Autonomous Screening for Diabetic Macular Edema Using Deep Learning Processing of Retinal Images

Bressler, Idan; Aviv, Rachelle; Margalit, Danny; Cohen, Gal; Ianchulev, Tsontcho; Savant, Shravan V.; Ramsey, David J.; Dvey-Aharon, Zack

doi:10.2139/ssrn.4474219

Cited by 1 publication

(1 citation statement)

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“…The effect that laser treatment has on imaging tasks was measured by applying the laser detection model as a postprocessing step for a model for the detection of DME, which was developed based on the EyePACs dataset, 35 and a model for age detection, also developed based on the EyePACs dataset. The performance on these tasks was measured in AUC on a separate validation set containing images both with and without laser treatment artefacts.…”

Section: Effect On Imaging Tasksmentioning

confidence: 99%

Autonomous screening for laser photocoagulation in fundus images using deep learning

Bressler¹,

Aviv²,

Margalit³

et al. 2023

Br J Ophthalmol

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BackgroundDiabetic retinopathy (DR) is a leading cause of blindness in adults worldwide. Artificial intelligence (AI) with autonomous deep learning algorithms has been increasingly used in retinal image analysis, particularly for the screening of referrable DR. An established treatment for proliferative DR is panretinal or focal laser photocoagulation. Training autonomous models to discern laser patterns can be important in disease management and follow-up.MethodsA deep learning model was trained for laser treatment detection using the EyePACs dataset. Data was randomly assigned, by participant, into development (n=18 945) and validation (n=2105) sets. Analysis was conducted at the single image, eye, and patient levels. The model was then used to filter input for three independent AI models for retinal indications; changes in model efficacy were measured using area under the receiver operating characteristic curve (AUC) and mean absolute error (MAE).ResultsOn the task of laser photocoagulation detection: AUCs of 0.981, 0.95, and 0.979 were achieved at the patient, image, and eye levels, respectively. When analysing independent models, efficacy was shown to improve across the board after filtering. Diabetic macular oedema detection on images with artefacts was AUC 0.932 vs AUC 0.955 on those without. Participant sex detection on images with artefacts was AUC 0.872 vs AUC 0.922 on those without. Participant age detection on images with artefacts was MAE 5.33 vs MAE 3.81 on those without.ConclusionThe proposed model for laser treatment detection achieved high performance on all analysis metrics and has been demonstrated to positively affect the efficacy of different AI models, suggesting that laser detection can generally improve AI-powered applications for fundus images.

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Section: Effect On Imaging Tasksmentioning

confidence: 99%