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Diagnostic accuracy of handheld fundus photography: A comparative study of three commercially available cameras.

Louisa LuSomsanguan AusayakhunSakarin AusayakuhnPreeyanuch KhunsongkietAtitaya ApivatthakakulCatherine Q SunTyson N KimMichele D LeeEdmund TsuiPlern SutraJeremy David Keenan
Published in: PLOS digital health (2022)
The objective of this study was to compare the sensitivity and specificity of handheld fundus cameras in detecting diabetic retinopathy (DR), diabetic macular edema (DME), and macular degeneration. Participants in the study, conducted at Maharaj Nakorn Hospital in Northern Thailand between September 2018 and May 2019, underwent an ophthalmologist examination as well as mydriatic fundus photography with three handheld fundus cameras (iNview, Peek Retina, Pictor Plus). Photographs were graded and adjudicated by masked ophthalmologists. Outcome measures included the sensitivity and specificity of each fundus camera for detecting DR, DME, and macular degeneration, relative to ophthalmologist examination. Fundus photographs of 355 eyes from 185 participants were captured with each of the three retinal cameras. Of the 355 eyes, 102 had DR, 71 had DME, and 89 had macular degeneration on ophthalmologist examination. The Pictor Plus was the most sensitive camera for each of the diseases (73-77%) and also achieved relatively high specificity (77-91%). The Peek Retina was the most specific (96-99%), although in part due to its low sensitivity (6-18%). The iNview had slightly lower estimates of sensitivity (55-72%) and specificity (86-90%) compared to the Pictor Plus. These findings demonstrated that the handheld cameras achieved high specificity but variable sensitivities in detecting DR, DME, and macular degeneration. The Pictor Plus, iNview, and Peek Retina would have distinct advantages and disadvantages when applied for utilization in tele-ophthalmology retinal screening programs.
Keyphrases
  • diabetic retinopathy
  • optical coherence tomography
  • editorial comment
  • optic nerve
  • structural basis
  • high speed
  • artificial intelligence
  • emergency department
  • high resolution
  • deep learning
  • acute care
  • adverse drug