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
| Autor: | Ryan Chambers, Catherine A Egan, Adnan Tufail, Tjebo F. C. Heeren, Louis Bolter, Abraham Olvera-Barrios, John Anderson, Konstantinos Balaskas |
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| Rok vydání: | 2020 |
| Předmět: |
Adult
Diagnostic Imaging Male medicine.medical_specialty 030209 endocrinology & metabolism Diagnostic accuracy Fundus (eye) Slit Lamp Microscopy Sensitivity and Specificity Retina 03 medical and health sciences Cellular and Molecular Neuroscience chemistry.chemical_compound 0302 clinical medicine Artificial Intelligence Predictive Value of Tests Ophthalmology Image Processing Computer-Assisted medicine Humans False Positive Reactions Grading (tumors) Aged Diabetic Retinopathy Microscopy Confocal Diagnostic Tests Routine business.industry Reproducibility of Results Retinal Diabetic retinopathy Middle Aged Reference Standards medicine.disease Wide field Sensory Systems Retinal image Cross-Sectional Studies chemistry 030221 ophthalmology & optometry Female business Algorithms Retinopathy |
| Zdroj: | British Journal of Ophthalmology. 105:265-270 |
| ISSN: | 1468-2079 0007-1161 |
| Popis: | BackgroundPhotographic 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.MethodsCross-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.ResultsWe 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.ConclusionEyeArt 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. |
| Databáze: | OpenAIRE |
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