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33 Machine Learning Trials Near You
Power is an online platform that helps thousands of Machine Learning patients discover FDA-reviewed trials every day. Every trial we feature meets safety and ethical standards, giving patients an easy way to discover promising new treatments in the research stage.
Learn More About PowerAI-Assisted Heart Monitoring for Heart Disease
Cincinnati, Ohio
Atrial fibrillation is an abnormal beating of the heart that can lead to stroke or heart failure. Structural heart diseases are conditions that affect the heart valves or heart muscle and can cause permanent heart damage if left untreated. Sometimes people have atrial fibrillation or structural heart disease and do not know it. The purpose of this study is to evaluate two devices that can predict who has or may develop atrial fibrillation or structural heart disease based on the results of an electrocardiogram.
No Placebo Group
Trial Details
Trial Status:Active Not Recruiting
Trial Phase:Unphased
Age:40+
Key Eligibility Criteria
Disqualifiers:Pacemaker, Atrial Fibrillation, Cardiac Surgery, Others
1000 Participants Needed
EHR Nudges for Opioid Overdose
Pittsburgh, Pennsylvania
The goal of this cluster randomized clinical trial is to test a clinician-targeted behavioral nudge intervention in the Electronic Health Record (EHR) for patients who are identified by a machine-learning based risk prediction model as having an elevated risk for an opioid overdose.
The clinical trial will evaluate the effectiveness of providing a flag in the EHR to identify individuals at elevated risk with and without behavioral nudges/best practice alerts (BPAs) as compared to usual care by primary care clinicians.
The primary goals of the study are to improve opioid prescribing safety and reduce overdose risk.
No Placebo Group
Trial Details
Trial Status:Recruiting
Trial Phase:Unphased
Key Eligibility Criteria
Disqualifiers:Malignant Cancer, Hospice Care
1350 Participants Needed
ThinkSono System for Deep Vein Thrombosis
Pittsburgh, Pennsylvania
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.
No Placebo Group
Trial Details
Trial Status:Recruiting
Trial Phase:Unphased
Key Eligibility Criteria
Disqualifiers:Consent Not Given, Incomplete Scan, Others
500 Participants Needed
Mobile App Support for Opioid Use Disorder
Indianapolis, Indiana
The COVID-19 pandemic puts individuals recovering from opioid use disorders (OUDs), an already vulnerable population, at increased risk of overdose due to decreased access to treatment, decreased social support, and increased psychosocial stress. This proposal will test the efficacy of a promising mobile app-based peer support program, compared to usual care, in increasing recovery capital, improving retention in treatment, and reducing psychosocial adverse effects, among a national sample of people in recovery from OUD. If effective, it would provide an accessible, personalized, and scalable approach to OUD recovery increasingly needed during the COVID-19 pandemic.
No Placebo Group
Trial Details
Trial Status:Recruiting
Trial Phase:Unphased
Key Eligibility Criteria
Disqualifiers:No Smartphone, Incarcerated, Others
1300 Participants Needed
Multi-Sensor Sleep Tracking for Nightshift Work
Novi, Michigan
Sleep is often a challenge for nightshift workers because their work and sleep schedules are inverted. Sleep is commonly measured using actigraphy, which is the standard measure of objective sleep in the general population; however, this method has substantial limitations for nightshift workers because the standard legacy algorithms only correctly identify 50.3% of daytime sleep. This significantly reduces the validity for nightshift workers. The purpose of this study is to test a novel method to expand actigraphy by using 1) a multi-sensor approach that 2) uses machine learning (ML) algorithms to increase the accuracy of detecting daytime sleep.
No Placebo Group
Trial Details
Trial Status:Not Yet Recruiting
Trial Phase:Unphased
Key Eligibility Criteria
Disqualifiers:Neurological Disorders, Alcohol Use Disorder, Pregnancy, Others
100 Participants Needed
Adaptive Step Goals for Health Behaviors
London, Ontario
This 12-week study compares the effectiveness of personalized daily step goals generated by a machine learning algorithm in the Sprout app versus fixed daily step goals of 10,000 steps among adults. Participants will be recruited through the Sprout app, and after a 1-week run-in period, they will be assigned to either the intervention or control group. The intervention group will receive adaptive goals based on their historical step data, while the control group will have a fixed goal. Both groups will receive financial incentives. This study aims to inform future interventions measuring changes in daily steps and app engagement levels (i.e., time spent on app, number of app opens) by studying how using financial incentives and an adaptive goal-setting design can improve physical activity levels of app users, informed by a machine learning algorithm.
No Placebo Group
Trial Details
Trial Status:Active Not Recruiting
Key Eligibility Criteria
Disqualifiers:Not Listed
500 Participants Needed
More than 70,000 total knee replacement procedures are performed annually in Canada, representing a growth of 17% over the past 5 years, with further increases anticipated due to an aging population. While total knee replacement offers improved quality of life for patients and is cost effective for the healthcare system, 20% of patients routinely report dissatisfaction with the procedure. Patient dissatisfaction has been strongly linked to unmet expectations of outcomes after the surgery, especially with respect to physical activity. Counselling patients on appropriate expectations has been suggested as a means to improve satisfaction. Recently, our group has developed a tool to predict the functional ability of an individual patient after total knee replacement. This tool employs machine learning to classify patients as more likely to maintain or improve function, based on a functional test performed in clinic while wearing a sensor system around each knee. Implementing this tool in clinic pre-operatively could assist in setting appropriate expectations for each patient. Our primary objective is to compare patient satisfaction scores at one year after total knee replacement in patients who were informed of their specific expected functional outcome compared to patients who were not informed of their predicted functional outcome. We hypothesize that patients who are given an informed expectation will have higher satisfaction scores. This in turn may decrease health system costs associated with additional clinic visits from dissatisfied patients.
No Placebo Group
Trial Details
Trial Status:Active Not Recruiting
Trial Phase:Unphased
Key Eligibility Criteria
Disqualifiers:Not Listed
40 Participants Needed
Eko Sensora Device for Heart Murmurs
Saint Joseph, Michigan
The Eko Artificial Intelligence (AI) has primarily been evaluated in the primary care setting. The digital stethoscope records a phonocardiogram of heart sounds of the patient and uses machine learning artificial intelligence to identify if there are abnormalities present (Eko Health, 2023).
The Eko SENSORA will be tested in the emergency department. Chest pain, fatigue, shortness of breath and syncope are all symptoms that could indicate a cardiac dysfunction.
The hypothesis is that this device will allow us increased ability to detect valvular heart disease that is clinically significant.
No Placebo Group
Trial Details
Trial Status:Recruiting
Trial Phase:Early Phase 1
Key Eligibility Criteria
Disqualifiers:Contact Isolation, Pediatric, Others
300 Participants Needed
Osteopathic Treatment for Low Back Pain
Blacksburg, Virginia
This Study is for our continued study of the Thoracolumbar Fascia (TFL) in patients with and without low back pain by our experienced multidisciplinary team:
Vincent Wang PhD, VT Biomedical Engineering \& Mechanics (BEAM). Albert J Kozar DO, FAOASM, R-MSK. P. Gunnar Brolinson, DO, FAOASM, FAOCFP. David T. Redden PhD, VCOM Research Biostatistician. Matthew Chung DO, VCOM and Team Physician at Virginia Tech. Edward Magalhaes, PhD, LPC, Psychiatry and Neuro- Behavioral Sciences, VCOM.
This listing is specifically for our renewed efforts via two, Department of Defense (DoD) and American Osteopathic Association (AOA), extramurally, simultaneously funded grants for similar but distinct projects. Both funding sources are aware of each other's funding and have approved their grant study moving forward simultaneously with some integration.
DoD: Machine Learning Analysis of Ultrasound Images for the Investigation of Thoracolumbar Myofascial Pain and Therapeutic Efficacy of Hydrodissection.
The primary objectives of the proposed project are to:
1. develop reliable, quantitative image analysis approaches to objectively distinguish images from subjects with acute or chronic TLF pain from those without pain and
2. to assess the preliminary clinical efficacy of hydrodissection of the TLF as a novel therapeutic treatment for chronic LBP.
AOA: Assessment of the Therapeutic Efficacy of OMT on Chronic Low Back Pain: An Integrated Sonographic and Machine Learning Analysis of Thoracolumbar Fascia Glide Impairment.
The primary objectives of the proposed project are to:
1. assess the preliminary clinical efficacy of OMT as a therapeutic treatment for CLBP of TLF origin and
2. develop reliable, quantitative image analysis approaches to objectively distinguish images from subjects with TLF pain from those without pain.
These projects will share 50 no LBP subjects as controls. The DOD study will include 50 acute LBP and 50 CLBP. The AOA study will include 50 CLBP.
This project uses standard surveys, physical exam, functional tests, and ultrasound imaging to obtain both static images of the TLF at multiple transition zones. It further uses ultrasound to evaluate the dynamic gliding motion, via cine loops, of this fascia in 2 different body movements in subjects with acute low back pain (ALBP), with chronic low back pain (CLBP), and without low back pain (WLBP). All images will be clinically analyzed and further assessed by textural and machine learning analysis. Patients with CLBP (only) will choose to enter one of the two studies (DoD vs AOA) at the time of consent.
All images will be clinically analyzed and further assessed by textural and machine learning analysis. Patients with CLBP (only) that are found to have TLF glide impairment or positive physical exam maneuvers suggesting TLF as etiology will enter the treatment arm of the chosen study at the time of consent, either ultrasound guided hydrodissection (USGH), or Osteopathic Manipulative Therapy (OMT). After receiving 3 treatments utilizing one of these modalities, the CLBP participants will have repeat standard surveys, physical exam, functional tests, and ultrasound imaging assessments at 2,4,6,12, and 24 weeks post-treatment.
At the conclusion of this project, the investigators expect to have developed, refined, and implemented robust and feasible experimental and computational approaches which can be further expanded in larger-scale studies. The development of our data-driven computer models for the objective analysis of sonographic images of the TLF has high potential impact as it seeks to transform the assessment of TLF integrity, injury and healing via establishment of reliable US imaging biomarkers. The investigators anticipate that the tools developed will have broad utility to assess a variety of clinical treatments for the TLF. The investigators also hope to validate physical exam maneuvers that may predict TLF mediated LBP and have preliminary evidence of the efficacy of hydrodissection and OMT in TLF mediated LBP.
In pursuit of these objectives, the investigators will adopt an innovative approach featuring a robust integration of clinical imaging, physical exam, pain and functional outcomes, quantitative image analysis, and machine learning analyses.
Specific Aim 1: Compare sonographic TLF imaging characteristics in individuals with acute versus chronic pain to those without low back pain.
Specific Aim 2: Develop a machine learning (ML) classification algorithm to reliably distinguish abnormal myofascial tissue in acute versus chronic pain stages from healthy tissue.
Specific Aim 3:
DoD Study: Assess the preliminary therapeutic efficacy of hydrodissection as a novel treatment for TLF pain using quantitative US imaging and ML tools.
AOA Study: Assess the preliminary therapeutic efficacy of OMT as a treatment for CLBP using quantitative US imaging and ML tools.
No Placebo Group
Trial Details
Trial Status:Recruiting
Trial Phase:Unphased
Age:18 - 50
Key Eligibility Criteria
Disqualifiers:BMI > 30, Pregnancy, Spinal Surgery, Others
Must Not Be Taking:Corticosteroids, Anticoagulants, Muscle Relaxants
200 Participants Needed
Blood Test for Lung Cancer
DuBois, Pennsylvania
The PROACT LUNG study is a prospective multi-center observational study to validate a blood-based test for the early detection of lung cancer by collecting blood samples from high-risk participants who will undergo a routine, standard-of-care screening Low-Dose Computed Tomography (LDCT).
No Placebo Group
Trial Details
Trial Status:Recruiting
Age:50+
Key Eligibility Criteria
Disqualifiers:Cancer Therapy, Recent Malignancy, Transplantation, Others
Must Not Be Taking:Chemotherapy, Immunotherapy, Biologics, Others
20000 Participants Needed
Fitbit Data for Detecting Infections After Appendicitis Surgery
Chicago, Illinois
The purpose of this study is to analyze Fitbit data to predict infection after surgery for complicated appendicitis and the effect this prediction has on clinician decision making.
No Placebo Group
Trial Details
Trial Status:Recruiting
Trial Phase:Unphased
Age:3 - 18
Key Eligibility Criteria
Disqualifiers:Non-ambulatory, Mobility Limitations, Language Barriers, Others
500 Participants Needed
Wearable Biosensor Accuracy for Alcohol Intoxication
Champaign, Illinois
The study will employ a combined laboratory-ambulatory design. Participants will engage in ambulatory assessment over the course of 14 days, wearing biosensors assessing transdermal alcohol concentration (TAC) and providing breathalyzer readings in real-world contexts. Also during this period, participants will attend three laboratory alcohol-administration sessions scheduled at one-week intervals, with alcohol dose and rate of consumption manipulated within and between participants, respectively. Laboratory visits will also double as ambulatory orientation, check-in, and close-out sessions.
No Placebo Group
Trial Details
Trial Status:Recruiting
Age:21+
Key Eligibility Criteria
Disqualifiers:Psychological Conditions, Pregnancy, Others
Must Not Be Taking:Alcohol Contraindicated Drugs
240 Participants Needed
Transcranial Electrical Stimulation for Mild Cognitive Impairment
Winston-Salem, North Carolina
The goal of this clinical trial is to learn about the ability of non-invasive brain stimulation during sleep to enhance people's deep sleep and its potential benefit on memory in people with mild cognitive impairment via home use sleep therapy device (SleepWISP) as well as learn about biomarkers associated with Alzheimer disease (AD). The clinical trial aims to answer the following main questions:
1. Whether the non-invasive transcranial electrical stimulation (TES) delivered by SleepWISP could provide short-term enhancement of deep sleep in a single night in the target population.
2. Whether TES delivered by SleepWISP could enhance deep sleep over multiple nights in the target population.
3. Whether enhance on deep sleep could improve memory performance in the target population.
Participants will be asked to wear non-invasive and painless devices that record their brain activity during sleep along with an actigraphy watch that measures their movement throughout the day. In addition, blood samples or nasal swab assays will be collected from participants multiple times during the study.
No Placebo Group
Trial Details
Trial Status:Recruiting
Trial Phase:Unphased
Age:40 - 85
Key Eligibility Criteria
Disqualifiers:Seizures, Epilepsy, Brain Injury, Others
Must Not Be Taking:EEG Affecting Drugs
60 Participants Needed
ML-Based Intervention for Pediatrics
Toronto, Ontario
The goal of this trial is to learn if a machine learning (ML) model can help optimize drug therapy in the pediatric population. The main question\[s\] it aims to answer are whether a machine learning model predicting receipt of a targeted medication within the next three months:
* Increases the offering of pharmacogenetic testing prior to receipt of a targeted medication
* Increases the number of patients with pharmacogenetic results prior to receipt of a targeted medication
* Increases the number of patients who have alteration in medication choice or dose based on pharmacogenetic results
This trial only focuses on the prediction and provision of participants with a high-risk of receiving a medication with a pharmacogenetic indication in the next three months.
No Placebo Group
Trial Details
Trial Status:Not Yet Recruiting
Trial Phase:Unphased
Age:6 - 18
Key Eligibility Criteria
Disqualifiers:Prior Pharmacogenetic Testing, ICU Admission, Others
275 Participants Needed
The goal of this trial is to determine the effectiveness of a machine-learning (ML) model predicting a serious cardiac event within the next three months, when compared pre- versus post-deployment, in pediatric cardiac inpatients. The main questions it aims to answer are whether deployment of the ML model:
1. Increases PACT consultation within the next three months among admissions without PACT involvement in the previous 100 days
2. Increases PACT consultation or visit within the next three months among those who experience a serious cardiac event during this period
3. Decreases time to PACT consultation or visit among those seen by PACT during this period
4. Decreases the incidence of death in the intensive care unit (ICU)
5. Increases documentation of goals of care
High-risk cardiology patients will be identified by an ML model each morning. If the patient has been seen by the PACT team within the past year, the update will go to the PACT team members. If the patient hasn't been seen by the PACT team, the email will be sent to the cardiology physician in charge of the patient. This physician will decide whether a PACT consultation is necessary based on their clinical judgment. If so, a referral will be made using the usual process. Outcomes of the identified patients will be compared pre- and post-deployment.
No Placebo Group
Trial Details
Trial Status:Not Yet Recruiting
Trial Phase:Unphased
Age:< 18
Key Eligibility Criteria
Disqualifiers:Expected Discharge Same Day
1000 Participants Needed
ML-Based Intervention for Vomiting in Pediatric Cancer
Toronto, Ontario
The goal of this single arm trial is to learn if a machine learning (ML) model predicting the risk of vomiting within the next 96 hours will impact vomiting outcomes in inpatient cancer pediatric patients.
The main questions it aims to answer are whether an ML model predicting the risk of vomiting within the next 96 hours will:
Primary
1. Reduce the proportion with any vomiting within the 96-hour window
Secondary
1. Reduce the number of vomiting episodes
2. Increase the proportion receiving care pathway-consistent care
3. Impact on number of administrations and costs of antiemetic medications
Newly admitted participants will have a ML model predict the risk of vomiting within the next 96 hours according to their medical admission information. The prediction will be made at 8:30 AM following admission. Pharmacists will be charged with bringing information about patients' vomiting risk to the attention of the medical team and implementing interventions.
No Placebo Group
Trial Details
Trial Status:Recruiting
Trial Phase:Unphased
Key Eligibility Criteria
Disqualifiers:Discharged Before Prediction Time
1332 Participants Needed
Acamprosate for Alcoholism
Bethesda, Maryland
Background:
Chronic heavy drinking can cause alcohol use disorder (AUD). AUD changes how the brain works. People with AUD may drink compulsively or feel like they cannot control their alcohol use. Acamprosate is an FDA-approved drug that reduces anxiety and craving in some, but not all, people with AUD.
Objective:
To learn more about how acamprosate affects brain function in people with AUD.
Eligibility:
People aged 21 to 65 years with moderate to severe AUD.
Design:
Participants will stay in the clinic for 21 days after a detoxification period of approximately 7 days.
Acamprosate is a capsule taken by mouth. Half of participants will take this drug 3 times a day with meals. The other half will take a placebo. The placebo looks like the study drug but does not contain any medicine. Participants will not know which capsules they are taking.
Participants will have a procedure called electroencephalography (EEG): A gel will be applied to certain locations on their scalp, and a snug cap will be placed on their head. The cap has sensors with wires. The sensors detect electrical activity in the brain. Participants will lie still and perform 2 tasks: they will look at different shapes and press a button when they see a specific one; and they will listen to tones and press dedicated buttons when they hear the corresponding tones.
Participants will have 2 EEGs: 1 on day 2 and 1 on day 23 of their study participation. They may opt to have up to 4 more EEG studies (one on day 13 and one on each of the three follow-up visits) and 2 sleep studies, in which they would have sensors attached to their scalp while they sleep.
Participants may have up to three follow-up visits for 6 months.
Trial Details
Trial Status:Recruiting
Trial Phase:Phase 4
Age:21 - 65
Key Eligibility Criteria
Disqualifiers:Pregnancy, Head Trauma, Epilepsy, Others
Must Not Be Taking:Naltrexone, Disulfiram, Benzodiazepines, Others
48 Participants Needed
SEPSys and RESCUE Scores for Sepsis Prediction
Largo, Maryland
This study is designed to test two new risk scores - one designed to predict a patient's four-hour risk of developing sepsis and one designed to predict a patient's four-hour risk of deterioration (cardiac arrest, death, unplanned ICU transfer, or rapid response team call). The goal of this study is to improve provider awareness of a patient's risk of these two negative outcomes by providing them with new risk scores. The primary outcome will be the time from when the risk score becomes elevated to when vital signs such as heart rate or blood pressure are measured, suggesting an increased awareness.
No Placebo Group
Trial Details
Trial Status:Not Yet Recruiting
Trial Phase:Unphased
Key Eligibility Criteria
Disqualifiers:Pediatric, Psychiatric, Sepsis, High RESCUE, Others
150000 Participants Needed
Machine Learning Monitoring for Clinical Deterioration
Madison, Wisconsin
In this study, the investigators will deploy a software-based clinical decision support tool (eCARTv5) into the electronic health record (EHR) workflow of multiple hospital wards. eCART's algorithm is designed to analyze real-time EHR data, such as vitals and laboratory results, to identify which patients are at increased risk for clinical deterioration. The algorithm specifically predicts imminent death or the need for intensive care unit (ICU) transfer. Within the eCART interface, clinical teams are then directed toward standardized guidance to determine next steps in care for elevated-risk patients.
The investigators hypothesize that implementing such a tool will be associated with a decrease in ventilator utilization, length of stay, and mortality for high-risk hospitalized adults.
No Placebo Group
Trial Details
Trial Status:Recruiting
Trial Phase:Unphased
Key Eligibility Criteria
Disqualifiers:Under 18, Not In ECART Unit
30000 Participants Needed
Pediatric eCART for High-Risk Outcomes
Madison, Wisconsin
This is a study comparing 3 years of retrospective data (pre-implementation) to 2 years of prospective data after the implementation of a pediatric version of Electronic Cardiac Arrest Risk Triage (pediatric eCART), a clinical decision support (CDS) tool that uses electronic health records (EHR) to identify patients with high risk for life threatening outcomes. Up to 30,000 encounters with pediatric patients will be assessed. Acceptability of the pediatric eCART intervention will also be measured from pediatric nurse clinicians.
No Placebo Group
Trial Details
Trial Status:Not Yet Recruiting
Trial Phase:Unphased
Age:< 17
Key Eligibility Criteria
Disqualifiers:Neonates, Birth Encounters, Others
30000 Participants Needed
Why Other Patients Applied
"I changed my diet in 2020 and I’ve lost 95 pounds from my highest weight (283). I am 5’3”, female, and now 188. I still have a 33 BMI. I've been doing research on alternative approaches to continue my progress, which brought me here to consider clinical trials."
WR
Obesity PatientAge: 58
"I've tried several different SSRIs over the past 23 years with no luck. Some of these new treatments seem interesting... haven't tried anything like them before. I really hope that one could work."
ZS
Depression PatientAge: 51
"As a healthy volunteer, I like to participate in as many trials as I'm able to. It's a good way to help research and earn money."
IZ
Healthy Volunteer PatientAge: 38
"My orthopedist recommended a half replacement of my right knee. I have had both hips replaced. Currently have arthritis in knee, shoulder, and thumb. I want to avoid surgery, and I'm open-minded about trying a trial before using surgery as a last resort."
HZ
Arthritis PatientAge: 78
"I've been struggling with ADHD and anxiety since I was 9 years old. I'm currently 30. I really don't like how numb the medications make me feel. And especially now, that I've lost my grandma and my aunt 8 days apart, my anxiety has been even worse. So I'm trying to find something new."
FF
ADHD PatientAge: 31
Integrated Machine Learning Approach for High Cholesterol
Philadelphia, Pennsylvania
The goal of this study is to identify individuals at high risk of FH, and to encourage the appropriate diagnosis and treatment of individuals at high risk of FH through the use of implementation science and behavioral economics principles.
Phase 1: Applying the FIND FH tool to the health system EHR and gathering data for pilot development; Phase 2: Pilot development and implementation; Phase 3: Conduct a large-scale pragmatic trial consistent with recommendations and learnings from the pilots in Phase 2
No Placebo Group
Trial Details
Trial Status:Enrolling By Invitation
Trial Phase:Unphased
Key Eligibility Criteria
Disqualifiers:Diagnosed FH, Lipid Specialist, Others
750 Participants Needed
Canary App for Alzheimer's Disease
Hackensack, New Jersey
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.
No Placebo Group
Trial Details
Trial Status:Recruiting
Trial Phase:Unphased
Age:50+
Key Eligibility Criteria
Disqualifiers:Parkinson's, Huntington's, Cancer, Others
100 Participants Needed
Pulse Oximetry Screening for Congenital Heart Defects
Queens, New York
The purpose of this study is to implement and externally validate an inpatient ML algorithm that combines pulse oximetry features for critical congenital heart disease (CCHD) screening.
No Placebo Group
Trial Details
Trial Status:Recruiting
Trial Phase:Unphased
Age:0 - 21
Key Eligibility Criteria
Disqualifiers:Echocardiogram, Cardiac Surgery, Others
Must Not Be Taking:Vasoactive Medications
240 Participants Needed
Novel Enuresis Alarm vs Standard Alarm for Bedwetting
New Haven, Connecticut
This trial compares two bedwetting devices, GoGoband® and another device, in children who wet the bed but do not have ADHD. Both devices work by waking the child when they start to wet the bed, helping them learn to stay dry. The body-worn alarm has been shown to be as effective as other methods and superior in terms of rapidity of response and consumer appeal.
No Placebo Group
Trial Details
Trial Status:Recruiting
Age:6 - 21
Key Eligibility Criteria
Disqualifiers:Not Listed
100 Participants Needed
Brain Interchange System for Epilepsy
Rochester, Minnesota
Overall, this study will investigate the functional utility of stereotyped HFOs by capturing them with a new implantable system (Brain Interchange - BIC of CorTec), which can sample neural data at higher rates \>=1kHz and deliver targeted electrical stimulation to achieve seizure control. In contrast to current closed-loop systems (RNS), which wait for the seizure to start before delivering stimulation, the BIC system will monitor the spatial topography and rate of stereotyped HFOs and deliver targeted stimulation to these HFO generating areas to prevent seizures from occurring. If the outcomes of our research in an acute setting become successful, the investigators will execute a clinical trial and run the developed methods with the implantable BIC system in a chronic ambulatory setting.
No Placebo Group
Trial Details
Trial Status:Not Yet Recruiting
Trial Phase:Unphased
Age:3 - 70
Key Eligibility Criteria
Disqualifiers:Unable To Continue Recordings
8 Participants Needed
Brain Interchange System for Epilepsy
Rochester, Minnesota
Overall, this study will investigate the functional utility of stereotyped HFOs by capturing them with a new implantable system (Brain Interchange - BIC of CorTec), which can sample neural data at higher rates \>=1kHz and deliver targeted electrical stimulation to achieve seizure control. In contrast to current closed-loop systems (RNS), which wait for the seizure to start before delivering stimulation, the BIC system will monitor the spatial topography and rate of stereotyped HFOs and deliver targeted stimulation to these HFO generating areas to prevent seizures from occurring. If the outcomes of our research in an acute setting become successful, the investigators will execute a clinical trial and run the developed methods with the implantable BIC system in a chronic ambulatory setting.
No Placebo Group
Trial Details
Trial Status:Recruiting
Trial Phase:Unphased
Age:3 - 70
Key Eligibility Criteria
Disqualifiers:Inability To Continue Recordings
12 Participants Needed
Ultrasound Algorithms for Pregnancy
Minneapolis, Minnesota
The goal of this clinical trial is to
* Collect ultrasound data from pregnant and non-pregnant individuals presenting to multiple study sites.
* Use the collected data and ultrasound images to train and validate Artificial intelligence algorithms developed by the Sponsor
Consented participants will be asked to take part in a research ultrasound scan
No Placebo Group
Trial Details
Trial Status:Not Yet Recruiting
Trial Phase:Unphased
Sex:Female
Key Eligibility Criteria
Disqualifiers:Unable To Consent, Compromised Care
700 Participants Needed
Dietary Intervention for Healthy Habits and Diet
Needham, Massachusetts
The PREDICT 3 study will build on previous research in over 2,000 individuals to further refine machine learning models that predict individual responses to foods, with the aim of advancing precision nutrition science and individualized dietary advice. The study incorporates both standardized and controlled dietary intervention, for the purpose of testing postprandial responses to specific mixed meals, in addition to a free-living period with a dietary record for measuring responses to a large variety of meals consumed in a realistic context, where the role of external factors (e.g. exercise, sleep, time of day) on postprandial responses may be determined. For the first time this PREDICT study is built on top of a commercial product which will allow access to a much larger group of participants who are already collecting large amounts of data through digital and biochemical devices that can contribute to science.
No Placebo Group
Trial Details
Trial Status:Recruiting
Trial Phase:Unphased
Key Eligibility Criteria
Disqualifiers:Pregnancy, Recent Heart Attack, Others
250000 Participants Needed
Mindfulness-Based Therapy for Chronic Lower Back Pain
Boston, Massachusetts
This study will identify unique signatures that people have which can cause pain by evaluating biological, psychological, and social markers using artificial intelligence. These markers can be used to accurately predict the response of diverse individuals with chronic low back pain (cLBP) to Mindfulness-Based Stress Reduction. This will help enhance clinician decision-making and the targeted treatment of chronic pain.
The overall objective is to use a unique machine learning (ML) approach to determine the biomarker signature of persons undergoing mindfulness based stress reduction (MBSR) treatment for their chronic low back pain (cLBP). This signature will facilitate clinical prediction and monitoring of patient response to MBSR treatment.
The design of the study is a single-arm clinical trial of the evidence-based MBSR program for patients with cLBP.
No Placebo Group
Trial Details
Trial Status:Recruiting
Trial Phase:Unphased
Key Eligibility Criteria
Disqualifiers:Recent Pain Worsening, Pregnancy, Cancer, Others
350 Participants Needed
Machine Learning Tool for Opioid Overdose
Gainesville, Florida
This clinical trial aims to evaluate the pilot implementation of a machine-learning (ML)-driven clinical decision support (CDS) tool designed to predict opioid overdose risk within the electronic health record (EHR) system at UF Health Internal Medicine and Family Medicine clinics in Gainesville, Florida. The study will use a pre- versus post-implementation design to compare outcomes within clinics, focusing on measures such as naloxone prescribing rates and opioid overdose occurrences. Researchers will also assess the usability, acceptability, and feasibility of the CDS tool through qualitative interviews with primary care clinicians (PCPs) in the participating clinics.
No Placebo Group
Trial Details
Trial Status:Recruiting
Trial Phase:Unphased
Key Eligibility Criteria
Disqualifiers:Malignant Cancer, Hospice Care
Must Be Taking:Opioids
2000 Participants Needed
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Frequently Asked Questions
How much do Machine Learning clinical trials pay?
Each trial will compensate patients a different amount, but $50-100 for each visit is a fairly common range for Phase 2–4 trials (Phase 1 trials often pay substantially more). Further, most trials will cover the costs of a travel to-and-from the clinic.
How do Machine Learning clinical trials work?
After a researcher reviews your profile, they may choose to invite you in to a screening appointment, where they'll determine if you meet 100% of the eligibility requirements. If you do, you'll be sorted into one of the treatment groups, and receive your study drug. For some trials, there is a chance you'll receive a placebo. Across Machine Learning trials 30% of clinical trials have a placebo. Typically, you'll be required to check-in with the clinic every month or so. The average trial length for Machine Learning is 12 months.
How do I participate in a study as a "healthy volunteer"?
Not all studies recruit healthy volunteers: usually, Phase 1 studies do. Participating as a healthy volunteer means you will go to a research facility several times over a few days or weeks to receive a dose of either the test treatment or a "placebo," which is a harmless substance that helps researchers compare results. You will have routine tests during these visits, and you'll be compensated for your time and travel, with the number of appointments and details varying by study.
What does the "phase" of a clinical trial mean?
The phase of a trial reveals what stage the drug is in to get approval for a specific condition. Phase 1 trials are the trials to collect safety data in humans. Phase 2 trials are those where the drug has some data showing safety in humans, but where further human data is needed on drug effectiveness. Phase 3 trials are in the final step before approval. The drug already has data showing both safety and effectiveness. As a general rule, Phase 3 trials are more promising than Phase 2, and Phase 2 trials are more promising than phase 1.
Do I need to be insured to participate in a Machine Learning medical study?
Clinical trials are almost always free to participants, and so do not require insurance. The only exception here are trials focused on cancer, because only a small part of the typical treatment plan is actually experimental. For these cancer trials, participants typically need insurance to cover all the non-experimental components.
What are the newest Machine Learning clinical trials?
Most recently, we added Brain Interchange System for Epilepsy, Non-Invasive Mapping-Guided Ablation for Rapid Heartbeat and Multi-Sensor Sleep Tracking for Nightshift Work to the Power online platform.
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