Publications by authors named "Renoh Chalakkal"
Article Synopsis
- - The text highlights the increasing availability of ophthalmic imaging datasets and open-source machine learning tools, enabling researchers to conduct deep-learning tasks independently.
- - It emphasizes the importance of selecting the right AI model based on specific ophthalmic problems, considering factors like accuracy, deployment capabilities, and adaptability to small datasets.
- - The article reviews current AI methods, evaluates their performance with various imaging modalities, and aims to enhance understanding of AI applications in ophthalmology.
: 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. : 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.
View Article and Find Full Text PDFCardiovascular Risk Stratification in Diabetic Retinopathy via Atherosclerotic Pathway in COVID-19/Non-COVID-19 Frameworks Using Artificial Intelligence Paradigm: A Narrative Review.
Diagnostics (Basel)
May 2022
Article Synopsis
- * Timely detection of CVD complications in DR patients is essential, and since traditional CAD risk assessments can be costly, low-cost imaging methods like carotid B-mode ultrasound can be utilized for better risk stratification.
- * The use of artificial intelligence (AI) in analyzing large data sets helps identify risk factors for atherosclerosis in DR patients, thus aiding in CVD risk assessment and highlighting the interconnection between DR, CAD, and their implications during the COVID-19 pandemic.
Introduction: 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.
Areas Covered: A new retinal illumination approach is proposed to address the main dilemma of the optical design, i.
Background: Over 700,000 New Zealanders (NZ), particularly elderly and Māori, live without timely access to specialist ophthalmology services. Teleophthalmology is a widely recognised tool that can assist in overcoming resource and distance barriers. Teleophthalmology gained unprecedented traction in NZ during the COVID-19 pandemic and subsequent lockdown.
View Article and Find Full Text PDFThe present study proposes a new approach to automated screening of Clinically Significant Macular Edema (CSME) and addresses two major challenges associated with such screenings, i.e., exudate segmentation and imbalanced datasets.
View Article and Find Full Text PDFAutomatic retinal image analysis has remained an important topic of research in the last ten years. Various algorithms and methods have been developed for analysing retinal images. The majority of these methods use public retinal image databases for performance evaluation without first examining the retinal image quality.
View Article and Find Full Text PDF