An innovative method for screening and evaluating the degree of diabetic retinopathy and drug treatment based on artificial intelligence algorithms.

Current methods of evaluating the degree of diabetic retinopathy are highly subjective and have no quantitative standard. To objectively evaluate the slight changes in tissue structure during the early stage of retinal diseases, a subjective interpretation and qualitative analysis of the pathological sections of retinal HE in diabetic animals is required for screening and evaluating the degree of diabetic retinopathy and drug efficacy. To develop an innovative method for screening and evaluating the degree of diabetic retinopathy and drug treatment based on artificial intelligence algorithms. Based on the change law of the early nerve fiber layer and the ganglion cells, we get disparate characteristics of the microscopic image of diabetes animal retina HE slices. Using image recognition and deep learning methods on these HE slices, we can identify the changes in the ganglion cells and nerve fiber layer for diagnosing early retinopathy and evaluated the therapeutic effect of the potential drugs. We conduct quantitative calculation per unit length of the nerve fiber layer and total area of the nerve fiber layer to identify biology significance of edema. Additionally, we also perform quantitative calculation with the number of unit area ganglion cells to identify the section in biology cell hyperplasia. Finally, we get the significance of quantitative calculation on the unit cell area to identify ganglion cell shriveling in biology. In addition to the evaluation of the disease degree and changes, we also obtained retinal HE sections after different drug interventions and evaluated the therapeutic effect of the drugs. This study presents a novel quantitative method for screening and evaluating of diabetic retinopathy and drug efficacy.