Predicting optical coherence tomography-derived diabetic macular edema grades from fundus photographs using deep learning

Diabetic eye disease is one of the fastest growing causes of preventable blindness. With the advent of anti-VEGF (vascular endothelial growth factor) therapies, it has become increasingly important to detect center-involved diabetic macular edema (ci-DME). However, center-involved diabetic macular edema is diagnosed using optical coherence tomography (OCT), which is not generally available at screening sites because of cost and workflow constraints. Instead, screening programs rely on the detection of hard exudates in color fundus photographs as a proxy for DME, often resulting in high false positive or false negative calls. To improve the accuracy of DME screening, we trained a deep learning model to use color fundus photographs to predict ci-DME. Our model had an ROC-AUC of 0.89 (95% CI: 0.87-0.91), which corresponds to a sensitivity of 85% at a specificity of 80%. In comparison, three retinal specialists had similar sensitivities (82-85%), but only half the specificity (45-50%, p<0.001 for each comparison with model). The positive predictive value (PPV) of the model was 61% (95% CI: 56-66%), approximately double the 36-38% by the retinal specialists. In addition to predicting ci-DME, our model was able to detect the presence of intraretinal fluid with an AUC of 0.81 (95% CI: 0.81-0.86) and subretinal fluid with an AUC of 0.88 (95% CI: 0.85-0.91). The ability of deep learning algorithms to make clinically relevant predictions that generally require sophisticated 3D-imaging equipment from simple 2D images has broad relevance to many other applications in medical imaging

Case Series: Unbiased Deep Sequencing Analysis of Acute Infectious Conjunctivitis in an Ambulatory Eye Center in Berkeley, California.

SIGNIFICANCE: Acute infectious conjunctivitis poses significant challenges to eye care providers. It can be highly transmissible, and because etiology is often presumed, correct treatment and management can be difficult. This study uses unbiased deep sequencing to identify causative pathogens of infectious conjunctivitis, potentially allowing for improved approaches to diagnosis and management. PURPOSES: This study aimed to identify associated pathogens of acute infectious conjunctivitis in a single ambulatory eye care center. CASE REPORTS: This study included patients who presented to the University of California Berkeley eye center with signs and symptoms suggestive of infectious conjunctivitis. From December 2021 to July 2021, samples were collected from seven subjects (ages ranging from 18 to 38). Deep sequencing identified associated pathogens in five of seven samples, including human adenovirus D, Haemophilus influenzae , Chlamydia trachomatis , and human coronavirus 229E. CONCLUSIONS: Unbiased deep sequencing identified some unexpected pathogens in subjects with acute infectious conjunctivitis. Human adenovirus D was recovered from only one patient in this series. Although all samples were obtained during the COVID-19 pandemic, only one case of human coronavirus 229E and no SARS-CoV-2 were identified

Predicting optical coherence tomography-derived diabetic macular edema grades from fundus photographs using deep learning

Center-involved diabetic macular edema (ci-DME) is a major cause of vision loss. Although the gold standard for diagnosis involves 3D imaging, 2D imaging by fundus photography is usually used in screening settings, resulting in high false-positive and false-negative calls. To address this, we train a deep learning model to predict ci-DME from fundus photographs, with an ROC-AUC of 0.89 (95% CI: 0.87-0.91), corresponding&nbsp;to 85% sensitivity at 80% specificity. In comparison, retinal specialists have similar sensitivities (82-85%), but only half the specificity (45-50%, p &lt; 0.001). Our model can also detect the presence of intraretinal fluid (AUC: 0.81; 95% CI: 0.81-0.86) and subretinal fluid (AUC 0.88; 95% CI: 0.85-0.91). Using deep learning to make predictions via simple 2D images without sophisticated 3D-imaging equipment&nbsp;and with better than specialist performance, has broad relevance to many other applications in medical imaging

Enhanced ELISPOT detection of antigen-specific T cell responses from cryopreserved specimens with addition of both IL-7 and IL-15: the Amplispot assay

The importance of the enzyme-linked immunosorbent spot (ELISPOT) assay as a tool for studying immune responses in vitro is becoming increasingly apparent. However, there remains a need for enhanced sensitivity for the detection of low frequency antigen-specific T cell responses. We reasoned that the addition of a combination of the cytokines interleukin (IL)-7 and IL-15 would selectively increase interferon-gamma (IFN-gamma) production from antigen-stimulated CD4+ and CD8+ effector memory T cells. Freshly isolated or cryopreserved peripheral blood mononuclear cells (PBMC) from four healthy donors were analysed by ELISPOT for the frequency of purified protein derivative (PPD)-specific CD4+ T cells or cytomegalovirus (CMV) peptide-specific CD8+ T cells. Addition of IL-7 and IL-15 increased the number of PPD-specific CD4+ T cells up to 2.4-fold in fresh PBMC and up to 18-fold in cryopreserved PBMC. The cytokines also increased the number of CMV peptide-specific CD8+ T cells in fresh PBMC up to 7.5-fold. No additional increases were seen when antibodies to co-stimulatory molecules CD28 and CD49d were applied together with the cytokine combination. These data demonstrate that the sensitivity of the ELISPOT assay may be significantly augmented by addition of the cytokines IL-7 and IL-15 to antigen-stimulated cells. This method will be particularly useful for the assessment of antigen-stimulated cytokine production by T cells in cryopreserved biological specimens

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