Two-Year Progressive Cranial Changes Following the Melbourne Technique for Sagittal Craniosynostosis. LM Harrison, K Prezelski, RR Hallac, AA Kane, P Sanati-Mehrizy.

3dMDhead images were obtained preoperatively and postoperatively at 3 weeks, 3 months, 1 year, and 2 years. Head circumference, cephalic index, scaphocephalic index (SCI), frontal bossing index (FBI), occipital bullet index (OBI), and vertex narrowing index (VNI) were measured automatically using the 3dMDvultus Craniometrics Calculator.

Three-dimensional analysis of facial morphology in nine-year-old children with different unilateral orofacial clefts compared to normative data. M Crins-de Koning, R Bruggink, M Nienhuijs, T Wagner, EM Bronkhorst, EM Ongkosuwito.

3dMD images of cleft patients were captured under standardized conditions between 2016 and 2022 and maintained in the database of the Amalia Cleft Palate and Craniofacial Unit of the Radboudumc, Nijmegen, the Netherlands.

Beyond the Surface: 3D and 4D Imaging for Craniofacial Assessment and Treatment. Rami R HALLAC.

Date: October 2024. Source: 3DBodyTech Conference, Lugano, Switzerland. Presenters: Rami R HALLAC, UT Southwestern Medical Center and Children’s Health, Dallas TX, USA, and Chris LANE, 3dMD. Rami R HALLAC Background: Dr. Hallac is an imaging scientist at Children’s Health and an associate professor at UT Southwestern Medical Center. Utilizing 3D imaging, modelling and printing, Dr.…

Three-dimensional Assessment of Longitudinal Surgical Outcome in Patients with Unilateral Cleft Lip and Palate: A Modified Rotation Advancement Technique. Y Xu, M Yao, B Shi, et al.

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: A visual computing pipeline for interactive landmarking of 3D photograms and patient reporting for assessing craniosynostosis. C Görg, C Elkhill, J Chaij, K Royalty, PD Nguyen, B French, AC Guerrero, AR Porras.

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.