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Diagnostic accuracy of diabetic retinopathy grading by an artificial intelligence-enabled algorithm compared with a human standard for wide-field true-colour confocal scanning and standard digital retinal images
  1. Abraham Olvera-Barrios1,2,
  2. Tjebo FC Heeren1,2,
  3. Konstantinos Balaskas1,
  4. Ryan Chambers3,
  5. Louis Bolter3,
  6. Catherine Egan1,2,
  7. Adnan Tufail1,2,
  8. John Anderson3
  1. 1Medical Retina, Moorfields Eye Hospital NHS Foundation Trust, London, UK
  2. 2University College London Institute of Ophthalmology, London, UK
  3. 3Diabetes, Homerton University Hospital NHS Foundation Trust, London, UK
  1. Correspondence to Abraham Olvera-Barrios, Medical Retina, Moorfields Eye Hospital NHS Foundation Trust, London EC1V 2PD, UK; a.olvera{at}nhs.net

Abstract

Background Photographic diabetic retinopathy screening requires labour-intensive grading of retinal images by humans. Automated retinal image analysis software (ARIAS) could provide an alternative to human grading. We compare the performance of an ARIAS using true-colour, wide-field confocal scanning images and standard fundus images in the English National Diabetic Eye Screening Programme (NDESP) against human grading.

Methods Cross-sectional study with consecutive recruitment of patients attending annual diabetic eye screening. Imaging with mydriasis was performed (two-field protocol) with the EIDON platform (CenterVue, Padua, Italy) and standard NDESP cameras. Human grading was carried out according to NDESP protocol. Images were processed by EyeArt V.2.1.0 (Eyenuk Inc, Woodland Hills, California). The reference standard for analysis was the human grade of standard NDESP images.

Results We included 1257 patients. Sensitivity estimates for retinopathy grades were: EIDON images; 92.27% (95% CI: 88.43% to 94.69%) for any retinopathy, 99% (95% CI: 95.35% to 100%) for vision-threatening retinopathy and 100% (95% CI: 61% to 100%) for proliferative retinopathy. For NDESP images: 92.26% (95% CI: 88.37% to 94.69%) for any retinopathy, 100% (95% CI: 99.53% to 100%) for vision-threatening retinopathy and 100% (95% CI: 61% to 100%) for proliferative retinopathy. One case of vision-threatening retinopathy (R1M1) was missed by the EyeArt when analysing the EIDON images, but identified by the human graders. The EyeArt identified all cases of vision-threatening retinopathy in the standard images.

Conclusion EyeArt identified diabetic retinopathy in EIDON images with similar sensitivity to standard images in a large-scale screening programme, exceeding the sensitivity threshold recommended for a screening test. Further work to optimise the identification of ‘no retinopathy’ and to understand the differential lesion detection in the two imaging systems would enhance the use of these two innovative technologies in a diabetic retinopathy screening setting.

  • diagnostic tests/investigation
  • epidemiology
  • imaging
  • retina
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Footnotes

  • Contributors As per ICMJE guidelines, all the authors agree to be accountable for all aspects of the work done on this study. In addition, each individual author’s contributions are: AO-B; statistical analysis, interpretation of data, manuscript preparation and manuscript approval. TH; statistical analysis, interpretation of data, manuscript preparation and manuscript approval. KB; acquisition of data, manuscript preparation and manuscript approval. RC; acquisition of data, manuscript preparation and manuscript approval. LB; acquisition of data, manuscript preparation and manuscript approval. AT; study conception and design, interpretation of data, manuscript preparation and manuscript approval. CE; study conception and design, interpretation of data, manuscript preparation and manuscript approval. JA; study conception and design, interpretation of data, manuscript preparation and manuscript approval.

  • Funding This work was funded by the North East London Diabetes Eye Screening Programme (RC, LB and JA). This research has received a proportion of its funding (salary support) from the Department of Health’s NIHR Biomedical Research Centre for Ophthalmology at Moorfields Eye Hospital and UCL Institute of Ophthalmology (AT and CE), the Lowy Medical Research Institute (TH) and from the Department of Ophthalmology and University Hospital, Universidad Autónoma de Nuevo León (AO-B).

  • Competing interests None declared.

  • Patient consent for publication Not required.

  • Provenance and peer review Not commissioned; externally peer reviewed.

  • Data availability statement The data that support the findings of this study are available from the North East London Diabetic Eye Screening Programme upon reasonable request.

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