Prospective evaluation of optical coherence tomography for disease detection in the Casey mobile eye clinic

A Rapid Review of Interventions to Improve Care for People Who Are Medically Underserved with Multiple Sclerosis, Diabetic Retinopathy, and Lung Cancer

In the United States, patients with chronic conditions experience disparities in health outcomes across the care continuum. Among patients with multiple sclerosis, diabetic retinopathy, and lung cancer, there is a lack of evidence summarizing interventions to improve care and decrease these disparities. The aim of this rapid literature review was to identify interventions among patients with these chronic conditions to improve health and reduce disparities in screening, diagnosis, access to treatment and specialists, adherence, and retention in care. Using structured search terms in PubMed and Web of Science, we completed a rapid review of studies published in the prior five years conducted in the United States on our subject of focus. We screened the retrieved articles for inclusion and extracted data using a standard spreadsheet. The data were synthesized across clinical conditions and summarized. Screening was the most common point in the care continuum with documented interventions. Most studies we identified addressed interventions for patients with lung cancer, with half as many studies identified for patients with diabetic retinopathy, and few studies identified for patients with multiple sclerosis. Almost two-thirds of the studies focused on patients who identify as Black, Indigenous, or people of color. Interventions with evidence evaluating implementation in multiple conditions included telemedicine, mobile clinics, and insurance subsidies, or expansion. Despite documented disparities and a focus on health equity, a paucity of evidence exists on interventions that improve health outcomes among patients who are medically underserved with multiple sclerosis, diabetic retinopathy, and lung cancer.

Reducing speckle in anterior segment optical coherence tomography images based on a convolutional neural network

Speckle noise is ubiquitous in the optical coherence tomography (OCT) image of the anterior segment, which greatly affects the image quality and destroys the relevant structural information. In order to reduce the influence of speckle noise in OCT images, a denoising algorithm based on a convolutional neural network is proposed in this paper. Unlike traditional algorithms that directly obtain denoised images, the algorithm model proposed in this paper learns the speckle noise distribution through the constructed trainable OCT dataset and indirectly obtains the denoised result image. In order to verify the performance of the model, we compare the denoising results of the algorithm proposed in this paper with several state-of-the-art algorithms from three perspectives: qualitative evaluation from the subjective visual perspective, quantitative evaluation from objective parameter indicators, and running time. The experimental results show that the proposed algorithm has a good denoising effect on different OCT images of the anterior segment and has good generalization ability. Besides, it retains the relevant details and texture information in the image, and it has strong edge preserving ability. The image of OCT speckle removal can be obtained within 0.4 s, which meets the time limit requirement of clinical application.