Surgical navigation system of mandibular angle osteotomy

Abstract

The invention discloses a surgical navigation system of mandibular angle osteotomy, which comprises the following steps: S1, establishing an osteotomy surface prediction model learning version based on a multitask convolutional neural network and according to the relevant data of past mandibular angle osteotomy patients, carrying out training by using the relevant data of a newly enrolled group ofmandibular angle osteotomy patients to obtain a stable osteotomy surface prediction model, and then superimposing data sets in a danger area to obtain an osteotomy surface prediction model stabilization version; S2, inputting the relevant information of mandibular angle osteotomy patients into an osteotomy surface prediction model, and predicting the change range of a post-operation face 3D effect; S3, drawing a projected three-dimensional image, which can be seen through and fit with a surgical field real time image, on the screen of a lens according to the maximum osteotomy amount and pre-operation CT; and S4, carrying out function superimposition on an osteotomy surface prediction model continuously, testing and improving continuously, and perfecting the surgical navigation system. Thesystem predicts a post-operation effect through establishing models, real-time rendering is performed during operation, and the operation accuracy is improved.

Description

technical field [0001] The invention relates to the technical field of surgical navigation, in particular to a surgical navigation system for mandibular angle osteotomy. Background technique [0002] The changes in the facial appearance of patients after mandibular angle osteotomy are not only related to the amount of mandibular osteotomy, but also partly related to the changes in soft tissue volume caused by changes in local soft tissue tension. Therefore, the amount of bone removed in mandibular angle osteotomy is not a subtraction between the preoperative facial appearance of the patient and the predicted postoperative effect. In the past, the 3D design of the surgical effect simply by means of 3D CT and facial 3D scanning camera system was not accurate. To predict the amount of bone removed and the shape and position of the osteotomy surface of the mandibular angle in order to achieve the postoperative prediction effect in the mandibular angle osteotomy. [0003] The ex...

AI Technical Summary

Problems solved by technology

However, there are currently three deficiencies in this system: (1) Additional fixed landmarks are required: the osteotomy line is determined by instructing the patient to wear a dental brace with a marking module. There is a high risk of relative displacement between the module and the mandible, which leads to errors in the judgment of the osteotomy line and reduces the safety of the operation; The stability of the relative positional relationship between the marking module and the mandibular body, but because the actual operation space of the mandibular angle osteotomy through the intraoral approach is narrow and deep, the operation of fixing the marking module itself is difficult, and the marking module is too large relative to the surgical field. Large, applicable cases that can achieve proper placement of the marker module are extremely limited

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