View clinical trials related to Disease.
Filter by:The primary goal of this study is to estimate the effectiveness of a medical decision support system based on artificial intelligence in the endoscopic diagnosis of benign tumors. Researchers will compare Adenoma detection rate between "artificial intelligence - assisted colonoscopy" and "conventional colonoscopy" groups to evaluate the clinical effectiveness of artificial intelligence model.
Prosthetic Joint Infection (PJI) is one of the severe complications following arthroplasty. With the global aging population, the number of patients undergoing primary joint replacement surgeries is increasing, leading to a rise in PJI cases. Although the incidence of PJI is generally low, the impact on patients can be catastrophic. Once an infection occurs, it is further complicated by the rising global antibiotic resistance, imposing a significant economic burden on patients. Therefore, improving the diagnostic rate of PJI is crucial. Currently, various infection markers are used in the diagnosis of PJI. However, there is still a lack of highly sensitive and specific markers to effectively diagnose PJI, necessitating the exploration of new infection markers. This study aims to investigate novel infection markers for the diagnosis of PJI, providing evidence for its diagnosis and subsequent treatment. In this research, we will prospectively collect data from patients undergoing primary joint replacement and those developing PJI from June 2024 to December 2026. These patients will be categorized into non-infection and infection groups. By collecting and analyzing general patient data, surgery-related information, and infection-related indicators from preoperative joint fluid and blood samples, we aim to further evaluate the predictive value of these infection markers for PJI.
The objective of this pilot trial is to implement a new perinatal palliative care intervention program tailored to the Flemish context, which aims to provide care to parents who receive a severe foetal/neonatal diagnosis for their (unborn) child and to their healthcare providers. Additionally, we aim to evaluate the feasibility and preliminary effectiveness by comparing measured variables to the baseline measurement done in the same hospital wards beforehand.
The RECLAIM study platform will be used to explore whether the use of Hyperbaric Oxygen therapy (HBOT) may be used to improve the symptoms of post covid condition. Hyperbaric oxygen therapy is a well-established medical treatment. HBOT promotes healing by delivering a high concentration of oxygen into the body. This high level of oxygen has a number of known benefits, such as growth of new blood vessels, as well as regulating immune and inflammation responses. It helps protect the brain and other nervous tissue from inflammation. HBOT may also have antiviral effects. Collectively, it has the potential to target the underlying mechanisms believed to play a critical role in the development of Long COVID. Many patients with Long COVID complain of fatigue, brain fog, muscle aches and other symptoms. There is evidence to suggest that these symptoms may be a problem with the blood vessels, resulting in abnormal delivery of oxygen to tissues. Thus, our group is investigating whether HBOT's ability to improve the delivery of oxygen to tissues may help these symptoms.
An Emergency Care Action Plan (ECAP) is a tool intended to be helpful to providers when treating a child with complex medical needs during an emergency. Once created, ECAPs are added to the Electronic Health Record (EHR), shared with the child's caregiver(s), and kept up by all of those involved in a child's care. The goal of this study is to measure important health outcomes (ex. inpatient days, emergency department visits) in terms of the use of the ECAP for infants discharged from the Neonatal Intensive Care Unit (NICU). This study will also measure other real-time potential challenges related to the use of the ECAP including, but not limited to, if it is being used, if providers and caregivers want to use it, and if they keep using it over a long period of time.
The goal of this observational study is to develop and validate a molecular heart rejection diagnostic system based on targeted transcriptome as a novel monitoring companion tool for heart allograft precision diagnostics applicable to formalin-fixed paraffin-embedded endomyocardial biopsies. The primary outcome will be the biopsy-proven rejection, that will be predicted with molecular classifiers (cellular and antibody-mediated rejection scores).
This clinical trial was designed as a prospective, multicenter, multi-reader multi-case (MRMC), superiority, parallel-controlled study. Participants who met the trial criteria and signed the informed consent form were enrolled. The trial group involved diagnoses of caries on panoramic radiographs using an artificial intelligence-assisted diagnostic system, while the control group involved diagnoses made by dental practitioners specializing in operative dentistry and endodontics with five years of experience, who interpreted oral panoramic radiographs to determine the presence and severity of caries.
Artificial intelligence (AI) is becoming prevalent in modern medicine and psychiatry. AI is based on a wide variety of computer algorithms classified under machine learning (ML). The purpose of the present study is to evaluate the potential for mental health diagnosis using AI. In the first part of the study, the AI will conduct an interview with standardized patients [SP] (actors) presenting a psychiatric illness. The AI will present a differential diagnosis and treatment plan. Immediately afterward, the actors will be interviewed by a board-certified psychiatrist, who will also give a differential diagnosis and a treatment plan. The results of the AI and psychiatrist will be compared. In the second part of the study, AI will examine patients coming for consultation by a psychiatrist in the inpatient units, outpatient units, or in the emergency room (ER) at Sheba Medical center. The AI results will be compered to the psychiatrist diagnosis.
Multicenter Prospective Cohort Study of Twin Maternal-Child Dyads in China (ChiTwiMC) is supported by National Key Research and Development Program of China - Reproductive Health and Women's and Children's Health Protection Project. This project is funded by the Ministry of Science and Technology of China under grant number 2023YFC2705900. The ChiTwiMC cohort is led by Professor Wei Yuan from the Department of Gynecology and Obstetrics at Peking University Third Hospital.
Gestational diabetes mellitus (GDM) is a condition that can affect pregnant women during pregnancy and may cause complications for the mother and the baby. Therefore, early and accurate detection is necessary to provide the woman and the baby with better health outcomes. Currently, the most commonly used criteria to detect GDM is the International Association of Diabetes and Pregnancy Study Groups (IADPSG) criterion. However, there is a suggestion that it results in over-diagnosis of GDM, and newer methods of diagnosis have been proposed. One such proposal is to have more than a binary outcome of assessment of dysglycemia in pregnancy. The investigator group created this criterion known as the National Priorities Research Program (NPRP) criterion. This clinical trial compares the IADPSG to the NPRP criteria in pregnant women in Qatar to determine if this newer method mitigates overdiagnosis and more accurately identifies women at risk of complications.