View clinical trials related to Diagnosis.
Filter by:Periodontal diseases and dental pathologies are highly prevalent oral diseases. Thirty-three to fifty percent of adult population presented at least one untreated caries and more than 50% of French population are affected by severe periodontitis. These diseases affect dental organ or periodontal attached system but could have negative impact on general health, quality of life, word and individual well-being. Association between chronic diseases as diabetes, rheumatoid arthritis, cardiovascular diseases, and oral health have been well investigated. Dental and periodontal diagnosis is dependent of various clinical parameters time consuming and dependent operator. It represents a public health challenge. Informatic analysis detecting diseases could be a time gain and a more precise diagnosis tool. Today, any software or algorithm allow automatized detection, clinical qualitative or quantitative indices recording while these informations are present in numeric models
Asthma is characterised by episodic symptoms (attacks) caused by airway inflammation and decreased airflow to the lungs. It affects 10% of the Canadian population and is the most common chronic disease in childhood. Despite its burden and its potential to be life-threatening, establishing the diagnosis takes time due to difficulty in accessing specialised breathing tests. Indeed, the current diagnostic strategy relies on a breathing test (spirometry) and, if non-diagnostic, a subsequent more complicated breathing test conducted in hospitals (a bronchial provocation test). Our dependence on the latter test must be confronted to the bottleneck created by our reliance on it and the difficulty to do these tests in children. Furthermore, within the current framework, people receiving a diagnosis do not know if they have active airway inflammation - a key feature with predicts increased susceptibility to asthma attacks and treatment responsiveness. Our study's goal is to validate clinically accessible and useful diagnostic tests for peoplesuspected to have asthma. Specifically, we are interested in alternative tests that are a) achievable outside the hospital; b) useful markers of airway inflammation/risk c) can identify people at with a higher likelihood of responding to anti-inflammatory therapy. The two tests we are mainly interested in are: - Exhaled nitric oxide (measured with a portable handheld machine) - The blood eosinophil count (obtained on a general blood test) +/- Other tests which we might be able to develop within this cohort (e.g. urine tests)
In this randomized controlled trial, we aim to evaluate the efficacy of incorporating mNGS in the management of pneumonia on efficiency and accuracy of causative pathogen identification, proportion of participants with effective antimicrobial therapy, length of hospitalization, and mortality.
Introduction: Early and rapid diagnosis of etiology is often an important part of saving the lives of patients in emergency department. Chest CT is an important examination method for emergency diagnosis because of its fast examination speed and accurate localization. Traditional medical imaging diagnosis relies on radiologists to report in a qualitative and subjective manner. Through the interdisciplinary combination of clinical, imaging and artificial intelligence, the integration of multi-omics data, the construction of large-scale language models, and the construction of the auxiliary diagnosis support system of "one check for multiple diseases" provide new ideas and means for the rapid and accurate screening of emergency critical diseases. Method： Study design Investigators retrospectively collected cardiovascular, respiratory, digestive, and neurological CT images, demographic data, medical history and laboratory date of emergency department patients during the period from 1 January 2018 and 30 December 2024. Regularly carry out standardized follow-up work, and complete the collection and database establishment of clinical-imaging multi-omics data of patients attending emergency department.The inclusion criteria are:1. adult emergency patients with cardiovascular, respiratory, digestive, and nervous system diseases; 2. These patients had CT images. Patients with incomplete clinical or radiographic data were excluded from the analysis. Regularly carry out standardized follow-up work, and complete the collection and database establishment of clinical-imaging multi-omics data of patients attending emergency department. Based on the collected medical text data, an artificial intelligence large-scale language model algorithm framework is built. After the structure annotation of chest CT images is performed by doctors above the intermediate level of imaging, the Transformer deep neural network is trained for CT image segmentation, and a series of tasks such as structural structure segmentation, damage detection, disease classification and automatic report generation are developed based on Vision Transformer self-attention architecture mechanism. A multi-disease diagnosis and treatment decision-making system based on chest CT images, clinical text and examination multimodal data was constructed and validated. Disscusion Emergency medicine deals mainly with unpredictable critical and sudden illnesses. Patients who come to the emergency department for medical treatment often have acute onset, hidden condition, rapid progress, many complications, high mortality and disability rate. Assisted diagnosis systems developed by combining clinical text, images and artificial intelligence can greatly improve the ability of emergency department doctors to accurately diagnose diseases. This study fills the blank of CT artificial intelligence aided diagnosis system for emergency patients, and provides a rapid diagnosis scheme for multi-system and multi-disease. Finally, the results will be transformed into clinical application software and used and promoted in clinical work to improve the diagnosis and treatment level.
The goal of this study is to learn about the patient's perspective regarding to psychological mood before and after an invasive urodynamic study.
The goal of this study is to learn whether the change or patient position might effect the results of invasive urodynamic study in males.
At present, there is a lack of effective screening methods. It is urgent to explore new non-invasive detection methods for early diagnosis of epithelial ovarian cancer and non-invasive differentiation methods for benign and malignant ovarian tumors. Liquid biopsy technology has great potential for early screening of tumors. The fragmentation patterns of cfDNA fragments in plasma and the uneven coverage of the genome can indirectly reflect the state of gene expression regulation in vivo. Its characteristics mainly include copy number variation (CNV), Nucleosome footprint, fragment length and motif. The number of proteins in a proteome can sometimes exceed the number of genomes. It includes "structural Proteomics" and "functional Proteomics". At present, research has explored the use of urinary protein biomarkers for early diagnosis of gastric cancer. "Deep Visual Proteomics (DVP)" reveals the mechanism driving tumor evolution and new therapeutic targets for tumors. Using the currently mature low depth WGS sequencing technology, this study aims to explore its clinical application in the differentiation and early screening of epithelial ovarian cancer, as well as monitoring the course of epithelial ovarian cancer, including the detection of minimal residual lesions (MRD) and monitoring of recurrence (MOR). This study also explores the role of urine proteomics in the differentiation of benign and malignant ovarian tumors, early screening and invasiveness of epithelial ovarian cancer, and monitoring the course of epithelial ovarian cancer.
102 patients who have suspicious ALNs were included in our prospective study, which was approved by a tertiary health care facility ethics committee. Each suspicious lymph nodes (LNs) were examined with PDUS and SMI in terms of distribution, appearance and number of vascular structures and the still images were stored. Subsequently, imaging findings were re-evaluated after histopathological or follow-up results and were compared between benign and malign groups. In addition, we revealed the diagnostic perfomance of using the each possible combination of these features in PDUS and SMI. Finally, two radiologists with 22 years and 4 years of experience analyzed the images and interobserver agreement was assessed
Prospective single centre non-randomised exploratory observational study to measure changes in tumour cellular redox status with 18F-FSPG PET in stage 3 non-small cell lung cancer (NSCLC) and stage 3 and 4 head and neck squamous cell cancer (HNSCC) at baseline and during standard of care treatment, and to compare this with 18F-FDG PET/CT and RECIST 1.1 response at 12 weeks.
To explore the potential efficacy of 18F-FAPI-04 PET/CT for PDAC tumour staging and compare the results with those obtained using 18F-FDG PET/CT.