Journal of Ophthalmology (Dec 2024)

Assessing the possibility of using portable and stationary non-mydriatic fundus cameras for diabetic retinopathy screening assisted by an artificial intelligence-based software platform in primary care

  • A.O. Nevska,
  • O.A. Pohosian,
  • K.O. Goncharuk,
  • O.O. Chernenko,
  • I.V. Hymanyk,
  • A.R. Korol

DOI
https://doi.org/10.31288/oftalmolzh202462226
Journal volume & issue
no. 6
pp. 22 – 26

Abstract

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Purpose: To assess the possibility of using portable and stationary non-mydriatic (NM) fundus cameras for diabetic retinopathy (DR) screening assisted by the artificial intelligence (AI)-based Retina-AI CheckEye© software platform in primary care. Material and Methods: In this prospective, open-label study, 609 subjects (1218 eyes) with either diagnosed diabetes mellitus (DM) or risk factors for DM were divided into two groups depending on whether the fundus camera was stationary or portable. NM single-field fundus photography was performed with a stationary fundus camera in group 1 and a portable camera in group 2. The AI-based Retina-AI CheckEye© software platform was used for the analysis of digital color fundus photographs of subject eyes for signs of DR. The numbers of poor-quality fundus images and the presence or absence of DR were noted, and the stage of DR was assessed. Results: In group 1 and group 2, there were 37 eyes and 339 eyes, respectively, whose images could not be processed by the neural network. DR was found in 15 subjects (5.17%) in group 1 and 8 subjects (2.51%) in group 2. Previously undiagnosed DM complicated by DR was discovered in 13 (4.5%) of the subjects included in group 1 versus 7 (2%) of the subjects included in group 2. Conclusion: Digital color fundus images taken with stationary and portable NM fundus cameras through non-dilated pupils and analyzed by the AI-based Retina-AI CheckEye© software platform provided DR detection and grading by stages among subjects with diagnosed DM as well those with undiagnosed DM. The percentage of poor-quality photographs can be reduced and the effectiveness of DR screening with the use of the AI-based Retina-AI CheckEye© software platform can be improved through the involvement of an experienced operator and better adherence to protocol for uploading fundus images to the cloud storage.

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