An Analysis of the Application of a Medical Imaging Artificial Intelligence System for a Spontaneous Fluorescence Imaging Recognition System Using Pre-Clinical Diabetic Retinopathy as an Example

Abstract

Objective: To analyze the application of a medical imaging artificial intelligence system for spontaneous fluorescence imaging recognition using pre-clinical diabetic retinopathy as an example so as to provide a technical exploration for early diagnosis and treatment. Methods: The fundus autofluorescence images of 102 patients (200 eyes) in a control group and 105 patients (200 eyes) in a study group were collected from August 2017 to May 2018. All patients were examined by a slit lamp microscope, preview lens, naked eye or corrected visual acuity and fundus autofluorescence images. The images from the control and study groups were used for analysis. The medical image extraction and recognition system is based on a two-dimensional lattice complexity measurement and was used to analyze the discernible differences between the fundus autofluorescence image of pre-clinical diabetic retinopathy and the normal retinal autofluorescence image. Results: Twenty-five features with comparative significance were extracted. The single and multiple features were tested by 10-fold and 5-fold cross tests for 25 features, and the accuracy rate was 82.47%. Conclusions: Complex analysis of a medical imaging artificial intelligence system can be used to identify the spontaneous fluorescence changes on the fundus of pre-clinical diabetic retinopathy with high accuracy. Key words: preclinical diabetic retinopathy; fundus autofluorescence image; complexity measure; feature extraction; artificial intelligence