View clinical trials related to Artificial Intelligence.
Filter by:Mammography is the most common method for breast imaging, and it provides information for model building and analysis. Radiomics applied to mammography has the potential to revolutionize clinical decision-making by providing valuable insights into risk assessment and disease detection. Despite this, the influence of imaging parameters and clinical and biological factors on radiological texture features remains poorly understood. There is a pressing need to overcome the obstacle of system-inherent effects on mammographic images to facilitate the translation of radiological texture features into routine clinical practice by enabling reliable and robust AI-based or AI-aided decision-making. Furthermore, understanding the relationship between imaging parameters, textural features, and clinical and biological information supports the clinical use of AI. The objective of this study is to evaluate AI methods for clinical practice and to study how it relates to clinical factors and biological features.
MRI scans were performed using 3 different 1.5T scanners with an eight-channel head coils. Following a 3D pre-contrast T1w scan, a low-dose contrast-enhanced 3D T1w scan was obtained using 20% (0.02 mmol/kg) of the standard dosage of gadoterate meglumine. The subjects were immediately administered the remaining 80% (0.08 mmol/kg) of the contrast agent to reach the standard dose of 0.1 mmol/kg, which served as a training ground truth for further quantitative assessment. All three acquisitions were performed during a single imaging session, with no additional gadolinium dose administered above the standard protocol.
Virtual Reality based training modulues have become a part of simulation based medical education and are nowadays used for undergraduate and postgraduate level training of healthcare professionals.In parallel to the advancements in Artificial Intelligence technology voice regotnition has the potential to be used as an interfeace during game play .The aim of this study is whether game interface with Artificial Intelligence based voice regognition may elevate the level of immersion during the use of Virtual Reality based serious gaming for Advanced Cardiac Life Support Training.
This study is a retrospective exploratory trial conducted at a single center, aiming to develop and validate a preoperative lymphatic metastasis model for cervical cancer using artificial intelligence deep learning. The model is trained using preoperative imaging and postoperative pathological findings of cervical cancer patients, with the goal of enhancing the accuracy of lymphatic metastasis prediction through preoperative imaging and offering insights for treatment decisions.
Background: Rapid developments in the field of artificial intelligence have begun to necessitate changes and transformations in nursing education. Objective: This study aimed to evaluate the impact of an artificial intelligence-supported case created in the in-class case analysis lecture for nursing students on students' case management performance and satisfaction. Design: This study was a randomized controlled trial. Method: The study involved 188 third-year nursing students who were randomly assigned to either the AI group (n=94) and control group (n=94). An information form, case evaluation form, knowledge test, and Mentimeter application were used to assess the students' case management performance and nursing diagnoses. The level of satisfaction with the case analysis lecture was evaluated using the VAS scale.
This clinical study aims to be used to implement and validate the AIDA tool in two phases: - Phase 1: Risk stratification and personalised recommendations & Model development - Phase 2: Mechanistic Model (Bioresource) development & testing
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.
Background: Atopic dermatitis (AD) is a chronic inflammatory skin disease characterized by recurrent rashes and itching, which seriously affects the quality of life of patients and brings heavy economic burden to society. The Treat to Target (T2T) strategy was proposed to guide optimal use of systemic therapies in patients with moderate to severe AD, and it is emphasized patients' adherence and combined evaluation from both health providers and patients. While effective treatments for AD are available, non-adherence of treatment is common in clinical practice due to the patients' unawareness of self-evaluation and lack of concern about the specific follow-up time points in clinics, which leads to the treatment failure and repeated relapse of AD. Hypothesis: An Artificial Intelligence assistant decision-making system (AIADMS) with implementation of the T2T framework could help control the disease progression and improve the clinical outcomes for AD. Overall objectives: We aim to develop an AIADMS in the form of smartphone app to integrate T2T approach for both clinicians and patients, and design clinical trials to verify the effectiveness and safety of the app. Methods: This project consists of three parts, AI training model for diagnosis and severity grading of AD based on deep learning, development of Artificial Intelligence assistant decision-making system (AIADMS) in the form of app, and design of a randomized controlled trial to verify the effectiveness and safety of AIADMS App for improvement of the clinical outcomes in AD patients. Expected results: With application of AIADMS based app, the goal of T2T for patients with AD could be realized better, the prognosis could be improved, and more satisfaction could be achieved for both patients and clinicians. Impact: This is the first AIADMS based app for AD management running through thediagnosis, patients' self-participation, medical follow-up, and evaluation of achievement of goal of T2T.
Accurate preoperative detection of muscle-invasive bladder cancer remains a clinical challenge. The investigators aimed to develop and validate a knowledge-guided causal diagnostic network for the detection of muscle-invasive bladder cancer with multiparametric magnetic resonance imaging(MRI).
The Collaborative Open Research Initiative Study (CORIS) is a groundbreaking international research endeavor aimed at exploring vital topics within the field of health professions education. At its core, CORIS embodies the spirit of inclusivity by opening its doors to contributors from all corners of the globe, putting the power of research into the hands of the global community and fostering an environment of open collaboration and meaningful contribution. We invite anyone and everyone to join as collaborators and suggest questions for inclusion in the survey, ensuring that the research process is enriched by diverse perspectives. As a collaborator, you will not only have the opportunity to actively engage in survey design, question formulation, and the entire research process from start to finish, but also gain the prospect of achieving valuable publications, which may boost your professional career.