Signals, in order.

Published intelligence from the original source record.

Source: EPFL News

The release adds peer metadata support, corrects the reported build-time version, mirrors binaries to CSCS ocf-share via FirecREST, and resolves symlink targets to absolute paths. These changes improve system transparency, distribution, and reliability. The update is part of ongoing Swiss AI research efforts.

Source: GitHub Releases & Activity

The repository swiss-ai/OpenTela is a research project located in Switzerland, and its latest development build is identified by commit f8088e1 from the main branch.

Source: GitHub Releases & Activity

This release updates the OpenTela project with new features and bug fixes, including ROCm stats printing, asynchronous CRDT synchronization, and a fix for orphan CID autoban issues. The update also marks the first contribution from user @xzyaoi. The release demonstrates ongoing development within the Swiss AI community, enhancing the project's capabilities and collaboration.

Source: GitHub Releases & Activity

OpenTela released version 0.0.4, adding ROCm support and a development deployment feature as described in pull request #3 by @robmsmt.

Source: GitHub Releases & Activity

OpenTela released version 0.0.3, reducing the log level for the boxo external library as part of a minor update.

Source: GitHub Releases & Activity

This retrospective cohort study uses radiographic and CT imaging data from Schulthess Klinik Zürich to develop and validate AI-based tools for assessing trapeziometacarpal (TMC) joint osteoarthritis and monitoring implants. The project comprises four subprojects: a new radiographic classification system for TMC OA, deep‑learning automation of that classification, automated detection of implant migration, and 3D reconstruction of the TMC joint from biplanar radiographs. Data are drawn from patients with the Touch implant and from other wrist‑related conditions, and the resulting prototypes are…

Source: ClinicalTrials.gov

Published intelligence from the original source record.

Source: ClinicalTrials.gov

The United States research project develops clinical decision support tools that combine established diagnostic variables with machine learning models to rapidly diagnose life‑threatening cardiovascular conditions in emergency department patients presenting with chest pain or dyspnea. The goal is to improve diagnostic accuracy, accelerate patient management, and reduce medical errors.

Source: ClinicalTrials.gov

This prospective, multinational, multicenter observational study follows patients with non-foveal, non-vision compromising atrophic age‑related macular degeneration for 24 months with 6‑month intervals. It aims to assess individual progression rates, identify focal and global retinal alterations, and evaluate monitoring using approved AI algorithms through routine imaging (Scanning Laser Fundus Photography, Color Fundus Photography, OCT, OCTA). No intervention is performed during the study, though treatment may be provided if approved in the EU.

Source: ClinicalTrials.gov