Diagnostic Accuracy of a Device for the Automated Detection of Diabetic Retinopathy in a Primary Care Setting.

Objective: To determine the diagnostic accuracy in a real-world primary care setting of a deep learning-enhanced device for automated detection of diabetic retinopathy (DR).

Research Design And Methods: Retinal images of people with type 2 diabetes visiting a primary care screening program were graded by a hybrid deep learning-enhanced device (IDx-DR-EU-2.1; IDx, Amsterdam, the Netherlands), and its classification of retinopathy (vision-threatening [vt]DR, more than mild [mtm]DR, and mild or more [mom]DR) was compared with a reference standard.

Multivariable risk-based patient selection for prostate biopsy in a primary health care setting: referral rate and biopsy results from a urology outpatient clinic.

Background: According to their guidelines, Dutch general practitioners (GPs) refer men with prostate-specific antigen (PSA) level ≥3.0 ng/mL to the urologist for risk-based patient selection for prostate biopsy using the Rotterdam Prostate Cancer Risk Calculator (RPCRC). Use of the RPCRC in primary care could optimize the diagnostic pathway even further by reducing unnecessary referrals.