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The efficacy of automated "disease/no disease" grading for diabetic retinopathy in a systematic screening programme
Open accessAbstract
AIM: To assess the efficacy of automated "disease/no disease" grading for diabetic retinopathy within a systematic screening programme. METHODS: Anonymised images were obtained from consecutive patients attending a regional primary care based diabetic retinopathy screening programme. A training set of 1067 images was used to develop automated grading algorithms. The final software was tested using a separate set of 14 406 images from 6722 patients. The sensitivity and specificity of manual and automated systems operating as "disease/no disease" graders (detecting poor quality images and any diabetic retinopathy) were determined relative to a clinical reference standard. RESULTS: The reference standard classified 8.2% of the patients as having ungradeable images (technical failures) and 62.5% as having no retinopathy. Detection of technical failures or any retinopathy was achieved by manual grading with 86.5% sensitivity (95% confidence interval 85.1 to 87.8) and 95.3% specificity (94.6 to 95.9) and by automated grading with 90.5% sensitivity (89.3 to 91.6) and 67.4% specificity (66.0 to 68.8). Manual and automated grading detected 99.1% and 97.9%, respectively, of patients with referable or observable retinopathy/maculopathy. Manual and automated grading detected 95.7% and 99.8%, respectively, of technical failures. CONCLUSION: Automated "disease/no disease" grading of diabetic retinopathy could safely reduce the burden of grading in diabetic retinopathy screening programmes.
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Cited by
Gulshan, V., Peng, L., Coram, M., Stumpe, M. C., Wu, D., Narayanaswamy, A., Venugopalan, S., Widner, K., Madams, T., Cuadros, J., et al. (2016) · JAMA · doi:10.1001/jama.2016.17216 · Cited by 7,697
Digital technology, tele-medicine and artificial intelligence in ophthalmology: A global perspective
Li, J.-P. O., Liu, H., Ting, D. S. J., Jeon, S., Chan, R. V. P., Kim, J. E., Sim, D. A., Thomas, P. B. M., Lin, H., Chen, Y., et al. (2021) · Progress in Retinal and Eye Research · doi:10.1016/j.preteyeres.2020.100900 · Cited by 626
van der Heijden, A. A., Abramoff, M. D., Verbraak, F., van Hecke, M. V., Liem, A., & Nijpels, G. (2018) · Acta Ophthalmologica · doi:10.1111/aos.13613 · Cited by 296
Automated Early Detection of Diabetic Retinopathy
Abràmoff, M. D., Reinhardt, J. M., Russell, S. R., Folk, J. C., Mahajan, V. B., Niemeijer, M., & Quellec, G. (2010) · Ophthalmology · doi:10.1016/j.ophtha.2010.03.046 · Cited by 287
Automated Diabetic Retinopathy Image Assessment Software
Tufail, A., Rudisill, C., Egan, C., Kapetanakis, V. V., Salas-Vega, S., Owen, C. G., Lee, A., Louw, V., Anderson, J., Liew, G., et al. (2017) · Ophthalmology · doi:10.1016/j.ophtha.2016.11.014 · Cited by 283
Metadata
- Authors
- Philip, S., Fleming, A. D., Goatman, K. A., Fonseca, S., Mcnamee, P., Scotland, G. S., Prescott, G. J., Sharp, P. F., & Olson, J. A.
- Venue
- British Journal of Ophthalmology
- Publisher
- BMJ
- Published
- 2007-05-15
- Volume
- 91
- Issue
- 11
- Pages
- 1512-1517
- ISSN
- 0007-1161 (ISSN-L)
- OpenAlex
- W2062393305
- Cited by
- 225
- References
- 0
- FWCI
- 5.47
- Type
- journal article
- Language
- en
- Open access
- green
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- Philip, S., Fleming, A. D., Goatman, K. A., Fonseca, S., Mcnamee, P., Scotland, G. S., Prescott, G. J., Sharp, P. F., & Olson, J. A. (2007). The efficacy of automated "disease/no disease" grading for diabetic retinopathy in a systematic screening programme. British Journal of Ophthalmology. https://doi.org/10.1136/bjo.2007.119453
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