Clinical Research Directory

AI-Assisted Saliva Diagnostics Using an Electrochemical Sensor Platform for Periodontitis Detection (SALIENCE)

This observational study aims to develop and validate a novel, AI-assisted electrochemical sensor platform for saliva-based diagnostics in periodontitis. Periodontitis is a chronic inflammatory disease affecting the gums and supporting tissues of the teeth. Despite its high global prevalence, early diagnosis remains challenging because the disease often progresses silently until irreversible damage has occurred. Saliva offers a promising, non-invasive diagnostic medium that reflects both oral and systemic health. However, its biological complexity and variability have limited its clinical use. This project addresses these challenges by combining advanced electrochemical sensing with artificial intelligence (AI) and synthetic data generation to improve diagnostic precision and reliability. The study involves the collection of saliva samples from adult participants with diagnosed periodontitis and from healthy controls. The samples will be analyzed using a modular sensor platform equipped with multiple electrodes that detect electrochemical signals from a wide range of salivary biomarkers. The sensor data will then be processed using machine learning models trained on both real and synthetic data to classify disease states. The main goals are to: Evaluate the performance of the electrochemical sensor array for saliva analysis. Develop and validate AI-based algorithms for detecting and differentiating between healthy and diseased samples. Generate feasibility data supporting future clinical implementation of saliva-based diagnostics for periodontitis. This interdisciplinary project combines expertise in clinical dentistry, biomedical engineering, and computer science. It is conducted in collaboration between Linköping University and Malmö University, with patient sampling carried out at an affiliated dental clinic. The study is expected to result in a working proof-of-concept device that enables real-time, non-invasive detection of periodontitis at the point of care. By enabling earlier diagnosis and more personalized treatment, this technology may transform periodontal care and serve as a foundation for future saliva-based diagnostics targeting other oral and systemic diseases.

Recovery Optimization Intervention to Reduce the Risk of Injuries in Soccer Players

The goal of this clinical trial is to evaluate if recovery interventions using cold and hot water immersion can reduce the risk of injuries in amateur soccer players. The main questions it aims to answer are: Do cold water immersions reduce muscle fatigue and improve performance more effectively than hot water immersions? What is the optimal timing and duration for these recovery interventions to maximize their effectiveness? Researchers will compare: Cold Water Immersion (CWI) Hot Water Immersion (HWI) No Immersion (NI) Thermographic Immersion (TI) based on individual thermal profiles Participants will: Undergo thermographic assessments to determine their thermal profiles. Perform physical tests such as isometric strength tests, countermovement jumps (CMJ), and squats with 50% body weight. Complete wellness questionnaires to assess their subjective recovery and fatigue levels. Provide blood and urine samples for biomarker analysis. Participate in recovery interventions (CWI, HWI, or TI) based on their assigned group.