View clinical trials related to Diagnosis.
Filter by:The primary goal of this study is to estimate the effectiveness of a medical decision support system based on artificial intelligence in the endoscopic diagnosis of benign tumors. Researchers will compare Adenoma detection rate between "artificial intelligence - assisted colonoscopy" and "conventional colonoscopy" groups to evaluate the clinical effectiveness of artificial intelligence model.
The goal of this prospective, diagnostic observational study is to learn about how imaging based markers for components of liver disease appear in children with obesity. It aims to determine whether the imaging markers (ultrasound and MRI) for liver disease can be tools to improve diagnostics for liver affection in children with obesity and to ascertain how the markers are related to multiple clinical measures, for example BMI and serology measure, and treatment effects over time.
This clinical trial was designed as a prospective, multicenter, multi-reader multi-case (MRMC), superiority, parallel-controlled study. Participants who met the trial criteria and signed the informed consent form were enrolled. The trial group involved diagnoses of caries on panoramic radiographs using an artificial intelligence-assisted diagnostic system, while the control group involved diagnoses made by dental practitioners specializing in operative dentistry and endodontics with five years of experience, who interpreted oral panoramic radiographs to determine the presence and severity of caries.
Gestational diabetes mellitus (GDM) is a condition that can affect pregnant women during pregnancy and may cause complications for the mother and the baby. Therefore, early and accurate detection is necessary to provide the woman and the baby with better health outcomes. Currently, the most commonly used criteria to detect GDM is the International Association of Diabetes and Pregnancy Study Groups (IADPSG) criterion. However, there is a suggestion that it results in over-diagnosis of GDM, and newer methods of diagnosis have been proposed. One such proposal is to have more than a binary outcome of assessment of dysglycemia in pregnancy. The investigator group created this criterion known as the National Priorities Research Program (NPRP) criterion. This clinical trial compares the IADPSG to the NPRP criteria in pregnant women in Qatar to determine if this newer method mitigates overdiagnosis and more accurately identifies women at risk of complications.
After neoadjuvant therapy, the primary lesion in breast cancer patients may experience tumor regression, which increases the difficulty of distinguishing between breast cancer and adjacent tissues. Raman spectroscopy is a form of scattering spectroscopy, which offers rapid and sensitive analysis, delivering detailed biochemical information and molecular signatures of internal molecular components within the sample. This study aims to discern between cancer and adjacent tissues after neoadjuvant therapy in breast cancer patients using label-free Raman spectroscopy.
Background：Gastric cancer is a globally important disease and the fifth most diagnosed malignant cancer in the world. Because it is usually diagnosed at an advanced stage, gastric cancer has a high mortality rate, making it the third most common cause of cancer-related death. Hot spots of gastric cancer incidence and mortality exist in East Asia, Eastern Europe and South America. It is still an urgent problem to find new diagnostic and prognostic markers and better understand the molecular mechanism of gastric cancer. Although radical resection and systemic chemotherapy have shown great improvement, the prognosis of gastric cancer (GC) patients is still depressing due to malignant proliferation and metastasis. Therefore, it is urgent to clarify the potential molecular mechanism of gastric cancer progression, which will contribute to the development of targeted therapy. Effective induction of tumor cell apoptosis is the most important feature of a new chemical agent for cancer treatment. There is increasing evidence that the cell cycle can act in concert with apoptosis to cause cell death under certain cellular stress conditions. A comprehensive understanding of the relationship between apoptosis and cell cycle is essential for developing effective cancer therapies. PWP1 is also known as endonuclein, which contains five WD40 repeated domains and belongs to the WD40-repeated superfamily. It is highly expressed in human pancreatic adenocarcinoma, where it functions as a cell-cycle regulator. However, the normal function of Pwp1 is largely unknown. Previous research data show that PWP1 plays a key role in regulating biological functions such as RNA processing, signal transduction, gene expression, vesicle transport, cytoskeleton assembly and cell cycle progression. Whether the high expression of PWP1 is ubiquitous in tumors, the relationship between the high expression and clinicopathological factors of tumors, and the mechanism of PWP1 in tumors are still unclear. Further exploration of the molecular mechanism of PWP1 in GC may provide new ideas and therapeutic targets for GC treatment in the future, and benefit clinical patients.
This study was conducted to investigate the relationship between diastolic notching on uterine artery Doppler and serum apelin-13 and 36 concentrations between 11 and 14 weeks of gestation.
Congenital heart disease (CHD) is the most common congenital disease in children. The early detection, diagnosis and treatment of CHD in children is of great significance to improve the prognosis and reduce the mortality of children, but the current screening methods have limitations. Electrocardiogram (ECG), as an economical and rapid means of heart disease detection, has a very important value in the auxiliary diagnosis of CHD.Big data and deep learning technologies in artificial intelligence (AI) have shown great potential in the medical field. The advent of the big data era provides rich data resources for the in-depth study of CHD ECG signals in children. The development of deep learning technology, especially the breakthrough in the field of image recognition, provides a strong technical support for the intelligent analysis of electrocardiogram. The particularity of children electrocardiogram requires the development of a special algorithm model. At present, the research on the application of deep learning models to identify children's electrocardiograms is limited, and the training and verification from large data sets are lacking. Based on the Chinese Congenital Heart Disease Collaborative Research Network, this project aims to integrate data and deep learning technology to develop a set of intelligent electrocardiogram assisted diagnosis system (CHD-ECG AI system) suitable for children with CHD, so as to improve the early detection rate of CHD and improve the efficiency of congenital heart disease screening.
The aim of this study is to propose an intelligent diagnosis and treatment system for for pelvic floor dysfunction in elderly women. The main question it aims to answer: 1) How can the investigators find out early if older women have different pelvic floor muscle functions? 2）How can the investigators give personalized treatment plans based on differences in pelvic floor function? Participants will be assigned different training programs by the system. The investigators will compare the treatment effects and costs of older women with pelvic floor dysfunction using and not using the system. All the participants will be offered examinations for pelvic floor function and different treatments. All examinations and treatments are non-invasive.
The aim of this Study is to collect radiologist feedback to support the further development and improvement of the imaging modes implemented on the embedded software in the SuperSonic® Ultrasound System (including the probe).