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Low Vision Aids clinical trials

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NCT ID: NCT06107881 Recruiting - Low Vision Clinical Trials

Beacon Sensors and Telerehabilitation to Assess and Improve Use of Devices (BeST-AID) for Low Vision

BeST-AID
Start date: October 2023
Phase: N/A
Study type: Interventional

One goal of this research is to conduct a non-inferiority trial of telerehabilitation versus in-office care to provide follow-up training to individuals with low vision to enhance their quality of life by using magnification devices and/or visual assistive mobile apps for important daily activities, such as reading and/or other valued tasks. This is a high priority given the increasing prevalence of low vision, paucity of low vision rehabilitation providers, and barriers related to access to care, such as transportation and geography, which can be essentially eliminated with telerehabilitation. Another goal of this project is to determine whether significant changes in environmental data collected by Bluetooth low energy beacon sensors can be used as a solution to monitor and indicate when low vision patients' have abandoned the use of their magnification devices, which has the potential to substantially enhance patient management by providing timely low vision rehabilitation services.

NCT ID: NCT04919837 Recruiting - Clinical trials for Artificial Intelligence

The Efficacy of an Artificial Intelligence Platform to Adapt Visual Aids for Patients With Low Vision: a Randomised Controlled Trial

AI
Start date: July 27, 2020
Phase: N/A
Study type: Interventional

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.

NCT ID: NCT04892316 Recruiting - Clinical trials for Artificial Intelligence

Using Machine Learning to Adapt Visual Aids for Patients With Low Vision

Start date: July 27, 2020
Phase:
Study type: Observational

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.