Predicting optical coherence tomography-derived diabetic macular edema grades from fundus photographs using deep learning

Center-involved diabetic macular edema (ci-DME) is a major cause of vision loss. Although the gold standard for diagnosis involves 3D imaging, 2D imaging by fundus photography is usually used in screening settings, resulting in high false-positive and false-negative calls. To address this, we train a deep learning model to predict ci-DME from fundus photographs, with an ROC–AUC of 0.89 (95% CI: 0.87–0.91), corresponding to 85% sensitivity at 80% specificity. In comparison, retinal specialists have similar sensitivities (82–85%), but only half the specificity (45–50%, p < 0.001). Our model can also detect the presence of intraretinal fluid (AUC: 0.81; 95% CI: 0.81–0.86) and subretinal fluid (AUC 0.88; 95% CI: 0.85–0.91). Using deep learning to make predictions via simple 2D images without sophisticated 3D-imaging equipment and with better than specialist performance, has broad relevance to many other applications in medical imaging

Deep learning to detect optical coherence tomography-derived diabetic macular edema from retinal photographs: a multicenter validation study

PURPOSE: To validate the generalizability of a deep learning system (DLS) that detects diabetic macular edema (DME) from two-dimensional color fundus photography (CFP), where the reference standard for retinal thickness and fluid presence is derived from three-dimensional optical coherence tomography (OCT). DESIGN: Retrospective validation of a DLS across international datasets. PARTICIPANTS: Paired CFP and OCT of patients from diabetic retinopathy (DR) screening programs or retina clinics. The DLS was developed using datasets from Thailand, the United Kingdom (UK) and the United States and validated using 3,060 unique eyes from 1,582 patients across screening populations in Australia, India and Thailand. The DLS was separately validated in 698 eyes from 537 screened patients in the UK with mild DR and suspicion of DME based on CFP. METHODS: The DLS was trained using DME labels from OCT. Presence of DME was based on retinal thickening or intraretinal fluid. The DLS's performance was compared to expert grades of maculopathy and to a previous proof-of-concept version of the DLS. We further simulated integration of the current DLS into an algorithm trained to detect DR from CFPs. MAIN OUTCOME MEASURES: Superiority of specificity and non-inferiority of sensitivity of the DLS for the detection of center-involving DME, using device specific thresholds, compared to experts. RESULTS: Primary analysis in a combined dataset spanning Australia, India, and Thailand showed the DLS had 80% specificity and 81% sensitivity compared to expert graders who had 59% specificity and 70% sensitivity. Relative to human experts, the DLS had significantly higher specificity (p=0.008) and non-inferior sensitivity (p 50%) and a sensitivity of 100% (p=0.02 for sensitivity > 90%). CONCLUSIONS: The DLS can generalize to multiple international populations with an accuracy exceeding experts. The clinical value of this DLS to reduce false positive referrals, thus decreasing the burden on specialist eye care, warrants prospective evaluation

Predicting optical coherence tomography-derived diabetic macular edema grades from fundus photographs using deep learning

Center-involved diabetic macular edema (ci-DME) is a major cause of vision loss. Although the gold standard for diagnosis involves 3D imaging, 2D imaging by fundus photography is usually used in screening settings, resulting in high false-positive and false-negative calls. To address this, we train a deep learning model to predict ci-DME from fundus photographs, with an ROC-AUC of 0.89 (95% CI: 0.87-0.91), corresponding&nbsp;to 85% sensitivity at 80% specificity. In comparison, retinal specialists have similar sensitivities (82-85%), but only half the specificity (45-50%, p &lt; 0.001). Our model can also detect the presence of intraretinal fluid (AUC: 0.81; 95% CI: 0.81-0.86) and subretinal fluid (AUC 0.88; 95% CI: 0.85-0.91). Using deep learning to make predictions via simple 2D images without sophisticated 3D-imaging equipment&nbsp;and with better than specialist performance, has broad relevance to many other applications in medical imaging