This data-driven method mitigates the risk of human biases in therapeutic evaluations and effectively transitions from static 2D images to dynamic 4D assessments of facial palsy state.
36 patients (25-72 years, 8 male, 28 female) with unilateral chronic synkinetic facial palsy, which is presumed to be a significant factor affecting classification, were recorded three times using the 30Hz 3dMDface system.
We present a method of computer-assisted orbital floor reconstruction using a mirroring technique and a custom-made titanium or high-density polyethylene mesh printed using computer-aided manufacturing techniques.
3dMD’s natural software evolution to more sophisticated dynamic-4D image analysis, landmark detection, and feature tracking capabilities.
In summary, this study will serve as evidence to guide clinicians in deciding whether to perform the T&A on patients prior to functional therapy based on a wide range of objectively and subjectively measured outcomes and provide a reference for multidisciplinary management of OSA with craniofacial alterations in growing children.
For infants up to age three months, 3D images were performed in our neonatal intermediate care unit in the supine position using a 10Hz 3dMDface.t System with this set-up being developed specifically for this study. Subsequently, 3D images were taken in an upright position with infants sitting on their parent’s lap in the department of orthodontics using a 10Hz 3dMDtrio.t system.
We make a step towards automatic realistic garment design and propose Design2Cloth, a high fidelity 3D generative model trained on a real world dataset. The scans of a total of 2010 distinct subjects spanning different ages, body types and ethnicities were captured using a 3dMDbody System.
This pilot study aimed to evaluate, for the first time, the changes in facial tissues following the placement of a single dental implant.
The aim of this study was to clarify the hard-soft tissue relationships of the lower 1/3 of the face in skeletal Class II-hyperdivergent patients compared with those in Class I-normodivergent patients using network analysis.
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.