Classification of Pachychoroid on Optical Coherence Tomographic En Face Images Using Deep Convolutional Neural Networks.

Purpose: To study the efficacy of deep convolutional neural networks (DCNNs) to differentiate pachychoroid from nonpachychoroid on en face optical coherence tomography (OCT) images at the large choroidal vessel.

Methods: En face OCT images were collected from eyes with neovascular age-related macular degeneration, polypoidal choroidal vasculopathy, and central serous chorioretinopathy. All images were prelabeled pachychoroid or nonpachychoroid based on quantitative and qualitative criteria for choroidal morphology on multimodal imaging by two retina specialists.

Classification of pachychoroid on optical coherence tomography using deep learning.

Purpose: Pachychoroid is characterized by dilated Haller vessels and choriocapillaris attenuation that are seen on optical coherence tomography (OCT) B-scans. This study investigated the feasibility of using deep learning (DL) models to classify pachychoroid and non-pachychoroid eyes from OCT B-scan images.

Methods: In total, 1898 OCT B-scan images were collected from eyes with macular diseases.

Classification of pachychoroid disease on ultrawide-field indocyanine green angiography using auto-machine learning platform.

Aims: Automatic identification of pachychoroid maybe used as an adjunctive method to confirm the condition and be of help in treatment for macular diseases. This study investigated the feasibility of classifying pachychoroid disease on ultra-widefield indocyanine green angiography (UWF ICGA) images using an automated machine-learning platform.

Methods: Two models were trained with a set including 783 UWF ICGA images of patients with pachychoroid (n=376) and non-pachychoroid (n=349) diseases using the AutoML Vision (Google).