View clinical trials related to Artificial Intelligence.
Filter by:The goal of this observational study is to assess the correlation between the artificial intelligence (AI) derived effective withdrawal time (EWT) during colonoscopy and endoscopists' baseline adenoma detection rate (ADR). The association between the AI derived EWT with ADR during the prospective colonoscopy series would also be determined. The colonoscopy video of participants will be monitored by the AI
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.
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.
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
HAAL project aims to test several technological devices in order to improve the quality of life of older people with dementia and their informal and formal caregiver.
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.
The goal of this observationl study is to evaluate the possibility of building a Deep Learning (DL) model capable of analyzing electrocardiographic traces of athletes and providing information in the form of a probability stratification of cardiovascular disease. Researchers will enroll a training cohort of 455 participants, evaluated following standard clinical practice for eligibility in competitive sports. The response of the clinical evaluation and ECG traces will be recorded to build a DL model. Researchers will subsequently enroll a validation cohort of 76 participants. ECG traces will be analyzed to evaluate the accuracy of the model to discriminate participants cleared for sports eligibility versus participants who need further medical tests
The goal of this clinical trial is to evaluate the effect of LearnGuide, a custom GPT developed with ChatGPT for supporting self-directed learning (SDL) in medical students. The main questions it aims to answer are: How does LearnGuide influence SDL skills among medical students? Can LearnGuide improve critical thinking and learning flow as measured by Cornell Critical Thinking Test (CCTT) Level Z score and Global Flow Score (GFS)? Participants will: Undergo a two-hour introduction to LearnGuide. Engage in 12 weeks of SDL task-based training with LearnGuide's support. If there is a comparison group: Researchers will compare the group utilizing LearnGuide for SDL and the group without this tool to see if there is a significant difference in SDL skills, critical thinking, and learning flow experiences.