Dental restoration automatic design method and system based on deep learning

Abstract

The invention discloses a dental restoration automatic design method and system based on deep learning, and the method comprises the steps: segmenting corresponding three-dimensional segmentation data from the dentition data of a patient, and obtaining corresponding target restoration data, so as to form a data set C comprising the three-dimensional segmentation data and the target restoration data; training a data processing model based on deep learning by adopting the data set C, so that the data processing model can generate a restoration model matched with the three-dimensional segmentation data of the patient according to the three-dimensional segmentation data of the patient; according to the design method, the dental restoration can be designed in a full-automatic manner, no human interaction is needed, the design efficiency is effectively improved, time and labor are saved, and the finally manufactured dental restoration is more personalized; besides, along with the increase of the design quantity of the prosthesis, the data processing model is continuously optimized, and the design capability is continuously improved, so that the requirements of dentists and patients are better met.

Description

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AI Technical Summary

Benefits of technology

The technical effect of this patented system is an automated way to create customizable teeth restored through computer-based methods without requiring human interference during or after treatment planning sessions. This improves the accuracy and quality of the resulting teeth while reducing costs associated therewith compared to traditional techniques such as casting trays filled with plaster moldings.

Problems solved by technology

This patented technical solution described earlier involves creation of customized teeth that can be restored without requiring much laborious work from experienced professionals who have been trained over years with different techniques. However, current methods lacked precision due to factors like variations between individuals' mouths shape and occlusivity caused during orthodontic treatment procedures.

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