Objective
To summarize the visual outcome of patients with severe ocular trauma treated with vitreous surgery.
Methods
Clinical data of 188(191 eyes) with severe ocular trauma treated with vitreous surgery in a period from November 1996 to April 1998 were analysed retrospectively.
Results
The study included penetrating injury in 56 eyes, foreign bodies in the posterior segment in 70 eyes, blunt injury in 41 eyes , and globe rupture in 24 eyes. Main complications included endophthalmitis in 35 eyes, choroidal bleeding in 20 eyes, retinal detachment in 60 eyes, and vitreous hemorrhage in 97 eyes. Post-opera-tively, out of 188 eyes, except for 3 of patients too young to examine, visual acuity improved in 133(70.7%), including 85(45.2%) with visal acuity 0.02-1.0, 46(24.5%) remained unchanged; and 9(4.8%) had worse vision. Among 34 with no-light-perception, 12 had light-perception or over.
Conclusion
A majority of severe trauma eyes can be salvaged with considerable visual recovery after adequate and timely vitreous surgery.
(Chin J Ocul Fundus Dis,1999,15:4-6)
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 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%.
Objective To explore the therapeutic value and effects of relaxing retinotomy for perforating traction retinal detachment(PTRD). Method A retrospective survey was done in 21 patinets (21 eyes) with PTRD who underwent vitrectomy combined with relaxing retinotomy in our hospital from 1998 to 2001.Results The retinae were completely reattached in all 21 cases. The visual acuity of 12 patients (57%) was counting finger, and the best visual acuity was 0.05. Among the 18 patients who were followed up for 6 to 25 months, 14(77.8%) remained retinal reattachment. Conclusions Relaxing retinotomy is effective for anatomic reattachment of PTRD, especially to the patients with retinal incarceration and severe proliferative vitreoretinopathy. (Chin J Ocul Fundus Dis，2003，19：5-7)
The fundus lesions caused by high myopia (HM) often lead to irreversible visual impairment or even blindness. However, the pathogenesis of HM and its fundus lesions is still unclear, the intraocular fluid detection technology of micro samples has brought new prospects for the early diagnosis, monitoring and intervention of the fundus lesions. The molecules associated with HM are various and functionally diverse, intermolecular interactions are staggered and the specific mechanism is complex. With the development of intraocular fluid detection technology, while gradually revealing the role of each molecule in the pathogenesis of HM, it is expected to successfully assist clinical work in the future, providing outpost markers for the progress of myopia and targets for early intervention, or providing a new therapy choice for HM fundus lesions at the molecular level targeting pathogenesis, which is expected to provide more accurate and effective treatment for HM patients in the future.
