27 Clinical Trials for Various Conditions
The goal of this clinical trial is to evaluate whether an AI tool that alerts providers to patients at high 6-year risk of lung cancer based on their chest x-ray images will improve lung cancer screening CT participation. The main question it aims to answer is: Does the AI tool improve lung cancer screening CT participation at 6 months after the baseline outpatient visit The intervention is an alert to the provider to discuss lung cancer screening CT eligibility, for patients considered at high risk of lung cancer based on CXR-LC AI tool. If there is a comparison group: Researchers will compare intervention and non-intervention arms to determine if lung cancer screen CT participation increases.
The DETECT-AS Diagnostic Study will assess the performance of artificial intelligence (AI) risk predictions to detect aortic stenosis using results from portable electrocardiogram (ECG) and cardiac ultrasound devices.
This study will evaluate the validity of a digital biomarker score for precision risk stratification among older adults with aortic sclerosis or mild aortic stenosis (AS) at three US health systems.
Hypothesis: BR's Gen3 DL algorithms, combined with its subxiphoid body sensor, can accurately diagnose OSA, categorize its severity, identify REM OSA and supine OSA, and detect central sleep apnea (CSA). Primary Objective: To rigorously evaluate the overall performance of the BR with Gen3 DL Algorithms and Subxiphoid Body Sensor in assessing SDB in individuals referred to the sleep labs with clinical suspicion of sleep apnea and a STOP-Bang score \> 3, by comparing to the attended in-lab PSG, the gold standard. Secondary Objectives: To determine the accuracy of BR sleep stage parameters using the Gen3 DL algorithms by comparing to the in-lab PSG; To assess the accuracy of the BR arrhythmia detection algorithm; To assess the impact of CPAP on HRV (both time- and frequency-domain), delta HR, hypoxic burden, and PWADI during split night studies; To assess if any of the baseline HRV parameters (both time- and frequency-domain), delta heart rate (referred to as Delta HR), hypoxic burden, and pulse wave amplitude drop index (PWADI) or the change of these parameters may predict CPAP compliance; To evaluate the minimum duration of quality data necessary for BR to achieve OSA diagnosis; To examine the performance of OSA screening tools using OSA predictive AI models formulated by National Taiwan University Hospital (NTUH) and Northeast Ohio Medical University (NEOMED).
After onset of Acute Ischemic Stroke (AIS), every minute of delay to treatment reduces the likelihood of a good clinical outcome. A key delay occurs in the time between completion of computed tomography (CT) angiography of the head and neck and interpretation in the setting of AIS care. The purpose of this study is to assess the effect of incorporating Viz.AI software, which via via a machine-learning algorithm performs artificial intelligence-based automated detection of large vessel occlusions (LVO) on CT angiography (CTA) images and alerts the AIS care team (diagnosis and treatment decisions will be based on the clinical evaluation and review of the images by the treating physician, per routine standard of care). The hypothesis is that integration of the software into the AIS care pathway will reduce delays in treatment. A cluster-randomized stepped-wedge trial will be performed across 4 hospitals in the greater Houston area.
This research involves retrospective and prospective studies for clinical validation of a DystoniaNet deep learning platform for the diagnosis of isolated dystonia.
This study will enroll end-stage renal disease (ESRD) patients on hemodialysis with a maturing arteriovenous fistula (AVF) for hemodialysis access. A study staff member will mark with indelible ink on each participant's skin the three sites on the upper extremity where the Eko CORE digital stethoscope will be used to take sound recordings. 9 recordings will be taken (3 at each site) once per week during weekly dialysis treatments.
The objective of this study is to clinically develop and evaluate a machine learning approach to improve the performance and data interpretation of the PhotoniCare OtoSight Middle Ear Scope in pediatric patients presenting at the primary care office for suspected ear infections. In this observational study, results of OtoSight imaging will not affect patient standard of care.
This study evaluates a second review of ultrasound images of breast lesions using an interactive "deep learning" (or artificial intelligence) program developed by Samsung Medical Imaging, to see if this artificial intelligence will help the Radiologist make more accurate diagnoses.
The primary objective of this study is to examine the role of machine learning and computer aided diagnostics in automatic polyp detection and to determine whether a combination of colonoscopy and an automatic polyp detection software is a feasible way to increase adenoma detection rate compared to standard colonoscopy.
This is a single center, diagnostic clinical trial in which the investigators aim to prospectively validate a deep learning model that identifies patients with features suggestive of cardiac amyloidosis, including transthyretin cardiac amyloidosis (ATTR-CA). Cardiac Amyloidosis is an age-related infiltrative cardiomyopathy that causes heart failure and death that is frequently unrecognized and underdiagnosed. The investigators have developed a deep learning model that identifies patients with features of ATTR-CA and other types of cardiac amyloidosis using echocardiographic, ECG, and clinical factors. By applying this model to the population served by NewYork-Presbyterian Hospital, the investigators will identify a list of patients at highest predicted risk for having undiagnosed cardiac amyloidosis. The investigators will then invite these patients for further testing to diagnose cardiac amyloidosis. The rate of cardiac amyloidosis diagnosis of patients in this study will be compared to rate of cardiac amyloidosis diagnosis in historic controls from the following two groups: (1) patients referred for clinical cardiac amyloidosis testing at NewYork-Prebysterian Hospital and (2) patients enrolled in the Screening for Cardiac Amyloidosis With Nuclear Imaging in Minority Populations (SCAN-MP) study.
Balance problems and falls are among the most common complaints in Veterans with Parkinson's Disease (PD), but there are no effective treatments and the ability to measure balance and falls remains quite poor. This study uses wearable sensors to measure balance and uses deep brain stimulation electrodes to measure electric signals from the brain in Veterans with PD. The investigators hope to use this data to better understand the brain pathways underlying balance problems in PD so that new treatments to improve balance and reduce falls in Veterans with PD can be designed.
The major goal of the study is to determine whether phonocardiography (using the Eko DUO stethoscope which can capture a three lead ECG reading) can present features that relate to the presence of PH diagnosed by echocardiography or right heart catheterization (RHC), and therefore have a potential to assist the provider to suspect PH.
Background: The genes a person is born with can sometimes cause serious diseases. Genetic diseases are rare, but they can have a big impact on the people they affect. Researchers have already made great strides in understanding how some genes cause disease. But they would like to have even better tools to analyze and understand genetic data. To create these new tools, they need to gather health and genetic data from a lot of people. Objective: This natural history study will gather medical information from people with genetic conditions. Eligibility: People of any age who (1) are known or suspected to have a genetic condition or (2) have a family member with a known or suspected genetic condition. Design: Participants will come to the clinic for up to 4 days. Tests to be performed will vary depending on the nature of each participant s health issue. The tests may include: Blood and saliva. Blood may be drawn from a vein; cells and saliva may be collected by rubbing the inside of the cheek with a swab. These would be used for genetic testing. Imaging scans. Participants may have X-rays or other scans of their bodies. They may lie still on a table while a machine records the images. Heart tests. Participants may lie still while a technician places a probe on their chest. They may also have stickers attached to wires placed on their chest. Photographs and recordings. Pictures may be taken of facial features, skin changes, or other effects of the genetic condition. Video and audio recordings may also be made. Some people may be able to participate via telehealth.
The PREVUE-VALVE study will establish reliable, population-based estimates of Valvular Heart Disease (VHD) prevalence among older Americans and allow for the development and validation of several innovative tools to aid in the detection and diagnosis of Valvular Heart Disease (VHD).
Biomarkers can be evaluated to provide information about disease presence or intensity and treatment efficacy. By recording these biomarkers through noninvasive clinical techniques, it is possible to gain information about the autonomic nervous system (ANS), which involuntarily regulates and adapts organ systems in the body. Machine learning and signal processing methods have made it possible to quantify the behavior of the ANS by statistically analyzing recorded signals. This work will aim to systematically measure ANS function by multiple modalities and use decoding algorithms to derive an index that reflects overall ANS function and/or balance in healthy able-bodied individuals. Additionally, this study will determine how transcutaneous auricular vagus nerve stimulation (taVNS), a noninvasive method of stimulating the vagus nerve without surgery, affects the ANS function. Data from this research will enable the possibility of detecting early and significant changes in ANS from "normal" homeostasis to diagnose disease onset and assess severity to improve treatment protocols.
The purpose of this study is to understand the effects of using an Artificial Intelligence algorithm for skeletal age estimation as a computer-aided diagnosis (CADx) system. In this prospective real-time study, the investigators will send de-identified hand radiographs to the Artificial Intelligence algorithm and surface the output of this algorithm to the radiologist, who will incorporate this information with their normal workflows to make an estimation of the bone age. All radiologists involved in the study will be trained to recognize the surfaced prediction to be the output of the Artificial Intelligence algorithm. The radiologists' diagnosis will be final and considered independent to the output of the algorithm.
To compare 2 different image creation/processing techniques during a standard CT scan in order to "see" problems in the liver and learn which method provides better image quality. The techniques use new artificial intelligence software to decrease image noise, which helps the radiologist to evaluate.
The goal is to assess the accuracy of an application that analyzes voice characteristics to diagnose patients with mild cognitive impairment (MCI) and Alzheimer's disease (AD). The main question is whether the application's diagnosis is the same as the clinician's for MCI and AD patients.
Childhood is an opportune time to intervene in obesity because behaviors that are developed during this time can have long-lasting effects and disrupt trajectories of obesity. This proposal aims to test the feasibility (i.e., participant acceptance, adherence, and retention) of a family-oriented intervention "AyUDA" (Aprender y Utilizar Decisiones Apreciables-Learning and Utilizing Significant Choices). The culturally tailored, two-arm adapted intervention to engage Latinx parents in healthy feeding and lifestyle practices for their children 2-5 years old, thereby reducing early childhood overweight and obesity. The investigators will use concepts of the Social-Ecological Framework for Obesity among Latinx, and the Social Learning Theory that emphasizes the importance of observing, modeling, and imitating behaviors. This approach includes a community engagement partnership with one clinic that serves a great number of Latinx families with 2-5 aged children in Central Kentucky (General Pediatric Clinic-Clinica Amiga). The investigators propose a two-arm randomized clinical trial (RCT) randomly assigning participants to either a telehealth deep cultural level group or a culturally traditional educational group in a sample of 40 Latinx families who will be followed for six months after the intervention. Moreover, investigators will explore short-term changes of the intervention on dietary behavior changes and anthropometric measurements among family members. The feasibility study will inform effect sizes that will be used to estimate statistical power for a future R01 on Community Level Interventions to Improve Minority Health and Reduce Health Disparities, National Institute of Health (NIH).
The objective of this study is to recruit influential community members using Snowball Sampling Methods. Community members identified through social network analysis as influential and well-connected will be trained as community health workers (CHW) using the Diabetes Empowerment Education Program (DEEP). These CHW will be used in a future trial to educate other members of the community.
International guidelines recommend deciding the treatment of colorectal lesions based on the estimated histology by endoscopic optical diagnosis. However, the theoretical and practical knowledge on optical diagnosis is not widely expanded The mail goal of this randomised controlled trial is to compare the pooled sensitivity of optical diagnosis for predicting deep submucosal invasion in large non-pedunculated polyps \> 20 mm assessed in routine colonoscopies of gastroenterologists attending a e-learning module (intervention group) vs gastroenterologists who do not (control group) The main questions the study aims to answer are: * Is the pooled sensitivity of optical diagnosis for predicting deep submucosal invasion in large non-pedunculated polyps assessed in routine colonoscopies increased in those gastroenterologists participating in the e-learning module? * Is the pooled diagnostic accuracy of optical diagnosis for predicting deep sm invasion in large non-pedunculated polyps ≥ 20 mm assessed in routine colonoscopies increased in those gastroenterologists participating in the e-learning module? * In lesions with submucosal invasion, is the en bloc and complete resection rate (R0) increased in those gastroenterologists participating in the e-learning module? * In lesions referred to surgery, is the pooled benign polyps rate decreased in those gastroenterologists participating in the e-learning module? * In lesions treated with advanced en bloc procedures (ESD, TAMIS, fullthickness resection), is the pooled rate of histology with high-grade dysplasia, intramucosal cancer or submucosal invasion increased in those gastroenterologists participating in the e-learning module? * In lesions treated with piecemeal endoscopic resection, is the pooled rate of histology with high-grade dysplasia, intramucosal cancer or submucosal invasion decreased in those gastroenterologists participating in the e-learning module? * Is the diagnostic accuracy for predicting deep submucosal invasion in a test with pictures increased after participating in the e-learning module? The participants (or subjects of study) are gastroenterologists. They will be randomised to do the e-learning course (intervention group) or not (control group). Researchers will compare clinical outcomes of gastroenterologists participating in the e-learning module vs gastroenterologists not participating in the e-learning module to see if: * the pooled sensitivity of optical diagnosis for predicting deep submucosal invasion in large non-pedunculated polyps \> 20 mm assessed in routine colonoscopies is increased. * the pooled diagnostic accuracy of optical diagnosis for predicting deep sm invasion in large non-pedunculated polyps \> 20 mm is increased. * the en bloc and complete resection rate (R0) is increased in lesions with submucosal invasion. * the pooled benign polyps rate decreased in lesions referred to surgery. * the pooled rate of histology with high-grade dysplasia, intramucosal cancer or submucosal invasion increased in lesions treated with advanced en bloc procedures (ESD, TAMIS, fullthickness resection). * the pooled rate of histology with high-grade dysplasia, intramucosal cancer or submucosal invasion decreased in lesions treated with piecemeal endoscopic resection. * the diagnostic accuracy for predicting deep submucosal invasion in a test with pictures after participating is increased.
This is a single-center phase I clinical study aiming to improve gait functions in patients with Parkinson's disease (PD) by using adaptive neurostimulation to the pallidum. The investigators will use a bidirectional deep brain stimulation device with sensing and stimulation capabilities to 1) decode the physiological signatures of gait and gait adaptation by recording neural activities from the motor cortical areas and the globus pallidus during natural walking and a gait adaptation task, and 2) develop an adaptive deep brain stimulation (DBS) paradigm to selectively stimulate the pallidum during different phases of the gait cycle and measure improvements in gait parameters. This is the first exploration of network dynamics of gait in PD using chronically implanted cortical and subcortical electrodes. In addition to providing insights into a fundamental process, the proposed therapy will deliver personalized neurostimulation based on individual physiological biomarkers to enhance locomotor skills in patients with PD. Ten patients with idiopathic Parkinson's disease undergoing evaluation for DBS implantation will be enrolled in this single treatment arm study.
A naturalistic study design, in which dTMS patients will be randomized to get a free add-on CBT treatment. The dTMS procedure will include treatment as usual, and participants will use the app from post randomization (Pre-treatment is defined as measures from the first three days of treatment) to the end of dTMS treatment (Post-treatment which is defined as measures from after twenty TMS sessions over a minimum of four weeks), and for an additional three months of FU (FU).
VTE associated harm is underappreciated among hospitalized patients and may be associated with missed doses of VTE prophylaxis medications. In order to ensure best practices, and administer a defect-free VTE prevention nurses must understand and educate patients on the importance of the VTE prophylaxis. We propose to conduct a randomized trial comparing the effect of a validated, real-time patient education bundle (PEB), to a program of nurse feedback and coaching (NFC) provided by nurse leaders.
The investigators have recently developed a registry of missed doses of VTE prophylaxis that includes retrospective data on missed doses of VTE prophylaxis. To decrease rates of VTE prophylaxis refusal, the group has developed a patient-centered education bundle that will be delivered as an in-person, 1-on-1 discussion session with a nurse educator. Supporting education materials include a 2-page education sheet and an educational video. The investigators hypothesize that patient refusal of VTE prophylaxis is associated with significant knowledge gaps among patients regarding patients' risk of developing VTE and the benefits of VTE prophylaxis and that delivering an education bundle to patients that refuse VTE prophylaxis will improve compliance with VTE prophylaxis and decrease rates of VTE.
The purpose of this study is to confirm the safety and efficacy of the ThinkSono Guidance System, a software data collection and communication tool designed to collect ultrasound data to help detect blood clots in veins. The ThinkSono system is CE Mark approved in the European Union and in clinical use in Europe. Usually, when an ultrasound is conducted to diagnose blood clots in veins, a sonographer (trained technologist who conducts ultrasounds) and/or radiologist will conduct the procedure, including a compression ultrasound exam, and the scan may require a bulky cart and ultrasound equipment. The ThinkSono Guidance System is a mobile software application that enables other healthcare professionals such as nurses, non-radiologist physicians including general practitioners, and other allied healthcare professionals to perform the ultrasound at the point of care using guidance from the software app. This is a multi-site non-randomized, double-blinded, prospective cohort pivotal study.