Clinical trials

This multicenter, prospective study aims to evaluate the real-world clinical utility of chest X-ray (CXR) for large-scale thoracic disease screening. Adult participants presenting for health examinations will undergo digital CXR screening, and any suspected abnormalities will be confirmed via a gold standard reference, such as a chest CT scan or clinical follow-up. The primary outcome measure is the detection rate (screening yield) of confirmed thoracic conditions. Secondary measures will assess diagnostic accuracy (sensitivity and specificity), false-positive rates, and multi-center reading consistency. By providing prospective, large-cohort evidence, this research seeks to validate the cost-effectiveness and feasibility of CXR in identifying early-stage lung, pleural, and cardiac abnormalities, ultimately guiding public health strategies and optimizing early medical intervention.

This study is a randomized controlled trial with 120 overweight or obese (body-mass index, BMI, ≥ 24 kg/m²) participants and 20 normal-weight participants. Twelve weeks of energy-restricted nutritional and lifestyle intervention with placebo, Lactobacillus paracasei LC-19 (LC-19), or semaglutide (a glucagon-like peptide-1 receptor agonist, GLP-1RA) will be randomly conducted in the overweight or obese participants. The primary goal is to clarify the roles of LC-19 and semaglutide in weight reduction and in improving energy, glucose, and lipid metabolism, while also comparing side effects, adverse events, and long-term outcomes such as weight regain between these two interventions. In addition, the study will explore key factors affecting intervention response to provide evidence for optimizing individualized intervention strategies.

This study is a randomized clinical trail with a parallel design, involving 120 overweight/obese (body-mass index, BMI ≥ 24 kg/m²) participants and 20 normal-weight participants. Overweight/obese participants will be randomly allocated to one of three groups: 1) calorie restricted balanced diet (CRD)group; 2) CRD + semaglutide group; or 3) CRD + segaglutide with protein supplementation. Through a 3-month weight loss intervention and 6-month follow-up, this study aims to investigate the effects of dietary protein supplementation combined with semaglutide on weight loss, energy and glucose and lipid metabolism, muscle loss, and weight regain. Additionally, the study will explore key factors affecting intervention efficacy, including obesity phenotypes, gut microbiota profiles, genetic backgrounds, and lifestyle factors, to provide evidence for optimizing individualized intervention strategies.

Hepatocellular carcinoma (HCC) is a common malignancy in China with a high mortality rate. Its early recurrence and long-term prognosis are closely associated with tumor aggressiveness. Microvascular invasion (MVI), defined as the presence of tumor cells within small branches of the portal or hepatic veins, is a key indicator of malignant biological behavior in HCC. Clinically, MVI is strongly correlated with postoperative early recurrence and serves as an important factor in determining surgical margin extension, adjuvant therapy, and postoperative management strategies. At present, definitive diagnosis of MVI still relies on postoperative pathological examination, and stable, effective preoperative assessment methods are lacking. Although some studies have attempted to predict MVI using preoperative imaging features, their clinical translation remains limited by poor generalizability, weak interpretability, and insufficient cross-center adaptability. This study aims to leverage multiphase preoperative CT imaging, artificial intelligence techniques, and clinical prior knowledge to develop a high-performance, generalizable, and interpretable computer-aided diagnostic system for preoperative prediction of HCC-MVI. An observational, prospective evaluation will be conducted to assess system performance and to facilitate the clinical translation of intelligent diagnostic technologies in real-world practice.

Laparoscopic cholecystectomy is a common surgical procedure, but it carries the potential for bile duct injury and other surgical risks. To provide visual assistance to surgeons during surgery and mitigate these risks, this research project aims to develop a real-time object recognition algorithm based on deep learning technology. This algorithm will label key anatomical structures in laparoscopic cholecystectomy videos, providing surgeons with immediate information on dangerous and safe areas.

This study seeks to develop a deep-learning-based intelligent predictive model for the efficacy of neoadjuvant chemotherapy in gastric cancer patients. By utilizing the patients' CT imaging data, biopsy pathology images, and clinical information, the intelligent model will predict the post-neoadjuvant chemotherapy efficacy and prognosis, offering assistance in personalized treatment decisions for gastric cancer patients.

This study will evaluate the utility of ChatGPT in recommending treatment plans for patients with gastrointestinal cancers, using both retrospective and prospective data.

This research integrates artificial intelligence to enhance early pregnancy ultrasonography quality control, focusing on specific fetal sections. In collaboration with prominent medical institutions, the investigators have amassed extensive fetal ultrasound data. The investigators aim to develop a deep learning model that can accurately identify essential anatomical areas in ultrasound images and evaluate their quality. This tool is expected to significantly decrease misdiagnoses of conditions like Down Syndrome and neural system deformities by ensuring real-time image quality assessment.

This study is to evaluate whether intraoperative fluorescence imaging targeting GPC3 can aid improve the surgical accuracy of hepatocellular carcinoma. The main purposes of this study include: ① To raise the detection rate of hepatocellular carcinoma intraoperatively using the novel NIR-II fluorescence molecular imaging and the GPC-3 targeted fluorophore. ② To validate the safety and effectiveness of the designed GPC-3 targeted fluorophore for clinical application.

Peritoneal metastasis of gastric cancer is difficult to be detected in time, thus delaying treatment. Based on the conventional CT images of gastric cancer, this study plans to develop, improve and validate an intelligent analysis system based on radiomics. By extracting and combining the radiomics features related to peritoneal metastasis of gastric cancer, the intelligent analysis system could predict the risk of peritoneal metastasis, and provide personalized decision suggestions for the treatment of gastric cancer.