View clinical trials related to Artificial Intelligence.
Filter by:Future predictive modeling in emergency medicine will likely combine the use of a wide range of data points such as continuous documentation, monitoring using wearables, imaging, biomarkers, and real-time administrative data from all health care providers involved. Subsequent extensive data sets could feed advanced deep learning and neural network algorithms to accurately predict the risk of specific health conditions. Moreover, predictive analytics steers towards the development of clinical pathways that are adaptive and continuously updated, and in which healthcare decision-making is supported by sophisticated algorithms to provide the best course of action effectively and safely. The potential for predictive analytics to revolutionize many aspects of healthcare seems clear in the horizon. Information on the use in emergency medicine is scarce. Aim of the study is to evaluate the performance of using routine-data to predict resource usage in emergency medicine using the commonly encountered symptom of acute neurologic deficit. As an outlook, this might serve as a prototype for other, similar projects using routine medical data for predictive analytics in emergency medicine.
By collecting non-image medical data of women undergoing cervical screening in multiple centers in China, including age, HPV infection status, HPV infection type, TCT results, and colposcopy biopsy pathology results, a multi-source heterogeneous cervical lesion collaborative research big data platform was established. Based on artificial intelligence (AI) machine learning, cervical lesion screening features are refined, a multi-modal cervical cancer intelligent screening prediction and risk triage model is constructed, and its clinical application value is preliminarily explored.
The overall aim of the project is to investigate how artificial intelligence (AI) can be used to streamline and at the same time increase diagnostic safety in breast cancer screening with mammography. AI has been shown in a number of studies to have great potential for both increasing diagnostic certainty (e.g. reduced occurrence of interval cancers) and at the same time reducing the workload for doctors. However, much research remains to clinically validate these new tools and to increase the understanding of how they affect the work of doctors. The specific goal of the project is to investigate whether the implementation of AI in breast cancer screening in Östergötland, Sweden, can increase the sensitivity (the mammography examination's ability to find breast cancer) and the specificity (that is, the right case is selected for further investigation: a minimum of healthy women are recalled but so many breast cancer cases that are possible are selected for further investigation) and at the same time make screening more efficient through reduced workload. AI will be implemented in the clinical routine and performance metrics such as cancer detection rate etc will be closely monitored. The study do not assign specific interventions to the study participants.
The present study will be performed to evaluate application of artificial intelligence in the prediction of clinical performance, marginal fit and fracture resistance of vertical "feather-edge" versus horizontal "shoulder" margin designs fabricated with two ceramic materials (zirconia and hybrid ceramic)
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.