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

The project aims to develop a predictive model for clinically significant depressive symptoms (CSDS) in patients undergoing coronary artery bypass graft (CABG) surgery using pre‑ and perioperative data. CSDS occur in about 30 % of CABG patients, which is four times higher than in the general population, and are linked to poor quality of life and increased morbidity and mortality. The model seeks to identify patients at risk so clinicians can make informed decisions and implement preventive measures.

Source: ClinicalTrials.gov

The AI-POD Clinical Validation Study targets patients with severe obesity undergoing a computerised tomography (CT) scan of the heart. It is a multi‑centre, controlled, open, randomised study involving 1,200 participants across Europe. Randomly selected participants will install a citizen app on their smartphone and receive a fitness tracker to record heart rate and daily steps.

Source: ClinicalTrials.gov

This prospective cohort study, part of the Clinnova programme, aims to identify clinical imaging and omics characteristics linked to early multiple sclerosis and its progression to a progressive form. It also seeks to investigate digital biomarkers that enable continuous clinical monitoring of patients.

Source: ClinicalTrials.gov

The study compares CT data with routine measurements to assess bone and muscle health in older adults. It uses AI algorithms to analyze existing CT scans for muscle adiposity and density, aiming for early detection of sarcopenia and osteoporosis. Functional tests such as hand strength and walking speed are also included to evaluate muscle condition.

Source: ClinicalTrials.gov

The pilot study, conducted in the United States, aims to provide evidence for a non-invasive, continuous, and transcutaneous method to determine blood glucose levels robustly and accurately. It employs advanced spectroscopy techniques combined with machine learning models to achieve precise glucose monitoring.

Source: ClinicalTrials.gov

This research evaluates the LarynGuide™ software, which runs on a video-laryngoscope platform, to determine whether it provides accurate and reliable guidance on the position of the tracheal tube following an intubation attempt. The study is conducted in the United States and focuses on the software’s ability to advise clinicians during laryngoscopy and intubation.

Source: ClinicalTrials.gov

This prospective, multinational, multicenter observational study follows patients with early and intermediate age‑related macular degeneration (AMD) over 24 months with 6‑month intervals to predict conversion to advanced AMD. It aims to identify focal and global retinal changes, assess individual risk of progression, map the sequence of events leading to advanced AMD, and improve AMD classification using artificial intelligence alongside traditional models. Imaging modalities include scanning laser fundus photography, color fundus photography, optical coherence tomography, and optical coheren…

Source: ClinicalTrials.gov