Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie
36303063
PURPOSE: To develop and evaluate an automated deep learning model to predict the anatomical outcome of rhegmatogenous retinal detachment (RRD) surgery.
BACKGROUND: The aim was to explore the feasibility and safety of performing common surgical steps in epiretinal membrane (ERM) peeling using the Preceyes Surgical System (PSS).
The purpose of this study is to compare robot-assisted and manual subretinal injections in terms of successful subretinal blistering, reflux incidences and damage of the retinal pigment epithelium (RPE). Subretinal injection was simulated on 84 ex-vi...
BACKGROUND: The purpose of this study was to develop a model that can predict the postoperative visual acuity in eyes that had undergone vitrectomy for an epiretinal membrane (ERM). The Light Gradient Boosting Machine (LightGBM) was used to evaluate ...
PURPOSE: To investigate the relationship between effective lens position (ELP) and patient characteristics, and to further develop a new intraocular lens (IOL) calculation formula for cataract patients with previous pars plana vitrectomy (PPV).
PURPOSE: To elaborate a deep learning (DL) model for automatic prediction of late recurrence (LR) of rhegmatogenous retinal detachment (RRD) using pseudocolor and fundus autofluorescence (AF) ultra-wide field (UWF) images obtained preoperatively and ...
PURPOSE: This study aimed to develop artificial intelligence models for predicting postoperative functional outcomes in patients with rhegmatogenous retinal detachment (RRD).
We focus on the utility of artificial intelligence (AI) in the management of macular hole (MH). We synthesize 25 studies, comprehensively reporting on each AI model's development strategy, validation, tasks, performance, strengths, and limitations. A...
PURPOSE: To evaluate various supervised machine learning (ML) statistical models to predict anatomical outcomes after macular hole (MH) surgery using preoperative optical coherence tomography (OCT) features.
PURPOSE: This study assessed the performance of various deep learning models in predicting the postoperative outcomes of idiopathic epiretinal membrane (ERM) surgery based on preoperative optical coherence tomography (OCT) images.