Signals, in order.

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

Published intelligence from the original source record.

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

This multi-center observational case-control study in ICU patients aims to identify novel biomarkers for early recognition of severe community acquired pneumonia-associated sepsis and to predict sepsis-related mortality. Patients with sCAP will be profiled over time and compared with controls, using modern omics technologies to explore mechanisms and influencing factors. The resulting data will be analyzed with machine learning algorithms and multi-dimensional mathematical models.

Source: ClinicalTrials.gov