ObjectiveTo study the research hot spots of ophthalmology-related coronavirus disease 2019 (COVID-19). MethodsPubMed database as the data source, the literatures of ophthalmology-related COVID-19 published on January 1, 2020 to February 22, 2022 were collected, limited to Medline included, the language type was limited to English and Chinese, and 1 592 literatures were included. By reading the titles and abstracts, the literatures of meeting notice, editor's note, etc. and the literature that was not quite relevant with ophthalmology-related COVID-19 were removed, and finally 1 547 literatures were included. Bibliographic Items Co-occurrence Matrix Builder (BICOMB 2.02 software) was used to collect the frequency of major Mesh terms/subheadings and the frequency of major Mesh terms after removing the subheadings, and the number of included articles published in the top 10 journals by the number of ophthalmology-related COVID-19 articles was recorded. VosViewer 1.6.18 software was used for cluster analysis of collaborator network and major Mesh terms, and the publication status and country or region distribution of active authors of ophthalmology-related COVID-19 were recorded. ResultsOf the 1 547 literatures, the active authors were mainly from India, Italy, Singapore, Spain, and Hong Kong, China, and so on; the top 10 journals published 617 articles in total (39.88%, 617/1 547). The high frequency major Mesh terms/subheadings included COVID-19, viral pneumonia, coronavirus infection, eye diseases/epidemiology, complications, prevention & control, diagnosis, virology, and Severe acute respiratory syndrome coronavirus 2, betacoronavirus/isolation & purification, ophthalmology/education, organization & administration, telemedicine, delivery of health care/organization & administration, and mucormycosis/diagnosis, etc. After taking out the subheadings, the high frequency of major Mesh terms also included conjunctivitis, orbital disease, retinal diseases, neuromyelitis optica, retinal vein occlusion, myopia and other eye diseases, eye diseases-related systemic diseases, such as multiple sclerosis and Miller Fisher syndrome, therapy and prevention-related drugs, such as hydroxyl chloroquine, angiogenesis inhibitors, and vaccination. ConclusionsOphthalmology-related COVID-19 researches have received extensive attention worldwide, COVID-19 is associated with multiple ocular diseases of anterior and posterior segments. COVID-19-related mucormycosis, hydroxychloroquine and possible retinal toxicity, and possible ocular adverse effects associated with vaccination are also noteworthy.
ObjectiveTo compare the quantitative measurements of the retinal capillary nonperfusion areas in a cohort of proliferative diabetic retinopathy (PDR) patients with fluorescein fundus angiography (FFA) and swept source optical coherence tomography angiography (SS-OCTA), and to determine the intrapersonal variability between examiners.MethodsA cross-sectional study. Eighteen eyes of eleven PDR patients diagnosed in Department of ophthalmology of Henan Provincial People's Hospital from September 2019 to January 2020 were included in this study. FFA was performed using Spectralis HRA+OCT (Germany Heidelberg Company) from and SS-OCTA was performed using VG200D (China Vision Micro Image Corporation). SS-OCTA was used to collect images of retinal layer, superficial capillary plexus (SCP) and deep capillary plexus (DCP). The same observation area was 80°×60° for SS-OCTA and 55° for FFA with both setting centered on the fovea. The forty-nine retinal capillary nonperfusion areas were observed. The area measurement was completed independently by three examiners. Paired sample t test or paired sample Wilcoxon test were used to compare the measured values of retinal capillary nonperfusion areas between the two examination methods and among the three examiners.ResultsThere was no significant difference in the retinal layer, SCP and DCP nonperfusion area measured by FFA and SS-OCTA among the three examiners (P＞0.05), and the consistency is good (consistency correlation coefficient＞0.9, P＜0.05). The nonperfusion area measured by FFA was 0.786 mm2. The median nonperfusion area of retinal layer and SCP measured by SS-OCTA were 0.787 mm2 and 0.791 mm2, respectively, and the average nonperfusion area of DCP was 0.878±0.366 mm2. The nonperfusion area of retinal layer and SCP measured by FFA and SS-OCTA showed no statistically significant difference (P=0.054, 0.198). The nonperfusion area of DCP measured by SS-OCTA was significantly larger than that of FFA, and the difference was statistically significant (P＜0.001). The results of repeatability analysis showed that 93.88% (46/49) of the DCP nonperfusion area data measured by SS-OCTA were greater than those measured by FFA.ConclusionThe retinal nonperfusion area of DCP in PDR patients measured by SS-OCTA is larger than that of FFA.
ObjectiveTo study the efficiency and difference of the artificial intelligence (AI) system based on fundus-reading in community and hospital scenarios in screening/diagnosing diabetic retinopathy (DR) among aged population, and further evaluate its application value. MethodsA combination of retrospective and prospective study. The clinical data of 1 608 elderly patients with diabetes were continuously treated in Henan Eye Hospital & Henan Eye Institute from July 2018 to March 2021, were collected. Among them, there were 659 males and 949 females; median age was 64 years old. From December 2018 to April 2019, 496 elderly diabetes patients were prospectively recruited in the community. Among them, there were 202 males and 294 female; median age was 62 years old. An ophthalmologist or a trained endocrinologist performed a non-mydriatic fundus color photographic examination in both eyes, and a 45° frontal radiograph was taken with the central fovea as the central posterior pole. The AI system was developed based on the deep learning YOLO source code, AI system based on the deep learning algorithm was applied in final diagnosis reporting by the "AI+manual-check" method. The diagnosis of DR were classified into 0-4 stage. The 2-4 stage patients were classified into referral DR group. ResultsA total of 1 989 cases (94.5%, 1 989/2 104) were read by AI, of which 437 (88.1%, 437/496) and 1 552 (96.5%, 1 552/1 608) from the community and hospital, respectively. The reading rate of AI films from community sources was lower than that from hospital sources, and the difference was statistically significant (χ2=51.612, P<0.001). The main reasons for poor image quality in the community were small pupil (47.1%, 24/51), cataract (19.6%, 10/51), and cataract combined with small pupil (21.6%, 11/51). The total negative rate of DR was 62.4% (1 241/1 989); among them, the community and hospital sources were 84.2% and 56.3%, respectively, and the AI diagnosis negative rate of community source was higher than that of hospital, and the difference was statistically significant (χ2=113.108, P<0.001). AI diagnosis required referral to DR 20.2% (401/1 989). Among them, community and hospital sources were 6.4% and 24.0%, respectively. The rate of referral for DR for AI diagnosis from community sources was lower than that of hospitals, and the difference was statistically significant (χ2=65.655, P<0.001). There was a statistically significant difference in the composition ratio of patients with different stages of DR diagnosed by AI from different sources (χ2=13.435, P=0.001). Among them, community-derived patients were mainly DR without referral (52.2%, 36/69); hospital-derived patients were mainly DR requiring referral (54.9%, 373/679), and the detection rate of treated DR was higher (14.3%). The first rank of the order of the fundus lesions number automatically identified by AI was drusen (68.4%) and intraretinal hemorrhage (48.5%) in the communities and hospitals respectively. Conclusions It is more suitable for early and negative DR screening for its high non-referral DR detection rate in the community. Whilst referral DR were mainly found in hospital scenario.