Objective To automatically segment diabetic retinal exudation features from deep learning color fundus images. Methods An applied study. The method of this study is based on the U-shaped network model of the Indian Diabetic Retinopathy Image Dataset (IDRID) dataset, introduces deep residual convolution into the encoding and decoding stages, which can effectively extract seepage depth features, solve overfitting and feature interference problems, and improve the model's feature expression ability and lightweight performance. In addition, by introducing an improved context extraction module, the model can capture a wider range of feature information, enhance the perception ability of retinal lesions, and perform excellently in capturing small details and blurred edges. Finally, the introduction of convolutional triple attention mechanism allows the model to automatically learn feature weights, focus on important features, and extract useful information from multiple scales. Accuracy, recall, Dice coefficient, accuracy and sensitivity were used to evaluate the ability of the model to detect and segment the automatic retinal exudation features of diabetic patients in color fundus images. Results After applying this method, the accuracy, recall, dice coefficient, accuracy and sensitivity of the improved model on the IDRID dataset reached 81.56%, 99.54%, 69.32%, 65.36% and 78.33%, respectively. Compared with the original model, the accuracy and Dice index of the improved model are increased by 2.35% , 3.35% respectively. Conclusion The segmentation method based on U-shaped network can automatically detect and segment the retinal exudation features of fundus images of diabetic patients, which is of great significance for assisting doctors to diagnose diseases more accurately.
ObjectiveTo deeply explore the clinical features and gene mutations of Waardenburg syndrome (WS) by tested of the eyes and genes of three patients. MethodsA Case series study. From 2019 to 2021, 3 children with WS who were diagnosed at Department of Ophthalmology, West China Hospital of Sichuan University were included in the study. Among them, there were 2 males and 1 female; the ages were 3, 4, and 12 months, respectively. All children underwent external eye, anterior segment, fundus and fluorescein fundus angiography, the clinical features of the eyes were observed. The peripheral venous blood of 3 children was collected, and the whole genome DNA was extracted for whole exome sequencing to analyze the gene mutation sites. ResultsAll children had different degrees of iris heterochromia and fundus pigment abnormalities, and were accompanied by sensorineural hearing impairment. Case 1 had dystopia canthorum; case 2 had macular fovea hypoplasia. The sequencing results of case 1 showed that there were large fragments of heterozygous deletion in exons 2-8 of the Paired box 3 (PAX3) gene, who was diagnosed as WS Ⅰ type. The sequencing results of of case 2 showed heterozygous mutation in exon 9 of Microphthalmia-associated transcription factor (MITF) gene (c.1066 C＞T), combined with heterozygous mutation in exon 1 of HPS6 gene (c.1417 G＞T), who was diagnosed as WS Ⅱ type. The sequencing result of case 3 showed that the exon 3 of SOX10 gene had loss of heterozygosity (c.497_500 delAAGA), who was diagnosed as WS Ⅳ type. Both PAX3 and SOX10 gene mutations were newly discovered mutations. ConclusionsThe ocular clinical features of Waardenburg syndrome include hypopigmentation of the iris and choroid, and dystopia canthorum, etc. Early screening of the eye and hearing will help to better diagnose the disease. The large fragments of heterozygous deletion in exons 2-8 of the PAX3 gene, the heterozygous mutation in exon 9 of MITF gene (c.1066 C＞T), and the loss of heterozygosity in exon 3 of SOX10 gene are pathogenic genetic variations of 3 children.
Objective To observe the characteristics of spectral-domain optical coherence tomography (SD-OCT) for leakage point in acute central serous chorioretinopathy (CSC). Methods A total of 21 acute CSC patients (21 eyes) were enrolled in this retrospective study, including 17 men (17 eyes) and 4 women (5 eyes). The mean age was (47.3±8.8) years (range 35 - 66 years). The mean duration was (1.6±0.8) months (range 0.5 - 3.0 months). All patients were underwent mydriatic fundus photography, SD-OCT examination and fluorescein fundus angiography (FFA). SD-OCT and FFA images were carefully compared to observe the SD-OCT examination characteristics of fluorescence leakage point. Results 21/21 eyes had one fluorescein leakage point. In addition to serous retinal detachment, leakage point in the SD-OCT examination showed retinal pigment epithelium (RPE) protrusion in 10 eyes (47.6％), RPE detachment in 7 eyes (33.3％), highly re?ective areas suggesting ?brinous exudate in the subretinal space in 3 eyes (14.3％), and RPE defect in 1 eye (4.8％). Conclusion The SD-OCT characteristics of acute CSC include RPE protrusion, RPE detachment, highly re?ective areas suggesting ?brinous exudate in the subretinal space and RPE defect.
ObjectiveTo compare the consistency of artificial analysis and artificial intelligence analysis in the identification of fundus lesions in diabetic patients.MethodsA retrospective study. From May 2018 to May 2019, 1053 consecutive diabetic patients (2106 eyes) of the endocrinology department of the First Affiliated Hospital of Zhengzhou University were included in the study. Among them, 888 patients were males and 165 were females. They were 20-70 years old, with an average age of 53 years old. All patients were performed fundus imaging on diabetic Inspection by useing Japanese Kowa non-mydriatic fundus cameras. The artificial intelligence analysis of Shanggong's ophthalmology cloud network screening platform automatically detected diabetic retinopathy (DR) such as exudation, bleeding, and microaneurysms, and automatically classifies the image detection results according to the DR international staging standard. Manual analysis was performed by two attending physicians and reviewed by the chief physician to ensure the accuracy of manual analysis. When differences appeared between the analysis results of the two analysis methods, the manual analysis results shall be used as the standard. Consistency rate were calculated and compared. Consistency rate = (number of eyes with the same diagnosis result/total number of effective eyes collected) × 100%. Kappa consistency test was performed on the results of manual analysis and artificial intelligence analysis, 0.0≤κ＜0.2 was a very poor degree of consistency, 0.2≤κ＜0.4 meant poor consistency, 0.4≤κ＜0.6 meant medium consistency, and 0.6≤κ＜1.0 meant good consistency.ResultsAmong the 2106 eyes, 64 eyes were excluded that cannot be identified by artificial intelligence due to serious illness, 2042 eyes were finally included in the analysis. The results of artificial analysis and artificial intelligence analysis were completely consistent with 1835 eyes, accounting for 89.86%. There were differences in analysis of 207 eyes, accounting for 10.14%. The main differences between the two are as follows: (1) Artificial intelligence analysis points Bleeding, oozing, and manual analysis of 96 eyes (96/2042, 4.70%); (2) Artificial intelligence analysis of drusen, and manual analysis of 71 eyes (71/2042, 3.48%); (3) Artificial intelligence analyzes normal or vitreous degeneration, while manual analysis of punctate exudation or hemorrhage or microaneurysms in 40 eyes (40/2042, 1.95%). The diagnostic rates for non-DR were 23.2% and 20.2%, respectively. The diagnostic rates for non-DR were 76.8% and 79.8%, respectively. The accuracy of artificial intelligence interpretation is 87.8%. The results of the Kappa consistency test showed that the diagnostic results of manual analysis and artificial intelligence analysis were moderately consistent (κ=0.576, P＜0.01).ConclusionsManual analysis and artificial intelligence analysis showed moderate consistency in the diagnosis of fundus lesions in diabetic patients. The accuracy of artificial intelligence interpretation is 87.8%.