Flow-Adjusted Trabeculectomy.

As one of the most efficacious glaucoma surgical techniques, trabeculectomy is considered by many surgeons to be the "gold standard" intra-ocular pressure (IOP)-reducing intervention. The purpose of this study is to present our intra-operative flow-adjusted surgical method, which aims to provide safety and efficacy more simply than previous methods. Retrospectively, we evaluated outcomes for trabeculectomy or phacotrabeculectomy in surgery-naïve eyes over three years for patients with glaucoma not associated with other ocular co-morbidities.

Better restoration of joint line obliquity in tibia first restricted kinematic alignment versus mechanical alignment TKA.

Introduction: In total knee arthroplasty (TKA), suboptimal restoration of joint line obliquity (JLO) and joint line height (JLH) may lead to diminished implant longevity, increased risk of complications, and reduced patient reported outcomes. The primary objective of this study is to determine whether restricted kinematic alignment (rKA) leads to improved restoration of JLO and JLH compared to mechanical alignment (MA) in TKA.

Materials And Methods: This retrospective study assessed patients who underwent single implant design TKA for primary osteoarthritis, either MA with manual instrumentation or rKA assisted with imageless navigation robotic arm TKA.

Is a Pre-Existent Cone-Beam Computed Tomography Able to Detect Metal Dental Posts?

(1) Background: In this study, the efficacy of cone-beam computed tomography (CBCT) in detecting dental posts was compared to periapical radiography. (2) Methods: A retrospective evaluation of 53 patients' periapical radiographs and CBCT images was performed. The presence and type of the intra-canal dental post were initially determined on the periapical images (PA) radiographs' examination and were then compared to the observer's ability to detect the dental post on a CBCT image.

Enhancing stroke risk and prognostic timeframe assessment with deep learning and a broad range of retinal biomarkers.

Stroke stands as a major global health issue, causing high death and disability rates and significant social and economic burdens. The effectiveness of existing stroke risk assessment methods is questionable due to their use of inconsistent and varying biomarkers, which may lead to unpredictable risk evaluations. This study introduces an automatic deep learning-based system for predicting stroke risk (both ischemic and hemorrhagic) and estimating the time frame of its occurrence, utilizing a comprehensive set of known retinal biomarkers from fundus images.