Automatic quantification of retinal photoreceptor integrity to predict persistent disease activity in neovascular age-related macular degeneration using deep learning

Abstract

PurposeUsing deep learning (DL)-based technique, we identify risk factors and create a prediction model for refractory neovascular age-related macular degeneration (nAMD) characterized by persistent disease activity (PDA) in spectral domain optical coherence tomography (SD-OCT) images.Materials and methodsA total of 671 typical B-scans were collected from 186 eyes of 186 patients with nAMD. Spectral domain optical coherence tomography images were analyzed using a classification convolutional neural network (CNN) and a fully convolutional network (FCN) algorithm to extract six features involved in nAMD, including ellipsoid zone (EZ), external limiting membrane (ELM), intraretinal fluid (IRF), subretinal fluid (SRF), pigment epithelium detachment (PED), and subretinal hyperreflective material (SHRM). Random forest models were probed to predict 1-year disease activity (stable, PDA, and cured) based on the quantitative features computed from automated segmentation and evaluated with cross-validation.ResultsThe algorithm to segment six SD-OCT features achieved the mean accuracy of 0.930 (95% CI: 0.916–0.943), dice coefficients of 0.873 (95% CI: 0.847–0.899), a sensitivity of 0.873 (95% CI: 0.844–0.910), and a specificity of 0.922 (95% CI: 0.905–0.940). The six-metric model including EZ and ELM achieved the optimal performance to predict 1-year disease activity, with an area under the receiver operating characteristic (ROC) curve (AUC) of 0.980, the accuracy of 0.930, the sensitivity of 0.920, and the specificity of 0.962. The integrity of EZ and ELM significantly improved the performance of the six-metric model than that of the four-metric model.ConclusionThe prediction model reveals the potential to predict PDA in nAMD eyes. The integrity of EZ and ELM constituted the strongest predictive factor for PDA in nAMD eyes in real-world clinical practice. The results of this study are a significant step toward image-guided prediction of long-term disease activity in the management of nAMD and highlight the importance of the automatic identification of photoreceptor layers.

Artificial Intelligence (AI) for Early Diagnosis of Retinal Diseases

“…Song et al used a CNN to develop an AI model to predict nAMD. They trained and tested the algorithm against 671 spectral domain optical coherence tomography images and reported an accuracy, sensitivity, and specificity of 93%, 87.3%, and 92.2%, respectively [55]. Romo-Bucheli D et al developed a DL model using DenseNet and a recurrent neural network (RNN) that analyzed OCT scans to predict treatment requirements in patients with nAMD.…”

Research progress on diagnosing retinal vascular diseases based on artificial intelligence and fundus images

“…The average accuracy of the model was 0.905, and the AUC was 0.762. Song et al (2022) constructed an AI model that predicted neovascular ANM based on a classified convolution neural network and a complete convolutional neural network algorithm. In total, 671 SD-OCT images were used to train and test the model.…”

Deep-Learning-Based Visualization and Volumetric Analysis of Fluid Regions in Optical Coherence Tomography Scans

“…Intraretinal Fluid (IRF), Subretinal Fluid (SRF), and Pigmented Epithelial Detachment (PED) are three of the most prevalent retinal pathologies [ 1 , 2 , 3 ]. Quantitative measures of retinal fluids are important biomarkers, and fluid volume computation from optical coherence tomography (OCT) scans by artificial intelligence (AI) algorithms can guide the treatment of exudative retinal conditions, such as Neovascular Age-related Macular Degeneration (NAMD).…”