Automated image curation in diabetic retinopathy screening using deep learning

Nderitu, Paul; Rio, Joan M. Nunez do; Webster, Ms Laura; Mann, Samantha; Hopkins, David; Cardoso, M. Jorge; Modat, Marc; Bergeles, Christos; Jackson, Timothy L.

doi:10.1038/s41598-022-15491-1

Cited by 9 publications

(2 citation statements)

References 40 publications

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“…It has been demonstrated that DL can predict progression of 2 or more ETDRS grades from fundus images [19], however the cohort size was small and images were from 7-field photography in clinical trial participants with macula oedema and hence are not representative of a screening programme population. Other studies have considered prediction of progression of DR using DL but did not quantify the improvement above current grading [20,21]. With respect to screening policy, a number of approaches have been proposed to improve screening programmes.…”

Section: Comparison With Other Studiesmentioning

confidence: 99%

Prediction of retinopathy progression using deep learning on retinal images within the Scottish screening programme

Mellor,

Jiang,

Fleming

et al. 2024

Br J Ophthalmol

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Background/aimsNational guidelines of many countries set screening intervals for diabetic retinopathy (DR) based on grading of the last screening retinal images. We explore the potential of deep learning (DL) on images to predict progression to referable DR beyond DR grading, and the potential impact on assigned screening intervals, within the Scottish screening programme.MethodsWe consider 21 346 and 247 233 people with type 1 diabetes mellitus (T1DM) and type 2 diabetes mellitus (T2DM), respectively, each contributing on average 4.8 and 4.4 screening intervals of which 1339 and 4675 intervals concluded with a referable screening episode. Information extracted from fundus images using DL was used to predict referable status at the end of interval and its predictive value in comparison to screening-assigned DR grade was assessed.ResultsThe DL predictor increased the area under the receiver operating characteristic curve in comparison to a predictor using current DR grades from 0.809 to 0.87 for T1DM and from 0.825 to 0.87 for T2DM. Expected sojourn time—the time from becoming referable to being rescreened—was found to be 3.4 (T1DM) and 2.7 (T2DM) weeks less for a DL-derived policy compared with the current recall policy.ConclusionsWe showed that, compared with using the current retinopathy grade, DL of fundus images significantly improves the prediction of incident referable retinopathy before the next screening episode. This can impact screening recall interval policy positively, for example, by reducing the expected time with referable disease for a fixed workload—which we show as an exemplar. Additionally, it could be used to optimise workload for a fixed sojourn time.

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Section: Comparison With Other Studiesmentioning

confidence: 99%

Prediction of retinopathy progression using deep learning on retinal images within the Scottish screening programme

Mellor,

Jiang,

Fleming

et al. 2024

Br J Ophthalmol

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“…The deep-learning AutoMorph is a publicly available pipeline that uses retinal vasculature morphology on fundus photographs through image preprocessing, quality grading, anatomical segmentation, and morphological feature measurement 56 . The opensource Nderitu et al algorithm detects laterality, retinal field, retinal presence, and gradability 57 .…”

Section: Automatic Quality Assessmentmentioning

confidence: 99%

Image quality assessment of retinal fundus photographs for diabetic retinopathy in the machine learning era: a review

Gonçalves,

Nakayama,

Ferraz

et al. 2023

Eye

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This study aimed to evaluate the image quality assessment (IQA) and quality criteria employed in publicly available datasets for diabetic retinopathy (DR). A literature search strategy was used to identify relevant datasets, and 20 datasets were included in the analysis.Out of these, 12 datasets mentioned performing IQA, but only eight specified the quality criteria used. The reported quality criteria varied widely across datasets, and accessing the information was often challenging. The findings highlight the importance of IQA for AI model development while emphasizing the need for clear and accessible reporting of IQA information. The study suggests that automated quality assessments can be a valid alternative to manual labeling and emphasizes the importance of establishing quality standards based on population characteristics, clinical use, and research purposes. In conclusion, image quality assessment is important for AI model development; however, strict data quality standards must not limit data sharing. Given the importance of IQA for developing, validating, and implementing deep learning (DL) algorithms, it's recommended that this information be reported in a clear, specific, and accessible way whenever possible.Automated quality assessments are a valid alternative to the traditional manual labeling process, and quality standards should be determined according to population characteristics, clinical use, and research purpose.

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