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Filter by: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.
Early diagnosis of cancer is key for improving patient outcomes, but cancers are difficult to diagnose if patients present with unspecific symptoms. The principal objective of the MEDECA (Markers in Early Detection of Cancer) study is to identify a multi-analyte blood test that can detect and map occult cancer within a mixed population of patients presenting with serious but unspecific symptoms. The study will include 1500 patients referred to the Diagnostic Center at Danderyd Hospital (DC DS), a multidisciplinary diagnostic center referral pathway for patients with radiological findings suggestive of metastasis without known primary tumor or suspicion of serious but unspecific symptoms. Blood samples are collected prior to a standardized and extensive cancer diagnostic work-up, including an expanded panel of biochemical analyses and extensive imaging such as computed tomography or magnetic resonance investigations. In collaboration with world-leading international scientists, the blood samples will be analyzed for a panel of novel and established blood biomarkers predictive of an underlying cancer, including markers of neutrophil extracellular traps, circulating tumor DNA, platelet mRNA profiling, affinity-based proteomics and nuclear magnetic resonance metabolomics. The diagnostic accuracy of the blood biomarkers with respect to cancer detection during the diagnostic work-up will be analyzed through machine learning.
It is important to diagnose pulmonary embolism in a timely manner to prevent death and long-term disability. More than half a million people (4-5% of emergency department patients) are tested for pulmonary embolism, although positive results are low. Imaging for PE testing exposes patients to radiation, is expensive, adds time to the emergency visit, and can lead to a false positive diagnoses. Existing protocols aimed at reducing unnecessary pulmonary embolism imaging are complex and seldom used by emergency physicians. Too many patients undergo unnecessary pulmonary embolism imaging. We have created a new tool (called Adjust-Unlikely) which could safely reduce pulmonary embolism imaging in Canada. Our research group composed of researchers, emergency physicians, and patients developed the Adjust-Unlikely clinical decision rule: a rule which has been customized for emergency physicians and emergency patients. Adjust-Unlikely is highly sensitive at the bedside, meaning there are very few false negative results. Our study aim is to prospectively validate Adjust-Unlikely pulmonary embolism testing in emergency patients with suspected pulmonary embolism.
- This study aim to develope a diagnostic method of pancreatic cancer by using a reagent for analyzing purine metabolite (Hypoxanthine, Xanthine) in urine. - It is safe and cost effective compare to radiologic or blood test. It can be used for initial screening test for healty population.
Aim of the study: To evaluate the value of Probe Confocal Laser Endomicroscopy (PCLE) in surgery for pancreatic tumor. Methods: Patients who are diagnosed with pancreatic tumor based on preoperative radiographic findings and will undergo radical resection are included in this clinical study. PCLE will be used in surgery to identify tumor is malignant or not, and surgeons will decide procedures of surgery based on outcomes of PCLE. In this present study, clinical trials will be divided into two phases. In the first phase, based on the definitive postoperative pathologic diagnosis, characteristic imaging structures that were collected by PCLE will be identified and primary diagnostic imaging criteria for pancreatic cancer would be developed. In the second phase, this criterion will be used for rapid intraoperative diagnosis of pancreatic cancer and predicting status of resection margin. In addition, accuracy of PCLE will be verified based on postoperative pathologic reports.
Breast phyllodes tumor (PT) is a rare fibroepithelial tumor, accounting for 1% to 3% of all breast tumors, categorized by the WHO into benign, borderline, and malignant, based on histopathology features such as tumor border, stromal cellularity, stromal atypia, mitotic activity and stromal overgrowth. Malignant PTs account for 18%-25%, with high local recurrence (up to 65%) and distant metastasis rates (16%-25%). Benign PT could progress to malignancy after multiple recurrences. Therefore, Early, accurate diagnosis and identification of therapeutic targets are crucial for improving outcomes and survival rates. In recent years, there has been growing interest in the application of artificial intelligence (AI) in medical diagnostics. AI can integrate clinical information, histopathological images, and multi-omics data to assist in pathological and clinical diagnosis, prognosis prediction, and molecular profiling.AI has shown promising results in various areas, including the diagnosis of different cancers such as colorectal cancer, breast cancer, and prostate cancer. However, PT differs from breast cancer in diagnosis and treatment approach. Therefore, establishing an AI-based system for the precise diagnosis and prognosis assessment of PT is crucial for personalized medicine. The research team, led by Dr. Nie Yan, is one of the few in Guangdong Province and even nationally, specializing in PT research. Their team has been conducting research on the malignant progression, metastasis mechanisms, and molecular markers for PT. The team has identified key mechanisms, such as fibroblast-to-myofibroblast differentiation, and the role of tumor-associated macrophages in promoting this differentiation. They have also identified molecular markers, including miR-21, α-SMA, CCL18, and CCL5, which are more accurate in predicting tumor recurrence risk compared to traditional histopathological grading. The project has collected high-quality data from nearly a thousand breast PT patients, including imaging, histopathology, and survival data, and has performed transcriptome gene sequencing on tissue samples. They aim to build a comprehensive multi-omics database for breast PT and create an AI-based model for early diagnosis and prognosis prediction. This research has the potential to improve the diagnosis and treatment of breast PT, address the disparities in breast PT care across different regions in China, and contribute to the development of new therapeutic targets.
Moesin deficiency was initially described in 7 male participants aged 4 to 69 years and is characterized by lymphopenia of the 3 lineages and moderate neutropenia. Genetically, 6 out of 7 participants had the same missense mutation in the moesin gene located on the X chromosome. The 7th patient has a mutation leading to the premature introduction of a STOP codon into the protein.Clinically the 7 participants with X-linked moesin-associated immunodeficiency all presented with recurrent bacterial infections of the respiratory, gastrointestinal or urinary tracts, and some had severe varicella.Therapeutically, in the absence of a molecular diagnosis and due to his SCID-like phenotype, one patient was treated with geno-identical hematopoietic stem cell transplantation . The remaining are untreated or treated with immunoglobulin substitution and/or prophylactic antibiotics. Since this study, the moesin gene has been integrated into DNA chips used for the molecular diagnosis of immune deficiencies in several countries. Physicians in Canada, the United States, Japan, South Africa and Europe have contacted us with a total of 16 known participants to date. Because of their very low severe, uncontrolled CMV infection and the absence of treatment recommendations, two 2 American participants were treated with allogeneic transplantation with severe post-transplant complications (1), and one of the participants died as a result of the transplant. Management of XMAID participants therefore varies widely from country to country, depending on age at diagnosis and clinical picture. It ranges from no treatment treatment (associated with recurrent infections and skin manifestations), IgIv substitution and/or antibiotic prophylaxis antibiotic prophylaxis, with low toxicity and apparent efficacy, and allogeneic transplantation, with all the risks risks involved (graft-related toxicity, graft versus host, disease, rejection, risk of infection). The Investigators therefore feel it is important to review the diagnosis, clinical presentation and management of X-MAID participants. The study the investigator propose will enable to understand the presentation of X-MAID participants, establish guidelines and provide the best treatment for each patient according to his or her clinical picture
The Global Initiative of Asthma Guideline (GINA) recommends a flowchart to diagnose asthma with first-step spirometry with reversibility and a bronchial challenge test (BPT) with histamine or methacholine as a second step. This multi-center prospective care evaluation study compares the 'standard asthma diagnostic work-up' (spirometry with reversibility and BPT) to the 'new asthma diagnostics work-up' (FeNO-test as an intermediate step between the spirometry with reversibility and the BPT), intending to determine the impact of the FeNO-based strategy, in terms of the number of avoided BPTs, cost-effectiveness and reduced burden to the patient and health care. The cost reduction of incorporating the FeNO-test in the new diagnostic algorithm will be established by the number of theoretically avoided BPT. The decrease in burden will be studied by calculating differences in the Visual Analogue Scale (VAS) -score and Asthma Quality of Life Questionnaire (AQLQ) -score after the BPT and FeNO-test with an independent T-test. The accuracy of the FeNO-test will be calculated by comparing the FeNO-test outcomes to the (gold standard) BPTs outcomes in terms of sensitivity and specificity.
The purpose of this study is to determine feasibility and safety of using an augmented reality system in patients undergoing MRI-Guided needle procedures.
Cardiac amyloidosis (CA) has recently been reported as a common cause of heart failure with preserved left ventricular ejection fraction (HFpEF), with a prevalence of 6% in elderly HFpEF patients. However, the diagnosis of CA is still challenging and requires multiple costly investigations. Regardless of the type of CA, TTR or AL, early diagnosis significantly improves prognosis. In this study, the investigators aimed to determine the prevalence of CA in Tunisian HFpEF patients and to identify clinical and ultrasound criteria predictive of CA.