ABSTRACT
In recent years, ophthalmology, as one of the medical fields highly dependent on auxiliary imaging, has been at the forefront of the application of deep learning algorithm. The morphological changes of the choroid are closely related to the occurrence, development, treatment and prognosis of fundus diseases. The rapid development of optical coherence tomography has greatly promoted the accurate analysis of choroidal morphology and structure. Choroidal segmentation and related analysis are crucial for determining the pathogenesis and treatment strategies of eye diseases. However, currently, choroidal mainly relies on tedious, time-consuming, and low-reproducibility manual segmentation. To overcome these difficulties, deep learning methods for choroidal segmentation have been developed in recent years, greatly improving the accuracy and efficiency of choroidal segmentation. The purpose of this paper is to review the features of choroidal thickness in different eye diseases, explore the latest applications and advantages of deep learning models in measuring choroidal thickness, and focus on the challenges faced by deep learning models.
ABSTRACT
The innovation of Chinese materia medica (CMM) industry must abandon the traditional development model, clarify the connotation of green value of CMM industry supply chain, identify the improvement point of green value, innovate CMM green supply chain model, and realize the unity of economic value and environmental value of CMM industry. On the basis of analyzing the current situation of CMM green supply chain, we constructed the green value flow model of the CMM supply chain, meanwhile, analyzed the green value strategy by using the analytic hierarchy method to improve the green value strategy of the CMM supply chain, and then identified the strategy and mechanism to develop the green productivity of the CMM industry, and provided a reference for the green value creation and promotion of the CMM supply chain.