Ultra-wide-field fluorescein angiography (UWFA) can obtain very wide retinal images (up to 200°), and is a very helpful tool to detect peripheral retinal lesions which cannot be found by other imaging methods. Analyzing the characteristics of the UWFA images may improve our understanding, treatment outcomes and management strategies of ocular fundus diseases. However this technology is still in its premature stage, there is still a lot of work to be done to improve its clinical application and study the characteristics and clinical meanings of these peripheral retinal lesions.
Ophthalmic imaging examination is the main basis for early screening, evaluation and diagnosis of eye diseases. In recent years, with the improvement of computer data analysis ability, the deepening of new algorithm research and the popularization of big data platform, artificial intelligence (AI) technology has developed rapidly and become a hot topic in the field of medical assistant diagnosis. The advantage of AI is accurate and efficient, which has great application value in processing image-related data. The application of AI not only helps to promote the development of AI research in ophthalmology, but also helps to establish a new medical service model for ophthalmic diagnosis and promote the process of prevention and treatment of blindness. Future research of ophthalmic AI should use multi-modal imaging data comprehensively to diagnose complex eye diseases, integrate standardized and high-quality data resources, and improve the performance of algorithms.
Objective To investigate the characteristics of ultrasonogram of eyes with silicon oil tamponade. Methods Forty-seven patients (47 eyes) who had undergone the operation of silicon-oil removing were examined by A-(to determine the length of ocular axis) and B-scan before and after the operation respectively. The length of ocular axis and cubage of vitreous chamber were detected and the characteristics of the ultrasonograms were observed according to the default parameters of ultrasonograph. Results The results of A-scan showed that the preoperative axial length was 1.465 times of the postoperative one in the eyes without lens, and 1.284 times in eyes with lens; after modified the parameter according to the acoustic velocity, the preoperative axial length was (0.78±0.34) mm longer than the postoperative one in the eyes without lens, and (0.56±0.32) mm in eyes with lens. The results of A-scan showed that the cubage of vitreous chamber enlarged obviously in eyes with silicon oil tamponade, and the acoustic features included complete filling and partial filling according to the amount of silicon oil. Several arc echoes at the posterior segment of eye were detected in the silicon-oil-filling eyes with retinal detachment. Conclusion In the silicon-oil-filling eyes with lengthened ocular axis, the characteristics of B-scan images are affected by acoustic velocity through silicon oil, the amount of silicon oil capacity and the emulsification of silicon oil. (Chin J Ocul Fundus Dis，2004，20：349-351)
ObjectiveTo analyze the consistency of diagnostic results using simple and comprehensive reading methods on stereoscopic color fundus photographs of diabetic retinopathy (DR) with diabetic macular edema (DME). Methods450 sets of 7-field stereoscopic color fundus photographs of DR DME were compared to standard fundus photographs of early treatment and DR study group. The pictures were read by two groups of reader with similar experience. Two strategies were used to make the judgments, including simple reading which based on the color fundus photographs only, and comprehensive reading which based on color fundus photographs, fundus fluorescein angiography (FFA) and optical coherence tomography (OCT). 15 parameters were scored, including micro-aneurysms (MA), intra-retinal hemorrhage (IRH), hard exudates (HE), cotton wood spot (CW), intra-retinal microvascular abnormalities (IRMA), neovascularization on optic disc (NVD), neovascularization elsewhere (NVE), optic fiber proliferation (FPD), fiber proliferation elsewhere (FPE), pre-retinal hemorrhage (PRH), vitreous hemorrhage (VH), retinal elevation (RE), retinal detachment of central macular (RDC), venous beading (VB), Venous leak (VL). The reliability was evaluated using weighted κ(κw) statistic values. According to Fleiss statistical theory, κw≥0.75, consistency is excellent; 0.60≤κw < 0.75, consistency is good; 0.40≤κw < 0.60, consistency is general; κw < 0.40, consistency is poor. ResultsThe κw values of these 15 parameters were 0.22-1.00, 0.28-1.00 for the simple reading and comprehensive reading respectively. For simple reading, the consistency was poor for 8 parameters (MA, NVD, NVE, FPE, PRH, IRMA, VB, VL), general for 3 parameters (CW, FPD, VH), good for 2 parameters (IRH, HE) and excellent for 2 parameters (RE, RDC). For comprehensive reading, the consistency was poor for 2 parameters (NVE, VB), general for 6 parameters (MA, IRH, CW, FPE, IRMA, VL), good for 2 parameters (NVD, HE), excellent for 5 parameters (FPE, PRH, VH, RE, RDC). ConclusionThe comprehensive reading has higher consistency to judge the abnormality parameters of the fundus photographs of DR with DME.
At present, artificial intelligence (AI) has been widely used in the diagnosis and treatment of various ophthalmological diseases, but there are still many problems. Due to the lack of standardized test sets, gold standards, and recognized evaluation systems for the accuracy of AI products, it is difficult to compare the results of multiple studies. When it comes to the field of image generation, we hardly have an efficient approach to evaluating research results. In clinical practice, ophthalmological AI research is often out of touch with actual clinical needs. The requirements for the quality and quantity of clinical data put more burden on AI research, limiting the transformation of AI studies. The prediction of systemic diseases based on fundus images is making progressive advancement. However, the lack of interpretability of the research lower the acceptance. Ophthalmology AI research also suffer from ethical controversy due to unconstructed regulations and regulatory mechanisms, concerns on patients’ privacy and data security, and the risk of aggravating the unfairness of medical resources.
ObjectiveTo observe the demographic data, disease composition and convenience of remote consultation in ophthalmology. MethodsA retrospective study. From 2015 to 2021, the demographic data, changing trends, disease classification of teleconsultation patients, and hospitals participating in teleconsultation, and the waiting time of patients for teleconsultation was analyzed retrospectively; remote consultation physician level composition and other data was analyzed. ResultsDuring the 7-year period, 1 216 patients with remote consultation were obtained through the platform of the telemedicine center. Among them, there were 680 males and 536 females; the average age was 50.8 years. In 2016 and 2017, the number of patients participating in telemedicine consultations reached a peak of 260 and 221 cases, respectively. Among the ophthalmic diseases, there were 490 cases (40.30%, 490/1 216) of retinal and optic nerve-related diseases, 212 cases (17.43%, 212/1 216) of ocular trauma. 678 cases (56.27%, 678/1 205) of remote consultation waiting time were less than 24 hours, 991 cases (82.24%, 991/1 205) were less than 48 hours. Among the physicians who participated in the remote consultation, there were 733 chief physicians (60.3%, 733/1 216) and 466 deputy chief physicians (38.3%, 466/1 216). ConclusionsDuring the seven-year period from 2015 to 2021, there are relatively few patients with ophthalmology teleconsultation; retinal and optic nerve-related diseases accounted for a high proportion. Remote consultation has high convenience.
Ultra-wide field fundus autofluorescence (FAF) imaging is a new noninvasive technique with an imaging range of about 200 °. It can detect peripheral retinal lesions that cannot be found in previous FAFs and more objectively reflect intracellular content and distribution of lipofuscin in the retinal pigment epithelium (RPE) and RPE cell metabolic status. The ultra-wide field FAF can find the abnormal autofluorescence (AF) in the peripheral retina of the eyes of age-related macular degeneration (AMD), and different AF manifestations may have an impact on the diagnosis and treatment of the different AMD subtypes. It is helpful to evaluate subretinal fluid in the eyes of central serous choroidal retinopathy and can accurately detect the changes in the outer retina of the eyes without subretinal fluid. It can help to determine the type of uveitis and fully display the evolution of the disease. It can also assess the peripheral photoreceptor cell layer and RPE in patients with retinal dystrophy and retinitis pigmentosa, and comprehensively evaluate their retinal function and monitor the progress of disease. It can also assist in the evaluation of the short-term efficacy and RPE cell function after the scleral buckling surgery for patients with rhegmatogenous retinal detachment. In the future, ultra-wide field FAF may change the knowledge and intervention strategy of ocular fundus diseases and promote the clinical and scientific research in this field.
With the rapid development of artificial intelligence (AI), especially deep learning, AI research in the field of ophthalmology has presented a trend of diversification in disease types, generalization in scenarios and deepening in researches. The AI algorithm has showed a good performance in the studies of diabetic retinopathy, age-related macular degeneration, glaucoma and other ocular diseases, yielding up the great potential of ophthalmic AI. However, most studies are still in their infancy, and the application of ophthalmic AI still faces many challenges such as lack of interpretability for results, deficiency of data standardization, and insufficiency of clinical applicability. At the same time, it should also be noted that the development of multi-modal imaging, the innovation of digital technologies (such as 5G and the Internet of Things) and telemedicine, and the new discovery that retina status can reflect systemic diseases have brought new opportunities for the development of ophthalmic AI. Learn the current status of AI research in the field of ophthalmology, grasp the new challenges and opportunities in its development process, successfully realizing the transformation of ophthalmic AI from research to practical application.
Optical coherence tomography (OCT) has developed from time-doma in into Fourier-domain OCT (FD-OCT) which indicates clearer details and higher resolution of images. FD-OCT can indicate the structure and pathological changes of each retinal layer, and reveal the retinal external limiting membranes and changes of inner- and outer-segment of visual cells by 3D solid reconstruction. FD-OCT not only provide detailed information of the images for the clinical diagnosis, but also help us investigting the characteristics and pthological mechanisms of ocular fundus diseases, which lead us to a new era of technology of observation on ocualr fundus diseases. In the application, we should pay attention to the significance of different colors of OCT images, and focus on the cohenrence of the position in the image acquistion during the follow-up period. Dynamic observation on the lesions by FD-OCT and aggregated anaylsis of resutls of several imageological examination would be the development direction of imageological examination of ocular fundus diseases.
The hallmark of the recent latest advances in diagnostic fundus imaging technology is combination of complex hierarchical levels and depths, as well as wide-angle imaging, ultra-wide imaging. The clinical application of wide-angle and ultra-wide imaging, not only can reevaluate the role of the peripheral retina, the classification types and treatment modalities of central retinal vein occlusion, and enhance the reliability of diabetic retinopathy screening, improve the classification and therapeutic decision of diabetic retinopathy, and but also can help guide and improve laser photocoagulation. However we must clearly recognize that the dominant role of ophthalmologists in the diagnosis of ocular fundus diseases cannot be replaced by any advanced fundus imaging technology including wide-angle imaging. We emphasize to use the three factors of cognitive performance (technology, knowledge and thinking) to improve the diagnosis of ocular fundus diseases in China.