Objective: Multiple studies have attempted to generate visual field (VF) mean deviation (MD) estimates using cross-sectional optical coherence tomography (OCT) data. However, whether such models offer any value in detecting longitudinal VF progression is unclear. We address this by developing a machine learning (ML) model to convert OCT data to MD and assessing its ability to detect longitudinal worsening.
View Article and Find Full Text PDFPurpose: Incisional hernias (IH) after kidney transplantation (KTx) can cause significant morbidity in kidney transplant recipients (KTR). We aimed to report the outcomes of surgical repair of IH in KTR from our centre.
Methods: We retrospectively analysed all the IH repairs in KTR from May 2018 to May 2023.
Purpose: Compare the use of optic disc and macular optical coherence tomography measurements to predict glaucomatous visual field (VF) worsening.
Methods: Machine learning and statistical models were trained on 924 eyes (924 patients) with circumpapillary retinal nerve fiber layer (cp-RNFL) or ganglion cell inner plexiform layer (GC-IPL) thickness measurements. The probability of 24-2 VF worsening was predicted using both trend-based and event-based progression definitions of VF worsening.
Purpose: To determine the associations between social vulnerability index (SVI) and baseline severity, worsening, and variability of glaucoma, as assessed by visual field (VF) and OCT.
Design: Retrospective longitudinal cohort study.
Participants: Adults with glaucoma or glaucoma suspect status in 1 or both eyes.