Publications by authors named "S H Schuman"
To assess the differences in measures of diabetic retinopathy (DR) disease burden between patients in high-income vs low-income ZIP codes when presenting to retina specialists. This retrospective cohort study comprised patients who presented to a retina specialist at Duke Eye Center between 2014 and 2023 for the management of DR. The quartile of patients with the highest income was compared with the quartile with the lowest income.
View Article and Find Full Text PDFRisk of Cancer Diagnosis in Patients With Eosinophilic Esophagitis Using a Nationwide Swedish Population Cohort.
United European Gastroenterol J
December 2024
Article Synopsis
- Eosinophilic esophagitis (EoE) is an inflammatory disease of the esophagus that may be linked to cancer, prompting a study to explore this association.
- A cohort study in Sweden analyzed 1,580 EoE patients and matched them with 7,533 reference individuals, tracking cancer development over a median of 7 years.
- While no overall cancer risk was found in EoE patients, there was a significant increase in the risk of esophageal cancer and Barrett's esophagus, though these cases were very rare, warranting caution in interpreting the results.
To assess the severity, progression, and treatment burden of diabetic retinopathy (DR) in patients after bariatric surgery compared with controls. A retrospective cohort study was performed of patients with type 2 diabetes and DR seen at the Duke Eye Center between 2014 and 2023. Clinical data included hemoglobin A (HbA), diagnostic stage of DR, diabetic macular edema (DME) or vitreous hemorrhage (VH), visual acuity (VA), and treatment burden at baseline and follow-up.
View Article and Find Full Text PDFImportance: The identification of patients at risk of progressing from intermediate age-related macular degeneration (iAMD) to geographic atrophy (GA) is essential for clinical trials aimed at preventing disease progression. DeepGAze is a fully automated and accurate convolutional neural network-based deep learning algorithm for predicting progression from iAMD to GA within 1 year from spectral-domain optical coherence tomography (SD-OCT) scans.
Objective: To develop a deep-learning algorithm based on volumetric SD-OCT scans to predict the progression from iAMD to GA during the year following the scan.