Deep learning and ensemble stacking technique for differentiating polypoidal choroidal vasculopathy from neovascular age-related macular degeneration

Abstract

Polypoidal choroidal vasculopathy (PCV) and neovascular age-related macular degeneration (nAMD) share some similarity in clinical imaging manifestations. However, their disease entity and treatment strategy as well as visual outcomes are very different. To distinguish these two vision-threatening diseases is somewhat challenging but necessary. In this study, we propose a new artificial intelligence model using an ensemble stacking technique, which combines a color fundus photograph-based deep learning (DL) model and optical coherence tomography-based biomarkers, for differentiation of PCV from nAMD. Furthermore, we introduced multiple correspondence analysis, a method of transforming categorical data into principal components, to handle the dichotomous data for combining with another image DL system. This model achieved a robust performance with an accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve of 83.67%, 80.76%, 84.72%, and 88.57%, respectively, by training nearly 700 active cases with suitable imaging quality and transfer learning architecture. This work could offer an alternative method of developing a multimodal DL model, improve its efficiency for distinguishing different diseases, and facilitate the broad application of medical engineering in a DL model design.

Application of Deep Learning for Automated Detection of Polypoidal Choroidal Vasculopathy in Spectral Domain Optical Coherence Tomography

“…The OCT-based model alone reached an accuracy of 83.2%, and the bimodal DL model combining fundus and OCT images had an improvement in accuracy of 87.4%. Another study from Chou et al 67 showed a comparable result. They proposed a bimodal model with an ensemble stacking technique, combining color fundus photographs (CFPs) and clinical features of OCT biomarkers to distinguish between PCV and nvAMD with an accuracy of 83.67%, sensitivity of 80.76%, and specificity of 84.72%.…”

Classifying neovascular age-related macular degeneration with a deep convolutional neural network based on optical coherence tomography images

“…Going one step further, there have been attempts to classify AMD using fundus photographs and OCT. Using the two modalities, Chou et al differentiated PCV from nAMD with EfficientNet and multiple correspondence analysis 22 . Moreover, Xu et al classified nAMD, Dry AMD, PCV, and normal groups using deep CNN networks 23 .…”

Recent developments on computer aided systems for diagnosis of diabetic retinopathy: a review

“…Wet AMD develops in these people when aberrant blood vessels develop into the retina and leak fluid, making the retina “wet.”. If CNV is left untreated it will lead to vision loss [ 34 ]. There are many works in the literature to find out the CNV using deep learning, machine learning techniques but the main concern of this work is to focus on the diabetic retinopathy-based lesions therefore, not going much deeper into this section it will review some of the work in the literature so that one may have the overview about this lesion type.…”

“…Yu-Bai Chou et al 2021 [ 34 ], uses a deep learning and ensemble stacking for CNV categorization. This research could provide an alternate way for constructing a multimodal DL framework, enhance its ability to differentiate between disorders, and clinical sciences more widely applicable in DL model construction.…”