Purpose: To develop deep learning (DL) models for the automatic detection of optical coherence tomography (OCT) measures of diabetic macular thickening (MT) from color fundus photographs (CFPs). Methods: Retrospective analysis on 17,997 CFPs and their associated OCT measurements from the phase 3 RIDE/RISE diabetic macular edema (DME) studies. DL with transfer-learning cascade was applied on CFPs to predict time-domain OCT (TD-OCT)-equivalent measures of MT, including central subfield thickness (CST) and central foveal thickness (CFT). MT was defined by using two OCT cutoff points: 250 μm and 400 μm. A DL regression model was developed to directly quantify the actual CFT and CST from CFPs. Results: The best DL model was able to predict CST ≥ 250 μm and CFT ≥ 250 μm with an area under the curve (AUC) of 0.97 (95% confidence interval [CI], 0.89-1.00) and 0.91 (95% CI, 0.76-0.99), respectively. To predict CST ≥ 400 μm and CFT ≥ 400 μm, the best DL model had an AUC of 0.94 (95% CI, 0.82-1.00) and 0.96 (95% CI, 0.88-1.00), respectively. The best deep convolutional neural network regression model to quantify CST and CFT had an R2 of 0.74 (95% CI, 0.49-0.91) and 0.54 (95% CI, 0.20-0.87), respectively. The performance of the DL models declined when the CFPs were of poor quality or contained laser scars. Conclusions: DL is capable of predicting key quantitative TD-OCT measurements related to MT from CFPs. The DL models presented here could enhance the efficiency of DME diagnosis in tele-ophthalmology programs, promoting better visual outcomes. Future research is needed to validate DL algorithms for MT in the real-world.
Purpose: To develop deep learning (DL) models for the automatic detection of optical coherence tomography (OCT) measures of diabetic macular thickening (MT) from color fundus photographs (CFPs). Methods: Retrospective analysis on 17,997 CFPs and their associated OCT measurements from the phase 3 RIDE/RISE diabetic macular edema (DME) studies. DL with transfer-learning cascade was applied on CFPs to predict time-domain OCT (TD-OCT)-equivalent measures of MT, including central subfield thickness (CST) and central foveal thickness (CFT). MT was defined by using two OCT cutoff points: 250 μm and 400 μm. A DL regression model was developed to directly quantify the actual CFT and CST from CFPs. Results: The best DL model was able to predict CST ≥ 250 μm and CFT ≥ 250 μm with an area under the curve (AUC) of 0.97 (95% confidence interval [CI], 0.89-1.00) and 0.91 (95% CI, 0.76-0.99), respectively. To predict CST ≥ 400 μm and CFT ≥ 400 μm, the best DL model had an AUC of 0.94 (95% CI, 0.82-1.00) and 0.96 (95% CI, 0.88-1.00), respectively. The best deep convolutional neural network regression model to quantify CST and CFT had an R2 of 0.74 (95% CI, 0.49-0.91) and 0.54 (95% CI, 0.20-0.87), respectively. The performance of the DL models declined when the CFPs were of poor quality or contained laser scars. Conclusions: DL is capable of predicting key quantitative TD-OCT measurements related to MT from CFPs. The DL models presented here could enhance the efficiency of DME diagnosis in tele-ophthalmology programs, promoting better visual outcomes. Future research is needed to validate DL algorithms for MT in the real-world.
Authors: David Le; Minhaj Alam; Cham K Yao; Jennifer I Lim; Yi-Ting Hsieh; Robison V P Chan; Devrim Toslak; Xincheng Yao Journal: Transl Vis Sci Technol Date: 2020-07-02 Impact factor: 3.283
Authors: Olivia E O'Leary; Andreas Schoetzau; Ludovic Amruthalingam; Nadine Geber-Hollbach; Kim Plattner; Paul Jenoe; Alexander Schmidt; Christoph Ullmer; Faye M Drawnel; Sascha Fauser; Hendrik P N Scholl; Jakob Passweg; Joerg P Halter; David Goldblum Journal: Transl Vis Sci Technol Date: 2020-08-03 Impact factor: 3.283
Authors: Melina Cavichini; Cheolhong An; Dirk-Uwe G Bartsch; Mahima Jhingan; Manuel J Amador-Patarroyo; Christopher P Long; Junkang Zhang; Yiqian Wang; Alison X Chan; Samantha Madala; Truong Nguyen; William R Freeman Journal: Transl Vis Sci Technol Date: 2020-10-20 Impact factor: 3.048