View clinical trials related to Artificial Intelligence.
Filter by: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.
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.
Acanthamoeba keratitis, caused by the pathogen Acanthamoeba spp, is recognized worldwide as a severe ocular infection that can pose potential risks to vision. This observational retrospective and single-center study, of exploratory nature, aims to determine the possibility of identifying patterns that may be useful for future rapid diagnosis of Acanthamoeba keratitis from confocal images, leveraging the normality of corneal examination and the high specificity and sensitivity of computational models. The data will be based on patients who have been confirmed positive through laboratory tests with proven effectiveness in detecting the infection. The laboratory tests considered for the division of patients into their respective groups are bacterial examination, PCR examination, and culture examination. Patients were divided into two groups, the first comprising patients positive for Acanthamoeba infection, while the second comprised patients negative for Acanthamoeba but positive for other pathogens. The study will last for 18 months. The cohort under study includes 151 patients from the IRCCS San Raffaele Hospital who underwent the aforementioned examinations, of which 76 cases will be included in the group of patients positive for Acanthamoeba and 75 in the group of controls positive for other pathogens. The confocal images of this cohort will be fed into artificial intelligence software. To evaluate the model, the test set will be used, and the AI model's ability will be assessed using the most commonly used metrics in the field of computer vision such as accuracy, specificity, sensitivity, and f1-score; culminating in a comprehensive evaluation of the model.
This is a prospective, observational study to be conducted at Sağlık Bilimleri University Istanbul Kanuni Sultan Süleyman Training and Research Hospital and Başakşehir Çam and Sakura City Hospital. The study aims to record various preoperative and postoperative data of patients who have undergone surgeries, specifically those with ASA scores of III and IV or those indicated to potentially need postoperative intensive care. Data points include patient demographics, type of surgery, ASA score, comorbidities, lab and imaging findings, and both actual and ChatGPT version 4 predicted outcomes regarding postoperative intensive care needs, anesthesia methods, duration of stay in intensive care and the hospital, and 30-day mortality rates. ChatGPT version 4's predictions will be compared with actual outcomes and anesthesiologist decisions.
The goal of this clinical trial is to test the performance of neuronal networks trained on ultrasonic raw Data (=radiofrequency data) for the assessment of liver diseases in patients undergoing a clinical ultrasound examination. The general feasibility is currently evaluated in a retrospective cohort. The main questions the study aims to answer are: - Can a neuronal network trained on RF Data perform equally good as elastography in the assessment of diffuse liver diseases? - Can a neuronal network trained on RF Data perform better than a neuronal network trained on b-mode images in the assessment of diffuse liver diseases? - Can a neuronal network trained on RF Data distinguish focal pathologies in the liver from healthy tissue? To answer these questions participants with a clinically indicated fibroscan will undergo: - a clinical elastography in Case ob suspected diffuse liver disease - a reliable ground truth (if normal ultrasound is not sufficient e.g. contrast enhanced ultrasound, biopsy, MRI or CT) in case of focal liver diseases, depending on the standard routine of the participating center - a clinical ultrasound examination during which b-mode images and the corresponding RF-Data sets are captured
In this study, we proposed a prospective study about the effect of the automatic surveillance system on surveillance rate of patients with gastric premalignant lesions. The enrolled patients were divided into group A with intelligent surveillance system, group B with manual reminder, and group C with natural state. The surveillance among the three groups will be compared.
The increasing abundance of clinical, genetic, radiological, and metabolic data in transplantation has led to a growing interest in applying artificial intelligence (AI) tools to optimize immunosuppression and overall patient management. However, the majority of these applications exist only in the preclinical state and the field of artificial intelligence remains unknown to the general public. In view of the potential applications of AI and the growing research interest in this topic, it is essential to assess the knowledge and perceptions of those directly involved in solid organ transplantation. Primary Objective: To assess the knowledge and perceptions of solid organ transplant patients and healthcare professionals working in the field of solid organ transplantation Primary endpoint: - Analysis of verbatim and closed-ended questionnaire data - Comparison of the averages obtained by the subgroups of patients and health professionals and analysis of the verbatim by grouping them by themes
The goal of this methodological, retrospective and prospective study is to; it is a tool to develop a risk estimator tool to detect risk gaps in individuals using artificial intelligence technology that is dangerous for those with CVC in adult intensive care patients, to test risk level estimation frameworks and to evaluate outcomes in the clinic. In our study, it is also our aim to protect, to present the security measures to prevent the risk of CVC with an artificial intelligence model, in an evidence-based way. The main question[s]it aims to answer are: - Can the risk of CVC-related infection be determined in adult intensive care patients using artificial intelligence? - To what degree of accuracy can the risk of CVC-associated infection be determined in adult intensive care patients using artificial intelligence? - What are the nursing practices that can reduce the risk of CVC-related infections? Methodology to develop an artificial intelligence-based CVC-associated infection risk level determination algorithm, retrospective using data from Electronic Health Records (EHR) patient data and manual patient files between January 2018 and December 2022 to create the algorithm and test the model accuracy, and the development stages of the model After the completion of the model, up-to-date data were collected for the use of the model and it was planned to be done prospectively.
In this study, we proposed a prospective study about the effect of the automatic surveillance system on surveillance rate of colorectal postpolypectomy patients. The enrolled patients were divided into group A with intelligent surveillance system reminding though telephone and message, group B with intelligent surveillance system reminding though message, group C with manual reminder, and group D with natural state. The surveillance among the four groups were compared.
The objective of this study is to assess the effectiveness of an AI-based reporting system for upper gastrointestinal endoscopy. The primary question that this study aims to address is whether the reporting system can enhance the completeness and accuracy of endoscopic reports when assisted by AI, as drafted by endoscopists. Patients will be randomly assigned to either the experimental group or the control group. In the experimental group, physicians will draft EGD reports with the assistance of the AI-based reporting system, while in the control group, physicians will use the conventional reporting system to draft EGD reports. At the same time, the AI-based reporting system will automatically generate a report of the EGD examination.