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ease progression and evaluating the effectiveness of new therapies.Major advances in the study of inherited retinal diseases (IRDs) have placed efforts to develop treatments for these blinding conditions at the forefront of the emerging field of precision medicine. As a result, the growth of clinical trials for IRDs has increased rapidly over the past decade and is expected to further accelerate as more therapeutic possibilities emerge and qualified participants are identified. Although guided by established principles, these specialized trials, requiring analysis of novel outcome measures and endpoints in small patient populations, present multiple challenges relative to study design and ethical considerations. This position paper reviews recent accomplishments and existing challenges in clinical trials for IRDs and presents a set of recommendations aimed at rapidly advancing future progress. The goal is to stimulate discussions among researchers, funding agencies, industry, and policy makers that will further the design, conduct, and analysis of clinical trials needed to accelerate the approval of effective treatments for IRDs, while promoting advocacy and ensuring patient safety.The Port Delivery System with ranibizumab (PDS) is an innovative, investigational drug delivery system designed for continuous delivery of ranibizumab into the vitreous to maintain therapeutic drug concentrations for extended durations. The phase 2 Ladder trial (NCT02510794) tested the efficacy of three customized formulations of ranibizumab in patients with neovascular age-related macular degeneration, and the phase 3 Archway trial (NCT03677934) will further assess the safety and efficacy of PDS 100 mg/mL with fixed 24-week refills. The insertion of the PDS implant into the vitreous cavity and subsequent refill-exchange of the drug require procedural skills that are not directly transferable from everyday experience for most eye surgeons today. Preoperative practice for the PDS implant insertion and refill-exchange procedures is therefore critical for achieving optimal surgical outcomes. Virtual reality (VR) as a training tool has long been used by the aeronautic industry and more recently adapted for physician training in medicine and surgery, with encouraging results. Besides the primary use of traditional training tools, physicians participating in Archway have an option to practice in computer-simulated environments provided by VR simulators before performing their first PDS implant insertion and refill-exchange procedures on patients. This Perspective article describes the unique advantages and technologic challenges that practice on VR simulators has to offer, and the experience of Archway physicians with VR technology as a first in any ophthalmic clinical trial.

Introducing a new technique to improve deep learning (DL) models designed for automatic grading of diabetic retinopathy (DR) from retinal fundus images by enhancing predictions' consistency.

A convolutional neural network (CNN) was optimized in three different manners to predict DR grade from eye fundus images. The optimization criteria were (1) the standard cross-entropy (CE) loss; (2) CE supplemented with label smoothing (LS), a regularization approach widely employed in computer vision tasks; and (3) our proposed non-uniform label smoothing (N-ULS), a modification of LS that models the underlying structure of expert annotations.

Performance was measured in terms of quadratic-weighted κ score (quad-κ) and average area under the receiver operating curve (AUROC), as well as with suitable metrics for analyzing diagnostic consistency, like weighted precision, recall, and F1 score, or Matthews correlation coefficient. While LS generally harmed the performance of the CNN, N-ULS statistically significantly improved performance with respect to CE in terms quad-κ score (73.17 vs. 77.69,

< 0.025), without any performance decrease in average AUROC. check details N-ULS achieved this while simultaneously increasing performance for all other analyzed metrics.

For extending standard modeling approaches from DR detection to the more complex task of DR grading, it is essential to consider the underlying structure of expert annotations. The approach introduced in this article can be easily implemented in conjunction with deep neural networks to increase their consistency without sacrificing per-class performance.

A straightforward modification of current standard training practices of CNNs can substantially improve consistency in DR grading, better modeling expert annotations and human variability.

A straightforward modification of current standard training practices of CNNs can substantially improve consistency in DR grading, better modeling expert annotations and human variability.

Optic disc (OD) and optic cup (OC) segmentation are fundamental for fundus image analysis. Manual annotation is time consuming, expensive, and highly subjective, whereas an automated system is invaluable to the medical community. The aim of this study is to develop a deep learning system to segment OD and OC in fundus photographs, and evaluate how the algorithm compares against manual annotations.

A total of 1200 fundus photographs with 120 glaucoma cases were collected. The OD and OC annotations were labeled by seven licensed ophthalmologists, and glaucoma diagnoses were based on comprehensive evaluations of the subject medical records. A deep learning system for OD and OC segmentation was developed. The performances of segmentation and glaucoma discriminating based on the cup-to-disc ratio (CDR) of automated model were compared against the manual annotations.

The algorithm achieved an OD dice of 0.938 (95% confidence interval [CI] = 0.934-0.941), OC dice of 0.801 (95% CI = 0.793-0.809), and CDR mean absolute error (MAE) of 0.077 (95% CI = 0.073 mean absolute error (MAE)0.082). For glaucoma discriminating based on CDR calculations, the algorithm obtained an area under receiver operator characteristic curve (AUC) of 0.948 (95% CI = 0.920 mean absolute error (MAE)0.973), with a sensitivity of 0.850 (95% CI = 0.794-0.923) and specificity of 0.853 (95% CI = 0.798-0.918).

We demonstrated the potential of the deep learning system to assist ophthalmologists in analyzing OD and OC segmentation and discriminating glaucoma from nonglaucoma subjects based on CDR calculations.

We investigate the segmentation of OD and OC by deep learning system compared against the manual annotations.

We investigate the segmentation of OD and OC by deep learning system compared against the manual annotations.