Abstract
Purpose :
COVID-19 has created a strong need for telemedicine and remote monitoring. Remote monitoring is especially important for diseases such as age-related macular degeneration (AMD), because prompt detection of subtle changes indicating neovascularization is critical to prevent blindness. This describes a novel, digital, handheld standalone device that delivers at-home AMD monitoring for patients and a hardware validation of that new device.
Methods :
A prototype device (KalEYEdoscope) was developed consisting of an injection-molded 3D printed shell with a graduated focusing mechanism with a 10x magnifying 22mm biconcave lens, two tactile buttons, a battery, a processing unit, and a small OLED screen (Figure 1). The electronics include a battery powered small single-board computer (Raspberry Pi 3) connected to a 1.5-inch RGB OLED screen and two pushbuttons. The device is inherently monocular. The software uses the concept of shape discrimination hyperacuity to identify the patient’s minimum distortion-detection threshold (MDDT). Upon use, the screen displays a series of circle-like images and, after each image, asks the user whether the image previously displayed appeared to be a perfect circle. The software then converges to the user’s MDDT based on user input. Data is collected longitudinally and analyzed to determine a change in patient condition, which is then communicated as a message on the screen.
Results :
Hardware validation was performed. The device had a weight of 0.211 kg and diameter of 61 mm. There was an appropriate push button resistance of 1.52 N, meeting the required engineering specifications of 1.4 to 5.6 N. The screen transition time was 0.7 seconds, and it took 1.834 seconds for the circle to appear on the screen. The total boot time of the software was 20.21 seconds, which was below the target of 30 seconds.
Conclusions :
A digital, hand-held standalone device can be created that is compact, lightweight, and meets targeted engineering and design specifications for hardware and software. This device could have potential in performing home monitoring of AMD progression using shape discrimination hyperacuity to identify the patient’s minimum distortion-detection threshold.
This is a 2021 ARVO Annual Meeting abstract.