View clinical trials related to Ophthalmology.
Filter by:Assessment of aging is central to health management. Compared to chronological age, biological age can better reflect the aging process and health status; however, an effective indicator of biological age in clinical practice is lacking. Human lens accumulates biological changes during aging and is amenable to a rapid and objective assessment. Therefore, the investigators will develop LensAge as an innovative indicator to reveal biological age based on deep learning using lens photographs.
This is an retrospective and prospective multicenter study to develop and validate an artificial intelligent (AI) aided diagnosis, therapeutic effect assessment model including chronic kidney disease (CKD) and dialysis patients starting from April 2009, which is based on ophthalmic examinations (e.g. retinal fundus photography, slit-lamp images, OCTA, etc.) and CKD diagnostic and therapeutic data (routine clinical evaluations and laboratory data), to provide a reliable basis and guideline for clinical diagnosis and treatment.
Virtual reality (VR) is a relatively new, emerging field within healthcare. Studies have analyzed public perceptions of virtual reality in healthcare using social media, but few have actually demonstrated and educated these modalities to communities. Because vision care can be costly and inaccessible, especially in communities with few physicians, this study aims to evaluate whether communities would be open to new technology. For example, it has been determined that 80% of vision loss is preventable with adequate screening technology, a key factor in ameliorating the economic and emotional burden of eye disease. Therefore, through demonstrations and educational presentations by medical students, gaps in understanding perceptions, willingness to adopt, and general demographics of those seeking better eye care will be understood.
According to the WHO's definition of visual impairment, as of 2018, there were approximately 1.3 billion people with visual impairment in the world, and only 10% of countries can provide assisting services for the rehabilitation of visual impairment. Although China is one of the countries that can provide rehabilitation services for patients with visual impairment, due to restrictions on the number of professionals in various regions, uneven diagnosis and treatment, and regional differences in economic conditions, not all visually impaired patients can get the rehabilitation of assisting device fitting. Traditional statistical methods were not enough to solve the problem of intelligent fitting of assisting devices. At present, there are almost no intelligent fitting models of assisting devices in the world. Therefore, in order to allow more low-vision patients to receive accurate and rapid rehabilitation services, we conducted a cross-sectional study on the assisting devices fitting for low-vision patients in Fujian Province, China in the past five years, and at the same time constructed a machine learning model to intelligently predict the adaptation result of the basic assisting devices for low vision patients.
According to the WHO's definition of visual impairment, as of 2018, there were approximately 1.3 billion people with visual impairment in the world, and only 10% of countries can provide assisting services for the rehabilitation of visual impairment. Although China is one of the countries that can provide rehabilitation services for patients with visual impairment, due to restrictions on the number of professionals in various regions, uneven diagnosis and treatment, and regional differences in economic conditions, not all visually impaired patients can get the rehabilitation of assisting device fitting. Traditional statistical methods were not enough to solve the problem of intelligent fitting of assisting devices. At present, there are almost no intelligent fitting models of assisting devices in the world. Therefore, in order to allow more low-vision patients to receive accurate and rapid rehabilitation services, we conducted a cross-sectional study on the assisting devices fitting for low-vision patients in Fujian Province, China in the past five years, and at the same time constructed a machine learning model to intelligently predict the adaptation result of the basic assisting devices for low vision patients.
The real-world electronic health records (EHR) were derived from the hospitalization of Zhongshan Ophthalmic Center (ZOC) of Sun Yat-sen University from 1998-2020 to investigate the ophthalmology diagnosis and treatment activities.
In this study, the investigators provide participants from ophthalmic clinic the AI visual inspection system and EDTRS in the purpose of seeking out a better way of visual inspection with high efficiency and accuracy, and report a prospective, randomized controlled study aiming at comparison of AI visual inspection system and EDTRS for visual inspection.