of craniofacial landmarks, allows for their manual confirmation and correction, and automatically computes both a series of standard clinical craniofacial measurements and machine learning-based metrics of head development prior to building an analysis report for upload to the patient’s electronic medical record.
Three-dimensional (3D) photography allows for the creation of surface models without the need for radiation and has been validated as reliable and equivalent to CT for calvarial measurements.
The 3D geometric morphometry in OAVS patients could be a useful tool for objective facial asymmetry assessment in patients with OAVS.
The purpose of this study was to evaluate the outcomes of a novel surgical technique using force balance reconstruction of the orbicularis oris.
3dMD’s natural software evolution to more sophisticated dynamic-4D image analysis, landmark detection, and feature tracking capabilities.
A deep learning-based approach for automated landmark extraction from 3dMD facial photographs was developed and its precision was evaluated. The results showed high precision and consistency in landmark annotation, comparable to manual and semi-automatic annotation methods.
3dMD facial images and deep transfer learning have been firstly combined for evaluating the facial attractiveness in patients undergoing Orthognathic surgery.
The 3dMDface system was used to capture the nasolabial morphology of the individuals.