Report From the 2022 Mary Tyler Moore Vision Initiative Diabetic Retinal Disease Clinical Endpoints Workshop.

The Mary Tyler Moore Vision Initiative Diabetic Retinal Disease (DRD) Clinical Endpoints Workshop was held on October 22, 2022 to accelerate progress toward establishment of useful clinical and research endpoints and development of new therapeutics that have important relevance across the full spectrum of DRD pathology. More than 90 patient representatives, clinicians, scientists, funding and regulatory agencies, diagnostic, therapeutic and biotech industry representatives discussed the needs for new diagnostic and therapeutic approaches to prevent and restore retinal neurovascular unit integrity. Phase I of the MTM Vision Initiative plans, notably updating the DRD staging system and severity scale, establishing a human ocular biorepository and resource, and clinical endpoints and biomarker development and validation, was emphasized.

Analysis of Deep Convolutional Neural Networks Using Tensor Kernels and Matrix-Based Entropy.

Analyzing deep neural networks (DNNs) via information plane (IP) theory has gained tremendous attention recently to gain insight into, among others, DNNs' generalization ability. However, it is by no means obvious how to estimate the mutual information (MI) between each hidden layer and the input/desired output to construct the IP. For instance, hidden layers with many neurons require MI estimators with robustness toward the high dimensionality associated with such layers.

A clinically motivated self-supervised approach for content-based image retrieval of CT liver images.

Deep learning-based approaches for content-based image retrieval (CBIR) of computed tomography (CT) liver images is an active field of research, but suffer from some critical limitations. First, they are heavily reliant on labeled data, which can be challenging and costly to acquire. Second, they lack transparency and explainability, which limits the trustworthiness of deep CBIR systems.

Regional performance variation in external validation of four prediction models for severity of COVID-19 at hospital admission: An observational multi-centre cohort study.

Background: Prediction models should be externally validated to assess their performance before implementation. Several prediction models for coronavirus disease-19 (COVID-19) have been published. This observational cohort study aimed to validate published models of severity for hospitalized patients with COVID-19 using clinical and laboratory predictors.

A Multidisciplinary Quality Improvement Initiative to Facilitate Penicillin Allergy Delabeling Among Hospitalized Pediatric Patients.

Background: Penicillin allergy is reported in up to 10% of the general population; however, >90% of patients reporting an allergy are tolerant. Patients labeled as penicillin allergic have longer hospital stays, increased exposure to suboptimal antibiotics, and an increased risk of methicillin-resistant and . The primary aim with our quality improvement initiative was to increase penicillin allergy delabeling to at least 10% among all hospitalized pediatric patients reporting a penicillin allergy with efforts directed toward patients determined to be low risk for true allergic reaction.