ORCID Profile
0000-0003-3452-5633
Current Organisations
The University of Auckland
,
oDocs Eye Care Ltd.
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Publisher: MDPI AG
Date: 14-05-2022
DOI: 10.3390/DIAGNOSTICS12051234
Abstract: Diabetes is one of the main causes of the rising cases of blindness in adults. This microvascular complication of diabetes is termed diabetic retinopathy (DR) and is associated with an expanding risk of cardiovascular events in diabetes patients. DR, in its various forms, is seen to be a powerful indicator of atherosclerosis. Further, the macrovascular complication of diabetes leads to coronary artery disease (CAD). Thus, the timely identification of cardiovascular disease (CVD) complications in DR patients is of utmost importance. Since CAD risk assessment is expensive for low-income countries, it is important to look for surrogate biomarkers for risk stratification of CVD in DR patients. Due to the common genetic makeup between the coronary and carotid arteries, low-cost, high-resolution imaging such as carotid B-mode ultrasound (US) can be used for arterial tissue characterization and risk stratification in DR patients. The advent of artificial intelligence (AI) techniques has facilitated the handling of large cohorts in a big data framework to identify atherosclerotic plaque features in arterial ultrasound. This enables timely CVD risk assessment and risk stratification of patients with DR. Thus, this review focuses on understanding the pathophysiology of DR, retinal and CAD imaging, the role of surrogate markers for CVD, and finally, the CVD risk stratification of DR patients. The review shows a step-by-step cyclic activity of how diabetes and atherosclerotic disease cause DR, leading to the worsening of CVD. We propose a solution to how AI can help in the identification of CVD risk. Lastly, we analyze the role of DR/CVD in the COVID-19 framework.
Publisher: MDPI AG
Date: 30-08-2023
DOI: 10.3390/DIAGNOSTICS13172810
Abstract: Purpose/Background: We evaluate how a deep learning model can be applied to extract refractive error metrics from pupillary red reflex images taken by a low-cost handheld fundus camera. This could potentially provide a rapid and economical vision-screening method, allowing for early intervention to prevent myopic progression and reduce the socioeconomic burden associated with vision impairment in the later stages of life. Methods: Infrared and color images of pupillary crescents were extracted from eccentric photorefraction images of participants from Choithram Hospital in India and Dargaville Medical Center in New Zealand. The pre-processed images were then used to train different convolutional neural networks to predict refractive error in terms of spherical power and cylindrical power metrics. Results: The best-performing trained model achieved an overall accuracy of 75% for predicting spherical power using infrared images and a multiclass classifier. Conclusions: Even though the model’s performance is not superior, the proposed method showed good usability of using red reflex images in estimating refractive error. Such an approach has never been experimented with before and can help guide researchers, especially when the future of eye care is moving towards highly portable and smartphone-based devices.
Publisher: Informa UK Limited
Date: 10-2021
DOI: 10.2147/OPTH.S294428
Publisher: Bentham Science Publishers Ltd.
Date: 10-11-2021
DOI: 10.2174/1874364102115010206
Abstract: The advancement of smartphone camera technology allowing a smaller, high-resolution forward-facing camera on a smartphone allows a user to see the image they are about to capture of themselves at arm’s length, therefore taking a ‘selfie’ image of themselves. The idea of a ‘selfie’ in a clinical setting is novel, but the exploration of this as a concept has been made necessary as COVID-19 infection and transmission risk is based on the proximity, that is, a susceptible person coming near to the person, who is infected. This report discusses an innovative smartphone-based device, oDocs nun IR, a retinal imaging device, as a tool for taking selfie retinal images/videos by patients, that could be later analyzed by the specialists/optometrists over the teleophthalmology portal.
Publisher: Elsevier
Date: 2020
Publisher: Informa UK Limited
Date: 03-04-2022
DOI: 10.1080/17434440.2022.2070004
Abstract: The present study proposes a new hand-held non-mydriatic fundus camera for retinal imaging. The goal is to design a fundus camera which is equally effective in both clinical and telemedicine scenarios. A new retinal illumination approach is proposed to address the main dilemma of the optical design, i.e. balancing efficacy with structural simplicity. This is achieved by symmetrical and co-axial placement of multiple illumination sources along the optical pathway. Each illumination source includes a white and a Near Infra-Red (NIR) LED, which are placed adjacent to each other. Hence, the camera can produce a view-finder with NIR illumination without the need for additional beam-splitters and filters. The proposed design blends the structural simplicity of the 'off-axis illumination with the wide field of view and uniform illumination of the 'ring' illumination. Moreover, the camera is designed to work with Android-based smartphones, which can easily be mounted and interfaced. The efficacy of the proposed camera is determined by ocular safety analysis and comparative evaluation with a table-top fundus camera. The results convincingly demonstrate the ability of the proposed camera as a primary driver of a wide-scale screening program in both clinical and remote resource constraint environments.
No related grants have been discovered for Renoh Johnson Chalakkal.