Deep learning of the retina enables phenome- and genome-wide analyses of the microvasculature.

Background: The microvasculature, the smallest blood vessels in the body, has key roles in maintenance of organ health as well as tumorigenesis. The retinal fundus is a window for human in vivo non-invasive assessment of the microvasculature. Large-scale complementary machine learning-based assessment of the retinal vasculature with phenome-wide and genome-wide analyses may yield new insights into human health and disease. Methods: We utilized 97,895 retinal fundus images from 54,813 UK Biobank participants. Using convolutional neural networks to segment the retinal microvasculature, we calculated fractal dimension (FD) as a measure of vascular branching complexity, and vascular density. We associated these indices with 1,866 incident ICD-based conditions (median 10y follow-up) and 88 quantitative traits, adjusting for age, sex, smoking status, and ethnicity. Results: Low retinal vascular FD and density were significantly associated with higher risks for incident mortality, hypertension, congestive heart failure, renal failure, type 2 diabetes, sleep apnea, anemia, and multiple ocular conditions, as well as corresponding quantitative traits. Genome-wide association of vascular FD and density identified 7 and 13 novel loci respectively, which were enriched for pathways linked to angiogenesis (e.g., VEGF, PDGFR, angiopoietin, and WNT signaling pathways) and inflammation (e.g., interleukin, cytokine signaling). Conclusions: Our results indicate that the retinal vasculature may serve as a biomarker for future cardiometabolic and ocular disease and provide insights on genes and biological pathways influencing microvascular indices. Moreover, such a framework highlights how deep learning of images can quantify an interpretable phenotype for integration with electronic health records, biomarker, and genetic data to inform risk prediction and risk modification

The variation of radiologists' performance over the course of a reading session

The radiologist's task of reviewing many cases successively is highly repetitive and requires a high level of concentration. Fatigue effects have, for example, been shown in studies comparing performance at different times of day. However, little is known about changes in performance during an individual reading session. During a session reading an enriched case set, performance may be affected by both fatigue (i.e. decreasing performance) and training (i.e. increasing performance) effects. In this paper, we reanalyze 3 datasets from 4 studies for changes in radiologist performance during a reading session. Studies feature 8-20 radiologists reading and assessing 27-60 cases in single, uninterrupted sessions. As the studies were not designed for this analysis, study setups range from bone fractures to mammograms and randomization varies between studies. Thus, they are analyzed separately using mixed-effects models. There is some indication that, as time goes on, specificity increases (shown with p<0.05 for 2 out of 3 datasets, no significant difference for the other) while sensitivity may also increase (p<0.05 for 1 out of 3 datasets). The difficulty of 'normal' (healthy / non-malignant) and 'abnormal' (unhealthy / malignant) cases differs (p<0.05 for 3 out of 3 datasets) and the reader's experience may also be relevant (p<0.05 for 1 out of 3 datasets). These results suggest that careful planning of breaks and session length may help optimize reader performance. Note that the overall results are still inconclusive and a targeted study to investigate fatigue and training effects within a reading session is recommended. © 2013 SPIE

Myeloid and plasmacytoid dendritic cells: reference ranges in the peripheral blood of healthy children.

To date, few publications report on dendritic cells values in healthy children and mostly are found as control groups in studies focused on either allergic and autoimmune diseases or malignancies. This report provides an overview of 8 publications regarding absolute dendritic cells quantification in the peripheral blood of healthy children by using minimum manipulated samples processed within 24 hours