Clinical Research Directory

Artificial Intelligence-assisted HER2 Expression Assessment in Urothelial Carcinoma Based on Imaging-pathology Omics

This study aims to build upon previous research by using artificial intelligence methods to fuse multimodal data from imaging and pathology to construct a predictive model for HER2 expression in urothelial carcinoma. The model's performance will be validated and optimized using a multicenter cohort study, ultimately achieving accurate and rapid prediction of HER2 expression. This will guide precise decision-making for further HER2-targeted therapy and improve patient prognosis. Big data analysis and deep learning will also assist physicians in more accurately diagnosing the disease and developing personalized treatment plans. The research findings will promote the integration and development of artificial intelligence technology with the healthcare industry in the application of multimodal data from clinical, imaging, and pathology perspectives.

Optical Characterization and Multi-modality, Multi-scale Modeling of Human Skin Applied to Cancer Diagnosis.

Skin carcinomas are the most commonly diagnosed cancers in fair-skinned populations, for example in France, Western Europe, and North America in particular. The OpticSkin project will build and make available to the general public and the scientific and medical community a histological and optical spectroscopic database of healthy, precancerous, and cancerous human skin in terms of absorption, elastic and inelastic scattering (Raman), steady-state and time-resolved autofluorescence, and polarization. The aim is to identify spectroscopic signatures that will be useful for diagnosis.

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.

Female Genital Schistosomiasis in Migrant Women: A Pilot Study

This pilot study, "GynoBizh," investigates the frequency and clinical characteristics of female genital schistosomiasis (FGS) among migrant women from sub-Saharan Africa living in non-endemic countries. Schistosomiasis is a significant global parasitic disease, with a high seroprevalence in migrants. The study aims to assess the presence of genital lesions through gynecological examinations, colposcopy, and molecular tests, identifying diagnostic markers and associated health conditions. Fifty participants will be followed over a year to improve understanding and management of FGS in underserved populations.

A Study on Diagnosis and Treatment Strategies for Atlantoaxial Dislocation

The most effective treatment for atlantoaxial dislocation is surgical treatment, with the principle of achieving reduction, reconstruction, and fusion of the atlantoaxial joint. The surgical strategies mainly include simple anterior approach, simple posterior approach, and combined anterior posterior approach. The investigators have summarized 904 cases of atlantoaxial instability or dislocation from 1998 to 2010 and preliminarily published the diagnosis and treatment strategy tree of the Third Hospital of Beijing Medical University. This strategy is divided into four types based on the severity of atlantoaxial dislocation: unstable, reversible, difficult to recover, and skeletal, and enters different surgical treatment processes. With the increase in the number of cases, accumulation of experience, and technological improvements in the past decade, spinal surgery colleagues have updated their classification diagnosis, diagnosis and treatment processes, and surgical techniques for atlantoaxial instability or dislocation. However, the selection of treatment strategies for atlantoaxial dislocation is mostly based on the surgeon's own experience, and there is a lack of standardized, large-scale, and high-level evidence-based medical research on the safety and effectiveness of current empirical strategies. Based on this, this study intends to adopt a multicenter, retrospective, and prospective study to construct a high-quality clinical cohort of atlantoaxial dislocation, update the classification and diagnosis and treatment strategies of atlantoaxial dislocation. And conduct long-term follow-up on patients to evaluate their safety and effectiveness, guide the surgical treatment of atlantoaxial dislocation, and thus form a recognized diagnostic and treatment standard for atlantoaxial dislocation.