Signals, in order.

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

The study is a prospective, multinational, multicenter observational trial that follows patients with early and intermediate age‑related macular degeneration (AMD) over 24 months to predict conversion to advanced AMD. It uses routine imaging modalities—scanning laser fundus photography, color fundus photography, optical coherence tomography, and OCT angiography—to identify focal and global retinal changes and assess individual risk of progression. Artificial intelligence is employed to enhance AMD classification alongside traditional models, with no intervention performed during the study per…

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

This research, conducted in the United States, examines the limited evidence supporting current clinical guidelines for diagnosing and treating rotator cuff injuries. The study compares the performance of various machine learning models that use pre‑operative data from an international, multicentric database to predict whether patients who undergo rotator cuff repair will achieve the minimal important change (MIC) for the single assessment numeric evaluation (SANE) at one‑year follow‑up.

Source: ClinicalTrials.gov

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 study evaluates if it is possible to identify quantitative parameters from audio signals to describe the changes in patient's state in relation to frailty and distress. It is conducted in the United States and falls under the research category.

Source: ClinicalTrials.gov