We aimed to investigate the detection of idiopathic macular holes (MHs) using ultra-wide-
field fundus images (Optos) with deep learning, which is a machine learning technology …

Aims To develop a deep learning (DL) model for automatic classification of macular hole
(MH) aetiology (idiopathic or secondary), and a multimodal deep fusion network (MDFN) …

Purpose: To study the etiology, clinical features, management options, and visual prognosis
in various types of atypical macular holes (MHs). Methods: A review of the literature was …

Macula fovea detection is a crucial molecular biological prerequisite for screening and
diagnosing macular diseases. Without early detection and proper treatment, any abnormality …

Purpose To evaluate the impact of different scan patterns and scan densities on small full-
thickness macular hole (MH) detection. Design Retrospective cross-sectional analysis …

Purpose: To establish a multilabel-based deep learning (DL) algorithm for automatic
detection and categorization of clinically significant peripheral retinal lesions using ultrawide …

Retinal tears and holes are breaks in the retinal tissue that allow fluid to enter the subretinal
space causing rhegmatogenous retinal detachment, a serious condition that if not treated in …

AIM To summarize the application of deep learning in detecting ophthalmic disease with
ultrawide-field fundus images and analyze the advantages, limitations, and possible …

Purpose To predict exudative age-related macular degeneration (AMD), we combined a
deep convolutional neural network (DCNN), a machine-learning algorithm, with Optos, an …

Purpose. To describe the characteristics of idiopathic macular holes (MH) on optical
coherence tomography (OCT) and correlate OCT with clinical assessment. Design. Cross …