Signals, in order.

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

The Kalibr repository, hosted by ETH Zurich's Autonomous Systems Lab, contains the Kalibr visual‑inertial calibration toolbox. It is a research tool used for calibrating visual and inertial sensors.

Source: GitHub Releases & Activity

The ADOmiARN study is a multicentre, prospective, longitudinal, non-interventional observational study conducted in France and Belgium. It evaluates the in vitro diagnostic medical device EndoTest® in adolescents aged 10 to 19 years with suspected or formally diagnosed endometriosis, while keeping the usual care pathway unchanged. The study collects saliva samples and self-reported symptom and quality-of-life questionnaires without altering standard treatments or diagnostic examinations.

Source: ClinicalTrials.gov

This research examines whether women experience more changes in melanocytic nevi during and after pregnancy compared to age‑matched non‑pregnant women. It also evaluates the psychological impact of total body mapping and dermoscopic examinations that incorporate artificial intelligence during pregnancy.

Source: ClinicalTrials.gov

The PANIC study is a research project based in the United States that seeks to develop a machine‑learning application for accurately predicting which patients are at risk of anastomotic insufficiency following colon and colorectal surgery. The goal is to provide a preoperative risk assessment tool to improve surgical outcomes.

Source: ClinicalTrials.gov

Published intelligence from the original source record.

Source: ClinicalTrials.gov

The project at Inselspital Bern will develop a clinical decision support system that uses self‑learning AI to analyze cardiotocography traces from 10,000–15,000 maternity records. CSEM will design, develop, and validate multiple machine‑learning architectures to predict the optimal fetal extraction time during labor, providing a superhuman support tool for obstetricians. The system will be trained on existing clinical outcomes to improve decision‑making accuracy.

Source: ClinicalTrials.gov