Kihara, Y; Heeren, TFC; Lee, CS; Wu, Y; Xiao, S; Tzaridis, S; Holz, FG; ... Lee, AY; + view all Kihara, Y; Heeren, TFC; Lee, CS; Wu, Y; Xiao, S; Tzaridis, S; Holz, FG; Charbel Issa, P; Egan, CA; Lee, AY; - view fewer (2019) Estimating Retinal Sensitivity Using Optical Coherence Tomography With Deep-Learning Algorithms in Macular Telangiectasia Type 2. JAMA Network Open , 2 (2) , Article e188029. 10.1001/jamanetworkopen.2018.8029.

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Abstract

IMPORTANCE: As currently used, microperimetry is a burdensome clinical testing modality for testing retinal sensitivity requiring long testing times and trained technicians. OBJECTIVE: To create a deep-learning network that could directly estimate function from structure de novo to provide an en face high-resolution map of estimated retinal sensitivity. DESIGN, SETTING, AND PARTICIPANTS: A cross-sectional imaging study using data collected between January 1, 2016, and November 30, 2017, from the Natural History Observation and Registry of macular telangiectasia type 2 (MacTel) evaluated 38 participants with confirmed MacTel from 2 centers. MAIN OUTCOMES AND MEASURES: Mean absolute error of estimated compared with observed retinal sensitivity. Observed retinal sensitivity was obtained with fundus-controlled perimetry (microperimetry). Estimates of retinal sensitivity were made with deep-learning models that learned on superpositions of high-resolution optical coherence tomography (OCT) scans and microperimetry results. Those predictions were used to create high-density en face sensitivity maps of the macula. Training, validation, and test sets were segregated at the patient level. RESULTS: A total of 2499 microperimetry sensitivities were mapped onto 1708 OCT B-scans from 63 eyes of 38 patients (mean [SD] age, 74.3 [9.7] years; 15 men [39.5%]). The numbers of examples for our algorithm were 67 899 (103 053 after data augmentation) for training, 1695 for validation, and 1212 for testing. Mean absolute error results were 4.51 dB (95% CI, 4.36-4.65 dB) when using linear regression and 3.66 dB (95% CI, 3.53-3.78 dB) when using the LeNet model. Using a 49.9 million–variable deep-learning model, a mean absolute error of 3.36 dB (95% CI, 3.25-3.48 dB) of retinal sensitivity for validation and test was achieved. Correlation showed a high degree of agreement (Pearson correlation r = 0.78). By paired Wilcoxon rank sum test, our model significantly outperformed these 2 baseline models (P < .001). CONCLUSIONS AND RELEVANCE: High-resolution en face maps of estimated retinal sensitivities were created in eyes with MacTel. The maps were of unequalled resolution compared with microperimetry and were able to correctly delineate functionally healthy and impaired retina. This model may be useful to monitor structural and functional disease progression and has potential as an objective surrogate outcome measure in investigational trials.

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