moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
The Clinical Value of Deep Learning-Based Reconstruction Techniques in Cardiac MRI Scanning
Trial ID: NCT07274436
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-08-01
Completion date: 2026-08-01
Sponsor: Lian Yang
Conditions: Cardiac Disease
Locations: Union Hospital，Tongji Medical College，Huazhong University of Science and Technology
Countries: China
Enrollment: 50
Age range: 18 Years - None
Sex: ALL

Summary:
By enrolling patients who underwent cardiac MR(Magnetic Resonance) examinations at our center and using randomized allocation, the patients were divided into a study group and a control group. The study group underwent scanning using AI-based(Artificial Intelligence-based) cardiac MRI(Magnetic Resonance Imaging) sequences, while the control group was scanned using non-AI cardiac MRI sequences

Eligibility criteria:
Inclusion Criteria:

1. Patients requiring cardiac MRI in clinical practice;
2. Patient age ≥ 18 years;
3. The patient has signed an informed consent form.

Exclusion Criteria:

1. Patients with contraindications to magnetic resonance imaging;
2. Patients who failed to complete the MRI examination or whose image quality was inadequate for diagnostic requirements;
3. Other circumstances deemed by clinical trial personnel as unsuitable for participation in this trial;
4. Subjects or their legal guardians voluntarily requesting to withdraw.

Outcomes:
Patient satisfaction
Optical Coherence Tomography: Prognostic Value and Artificial Intelligence
Trial ID: NCT07293858
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2021-11-26
Completion date: 2028-12-31
Sponsor: Radboud University Medical Center
Conditions: Coronary Arterial Disease
Locations: Radboudumc
Countries: Netherlands
Enrollment: 1000
Age range: None - None
Sex: ALL

Summary:
This study aims to evaluate the prognostic value of optical coherence tomography (OCT) imaging of the coronary arteries. OCT provides high-resolution images of the vessel wall and stented segments during routine cardiac catheterization. All patients who undergo OCT as part of their clinical care are invited to participate in this prospective registry.

The study will examine whether specific OCT-derived characteristics-such as plaque morphology, vulnerable features, or indicators of stent optimization-are associated with long-term clinical outcomes. Follow-up information on symptoms, medication use, hospitalizations, cardiac procedures, and major cardiac events will be collected through medical records, questionnaires, and national registry data over a period of up to 10 years.

In addition, pseudonymized OCT pullbacks will be used to support the development of an artificial intelligence (AI) algorithm for automated annotation of OCT images. This algorithm may help improve the clinical interpretation of OCT by identifying relevant imaging features in a consistent and efficient manner.

Participation is voluntary and includes permission to use clinical data, OCT images, and follow-up information for research purposes. Data are coded to protect participant privacy and stored securely according to applicable regulations. The results of this study may contribute to better understanding of coronary plaque characteristics and may support improved decision-making in interventional cardiology.

Eligibility criteria:
Inclusion Criteria:

* All consecutive patients undergoing intracoronary OCT imaging at the Radboud University Medical Center, and any additional centers that may join the study in the future.
* OCT performed at the discretion of the treating physician, according to contemporary clinical guidelines.
* Written informed consent obtained after the procedure.

Exclusion Criteria:

* Patients who decline or are unable to provide informed consent. (Only patients who provide consent can be included.
* Patients for whom follow-up or data collection is not feasible (e.g., no valid contact details).

Outcomes:
Relationship between OCT-derived plaque and stent characteristics and long-term clinical outcomes
A Randomized Clinical Trial Investigating the Impact of Artificial Intelligence Electrocardiography on Occlusion Myocardial Infarction Management Under the Value-Based Payment System
Trial ID: NCT06887699
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-08-01
Completion date: 2028-06-30
Sponsor: National Defense Medical Center, Taiwan
Conditions: OMI - Occlusion Myocardial Infarction; Artificial Intelligence (AI); Electrocardiogram; Cost-effectiveness Analysis
Interventions: AI-ECG P4P program
Locations: Kaohsiung Armed Forces General Hospital; Taipei Municipal Wanfang Hospital; Tri-Service General Hospital
Countries: Taiwan; Taiwan; Taiwan
Enrollment: 212000
Age range: 18 Years - None
Sex: ALL

Summary:
This trial will prospectively evaluate the impact of integrating AI-ECG within the pay-for-performance program on improving the diagnosis, treatment, and clinical outcomes of occlusion myocardial infarction patients by promoting accurate and timely diagnoses through financial incentives.

Eligibility criteria:
Inclusion Criteria:

* Patients in the emergency department
* Patients received at least 1 ECG examination.

Exclusion Criteria:

* The patients received ECG at the period of inactive AI-ECG system.
* Patients with a history of coronary angiography within the past 3 days.

Outcomes:
D-to-W time (Door to Wire Time); Cost Outcome Measure
Impact of Computer-aided Optical Diagnosis (CADx) in Predicting Histology of Diminutive Rectosigmoid Polyps: a Multicenter Prospective Trial.
Trial ID: NCT05500248
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2022-10-01
Completion date: 2024-08-30
Sponsor: University of Roma La Sapienza
Conditions: Colonic Neoplasms
Interventions: Leave In Situ; Standard
Locations: Ospedale Dei Castelli
Countries: Italy
Enrollment: 919
Age range: 18 Years - None
Sex: ALL

Summary:
This two parallel-arms, randomized, multicenter trial is aimed at investigating safety and effectiveness of a Computer-Aided-Diagnosis (CADx)-assisted leave-in-situ strategy (Leave-In-Situ Arm) as opposed to a resect-all strategy (Standard Arm) as implemented by endoscopists in a real-world setting.

With this study it will be possible to understand the impact of CADx in patient treatment and management both in terms of clinical outcome and costs.

Eligibility criteria:
Inclusion criteria:

\- All \>18 years-old patients undergoing elective colonoscopy

Exclusion criteria:

* patients with personal history of colorectal cancer (CRC), or Inflammatory Bowel Disease (IBD).
* Patients affected with Lynch syndrome or Familiar Adenomatous Polyposis.
* patients with inadequate bowel preparation (defined as Boston Bowel Preparation Scale \<2 in any colonic segment).
* patients with previous colonic resection.
* patients on antithrombotic therapy, precluding polyp resection.
* patients who were not able or refused to give informed written consent.

Outcomes:
Adenoma Detection Rate (ADR)
Phase 1 Trial of the Implementation of an Artificial Intelligence-powered Virtual Assistant for Emergency Triage in Neurology
Trial ID: NCT06334796
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: EARLY_PHASE1
Start date: 2023-10-01
Completion date: 2024-01-01
Sponsor: Fundación para la Lucha contra las Enfermedades Neurológicas de la Infancia
Collaborators: Entelai
Conditions: Stroke; Guillain-Barre Syndrome; Facial Palsy; Migraine; Status Epilepticus; Vertigo Benign Positional; Delirium; Trigeminal Neuralgia; Meningitis; Subarachnoid Hemorrhage
Interventions: Virtual Assistant
Locations: Fleni
Countries: Argentina
Enrollment: 10
Age range: 18 Years - None
Sex: ALL

Summary:
This study examines the use of an AI-powered virtual assistant for quickly identifying and handling neurological emergencies, particularly in places with limited medical resources. The research aimed to check if this AI tool is safe and accurate enough to move on to more advanced testing stages. In a first-of-its-kind trial, the virtual assistant was tested with patients having urgent neurological issues. Neurologists first reviewed the AI's recommendations using clinical records and then assessed its performance directly with patients. The findings were as follows: neurologists agreed with the AI's decisions nearly all the time, and the AI outperformed earlier versions of Chat GPT in every tested aspect. Patients and doctors found the AI to be highly effective, rating it as excellent or very good in most cases. This suggests the AI could significantly enhance how quickly and accurately neurological emergencies are dealt with, although further trials are needed before it can be widely used.

Eligibility criteria:
Inclusion Criteria:

* Patients over 18 years old consulting in the ER due to a neurological emergency

Exclusion Criteria:

* Pregnancy

Outcomes:
Diagnostic performance
Clinical Validation of Transrectal Multiparametric Ultrasound Imaging Strategy (PCaVision) for the Detection of Clinically Significant Prostate Cancer: a Head-to-head Comparison With the MRI-based Strategy
Trial ID: NCT06281769
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-02-26
Completion date: 2025-08-15
Sponsor: Academisch Medisch Centrum - Universiteit van Amsterdam (AMC-UvA)
Collaborators: Angiogenesis Analytics
Conditions: Prostate Cancer
Interventions: 3D multi-parametric ultrasound targeted biopsy pathway using PcaVision; MRI targeted biopsy pathway
Locations: Amsterdam UMC - location VUmc; Andros Clinics; Spaarne Gasthuis; St. Antonius; Fransiscus Gasthuis
Countries: Netherlands; Netherlands; Netherlands; Netherlands; Netherlands
Enrollment: 467
Age range: 18 Years - None
Sex: MALE

Summary:
The primary objective is to demonstrate non-inferiority of the detection rate of clinically significant prostate cancer (csPCa) in targeted biopsies based on PCaVision imaging (PCaVision pathway) in comparison with the detection rate of clinically significant cancer in targeted biopsies based on MRI (MRI pathway).

Eligibility criteria:
Inclusion Criteria:

* be male
* have an age of 18 years or older
* be biopsy naïve
* have a clinical suspicion of prostate cancer
* be scheduled for evaluation by prostate MRI based on a suspicious DRE and/or elevated serum PSA
* have signed informed consent

Exclusion Criteria:

* active (urinary tract) infection or prostatitis
* a patient history with a cardiac right-to-left shunt.
* allergic to sulphur hexafluoride or any of the other ingredients of the ultrasound contrast agent SonoVue
* current treatment with dobutamine
* known severe pulmonary hypertension (pulmonary artery pressure \>90 mmHg), uncontrolled systemic hypertension or respiratory distress syndrome
* any (further) contraindication to undergo MRI or 3D mpUS imaging
* incapable of understanding the language in which the patient information is given.
* medical history of prostate surgery
* treatment of 5 alpha-reductase inhibitors for at least 3 months

Outcomes:
Detection rate of clinically significant prostate cancer (csPCa) in targeted biopsies based on PCaVision imaging in comparison with the detection rate of clinically significant cancer in targeted biopsies based on MRI.
An Open Label Data Collection Study of My Connect Post-Op, a Post-Operative Communication Tool
Trial ID: NCT07120191
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-05-01
Completion date: 2025-11-30
Sponsor: Concentra AI, inc
Conditions: Total Hip Replacement; Total Knee Replacement
Interventions: post op communication software
Locations: Kansas Joint and Spine
Countries: United States
Enrollment: 200
Age range: 22 Years - 80 Years
Sex: ALL

Summary:
This open label, multi-center study will collect data from the participant's use of My Connect Post-Op Software as a post-operative communication solution in supporting patient recovery through Technology-Assisted Conversation (TAC). The application will be tested by patients who have undergone total joint replacement as a post-op communication tool. Data will be collected on how effectively the product guides patients through their recovery, identifying potential complications, and providing appropriate recommendations based on structured question pairs.

Eligibility criteria:
Inclusion Criteria:

Orthopedic patients scheduled for elective joint arthroplasty (single or double knee, or single or double hip replacement surgery).

Age 22-80 years. Owns a smart phone. Speaks and understands written and spoken English. Ability to give written informed consent.

Exclusion Criteria:

Known diagnosis of a psychiatric disorder. Known opioid dependence in the Investigator's opinion that may complicate participation.

Participation in another clinical trial investigating a drug, medical device, or a medical procedure in the 30 days preceding trial inclusion and throughout the duration of the trial.

Any condition that in the Investigator's opinion may complicate or preclude the subject from participating in this trial, such as physical impairment or noncooperation

Outcomes:
Collect structured conversation logs
Speech and Image Recognition Based System in Improving Reporting Quality During Colonoscopy
Trial ID: NCT05829590
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2023-05-15
Completion date: 2023-07-31
Sponsor: Renmin Hospital of Wuhan University
Conditions: Artificial Intelligence; Colonoscopy
Interventions: Artificial intelligence assistant system
Locations: Renmin Hospital of Wuhan Univercity
Countries: China
Enrollment: 10
Age range: 18 Years - 70 Years
Sex: ALL

Summary:
In this study, the investigators proposed a prospective study about the effectiveness of speech and image recognition-based system in improving reporting quality during colonoscopy for colonoscopy report quality in endoscopists. The participants would be divided into two groups. For the collected colonoscopy videos, group A would record their observations with the assistance of the artificial intelligence system. The artificial intelligence assistant system can automatically capture bowel segment images and prompt abnormal lesions. Group B would complete the endoscopy report without special prompts. After a period of washout period, the two groups switched, that is, group A without AI assistance and group B with AI assistance to complete the colonoscopy report. Then, the completeness of the colonoscopy report, the completeness of capturing anatomical landmarks and detected lesions, the completeness of structured description, the accuracy of lesion reporting, the time for reporting and the satisfaction with the reporting system are compared with or without AI assistance.

Eligibility criteria:
Inclusion Criteria:

Patients:

1. Male or female ≥18 years old;
2. Able to read, understand and sign an informed consent;
3. The investigator believes that the subjects can understand the process of the clinical study, are willing and able to complete all study procedures and follow-up visits, and cooperate with the study procedures;
4. Patients requiring colonoscopy.

Doctors:

1. Males or females who are over 18 years old;
2. After qualified medical education and obtaining the Physician's Practice License.

Exclusion Criteria:

Patients:

1. Have drug or alcohol abuse or mental disorder in the last 5 years;
2. Pregnant or lactating women;
3. Patients with known multiple polyp syndrome;
4. patients with known inflammatory bowel disease;
5. known intestinal stenosis or space-occupying tumor;
6. known colon obstruction or perforation;
7. patients with a history of colorectal surgery;
8. Patients with a previous history of allergy to pre-used spasmolysis;
9. Unable to perform biopsy due to coagulation disorders or oral anticoagulants;
10. High-risk diseases or other special conditions that the investigator considers the subject unsuitable for participation in the clinical trial.

Doctors:

1\. The researcher believes that the subjects are not suitable for participating in clinical trials.

Outcomes:
The integrity of colonoscopy report
A Crossover Study to Assess the Effect of an Artificial Intelligence (AI)-Based Bedtime Smart Snack Intervention in Preventing Overnight Low Glucose in People With T1D on Multiple Daily Injections.
Trial ID: NCT05967260
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2023-09-01
Completion date: 2024-09-20
Sponsor: Oregon Health and Science University
Conditions: Type 1 Diabetes Mellitus With Hypoglycemia
Interventions: DailyDose Smart Snack app; Dexcom G6 CGM
Locations: Oregon Health and Science University
Countries: United States
Enrollment: 21
Age range: 18 Years - 100 Years
Sex: ALL

Summary:
This is a single-center, open-label, crossover trial with two arms and two periods (2x2) and one-week washout period. The study is designed to evaluate the efficacy of an AI-based bedtime smart snack intervention in reducing nocturnal low glucose in people living with T1D on MDI therapy compared with traditional CGM-augmented MDI therapy as the control.

Eligibility criteria:
Inclusion Criteria:

* Diagnosis of type 1 diabetes mellitus for at least 1 year
* Male or female participants 18 years of age or older
* Using multiple daily injections
* HbA1c \<10% at screening
* Current use of a continuous glucose monitoring system with at least two episodes of overnight hypoglycemia (defined as sensed glucose \<70 mg/dL for at least 10 minutes between the hours of 10 PM and 6 AM) within 30 days prior to screening
* Individuals with history of severe hypoglycemia requiring third party assistance must have a companion in the same dwelling as the study participant who will be linked to the participant's Dexcom app during the control arm of the study, and who is trained in the administration of glucagon.
* Willingness to follow all study procedures
* Willingness to sign informed consent and HIPAA documents

Exclusion Criteria:

* Individual of childbearing potential who is pregnant or intending to become pregnant or breast-feeding or is not using adequate contraceptive methods. Acceptable contraception includes birth control pill / patch / vaginal ring, Depo-Provera, Norplant, an IUD, the double barrier method (the woman uses a diaphragm and spermicide, and the man uses a condom), or abstinence
* Any active infection
* Known or suspected abuse of alcohol, narcotics, or illicit drugs (except marijuana use)
* Seizure disorder
* Use of non-insulin glucose lowering medications
* Use of steroids
* Stage-three or more advanced chronic kidney disease
* Hypo- or hyper- thyroidism that is not medically optimized and on a stable regimen define as Thyroid-stimulating hormone (TSH) outside of the normal reference range based on screening labs
* Adrenal insufficiency
* Cirrhosis
* Any life-threatening disease, including malignant neoplasms and medical history of malignant neoplasms within the past 5 years prior to screening (except basal and squamous cell skin cancer).
* Any clinically significant disease or disorder which in the opinion of the Investigator may jeopardize the participant's safety or compliance with the protocol
* Individual working night shifts

Outcomes:
Probability of Overnight Hypoglycemia
Early ECG Prediction of Multi-system Disease Cohort Establishment and Follow Up
Trial ID: NCT06924580
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2017-01-18
Completion date: 2026-12-30
Sponsor: RenJi Hospital
Conditions: Public Health; Public Health System Research; Multi-system Disease Diagnosis
Interventions: ECG screening
Locations: Ren Ji Hospital Afflited to School of Medicine, Shanghai Jiao Tong University
Countries: China
Enrollment: 500000
Age range: None - None
Sex: ALL

Summary:
This registered multicenter study aims to investigate the diagnostic efficacy of artificial intelligence-enhanced electrocardiography (AI-ECG) in detecting multi-system diseases. The research will utilize prospectively collected data from inpatient, emergency, and outpatient populations to develop ECG-based diagnostic, screening, and predictive models for multi-system diseases.

Eligibility criteria:
Inclusion Criteria:

1. Patients who visited the study hospital.
2. Patients included should have both ECG data and discharge diagnosis codes (ICD-10) for inpatients and emergency patients.

Exclusion Criteria:

1\. Patients who declined participation, cases with incomplete or missing clinical data, and pregnant individuals.

Outcomes:
Multi-system disease predicting based on ECG
The Relationship Between Quality of Life, Anxiety Levels, and Attitudes Toward Artificial Intelligence Among Women Undergoing Infertility Treatment
Trial ID: NCT07308561
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-10-23
Completion date: 2026-10-23
Sponsor: Acibadem University
Conditions: Infertility, Female; Artificial Intelligence; Quality of Life; Anxiety
Locations: Acıbadem Health Group
Countries: Turkey (Türkiye)
Enrollment: 191
Age range: 18 Years - 45 Years
Sex: FEMALE

Summary:
Infertility affects approximately one in six individuals worldwide and is associated with significant psychological distress, particularly among women undergoing treatment. Increased anxiety levels are strongly linked to reduced quality of life during the infertility process. With the growing integration of artificial intelligence (AI) into healthcare, AI-based tools are increasingly used in infertility care to support decision-making and patient engagement. While many patients are familiar with AI technologies, individual attitudes toward AI may influence their acceptance and potential psychosocial benefits. This study aims to examine the relationship between attitudes toward artificial intelligence, anxiety levels, and quality of life among women undergoing infertility treatment.

Eligibility criteria:
Inclusion Criteria:

* Women aged 18-45 years diagnosed with infertility (primary or secondary infertility).
* Women undergoing infertility treatment and those who have experienced various treatment modalities (IUI, IVF, ICSI).
* Women who voluntarily agree to participate in the study.
* Women who are able to understand and speak Turkish.

Exclusion Criteria:

* Women with diagnosed psychological disorders (e.g., clinical depression, anxiety disorders).
* Women who are not undergoing infertility treatment.

Outcomes:
Sociodemographic and Descriptive Information Form; State-Trait Anxiety Inventory (STAI) - Trait Anxiety Scale; General Attitudes Toward Artificial Intelligence Scale; Fertility Quality of Life Scale (FertiQol) - Core Module