moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Realtime Diagnosis From Electrocardiogram Artificial Intelligence-Guided Screening for Atrial Fibrillation With Long Follow-Up (REGAL)
Trial ID: NCT05923359
Status: ACTIVE_NOT_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-04-10
Completion date: 2026-09
Sponsor: Mayo Clinic
Conditions: Atrial Fibrillation
Interventions: Apple Watch
Locations: Mayo Clinic
Countries: United States
Enrollment: 2000
Age range: 18 Years - None
Sex: ALL

Summary:
The purpose of this research is to test whether Apple Watch, used as a long-term monitoring device, can enable early detection of atrial fibrillation.

Eligibility criteria:
Inclusion Criteria:

* Had a 10-second 12-lead ECG done at Mayo Clinic
* Have a high AI-ECG risk score indicating a high likelihood of previously unrecognized AF
* Men with CHA2DS2-VASc2 ≥2 or women with CHA2DS2-VASc ≥ 3

Exclusion Criteria:

* Diagnosed atrial fibrillation
* Diagnosed dementia
* Diagnosed end-stage kidney disease
* History of intracranial bleeding
* Have an implantable cardiac monitoring device, including a pacemaker, a defibrillator, or implanted loop recorder
* Missing date of birth
* Residence outside of the U.S. or missing address information

Outcomes:
Atrial fibrillation
Effectiveness of Virtual Health Coaching Based on Artificial Intelligence to Improve Glycemic Control in Patients With Type 2 Diabetes Mellitus
Trial ID: NCT07165730
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-09-15
Completion date: 2026-06-15
Sponsor: Hasanuddin University
Conditions: Type 2 Diabetes Mellitus (T2DM)
Interventions: Virtual Health Coaching with Artificial Intelligence (DIACOACH); Standard Diabetes Education
Locations: Public Health Center of Bontomatene and Public Health Center of Binamu Kota
Countries: Indonesia
Enrollment: 206
Age range: 18 Years - 65 Years
Sex: ALL

Summary:
This study aims to evaluate the effectiveness of a virtual health coaching program based on artificial intelligence (AI) in improving blood sugar control among people with type 2 diabetes in primary health care settings in Jeneponto and Bantaeng, Indonesia. The intervention will be delivered through the DIACOACH application, which provides personalized coaching, lifestyle guidance, and self-care support. Participants will be randomly assigned to either the intervention group, receiving virtual health coaching for 12 weeks, or the control group, receiving standard diabetes education. Main outcomes include fasting blood sugar and HbA1c levels. Other outcomes include self-care adherence, dietary behavior, and quality of life. This trial seeks to provide evidence on whether AI-based virtual coaching is a practical and effective digital health solution to support diabetes self-management in community health settings with limited resources.

Eligibility criteria:
Inclusion Criteria:

Diagnosed with Type 2 Diabetes Mellitus for more than 3 months

Age between 30 and 56 years

Able to read and understand instructions

Have access to a digital device (smartphone or similar)

In stable clinical condition

Exclusion Criteria:

Diagnosed with Type 1 Diabetes Mellitus

Patients with severe complications (e.g., advanced nephropathy, retinopathy, cardiovascular disease)

Patients currently undergoing other structured interventions

Patients on long-term steroid therapy

Patients without access to a digital device

Outcomes:
Fasting Blood Glucose (FBG); Glycated Hemoglobin (HbA1c)
Post-radiotherapy MRI Based AI System to Predict Radiation Proctitis for Pelvic Cancers
Trial ID: NCT04918992
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2021-06-22
Completion date: 2024-08-01
Sponsor: Sixth Affiliated Hospital, Sun Yat-sen University
Conditions: Pelvic Cancer
Interventions: Artificial Intelligence
Locations: the Sixth Affiliated Hospital of Sun Yat-sen University; the Sixth Affiliated Hospital of Sun Yat-sen University; The Third Affiliated Hospital of Kunming Medical College; Sir Run Run Shaw Hospital
Countries: China; China; China; China
Enrollment: 400
Age range: 18 Years - 75 Years
Sex: ALL

Summary:
In this study, investigators utilize a Artificial Intelligence (AI) supportive system to predict radiation proctitis for patients with pelvic cancers underwent radiotherapy. By the system, whether the participants achieve the radiation proctitis will be identified based on the radiomics features extracted from the post radiotherapy Magnetic Resonance Imaging (MRI) . The predictive power to discriminate the radiation proctitis individuals from non-radiation proctitis patients, will be validated in this multicenter, prospective clinical study.

Eligibility criteria:
Inclusion Criteria:

* pathologically diagnosed as pelvic tumours
* intending to receive or undergoing radiotherapy
* MRI (high-solution T2-weighted imaging, contrast-enhanced T1-weighted imaging, and diffusion-weighted imaging are required) examination is completed after radiotherapy

Exclusion Criteria:

* insufficient imaging quality of MRI (e.g., lack of sequence, motion artifacts)
* incomplete radiotherapy

Outcomes:
The area under curve (AUC) of Receiver Operating Characteristic (ROC) curves of AI prediction system in prediction radiation proctitis
Development and Validation of a Deep Learning Model for the Prediction of Hepatocellular Cancer Recurrence After Transplantation: The Time-Radiological Response- AlphafetoproteIN-Artificial Intelligence Model
Trial ID: NCT05200195
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2020-01-15
Completion date: 2022-03-15
Sponsor: European Hepatocellular Cancer Liver Transplant Group
Conditions: Liver Transplant Disorder; Liver Cancer; Recurrent Cancer
Interventions: Liver transplantation
Locations: Quirino Lai
Countries: Italy
Enrollment: 4026
Age range: 18 Years - None
Sex: ALL

Summary:
Identifying patients at high risk for recurrence of hepatocellular carcinoma (HCC) after liver transplantation (LT) represents a challenging issue. The present study aims to develop and validate an accurate post-LT recurrence prediction calculator using the machine learning method.

Eligibility criteria:
Inclusion Criteria:

* Consecutive adult (≥18 years) patients enlisted and transplanted with the primary diagnosis of HCC during the period 2000-2018.

Exclusion Criteria:

* Patients with HCC diagnosed only at pathological examination (incidental HCC)
* Patients with mixed hepatocellular-cholangiocellular cancer misdiagnosed as HCC
* Patients with cholangiocellular cancer misdiagnosed as HCC
* Patients dying early after LT (≤ one month)

Outcomes:
Post-transplant HCC recurrence
Predicting an Opaque Bubble Layer During Small-Incision Lenticule Extraction Surgery Based on Deep Learning
Trial ID: NCT06577012
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2024-05-01
Completion date: 2024-08-01
Sponsor: Second Affiliated Hospital of Nanchang University
Conditions: Myopia; Small-incision Lenticule Extraction (SMILE) Surgery
Interventions: Small incision lenticule extraction
Locations: The Second Affiliated Hospital of Nanchang University
Countries: China
Enrollment: 4678
Age range: 18 Years - 45 Years
Sex: ALL

Summary:
To explore the prediction of OBL by deep learning model in SMILE surgery

Eligibility criteria:
Inclusion Criteria:

* A condition in which the spherical equivalent refractive error of an eye is ≤ -0.50 D when ocular accommodation is relaxed;
* Age ≥18 years;
* Spherical equivalent (SE) ≥ -10.0D;
* Corrected distance visual acuity (CDVA) ≥ 16/20;
* Stable myopia for at least 2 years;
* No contact lenses wearing for at least 2 weeks

Exclusion Criteria:

* The presence or history of eye conditions other than myopia and astigmatism, such as keratoconus or external eye injury;
* A history of eye surgery;
* The presence or history of systemic diseases.

Outcomes:
The area of patients with opaque bubble layer in the SMILE surgeries
Use of Donor Derived-cell Free DNA (AlloSure) to Facilitate Belatacept Monotherapy in Kidney Transplant Patients
Trial ID: NCT04786067
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: PHASE4
Start date: 2021-07-28
Completion date: 2025-07-30
Sponsor: University of Texas Southwestern Medical Center
Collaborators: CareDx
Conditions: Kidney Transplant Immunosuppression
Interventions: Belatacept
Locations: UT Southwestern Medical Center
Countries: United States
Enrollment: 25
Age range: 18 Years - None
Sex: ALL

Summary:
The purpose of the study is to identify kidney transplant patients that can be transitioned from multi-drug immunosuppression therapy to Belatacept monotherapy, using cell free DNA and gene expression as markers of immune quiescence. The primary objective will be to determine if donor derived-cell free DNA (AlloSure) can be utilized to facilitate Belatacept monotherapy, and to determine if Belatacept is safe and effective as immunosuppression in kidney transplant recipients. The secondary objective is to determine the utility of AlloMap as a predictor of immune quiescence and tolerance of immunosuppressive de-escalation to Belatacept monotherapy, and to evaluate the performance of iBox in predicting adverse outcomes in patients transitioned to Belatacept monotherapy

Eligibility criteria:
Inclusion Criteria:

* Adult (\>18 years) recipients of a kidney-only transplant, including re-transplants
* Non-HLA identical Living or Deceased Donor Grafts
* Able to provide informed consent
* Absence of donor specific antigens
* Stable renal function (eGFR\>40mL/min for 3 months prior to enrollment)
* Patients treated with Belatacept as part of de novo immunosuppression or converted to Belatacept with stable kidney function for 3 months (as stated above)
* Patients who underwent kidney transplantation at least 9 months prior to study entry

Exclusion Criteria:

* Prior or concurrent non-kidney organ transplants
* Presence of BK nephropathy in current graft
* Recipient on any other investigational drug in the 12 weeks prior to inclusion
* Patient with history of recent (\<3mo), recurrent, or severe (Banff Grade 2 or greater or unable to be treated with steroids) acute rejection episodes
* Female participant who is pregnant, lactating or planning pregnancy during the course of the trial
* Significant hepatic impairment
* Bilateral kidney transplantation
* Any other significant disease or disorder which, in the opinion of the Investigator, may either put the participants at risk because of participation in the trial, or may influence the result of the trial, or the participant's ability to participate in the trial

Outcomes:
Number of patients with acute kidney graft rejection
Design and Evaluation of a Train the Trainer Curriculum to Scale Up Automated Intelligence Supported Echocardiography for Rheumatic Heart Disease Screening in a Public Health Care System
Trial ID: NCT05668611
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2023-01-03
Completion date: 2023-12-20
Sponsor: Children's Hospital Medical Center, Cincinnati
Collaborators: Uganda Heart Institute
Conditions: Rheumatic Heart Disease
Interventions: Primary Training Phase; Primary Assessment Phase; Primary Screening Phase; Secondary Training Phase; Secondary Assessment Phase; Secondary Screening Phase
Locations: Uganda Heart Institute
Countries: Uganda
Enrollment: 1628
Age range: 5 Years - 40 Years
Sex: ALL

Summary:
The first primary objective of this study is to determine the diagnostic performance (sensitivity and specificity) of primary MOH nurses who have completed certification in focused echocardiography acquisition and interpretation by expert trainers to accurately identify patients who are screen positive and screen negative for rheumatic heart disease. The second primary objective is to determine the diagnostic performance (sensitivity and specificity) of secondary MOH nurses who have completed certification in focused echocardiography acquisition and interpretation by MOH nurse trainers to accurately identify patients who are screen positive and screen negative for rheumatic heart disease.

Eligibility criteria:
Inclusion Criteria:

* Attend same HCIII/IV sites that the MOH nurses work at
* Agree to participate via the study's informed consent

Exclusion Criteria:

* Very sick participants requiring transfer to higher level facility or inpatient care

Outcomes:
RHD Echocardiographic Diagnostic accuracy, sensitivity, specificity of MOH nurses
Technology Development of Diagnostic Ultrasound System for Obstetric Imaging
Trial ID: NCT07590063
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2026-05-06
Completion date: 2026-09-30
Sponsor: United Imaging Healthcare North America, Inc.
Collaborators: Oregon Health and Science University
Conditions: Pregnancy
Interventions: Diagnostic Ultrasound System
Locations: OHSU Perinatology and Genetics Clinic
Countries: United States
Enrollment: 50
Age range: 18 Years - 45 Years
Sex: FEMALE

Summary:
This study collects obstetric ultrasound images from pregnant participants using the UIH uSONIQUE Nova diagnostic ultrasound system (uC7-1s transducer). The goal is to obtain standardized, high-quality images to support artificial intelligence (AI) algorithm development, AI training and validation, and automated obstetric measurement workflows. Results will support a premarket notification (510(k)) submission to the U.S. FDA.

Eligibility criteria:
Inclusion Criteria:

To be eligible to participate in this study, an individual must meet all of the following criteria:

1. Pregnant persons age 18-45 years
2. Single intrauterine pregnancy
3. Gestational duration: 18 - 28 weeks

Exclusion Criteria:

An individual who meets any of the following criteria will be excluded from participation in this study:

1. Pregnant individuals with multi-fetal gestation (e.g., twins, triplets, etc.).
2. Pregnant individuals with suspected or known fetal anomaly, undergoing evaluation for genetic abnormality, requiring non-standard care, or diagnostic intervention.
3. Pregnant individuals with a medical or surgical history that may impact quality of ultrasound imaging:

   * Body Mass Index (BMI) \> 40
   * Prior surgeries including: abdominal mesh placement; abdominoplasty
   * Vaginal bleeding within the last 6 weeks
4. Patients with serious medical comorbidities including: maternal congenital heart abnormalities; history of organ transplant; asthma exacerbation (during pregnancy); pre-eclampsia; recent mechanical trauma.
5. Use of any of the following during pregnancy: marijuana (or similar derivatives); alcohol; tobacco; methamphetamines; warfarin; isotretinoin; methotrexate; valproic acid; retinoids; oral steroids
6. Non-English speaking.
7. Participants deemed unsuitable by the investigator due to safety or data integrity.

Outcomes:
Complete Fetal Biometric Image Set Collection
A Pragmatic Randomized Controlled Trial of a New Artificial Intelligence-Assisted Clinical Model in Opportunistic Screening for Glaucoma in the Singapore Integrated Diabetic Retinopathy Program
Trial ID: NCT07243665
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-11-17
Completion date: 2027-03
Sponsor: Singapore Eye Research Institute
Collaborators: Singapore General Hospital; SingHealth Polyclinics
Conditions: Glaucoma
Interventions: Artificial Intelligence model to detect glaucoma; No intervention
Locations: Singapore National Eye Centre
Countries: Singapore
Enrollment: 1040
Age range: 21 Years - None
Sex: ALL

Summary:
Glaucoma is major cause of irreversible blindness and is characterized by optic nerve damage and visual field loss. Screening for glaucoma is challenging due to lack of a simple, accurate, cost-efficient and standardized process. Artificial intelligence, (AI) especially deep learning (DL) algorithms have potential to automate glaucoma detection, but have to be evaluated in real world settings, before public deployment. This study aims to evaluate the screening accuracy of a DL algorithm for glaucoma detection using colour fundus photographs (CFP) in a pragmatic randomised control trial (RCT). The algorithm will be tested in 1040 eligible patients with diabetes, recruited from the Diabetes \& Metabolism Centre's clinics under the Singapore Integrated Diabetic Retinopathy Program (SiDRP) and randomized to 2 arms: AI-assisted model vs current standard of care (grader assessment). The performance of both arms will be compared to performance of study ophthalmologist in diagnosing glaucoma. We hypothesize that the DL model has better screening performance in detecting glaucoma in the community, compared to the current practice method.

Eligibility criteria:
Inclusion Criteria: We aim to recruit all eligible patients who attend Singapore General Hospital (SGH) Diabetes \& Metabolism Centre's (DMC) clinics and SingHealth Polyclinics (SHP)-Bukit Merah under the Singapore Integrated Diabetic Retinopathy Programme (SiDRP). Patients are eligible for the study if

1. Aged 21 years old and above, with diabetes, including type 1 and type 2,
2. Retinal photos of the patients can be taken with the fundus camera in the clinics, regardless of photos' quality, and
3. They are willing and capable of providing a written informed consent form.

Exclusion Criteria: Patients meeting any of the exclusion criteria will be excluded from participation:

1. Patients who have difficulty in having retinal photos taken or have difficulties in completing the ocular examination protocols according to investigator's decision.
2. Any other contraindication(s) as indicated by the endocrinologists responsible for the patients.

   \-

Outcomes:
Evaluation of model performance
A Study on the Evaluation of Artificial Intelligence-Generated Clinical Advisory Outputs in Medical Professional Contexts
Trial ID: NCT06980467
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-05-15
Completion date: 2025-05-23
Sponsor: Tsinghua University
Conditions: Artificial Intelligence (AI)
Interventions: Large Language Model
Locations: Tsinghua University; Shanghai Health and Medical Center
Countries: China; China
Enrollment: 8
Age range: 18 Years - 80 Years
Sex: ALL

Summary:
This study examines how artificial intelligence systems interact with medical professionals. Researchers will compare physician responses to different AI-generated outputs in simulative medical scenarios.

Eligibility criteria:
Inclusion Criteria:

* Age ≥ 18 years old;
* Licensed physician or medical professional qualified to perform clinical evaluations;
* Willing to sign informed consent and commit time to participate in the study.

Exclusion Criteria:

* Unable to dedicate sufficient time to complete the study tasks;
* Unable or unwilling to understand and follow study instructions.

Outcomes:
Physician Response to AI-Generated Medical Content
Assessment of Accuracy and Aesthetics Following Automated Mandibular Defect Reconstruction Using Artificial Intelligence: A Case Series Study
Trial ID: NCT06945692
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-05-01
Completion date: 2026-05-01
Sponsor: Cairo University
Conditions: Mandibular Tumor
Interventions: patient specific reconstruction plates
Enrollment: 4
Age range: 18 Years - 55 Years
Sex: ALL

Summary:
The Aim of the study is to evaluate Accuracy of automated mandibular defect reconstruction using Artificial intelligence and assessing impact on aesthetic and occlusion outcomes using patient-specific reconstruction plates.

Eligibility criteria:
Inclusion Criteria:

* Patients with mandibular tumors, cysts or any benign disease resulting in mandibular continuity defect.
* Age group: from 18 - 55 years old.
* No sex predilection.
* CTs or CBCTs of only healthy mandibles from an online database and real data.

Exclusion Criteria:

* Patients with mandibular malignant lesions.
* Children age group from 2-17.
* CTs Of maxilla.
* Elderly patients to be excluded due to the normal physiologic bony change.

Outcomes:
Accuracy Of the virtually Generated 3D model using AI; Accuracy of AI generated model clinically
Adenoma Detection Using Real-Time Computer-Aided Colon Polyp Detection System to Compare Water Exchange and Air Insufflation - A Randomized Controlled Trial
Trial ID: NCT05448300
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2022-05-16
Completion date: 2023-05-15
Sponsor: Dalin Tzu Chi General Hospital
Conditions: Colon Adenoma; Colonoscopy
Interventions: Colonoscopy with water exchange or air insufflation methods
Locations: Sepulveda Ambulatory Care Center; Chia Pei Tang
Countries: United States; Taiwan
Enrollment: 250
Age range: 50 Years - 80 Years
Sex: ALL

Summary:
Water exchange (WE) is an effective insertion method to minimize insertion discomfort and maximize ADR. It is characterized by infusing water to guide insertion in an airless lumen with suctioning of infused water during insertion and almost complete removal of the infused water when cecal intubation is achieved. A modified Delphi review reported water exchange showed the highest overall ADR, ADR in screening cases, and in the right side of the colon compared with water immersion and air (or CO2) insufflation. One of the plausible mechanisms of improving ADR by water exchange is salvage cleaning during insertion, which might help artificial intelligence by removing the interference of fecal debris and bubbles. However, no RCT has been performed to evaluate the effect of CADe on WE colonoscopy.

Therefore, investigators will conduct a RCT comparing the ADR of CADe assisted colonoscopy inserted with either WE or insufflation. Our hypothesis will be that CADe assisted WE colonoscopy achieves higher ADR than CADe assisted air insufflation colonoscopy.

Eligibility criteria:
Inclusion Criteria:

* Consecutive patients, undergoing screen, diagnostic or surveillance colonoscopy performed by the participating endoscopists will be enrolled.

Exclusion Criteria:

* We excluded patients age \< 50 or \> 80 years old, undergone colonoscopy in the past 3 years, partial intake of bowel preparation, renal failure, previous colonic resection, scheduled for polypectomy, American Society of Anesthesiology (ASA) Risk Class 3 or higher, and lack of written informed consent.

Outcomes:
adenoma detection rate