View clinical trials related to Artificial Intelligence.
Filter by:Endoscopic ultrasonography (EUS) is a key procedure for diagnosing biliopancreatic diseases. However, the performance among EUS endoscopists varies greatly and leads to blind areas during operation, which impaired the health outcome of patients. We previously developed an artificial intelligence (AI) device that accurately identifies EUS standard stations and significantly reduces the difficulty of ultrasound image interpretation. In this study, we updated the device (named EUS-IREAD) and assessed its performance in improving the quality of EUS examination in a single-center randomized controlled trial.
Diabetic retinopathy is frequent, potentially severe with visual threat, health costly and represents a major public health problem. However, screening compliance for retinopathy remains too low in France, approximately 40% patients with diabetes laking diabetic retinopathy screening for at least 2 years. DIABeyeIA is a prospective pilot study evaluating the effectiveness and acceptability of diabetic retinopathy screening in 11 pharmacies in Normandy (north of France) using a non-mydriatic portable retinophotograph enhanced by artificial intelligence software. The main goal of this work is to evaluate a potential increase rate of diabetic retinopathy screening, compared to the actual rate (64% in France). Secondary goals are faisability, satisfaction and economical considerations for implementation of such a new screening program.
The OLGIM staging system is highly recommended for a comprehensive assessment of GIM severity to evaluate patients' gastric cancer risk. However, its need to take at least 4 biopsies is not clinically feasible due to a serious shortage of pathologists compared with the large number of gastric cancer screening population. We plan to develop a Digital Pathology artificial intelligence diagnosis system (DPAIDS), to automatically identify tumor areas in whole slide images(WSI) and quickly and accurately quantify the severity of intestinal metaplasia according to the proportion of intestinal metaplasia areas.
In this study, the investigators used the optical flow method to measure the colonoscopy withdrawal speed, and doctors were selected from multiple hospitals to collect prospective colonoscopy screening videos, and the percentage of colonoscopy withdrawal overspeed was calculated to explore the relationship between it based on optical flow method and the adenoma detection rate.
In the high cold environment, mobile digital radiography (DR) is used to collect the imaging of bone injury patients, and the artificial intelligence diagnosis module is used to diagnose the injury. The quality of images obtained by mobile or conventional DR and the consistency between artificial intelligence and pure artificial diagnosis will be analyzed respectively. Finally, the application value of mobile DR loaded with artificial intelligence diagnosis function in high cold environment is determined.
Study objective: To establish a quality control system for gastrointestinal endoscopy based on artificial intelligence technology and an auxiliary diagnosis system that can perform lesion identification, improving the detection rate of early gastrointestinal cancer while standardizing, normalizing, and homogenizing the endoscopic treatment in primary hospitals (including some of the primary hospitals, which are participating in Beijing-Tianjin-Hebei Gastrointestinal Endoscopy Medical Consortium) under Gastrointestinal Endoscopy Artificial Intelligence Cloud Platform as the hardware base. Study design: This study is a prospective, multi-center, real-world study.
To improve the accuracy of risk prediction, screening and treatment outcome of cancer, we aim to establish a medical database that includes standardized and structured clinical diagnosis and treatment information, image features, pathological features, and multi-omics information and to develop a multi-modal data fusion-based technology system using artificial intelligence technology based on database.
Spontaneous intracerebral hemorrhage(SICH) is the most lethal and disabling stroke. Timely and accurate assessment of patient prognosis could facilitate clinical decision making and stratified management of patients and is important for improving patient clinical prognosis. However, current studies on the prediction of prognosis of patients with SICH are limited and only include a single variable, with less precise results and inconvenient clinical application, which may lead to delays in effective patient treatment. Our group's previous studies on SICH showed that hematoma heterogeneity and the degree of contrast extravasation within the hematoma are closely related to the clinical outcome of patients, but they are difficult to describe quantitatively based on imaging signs. Based on this, we propose to use radiomics to quantitatively extract hematoma features from NCCT and CTA images, combine them with patients' clinical information and laboratory tests, study their relationship with the prognosis of cerebral hemorrhage, and use artificial intelligence to establish a rapid and accurate prognostic prediction model for patients with SICH, which is of great significance to guide clinical individualized treatment.
Today the standard for the diagnosis and monitoring of bladder tumors is bladder endoscopy. The performance of this exam is not perfect. With this work, based on artificial intelligence, the investigators wish to combine endoscopy with a complementary diagnostic tool in order to improve patient care. The main objective will be to reduce diagnostic errors / wanderings in patients treated or followed for bladder tumors, by imposing a new standard of diagnostic bladder mapping (high PPV and VPN, high precision)(primary purpose diagnostic). The secondary objective will be to homogenize and systematize the descriptive part of the lesions, and to use AI to better characterize tumor aggressiveness. The final objective being to validate a new precision tool (diagnostic companion) essential for developing and standardizing the therapeutic management of bladder tumors (correcting inter-observer heterogeneity). In this project, video frame will be first extracted from our dataset of cystoscopy videos hosted in in the Next Cloud Recherche. Selected medical image will be segmented and analyzed using our pre-trained CNN model with a feature detection algorithm to obtain features. Data will be analyzed on both patient and lesion levels. The study will assess the Bladder-PAD accuracy on the detection of bladder tumors, and its ability to predict tumor risk of recurrence and progression.
Insulin therapy is the mainstream glucose-lowering program for hospital glucose management. Intelligent insulin dose calculation software based on fingertip glucose monitoring is born in response to the situation. A new generation of continuous blood glucose monitoring technology compared with traditional monitoring technology provides more abundant blood sugar change information.The development of the continuous glucose monitoring technology espcially in blood sugar change trend arrow information, the intelligent insulin dose adjustment sequence based on WeChat program is developed. We plan to carry out a randomized controlled study on patients receiving insulin therapy, in which the insulin dose is adjusted according to the information of four blood glucose monitoring points (before meals and before bed), and randomly divided into two groups, one group is adjusted according to the experience of clinicians, the other group is adjusted according to Wechat program, and glucose monitoring is continued for 1 week. bBood glucose control index of TIR , and the incidence of hypoglycemia and hyperglycemia, hospitalization days and cost was observed. This study ihas great clinical value.