View clinical trials related to Artificial Intelligence.
Filter by:Assessing the Efficacy of Artificial Intelligence in Left Ventricular Function Screening Using Parasternal Long Axis View Cardiac Ultrasound Video Clips ABSTRACT BACKGROUND: Echocardiography serves as a fundamental diagnostic procedure for managing heart failure patients. Data from Thailand's Ministry of Public Health reveals that there is a substantial patient population, with over 100,000 admissions annually due to this condition. Nevertheless, the widespread implementation of echocardiography in this patient group remains challenging, primarily due to limitations in specialist resources, particularly in rural community hospitals. Although modern community hospitals are equipped with ultrasound machines capable of basic cardiac assessment (e.g., parasternal long axis view), the demand for expert cardiologists remains a formidable obstacle to achieving comprehensive diagnostic capabilities. Leveraging the capabilities of Artificial Intelligence (AI) technology, proficient in the accurate prediction and processing of diverse healthcare data types, offers a promising for addressing this prevailing issue. This study is designed to assess the effectiveness of AI in evaluating cardiac performance from parasternal long axis view ultrasound video clips obtained via the smartphone application. OBJECTIVES: To evaluate the effectiveness of artificial intelligence in screening cardiac function from parasternal long axis view cardiac ultrasound video clips obtained through the smartphone application.
Patients applied to the anesthesia clinics of Health Science University Istanbul Kanuni Sultan Suleyman Training and Research Hospital and Basaksehir Cam and Sakura City Hospital were included in the study. Evaluation forms which will be filled in every preoperative examinations will be saved in the hospitals systems. Patients datas without indentification informations will be asked to ChatGpt to give anesthesiological risc scores. This scores will be compared with the scores already given by anesthesiologists.
Renal cell carcinoma (RCC) is the most common malignant tumor in the kidney with a high mortality rate. Traditional imaging techniques are limited in capturing the internal heterogeneity of the tumor. Radiomics provides internal features of lesions for precise diagnosis, prognosis prediction, and personalized treatment planning. Early and accurate diagnosis of renal tumors is crucial, but it's challenging due to morphological and pathological overlap between benign and malignant lesions. The accurate diagnosis of RCC, especially for small tumors, remains a significant challenge. Recent studies have shown a relationship between body composition, obesity, and renal tumors. Common indicators like body weight and BMI fail to reflect body composition accurately. Research on the role of body composition, including adipose tissue, in tumor pathology could improve clinical diagnosis and treatment planning.
The emergence of artificial intelligence (AI) and specifically deep learning (DL) have shown great potentials in finding radiographic features and treatment planning in the field of cariology and endodontics . A growing body of literature suggests that DL models might assist dental practitioners in detecting radiographical features such as carious lesions, periapical lesions, as well as predicting the risk of pulp exposure when doing caries excavation therapy. Although, current literature lacks sufficient research on the effect of sufficient training of dental practitioners for using AI-based platforms. This prospective randomized controlled trial aims to assess the performance of students when using an AI-based platform for pulp exposure prediction with and without sufficient preprocedural training. The hypothesis is that participants performance at group with sufficient training is similar to the group without sufficient training.
In this study, the EUS intelligent picture reporting system can automatically generate reports after reading videos of EUS examinations. This function can standardize the quality of endoscopic ultrasound image reporting and reduce the work burden of ultrasound endoscopists.
To investigate the degree of the real-time detection and classification system for increasing the adenoma detection rate during colonoscopy.
A clinical trial of the effectiveness and safety of intestinal polyp digestive endoscopy-assisted diagnosis software used in the analysis of colonoscopy medical images generated by electronic digestive endoscopy equipment.
The purpose of this study is to describe the design, methodology and evaluation of the preclinical test of Carebot AI CXR software, and to provide evidence that the investigated medical device meets user requirements in accordance with its intended use. Carebot AI CXR is defined as a recommendation system (classification "prediction") based on computer-aided detection. The software can be used in a preclinical deployment at a selected site before interpretation (prioritization, display of all results and heatmaps) or after interpretation (verification of findings) of CXR images, and in accordance with the manufacturer's recommendations. Given this, a retrospective study is performed to test the clinical effectiveness on existing CXRs.
A test-retest study on the stability and repeatability of healthy skin features on OCT
This is a study to validate the effect of the intelligent diagnostic evidence-based analytic system in acute abdominal pain augmentation. Included physicians were randomly assigned into control or AI-assisted group. In this experiment, the whole electronic health record of each acute abdominal pain patient was divided into two parts, signs and symptoms recording (including chief complaint, present history, physical examination, past medical history, trauma surgery history, personal history, family history, obstetrical history, menstrual history, blood transfusion history, drug allergy history) and auxiliary examination recording (including laboratory examination and radiology report). For each case, the control group readers will first read the signs and symptoms recording of electronic health record and make a clinical diagnosis. Then the readers have to decide to either order a list of auxiliary examinations or confirm the clinical diagnosis without further examination. If the readers choose to order examinations, the corresponding examination results will be feedback to the readers, and the readers can then decide to either continue to order a list of auxiliary examinations or make a confirming diagnosis. Such cycle will last until the reader make a confirming diagnosis. For the AI-assisted readers, the physicians were additionally provided with the feature extracted by IDEAS-AAP, a list of suspicious diagnoses predicted by IDEAS-AAP, and corresponding diagnostic criteria according to guidelines. After the readers get the examination results, the IDEAS-AAP will renew its diagnosis prediction