DISCOVER: 2-D multiview summarization of Optical Coherence Tomography Angiography for automatic diabetic retinopathy diagnosis.

Diabetic Retinopathy (DR), an ocular complication of diabetes, is a leading cause of blindness worldwide. Traditionally, DR is monitored using Color Fundus Photography (CFP), a widespread 2-D imaging modality. However, DR classifications based on CFP have poor predictive power, resulting in suboptimal DR management. Optical Coherence Tomography Angiography (OCTA) is a recent 3-D imaging modality offering enhanced structural and functional information (blood flow) with a wider field of view. This paper investigates automatic DR severity assessment using 3-D OCTA. A straightforward solution to this task is a 3-D neural network classifier. However, 3-D architectures have numerous parameters and typically require many training samples. A lighter solution consists in using 2-D neural network classifiers processing 2-D en-face (or frontal) projections and/or 2-D cross-sectional slices. Such an approach mimics the way ophthalmologists analyze OCTA acquisitions: (1) en-face flow maps are often used to detect avascular zones and neovascularization, and (2) cross-sectional slices are commonly analyzed to detect macular edemas, for instance. However, arbitrary data reduction or selection might result in information loss. Two complementary strategies are thus proposed to optimally summarize OCTA volumes with 2-D images: (1) a parametric en-face projection optimized through deep learning and (2) a cross-sectional slice selection process controlled through gradient-based attribution. The full summarization and DR classification pipeline is trained from end to end. The automatic 2-D summary can be displayed in a viewer or printed in a report to support the decision. We show that the proposed 2-D summarization and classification pipeline outperforms direct 3-D classification with the advantage of improved interpretability.

Federated Learning for Diabetic Retinopathy Detection in a Multi-center Fundus Screening Network.

Federated learning (FL) is a machine learning framework that allows remote clients to collaboratively learn a global model while keeping their training data localized. It has emerged as an effective tool to solve the problem of data privacy protection. In particular, in the medical field, it is gaining relevance for achieving collaborative learning while protecting sensitive data. In this work, we demonstrate the feasibility of FL in the development of a deep learning model for screening diabetic retinopathy (DR) in fundus photographs. To this end, we conduct a simulated FL framework using nearly 700,000 fundus photographs collected from OPHDIAT, a French multi-center screening network for detecting DR. We develop two FL algorithms: 1) a cross-center FL algorithm using data distributed across the OPHDIAT centers and 2) a cross-grader FL algorithm using data distributed across the OPHDIAT graders. We explore and assess different FL strategies and compare them to a conventional learning algorithm, namely centralized learning (CL), where all the data is stored in a centralized repository. For the task of referable DR detection, our simulated FL algorithms achieved similar performance to CL, in terms of area under the ROC curve (AUC): AUC =0.9482 for CL, AUC = 0.9317 for cross-center FL and AUC = 0.9522 for cross-grader FL. Our work indicates that the FL algorithm is a viable and reliable framework that can be applied in a screening network.Clinical relevance- Given that data sharing is regarded as an essential component of modern medical research, achieving collaborative learning while protecting sensitive data is key.

Prediction of skin anti-aging clinical benefits of an association of ingredients from marine and maritime origins: Ex vivo evaluation using a label-free quantitative proteomic and customized data processing approach.

BACKGROUND: The application of ingredients from marine and maritime origins is increasingly common in skin care products, driven by consumer expectations for natural ingredients. However, these ingredients are typically studied for a few isolated in vitro activities. OBJECTIVES: The purpose of this study was to carry out a comprehensive evaluation of the activity on the skin of an association of ingredients from marine and maritime origins using label-free quantitative proteomic analysis, in order to predict the clinical benefits if used in a skin care product. METHODS: An aqueous gel containing 6.1% of ingredients from marine and maritime origins (amino acid-enriched giant kelp extract, trace element-enriched seawater, dedifferentiated sea fennel cells) was topically applied on human skin explants. The skin explants' proteome was analyzed in a label-free manner by high-performance liquid nano-chromatography coupled with tandem mass spectrometry. A specific data processing pipeline (CORAVALID) providing an objective and comprehensive interpretation of the statistically relevant biological activities processed the results. RESULTS: Compared to untreated skin explants, 64 proteins were significantly regulated by the gel treatment (q-value ≤ 0.05). Computer data processing revealed an activity of the ingredients on the epidermis and the dermis. These significantly regulated proteins are involved in gene expression, cell survival and metabolism, inflammatory processes, dermal extracellular matrix synthesis, melanogenesis and keratinocyte proliferation, migration, and differentiation. CONCLUSIONS: These results suggest that the tested ingredients could help to preserve a healthy epidermis and dermis, and possibly to prevent the visible signs of skin aging.