View clinical trials related to Artificial Intelligence.
Filter by:This is a prospective study that is the first to implement resect and discard and diagnose and leave strategies in real-time practice using stringent documentation and adjudication by 2 expert endoscopists as the gold standard. Therefore, this study mainly aims to evaluate the agreement between (CADx) assisted optical diagnosis and adjudication by two expert endoscopists in establishing surveillance intervals concordant with the European Society for Gastrointestinal Endoscopy (ESGE) and US Multisociety task force (USMSTF) guidelines.
As part of the management of a patient with suspected bone fractures, emergency physicians are required to make treatment decisions before obtaining the imaging reading report from the radiologist, who is generally not available only a few hours after the patient's admission, or even the following day. This situation of the emergency doctor, alone interpreting the radiological image, in a context of limited time due to the large flow of patients to be treated, leads to a significant risk of interpretation error. Unrecognized fractures represent one of the main causes of diagnostic errors in emergency departments. This comparative study consists of two cohorts of patients referred to the emergency department for suspected bone fracture. The first will be of interest to patients whose radiological images will be interpreted by the reading of the emergency doctor systematically doubled by the reading of the artificial intelligence. The other will interest a group of patients cared for by the simple reading of the emergency doctor. All of the images from both groups of patients will be re-read by the establishment's group of radiologists no later than 24 hours following the patient's treatment. A centralized review will be provided by two expert radiologists. Also, patients in both groups will be systematically recalled in the event of detection of an unknown fracture for hospitalization.
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 goal of this in-silico clinical trial is to learn about the usability and clinical effectiveness of an interpretable deep learning framework (VAE-MLP) using counterfactual explanations and layerwise relevance propagation for prediction of post-hepatectomy liver failure (PHLF) in patients with hepatocellular carcinoma (HCC). The main questions it aims to answer are: - To investigate the usability of the VAE-MLP framework for explanation of the deep learning model. - To investigate the clinical effectiveness of VAE-MLP framework for prediction of post-hepatectomy liver failure in patients with hepatocellular carcinoma. In the usability trial the clinicians and radiologists will be shown the counterfactual explanations and layerwise relevance propagation (LRP) plots to evaluate the usability of the framework. In the clinical trial the clinicians and radiologists will make the prediction under two different conditions: with model explanation and without model explanation with a washout period of at least 14 days to evaluate the clinical effectiveness of the explanation framework.
The aim of this study to predict carbapenem resistant Klebsiella spp. earlier in our patients monitored in our Intensive Care Unit in the future, using artificial intelligence. Patients with bloodstream infection and pneumonia caused by Klebsiella spp. will be comparatively examined in two groups, as sensitive and resistant. Resistance will be attempted to be predicted with deep machine learning.
The investigators will revise the Checklist for AI in Medical Imaging (CLAIM) guideline using Delphi consensus methods. An international panel of physicians, researchers, and journal editors with expertise in AI in medical imaging -- including radiology, pathology, dermatology, GI endoscopy, and ophthalmology -- will complete up to 3 web-based surveys. Participants who complete all survey rounds will be credited as contributors on resulting publications.
The objective of this study is to apply an artificial intelligence algorithm to diagnose multi-retinal diseases in real-world settings. The effectiveness and accuracy of this algorithm are evaluated by sensitivity, specificity, positive predictive value, negative predictive value, and area under curve.
This study is a multicenter evaluation of diagnostic performance using simulated clinical vignettes. It aims to test the effectiveness of the POSOS app in detecting drug-induced iatrogenesis in urgent medical situations, an issue of public health importance. Participating physicians, who are randomly assigned to either use or not use POSOS, are categorized based on their years of experience. Vignettes, including a mixture of complex, simple, and non-iatrogenesis cases, are assigned to these doctors. During the simulation, physicians respond to their respective vignettes on the YgheniVi platform, with responses recorded at two intervals (5 min and 15 min). The supervising physicians subsequently fill out an e-CRF, providing further data on the time spent, the number of medical research applications used, and the overall user experience of POSOS. A doctor/pharmacist pair then corrects the answers to the vignettes.
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
Grading endoscopic atrophy according to the Kimura-Takemoto classification can assess the risk of gastric neoplasia development. However, the false negative rate of chronic atrophic gastritis is high due to the varying diagnostic standardization and diagnostic experience and levels of endoscopists. Therefore, this study aims to develop an AI model to identify the Kimura-Takemoto classification.