Automated diabetic retinopathy imaging in Indian eyes: A pilot study

Abstract

Aim:To evaluate the efficacy of an automated retinal image grading system in diabetic retinopathy (DR) screening.Materials and Methods:Color fundus images of patients of a DR screening project were analyzed for the purpose of the study. For each eye two set of images were acquired, one centerd on the disk and the other centerd on the macula. All images were processed by automated DR screening software (Retmarker). The results were compared to ophthalmologist grading of the same set of photographs.Results:5780 images of 1445 patients were analyzed. Patients were screened into two categories DR or no DR. Image quality was high, medium and low in 71 (4.91%), 1117 (77.30%) and 257 (17.78%) patients respectively. Specificity and sensitivity for detecting DR in the high, medium and low group were (0.59, 0.91); (0.11, 0.95) and (0.93, 0.14).Conclusion:Automated retinal image screening system for DR had a high sensitivity in high and medium quality images. Automated DR grading software's hold promise in future screening programs.

Renewable thermoset copolymers from tung oil and natural terpenes

“…However, as terpenes exhibit carbon-carbon double bonds amenable to reactions, several works have been published concerning the polymerization of terpenes with vinyl monomers in order to develop biodegradable polymeric materials. [22][23][24] Envisioning the oil molecules as chains bearing multiple vinyl monomers, the main purpose of this project is to cover the major aspects related to the synthesis, characterization as well as properties of new macromolecular bio-based thermoset polymers, produced through the cationic copolymerization of tung (a natural oil) with three different terpenes; such as limonene (Lim), myrcene (Myr), and linalool (Lin) as reactive comonomers. Also, in order to compare the properties of the resulting materials, TO was copolymerized with styrene, a widely studied synthetic monomer.…”

Capacity building for diabetic retinopathy screening by optometrists in India

“…AI technology is dependent on accurate, good quality fundus images, and current AI programs are unable to accurately detect DME solely based on photographs. [ 23 25 26 ] So, the fundamental step in building an ideal DR screening model is to capture a good quality fundus image and a comprehensive assessment of the people with DM. This includes the assessment of visual acuity, refraction, IOP, glycemic control, and comorbidities.…”

Artificial intelligence for diabetic retinopathy

“…EyeArt (Eyenuk Inc., Los Angeles, USA) was approved by the Food and Drug Administration in August 2020 [35] . Retmarker DR (Retmarker, Coimbra, Portugal) has been certified as a Class IIa medical device in Europe [36] and has been used in the DR screening project in Portugal since 2011 [37] . In China, AI-based screening software for DR by Shenzhen SiBionics Co. Ltd. (Shenzhen, China) and the AI-based analysis software for DR by Airdoc (Beijing, China) were also approved in August 2020 [38] Table 1.…”