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.
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.
With the perspective of evidence-based medicine, this review aims to investigate the effectiveness and safety of off-label drug use of bevacizumab for eye disease, and explore the barriers to further study. And then, suggestions for the supported evidence and clinical use of off-label drug use will be provided based on this case.
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.
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.
ObjectiveTo investigate and analyze the ophthalmic resource distribution and service ability of Leshan City, and provide scientific basis for development of ophthalmology and prevention of blindness. MethodsWe statistically analyzed all departments of ophthalmology in 17 general hospitals of Leshan, including numbers of beds, numbers of health technicians, professional title structure, ophthalmic instruments, levels of operation and service ability in 2012. ResultsThere were 186 ophthalmic beds, 84 ophthalmologists, 6 technicians, 64 nurses, 16 professors, 28 doctors with medium-level title, and 40 residents in the 17 general hospitals of Leshan. There were 184 300 out-patients and 9 920 in-patients with 12 320 operations including 6 211 cataract operations in the year of 2012. ConclusionThe ophthalmic resources and service ability are not equally distributed in Leshan. Most resources are distributed in big hospitals of the urban district. Meanwhile, hospitals in remote areas do not have ophthalmologists or ophthalmologic instruments. We should develop our service ability and work efficiency by continuous learning in order to improve the three-level primary blindness prevention system.