Our client wanted to use artificial intelligence to leverage the data generated by their 4D jaw capture system (3D representation of the jaw + motion simulation) to perform the following tasks :
– Dental occlusion diagnostic assistance considering movement
– Assistance in generating aligners for the patient
Using 3D mesh data provided by an intraoral camera and motion data (such as chewing movements captured by our client’s device), we have developed algorithms capable of simulating occlusions and assisting practitioners in generating aligners.
The solution provides significant time savings for practitioner clients, who benefit from powerful simulation tools. The solution also helps them avoid trial and error by providing an immediately well-fitted aligner for the patient.
Occlusion diagnostic assistance
Neovision has designed tooth segmentation algorithms using deep learning and 3D geometric calculations. The information provided by experts has allowed us to develop a functionality that calculates occlusions during the simulation of a person’s chewing movements. These occlusion data then enable practitioners to adapt the proposed treatments accordingly.
Aligner generation assistance
Practitioners define a “smile design,” which represents an ideal smile curve based on a photo. Using the previously generated data and the ideal curve, an image is generated, taking into account the patient’s aesthetic and functional characteristics.
By leveraging 3D tooth libraries, our algorithms suggest an ideal positioning that adapts to the smile curve. The practitioner can then simulate chewing to verify that there are no occlusions during movement.
18 July 2023
Computer Vision, Deep Learning, Imagerie 3D, Machine learning, R&D, Santé