View clinical trials related to Artificial Intelligence.
Filter by: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.
This is a pragmatic trial that will measure if the use of AI to identify patients with complex care needs and language barriers, as well as active reaching out to clinicians to offer the use of interpreter services will improve the frequency of interpreter use and reduce the time to first interpreter use
Validation of the accuracy of AI in assisting gastroscopic varices diagnosis through a prospective multicenter study
Lower limbs of stroke patients gradually recover through Brunnstrom stages, from initial flaccid status to gradually increased spasticity, and eventually decreased spasticitiy. Throughout this process. after stroke patients can start walking, their gait will show abnormal gait patterns from healthy subjects, including circumduction gait, drop foot, hip hiking and genu recurvatum. For these abnormal gait patterns, rehabilitation methods include ankle-knee orthosis(AFO) or increasing knee/pelvic joint mobility for assistance. Prior to this study, similar research has been done to differentiate stroke gait patterns from normal gait patterns, with an accuracy of over 96%. This study recruits subject who has encountered first ever cerebrovascular incident and can currently walk independently on flat surface without assistance, and investigators record gait information via inertial measurement units strapped to their bilateral ankle, wrist and pelvis to detect acceleration and angular velocity as well as other gait parameters. The IMU used in this study consists of a 3-axis accelerometer, 3-axis gyroscope and 3-axis magnetometer, with a highest sampling rate of 128Hz. Afterwards, investigators use these gait information collected as training data and testing data for a deep neural network (DNN) model and compare clinical observation results by physicians simultaneously, in order to determine whether the DNN model is able to differentiate the types of abnormal gait patterns mentioned above. If this model is applied in the community, investigators hope it is available to early detect abnormal gait patterns and perform early intervention to decrease possibility of fallen injuries. This is a non-invasive observational study and doesn't involve medicine use. Participants are only required to perform walking for 6 minutes without assistance on a flat surface. This risk is extremely low and the only possible risk of this study is falling down during walking.
In this study, the investigators proposed a prospective study about the effectiveness of artificial intelligence system for Retrograde cholangiopancreatography. The subjects would be include in an analyses groups. The AI-assisted system helps endoscopic physicians estimate the difficulty of Endoscopic retrograde cholangiopancreatography for choledocholithiasis and make recommendations based on guidelines and difficulty scores. The investigators used the stone removal times, success rate of stone extraction and Operating time to reflect the difficulty of the operation, and evaluated whether the results of the AI system were correct.