Sichuan University

Zhihe Zhao

Bo Zhang

Yu Li

Wenxin Lu

Xinqi Huang

Lixia Yu

Jingyang Lou

Guanyin Zhu

Jie Fang

Yanbo Liao

A deep learning-based MRI examination and diagnosis system for displacement of a temporomandibular joint disc includes: an acquisition unit, configured to acquire a human head MRI image; a labeling unit, configured to obtain temporomandibular joint area images based on the human head MRI image, and label the temporomandibular joint area images to obtain a training set, the temporomandibular joint area images being classified into images in which a joint disc is in normal and abnormal positions in open and closed states; and a diagnosis unit, configured to construct a first temporomandibular joint disc displacement diagnosis model, and train a first jaw joint disc displacement classification model based on the training set to obtain a second temporomandibular joint disc displacement diagnosis model which is configured to classify and recognize temporomandibular joint area images to be diagnosed, and output a temporomandibular joint disc displacement examination and diagnosis result.

Deep learning-based MRI examination and diagnosis system for displacement of temporomandibular joint disc

The present invention belongs to the field of animal gas anesthesia, and provides a quantitative and precise gas anesthesia method and device for experimental animals. The quantitative and precise gas anesthesia device for experimental animals includes an anesthetic fluid tank, a drainage tube, catheters, closed animal anesthetic tanks, and a recycling container, where anesthetic fluid is put into the anesthetic fluid tank, the bottom of the anesthetic fluid tank is connected to an inlet of the drainage tube, a set of holes are evenly arranged in the drainage tube, each hole is connected to an inlet of each catheter, and outlets of the catheters extend into the closed animal anesthetic tanks.

Quantitative and precise gas anesthesia method and device for experimental animals

Growing community of inventors

Sichuan, China

Zhihe Zhao