View clinical trials related to Artificial Intelligence.
Filter by:The goal of this clinical trial is to study whether the use of our blood culture prediction tool is non-inferior to current practice and if it can improve certain outcomes in all adult patients presenting to the emergency department with a clinical indication for a blood culture analysis (according to the treating physician). The primary endpoint is 30-day mortality. Key secondary outcomes are: - hospital admission rates - in-hospital mortality - hospital length-of-stay. In the intervention group, the physician will follow the advice of our blood culture prediction tool. In the comparison group all patients will undergo a blood culture analysis.
Building artificial intelligence models for diagnosis of anterior open bite malocclusion
Primary objective of the study is to evaluate if the novel virtual polyp sizing tool accuracy in determining the size class of polyps among diminutive (0-5 mm), small (6-9 mm) and large (10 mm and above).
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 and the result of EWT will be blinded to the endoscopists
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.