moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Comparison of Colonoscopy Adenoma Detection Yield of Standard Colonoscopy, Discovery Aided Colonoscopy, and Discovery and G-EYE® Aided Colonoscopy
Trial ID: NCT05158725
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2021-11-08
Completion date: 2022-06
Sponsor: Dr. Horst Schmidt Klinik GmbH
Conditions: Adenoma Colon
Interventions: Standard Colonoscopy; Discovery aided colonoscopy; Discovery and G-EYE
Locations: Helios Dr. Horst Schmidt Kliniken Wiesbaden GmbH
Countries: Germany
Enrollment: 1320
Age range: None - None
Sex: ALL

Summary:
A Prospective Randomized Comparison of colonoscopy Adenoma Detection Yield of (i) Standard Colonoscopy (SC), (ii) artificial intelligence (Discovery) aided colonoscopy, and (iii) artificial intelligence (Discovery) and permanently mounted balloon (G-EYE®) aided colonoscopy.

Eligibility criteria:
Inclusion Criteria:

1. Screening and surveillance population for Adenoma and CRC.
2. The patient must understand and sign a written informed consent for the procedure.

Exclusion Criteria:

1. Subjects with inflammatory bowel disease;
2. Subjects with a personal history of hereditary polyposis syndrome;
3. Subjects with suspected chronic stricture potentially precluding complete colonoscopy;
4. Subjects with diverticulitis or toxic megacolon;
5. Subjects with prior colonic surgery (exclusion appendectomy)
6. Subjects with a history of radiation therapy to abdomen or pelvis;
7. Pregnant or lactating female subjects;
8. Subjects who are currently enrolled in another clinical investigation.
9. Subjects with current oral or parenteral use of anticoagulants, not considered eligible by the investigator.
10. Subjects with recent (within the last 3 mounts) coronary ischemia or CVA (stroke)
11. Any patient condition deemed too risky for the study by the investigator

Outcomes:
Adenoma detection yield
Feasibility of Internet Delivered Parent Child Interaction Therapy (AI I-PCIT) Real-Time Monitoring of Sleep and Behavior of Children 3-7 Years-Old Receiving Remote PCIT
Trial ID: NCT05725525
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2023-03-02
Completion date: 2023-12-01
Sponsor: Mayo Clinic
Conditions: Disruptive Behavior
Interventions: Remote PCIT augmented with wearable devices
Locations: Mayo Clinic Minnesota
Countries: United States
Enrollment: 5
Age range: 3 Years - 99 Years
Sex: ALL

Summary:
The purpose of this study is to learn more about how feasible remote parent child interaction therapy (PCIT) is for children with disruptive behaviors and how to augment treatment with wearable devices.

Eligibility criteria:
Inclusion Criteria - Children (Aged 3-7):

* Outpatients.
* Able to provide developmentally appropriate informed assent, and legal guardians able to provide informed consent.
* EBP Severity rated above the clinically significant range (≥120; T-score ≥ 60) (Eyberg Child Behavior Inventory- ECBI; Eyberg \& Pincus, 1999).
* Families approached for participation will be asked to commit to complete the treatment:
* At least one primary caregiver and the identified child will have to be able to speak and understand English;
* Must have the ability, technology, and internet access for remote therapy/research visits.

Inclusion Criteria - Adults (Any age):

* Agree to wear Garmin watch.
* Able to provide informed consent.
* Able to speak and understand English.
* Has the ability, technology, and internet access for remote therapy/research visits.

Exclusion Criteria - Children:

* Formal diagnosis of Severe Intellectual disability, Autistic Spectrum Disorder Level 3, or a psychotic disorder for the child.
* Parents not consenting to the study.
* Parents or child is not able to adhere to the study protocol.
* A Child who is reasonable expected to be unable to tolerate wearing the Garmin device for at least 70% of the time during the day and night 70% of the days during the treatment (12 weeks). This is based on the principal investigator's discretion.
* Unable to speak and understand English.
* Refusal or withdrawal of consent, inability, or unwillingness to adhere to study procedures.
* Children in foster care.
* Does not have the ability, technology, and/or internet access for remote therapy/research visits.
* Need for more intensive behavioral treatments such as ER visit for behavioral dyscontrol or hospitalization will not be exclusionary or exit criteria.

Exclusion Criteria - Adults:

* Unable to speak and understand English.
* Refusal or withdrawal of consent, inability, or unwillingness to adhere to study procedures.

Outcomes:
How wearing a Garmin watch will help the remote PCIT clinical outcomes; Number of participants able to wear the Garmin watch throughout remote PCIT; Evaluating parental acceptance of remote PCIT augmented with a Garmin device
Development and Clinical Validation of an AI-Based Camera System for Real-Time Biomechanical Monitoring in Upper-Limb Rehabilitation
Trial ID: NCT07492797
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-03-15
Completion date: 2027-03-14
Sponsor: Mississippi State University
Collaborators: University of Mississippi Medical Center
Conditions: Hand Injury Rehabilitation; Postoperative Rehabilitation
Interventions: AI-Based Camera Tele-Rehabilitation Monitoring System; Standard Telehealth Rehabilitation
Locations: University of Mississippi Medical Center; Mississippi State University
Countries: United States; United States
Enrollment: 40
Age range: 18 Years - None
Sex: ALL

Summary:
This study evaluates a novel camera-based system designed to support remote rehabilitation by measuring hand and upper-limb biomechanics in real time. Many patients recovering from musculoskeletal or neurological conditions require frequent monitoring during rehabilitation, but regular clinic visits may be difficult due to distance, cost, or limited access to specialized care. Current telehealth approaches typically rely on qualitative assessments or self-reported feedback rather than objective biomechanical measurements.

The purpose of this study is to determine whether a computer vision-based system can accurately estimate biomechanical parameters such as joint angles, range of motion, muscle force, and joint torque using only a standard camera. The system analyzes hand movement using artificial intelligence and biomechanical modeling to provide real-time measurements during rehabilitation exercises.

Participants will perform guided hand-movement tasks while the system records video and extracts anatomical landmarks. These data will be used to compute biomechanical parameters and assess whether the system can reliably monitor rehabilitation progress remotely. The results will help determine whether this technology can provide clinicians with objective, continuous data to support personalized rehabilitation and improve patient outcomes.

Eligibility criteria:
Inclusion Criteria:

* Adults aged 18 years or older.
* Individuals undergoing or recovering from upper-limb or hand rehabilitation following musculoskeletal or neurological injury or surgery.
* Ability to perform basic hand or upper-limb movement tasks required for the rehabilitation exercises.
* Ability to understand study instructions and provide informed consent.

Exclusion Criteria:

* Severe cognitive impairment preventing understanding of study procedures.
* Medical conditions that prevent safe participation in hand or upper-limb rehabilitation exercises.
* Severe visual impairment preventing interaction with the camera-based monitoring system.
* Participation in another interventional study that could affect rehabilitation outcomes.

Outcomes:
Accuracy of Camera-Based Joint Torque Estimation; Correlation Between Camera-Based and Clinical Biomechanical Measurements
Evaluating the Sensitivity to Change of AI-Feedback in Ultrasound Biometry: A Stratified Randomized Controlled Trial Across the Expertise Gradient
Trial ID: NCT07476638
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-03-01
Completion date: 2027-03-01
Sponsor: Copenhagen Academy for Medical Education and Simulation
Collaborators: Rigshospitalet, Denmark; Slagelse Hospital
Conditions: Fetal Growth Abnormalities; Fetal Weight
Interventions: AI interventional group
Locations: Rigshospitalet
Countries: Denmark
Enrollment: 75
Age range: None - None
Sex: ALL

Summary:
Objective: To evaluate the impact of real-time AI feedback on fetal biometry accuracy and investigate the Expertise Reversal Effect-whether AI benefits diminish as user experience increases.

Design: A stratified randomized trial of 75 participants (25 Novices, 25 Intermediates, 25 Experts). Users are randomized 1:1 to either AI-assisted or manual measurement groups.

Outcomes:

* Primary: EFW accuracy (MAPE) compared to actual birthweight.
* Secondary: Procedure time, image quality, error relative to baseline scans, and cognitive workload (NASA-TLX).

Eligibility criteria:
Clinical Target Population: Healthcare professionals and students, including but not limited to:

* Medical students (doing their masters.
* Resident physicians and Senior Consultants in Obstetrics and Gynecology.

Exclusion:

\- If the participants do not understand and speak either Danish or English

Pregnant women:

Inclusion Criteria:

* Pre pregnancy BMI \< 40
* Singelton pregnancy
* GA ≥ 37+0 at time of induction
* Intact membranes (to ensure consistent amniotic fluid index)

Exclusion Criteria:

* Major fetal anatomical anomaly
* Anhydramnios (DVP \< 2 cm)
* CPR ratio \< 2.5th percentile

Outcomes:
To evaluate the sensitivity to change in ultrasound measurement accuracy when using AI-feedback compared to standard scanning
Use of Machine-learning Algorithms, Biomarkers and Measures of Quality of Life to Personalize Medical Management of Liver and Heart Transplant Recipients
Trial ID: NCT06774768
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-01-30
Completion date: 2029-12-31
Sponsor: IRCCS Azienda Ospedaliero-Universitaria di Bologna
Conditions: Artificial Intelligence (AI); Hepatocellular Carcinoma (HCC); Heart Transplantation; Liver Transplantation; Machine Learning; Major Cardiovascular Event
Locations: IRCCS - Azienda ospedaliero-universitaria di Bologna; IRCCS - Azienda ospedaliero-universitaria di Bologna
Countries: Italy; Italy
Enrollment: 1000
Age range: 18 Years - None
Sex: ALL

Summary:
This is an observational, low risk tissue based, non-pharmacological, retrospective-prospective study for adults heart and liver transplant patients, related to IRCCS Azienda Ospedaliero-Universitaria di Bologna (IRCCS AOUBO).

This clinical study is part of the national multicentric project DARE. The project has the wide overarching aim to develop digital solutions for personalized healthcare.

Eligibility criteria:
Retrospective cohort Inclusion criteria Receiving a heart or liver transplantation at IRCCS AOUBO between January 2008 and December 2020.

Surviving at least 6 months after surgery Receiving at least one outpatient clinical assessment, comprising clinical evaluation, standard laboratory tests, and graft ultrasound Older than 18 years old

Exclusion criteria Unavailability of medical records in the standard data repositories of IRCCS AOUBO

Prospective cohort

Inclusion criteria Older thant 18 years old Receiving a heart or liver transplantation at least 6 months before study entry and being in active follow up at IRCCS AOUBO Obtaining informed consent

Exclusion criteria None

Outcomes:
Infection; Major Cardiovascular Event (MACE); New onset malignancy; Chronic graft dysfunction
Development of Predictive Models Using Artificial Intelligence for Postoperative Chronic Pain and Opioid Use Following Breast Surgery: A Prospectively-Designed Study
Trial ID: NCT04967352
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2021-07-19
Completion date: 2023-12-31
Sponsor: University of California, San Diego
Conditions: Chronic Pain; Opioid Use; Breast Pain; Breast Cancer
Locations: University of California San Diego
Countries: United States
Enrollment: 500
Age range: 18 Years - None
Sex: ALL

Summary:
Breast surgery, which includes mastectomy, breast reconstructive surgery, or lumpectomies with sentinel node biopsies, may lead to the development of chronic pain and long-term opioid use. In the era of an opioid crisis, it is important to risk-stratify this surgical population for risk of these outcomes in an effort to personalize pain management. The opioid epidemic in the United States resulted in more than 40,000 deaths in 2016, 40% of which involved prescription opioids. Furthermore, it is estimated that 2 million patients become opioid-dependent after elective, outpatient surgery each year. After major breast surgery, chronic pain has been reported to develop anywhere between 35% - 62% of patients, while about 10% use long-term opioids. Precision medicine is a concept at which medical management is tailored to an individual patient based on a specific patient's characteristics, including social, demographic, medical, genetic, and molecular/cellular data. With a plethora of data specific to millions of patients, the use of artificial intelligence (AI) modalities to analyze big data in order to implement precision medicine is crucial. We propose to prospectively collect rich data from patients undergoing various breast surgeries in order to develop predictive models using AI modalities to predict patients at-risk for chronic pain and opioid use.

Eligibility criteria:
Inclusion Criteria:

* Patient undergoing major breast surgery (except for simple lumpectomy)

Exclusion Criteria:

* refusal to consent
* lack of independent decision-making capacity
* inability to communicate effectively with research personnel

Outcomes:
Persistent opioid use after 90 days; Persistent pain after 90 days
An Artificial Intelligence Algorithm for Lymphocyte Focus Score in Whole Slide Images of Minor Salivary Gland Biopsy Samples for Diagnosing Sjogren's Syndrome : a Blinded Clinical Validation and Deployment Study
Trial ID: NCT06437652
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2023-10-01
Completion date: 2024-10-30
Sponsor: Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
Conditions: Sjogren's Syndrome
Locations: Ying-Qian Mo
Countries: China
Enrollment: 1000
Age range: 18 Years - 70 Years
Sex: ALL

Summary:
The aim of this research is to discover an artificial intelligence (AI) algorithm for lymphocyte focus score in whole slide images of labial minor salivary gland (SG) biopsy samples for diagnosing Sjogren's Syndrome, in order to enhance the precision of pathological interpretation of labial minor SG biopsy samples in patients with suspected Sjogren's syndrome and aid clinicians make an accurate diagnose. A remote AI-assisted pathological interpretation platform for lymphocyte focus score in labial SG will be built for the global based on the research results. The research will propose the AI-assisted pathological interpretation of lymphocyte focus score in labial minor SG biopsy samples in the future guidelines for the diagnosis and treatment of Sjogren's syndrome.

The research will：

1. Develop and debug the AI algorithm for lymphocyte focus score in whole slide images of labial minor SG biopsy samples for diagnosing Sjogren's Syndrome;
2. Internal test of the AI algorithm;
3. Clinical validation of the AI algorithm with blind method in multiple centers; 4）Built a remote AI-assisted pathological interpretation platform for lymphocyte focus score in labial SG for the global and Explore its clinical application.

Eligibility criteria:
Inclusion Criteria:

* The original format of digital pathological images of labial gland biopsy tissue from patients with suspected Sjögren's Syndrome uploaded to the designated platform.

Exclusion Criteria:

1. Overlapping layers of cells due to excessively thick sections;
2. Excessive tissue defects caused by incomplete sectioning or poor staining on slides;
3. Absence of labial gland;
4. Insufficient clarity in the image.

Outcomes:
The accuracy of pathological interpretation of focal index of labial gland tissue
A Prospective Real-World Study of Pathology Artificial Intelligence for Predicting Molecular Alterations in Gliomas
Trial ID: NCT07263711
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-09-01
Completion date: 2030-09-01
Sponsor: Nanfang Hospital, Southern Medical University
Conditions: Glioma
Locations: Nanfang Hospital, Southern Medical University
Countries: China
Enrollment: 2000
Age range: 18 Years - 100 Years
Sex: ALL

Summary:
The goal of this clinical study is to learn if an artificial intelligence (AI) model can accurately predict important molecular changes in gliomas, a type of brain tumor, using digital pathology images.

The main questions this study aims to answer are:

How accurate is the AI model in predicting key molecular alterations compared with standard molecular testing? Can the AI model shorten the time needed for diagnosis and reduce the need for expensive molecular tests?

Researchers will collect whole slide images from multiple hospitals and use the AI model to predict molecular results. The predictions will be compared with the actual test results from standard laboratory methods.

Participants will:

Allow the use of their pathology images and molecular test results for research.

Have no additional treatments or procedures beyond standard medical care.

This study will help determine whether AI-assisted tools can provide faster and lower-cost molecular diagnosis for glioma, improving patient care and supporting equal access to precision medicine.

Eligibility criteria:
Inclusion Criteria:

* Participant (or legally authorized representative) has voluntarily signed the informed consent form.
* Age ≥ 18 years at the time of enrollment.
* Histologically suspected diffuse glioma based on biopsy or surgical resection.
* Availability of complete clinical information and usable digital pathology slides with hematoxylin and eosin (H\&E) staining.
* Postoperative molecular pathology results available for comparison.

Exclusion Criteria:

* Poor-quality pathology samples (e.g., insufficient tissue, large folding or contamination of slides, or substandard digital scanning quality).
* Determined by the investigator to be unsuitable for participation in the study for any reason.

Outcomes:
Accuracy of AI model in predicting key molecular alterations in glioma
Lymphocyte Sub-populations and Auto Immune Diseases : What Type of Profile ? (SPLIT)
Trial ID: NCT03901664
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2018-02-01
Completion date: 2018-04-01
Sponsor: University Hospital, Brest
Conditions: Autoimmune Diseases
Locations: CHRU de Brest
Countries: France
Enrollment: 3500
Age range: None - None
Sex: ALL

Summary:
To determine if artificial intelligence allow to detect lymphocyte subpopulations specific of diseases (such as primary Sjogren' syndrome and systemic lupus erythematosus) among patients with suspicion of autoimmune diseases.

Eligibility criteria:
Inclusion Criteria:

* autoimmune or infectious disease

Exclusion Criteria:

* No

Outcomes:
Diagnosis of autoimmune disease
Improving Blood Pressure Control in End-Stage Renal Disease Through a Supportive-Educative Nursing Intervention
Trial ID: NCT01052220
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2009-06
Completion date: 2010-07
Sponsor: Wayne State University
Conditions: Hypertension; Hemodialysis
Interventions: BP education and BP self-regulation; Supportive Educative Nursing Intervention
Locations: Davita Detroit Kresge Hemodialysis Unit; William Beaumont Hemodialysis Units
Countries: United States; United States
Enrollment: 118
Age range: 18 Years - None
Sex: ALL

Summary:
The major purpose of this quantitative study is to determine if a 3 month supportive educative nursing intervention incorporating Blood Pressure (BP) education and BP, salt and fluid monitoring, in addition to goal setting and reinforcement will improve BP control in a chronic end-stage renal disease population.

Eligibility criteria:
Inclusion Criteria:

1. \> 18 years
2. average 4 week pre HD systolic BP \> 150 mmHg or diastolic BP \> 90 mmHg
3. read and speak English.

Exclusion Criteria:

1. on HD \< 6 months
2. history of illicit drug use
3. current major depression
4. lack of orientation to person, time or place or score or \< 80 on the Modified Mini Mental Status Screen
5. major health problems ie. terminal cancer or HIV
6. missed \> 2 HD treatments over 4 weeks.

Outcomes:
Blood Pressure
AI-Enabled Direct-from-ECG Ejection Fraction (EF) Severity Using COR ECG Wearable Monitor
Trial ID: NCT06699056
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2024-11-21
Completion date: 2025-09
Sponsor: Peerbridge Health, Inc
Conditions: Ventricular Ejection Fraction; LVF; LV Dysfunction; Atrial Enlargement; Conduction Defect; Heart Failure; Valvular Heart Disease; Ischemic Heart Disease; Cardiotoxicity; Myocardial Infarction; Dilated Cardiomyopathy; HFrEF - Heart Failure With Reduced Ejection Fraction; HFpEF - Heart Failure With Preserved Ejection Fraction; Syncope; Remodeling, Cardiac
Interventions: 15-minutes of sitting during COR ECG Acquistion
Locations: Orange County Heart Institute; Peerbridge Health; Henry Ford Hospital; Hackensack University Medical Center; Mount Sinai Hospital; Moses H. Cone Memorial Hospital; Texas Cardiac Arrhythmia Research Foundation; South Heart Clinic
Countries: United States; United States; United States; United States; United States; United States; United States; United States
Enrollment: 1500
Age range: 18 Years - None
Sex: ALL

Summary:
This prospective, multicenter, cluster-randomized controlled study aims to evaluate the accuracy of an investigational artificial intelligence (AI) Software as a Medical Device (SaMD) designed to compute ejection fraction (EF) severity categories based on the American Society of Echocardiography's (ASE) 4-category scale. The software analyzes continuous ECG waveform data acquired by the FDA-cleared Peerbridge COR® ECG Wearable Monitor, an ambulatory patch device designed for use during daily activities. The AI software assists clinicians in cardiac evaluations by estimating EF severity, which reflects how well the heart pumps blood.

In this study, EF severity determination will be made using 5-minute ECG recordings collected during a 15-minute resting period with participants seated upright. The results will be compared to EF severity obtained from an FDA-cleared, non-contrast transthoracic echocardiogram (TTE) predicate device. This comparison aims to validate the accuracy of the AI software.

Eligibility criteria:
Inclusion Criteria:

* Age ≥ 18 years
* Able and eligible to wear a Holter monitor

Exclusion Criteria:

* Receiving mechanical respiratory or circulatory support, or renal support therapy, at the time of screening or during Visit #1
* Any condition that, in the investigator's opinion, could interfere with compliance with the study protocol or pose a safety risk to the participant
* History of poor tolerance or severe skin reactions to ECG adhesive materials

Outcomes:
Agreement of CorEFS Software EF Severity Categories Using Peerbridge COR™ ECG Data with ASE EF Severity Categories Established by Ultrasound Echocardiography