moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Detection of the Occurrence of Infiltration of Gadolinium Injection in Brain MR Scans Using Artificial Intelligence
Trial ID: NCT05235646
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2022-03-14
Completion date: 2022-04-05
Sponsor: Mayo Clinic
Conditions: Magnetic Resonance Imaging
Locations: Mayo Clinic Rochester
Countries: United States
Enrollment: 40
Age range: 18 Years - None
Sex: ALL

Summary:
The purpose of this research is to develop machine learning algorithms to analyze images from brain MRI to confirm that contrast agent has been correctly administered.

Eligibility criteria:
Inclusion Criteria:

* Patients undergoing a MR brain without and with contrast examination on any GE or Siemens 1.5T and 3T MRI system within Mayo Clinic, Rochester, as part of their care plan.

Exclusion Criteria:

* None

Outcomes:
Detection of Infiltration of Gadolinium contrast agent into tissue and muscle using artificial intelligence model
Exploring the Potential of Artificial Intelligence for Earlier Breast Cancer Detection: A Retrospective Multi-Reader Study Based on AI-Assisted Mammographic Interpretation (EARLIEST-AI)
Trial ID: NCT07556900
Status: ACTIVE_NOT_RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-10-01
Completion date: 2026-09
Sponsor: Mammograaf Radioloogiakliinik
Collaborators: Hera-MI, SAS
Conditions: Breast Cancer; AI (Artificial Intelligence); Early Detection of Cancer
Locations: Mammograaf Radioloogiakliinik
Countries: Estonia
Enrollment: 785
Age range: 30 Years - None
Sex: FEMALE

Summary:
EARLIEST-AI Study type: Two-phase, multi-reader, blinded retrospective observational study based on data from a single clinic.

The primary objective of the study is to assess the sensitivity and specificity of radiologists and Artificial Intelligence (AI) in interpreting mammographic examinations for breast cancer detection in a scenario where no previous examinations are available, and to compare the diagnostic performance of radiologists with and without AI support.

The secondary objectives of the study are to assess the independent diagnostic performance of Computer Aided Detection (CAD) software, including sensitivity and specificity in identifying histopathologically confirmed breast cancer cases; to assess inter-reader and intra-reader variability in interpretation with and without AI support; to assess the agreement between AI outputs and histopathological findings; and to assess the impact of mammogram technical parameters on AI performance.

Time frame: Review of a subset of mammograms randomly selected from those performed between January 1, 2012 and December 31, 2024. Imaging findings were classified according to the Breast Imaging Reporting and Data System (BI-RADS).

Inclusion criteria:

1. Female patients aged 30 years or older.
2. One or more mammograms performed between January 1, 2012 and December 31, 2024.
3. Meets one of the following criteria:

3.1 Histopathologically confirmed diagnosis of breast cancer within 6 months of mammogram, 3.2 or two consecutive mammograms with BI-RADS 1 or BI-RADS 2.

Exclusion criteria:

1. Mammograms of poor quality or artifacts that do not allow for reliable assessment.
2. History of breast surgery or previous breast cancer treatment that has significantly altered breast morphology.
3. Data deficiencies, including:

3.1. lack of previous histopathological data 3.2. or lack of available follow-up data.

Eligibility criteria:
Inclusion Criteria:

* .

Exclusion Criteria:

* .

Outcomes:
Standalone reader sensitivity; Standalone CAD sensitivity; Augmented Reader sensitivity; Standalone reader detection to diagnostic time (MAE); Augmented reader detection to diagnostic time (MAE)
The Multicentre Wavy Stress Study: Assessing the Effect of the 'Wavy' Application, on Stress and Burden of Disease in Women With INOCA Caused by Vasospasms.
Trial ID: NCT06171893
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2022-08-01
Completion date: 2022-12-31
Sponsor: Radboud University Medical Center
Collaborators: VieCuri Medical Centre; The Elisabeth-TweeSteden Hospital; Maastricht University Medical Center; Maasstad Hospital; Catharina Ziekenhuis Eindhoven; ZonMw: The Netherlands Organisation for Health Research and Development; Health Holland; Netherlands Organisation for Scientific Research
Conditions: Stress; Ischemia; Vasospasm, Coronary; Vasomotor; Spasm; Angina, Stable; Prinzmetal Angina
Interventions: Wavy stress application
Locations: Radboud universty medical center; Maastricht UMC; VieCuri; Catharina Hospital; Elisabeth-TweeSteden Hospital; Maasstad Hospital
Countries: Netherlands; Netherlands; Netherlands; Netherlands; Netherlands; Netherlands
Enrollment: 117
Age range: 18 Years - 70 Years
Sex: FEMALE

Summary:
For women that experience angina symptoms with underlying vascular spasm as the cause, stress has an aggravating role. Coping with stress is therefore included as an important pillar in dealing with this chronic disease, see the European Association of Percutaneous Cardiovascular Interventions (EACPI) consensus document on INOCA. In practice, stress management focuses on informing and identifying the role stress plays in their lives. A potential stress management tool: "Wavy" aims to help users manage stress more consciously through biofeedback. This research focuses on the effectiveness of stress management applications. The hypothesis is that the app will help to avoid the trigger stress as much as possible and thus reduce the burden of disease.

Eligibility criteria:
Female participants are recruited from the cardiology outpatient clinics at the department of cardiology Radboudumc, Maastricht UMC, Catharina Hospital Eindhoven, St. Elisabeth Hospital Tilburg, Maasstad Hospital and in VieCuri MC, Venlo. All participants have must meet

Inclusion criteria:

1. Female.
2. Aged 18-70 (upper-limit, because of increased comorbidities and potential difficulties with using the technology).
3. Suffering from chronic angina pectoris.
4. Diagnosed with INOCA, documented within 2 years, with a coronary angiogram and an abnormal coronary function test (CFT), indicative of either epicardial or microvascular vasospasms.
5. Daily usage of a smartphone with an operating system equal to or higher than Android 5.0 (Lollipop) or IOS 10.0.
6. Signed written informed consent.

Excluded criteria:

1. She is not available for the assigned research period of approximately 10 weeks from May 2021 till October 2022. The application can be used during the planned holidays.
2. She is unable to participate in the stress-reducing exercise, e.g., due to hearing problems.
3. A language barrier is present.

Outcomes:
seattle angina questionnaire
Application Value of Deep Learning in Diagnosis of Cervical Spondylosis
Trial ID: NCT04952233
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2021-01-30
Completion date: 2021-07-30
Sponsor: Peking University Third Hospital
Conditions: Artificial Intelligence
Locations: Peking University Third Hospital
Countries: China
Enrollment: 2000
Age range: 18 Years - 80 Years
Sex: ALL

Summary:
Compared with the personal experience judgment of physicians, deep learning can identify something more quickly, efficiently, and accurately The identification and diagnosis of diseases save the energy of clinical and imaging doctors and achieve an individualized diagnosis of patients Diagnosis and evaluation are beneficial to the formulation of clinical surgical methods and the improvement of patients' prognoses.

This study uses deep learning technology, through the big data of cervical spondylosis cases learn, to explore the use of deep learning The feasibility of identifying and analyzing the characteristic imaging findings of cervical CT images that may be suggestive of a diagnosis It is attempted to reach the level of artificial intelligence-assisted diagnosis of cervical spondylosis.

Eligibility criteria:
Inclusion Criteria:

1. No surgical treatment was performed before the imaging scan. Imaging report and clinical diagnosis of cervical spondylosis with or without ossification of the posterior longitudinal ligament.
2. Patients who visited the orthopedics department and emergency department of our hospital without any surgical treatment before image scan and no obvious abnormalities were found in cervical imaging CT.

Exclusion Criteria:

1. Surgery before image data acquisition;
2. Cervical cancer, tuberculosis, and fracture;
3. The lack of image data, the image is not clear.

Outcomes:
Compare the consistency between AI and clinicians in identifying cervical CT features (cervical curvature, alignment, intervertebral space, disc herniation, ossification of the posterior longitudinal ligament, spinal stenosis)
A Novel Imaging Based Quantitative Model-aided Detection of Portal Hypertension in Patients With Cirrhosis (CHESS2104): A Prospective, Multicenter Study
Trial ID: NCT05068492
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2023-12-10
Completion date: 2025-12-01
Sponsor: Hepatopancreatobiliary Surgery Institute of Gansu Province
Collaborators: Portal Hypertension Alliance in China
Conditions: Portal Hypertension
Interventions: CT; MRI; HVPG; Ultrasound
Enrollment: 2000
Age range: 18 Years - None
Sex: ALL

Summary:
How to construct a novel, non-invasive, accurate, and convenient method to achieve prediction of hepatic venous pressure gradient (HVPG) is an important general problem in the management of portal hypertension in cirrhosis. We plan to investigate the ability of AI analysis of Ultrasound, computed tomography (CT) or magnetic resonance (MR) to establish a risk stratification system and perform tailored management for portal hypertension in cirrhosis.

Eligibility criteria:
Inclusion Criteria:

1. age \> 18 years old;
2. confirmed cirrhosis (laboratory, imaging and clinical symptoms);
3. with ultrasound/CT/MRI within 1 month prior to HVPG measurement;
4. written informed consent.

Exclusion Criteria:

1. any previous liver or spleen surgery;
2. liver cancer; chronic acute liver failure;
3. acute portal hypertension;
4. unreliable HVPG or ultrasound/CT/MRI results due to technical reasons.
5. with liver interventional therapy between HVPG and ultrasound/CT/MRI

Outcomes:
Diagnostic value
Evaluation of Artificial Intelligence for Detection of Gastrointestinal Lesions in Endoscopy (EAGLE)
Trial ID: NCT05730192
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2023-05-10
Completion date: 2023-11-29
Sponsor: Odin Medical
Collaborators: Meditrial Europe Ltd.
Conditions: Colorectal Cancer; Polyp of Colon; Adenoma Colon
Interventions: CADDIE
Locations: Israeli-Georgian Medical Research Clinic Healthycore; Sana Klinikum Lichtenberg; Humanitas Mater Domini; Humanitas Catania; Humanitas Research Hospital; H-T. Centrum Medyczne; The Maria Sklodowska-Curie National Research Institute of Oncology; Przychodnia Polskiej Fundacji Gastroenterologii; University Hospital "Clinico Lozano Blesa" of Zaragoza
Countries: Georgia; Germany; Italy; Italy; Italy; Poland; Poland; Poland; Spain
Enrollment: 985
Age range: 40 Years - None
Sex: ALL

Summary:
The EAGLE study is a prospective randomized controlled multicenter parallel design trial, for the assessment of clinical performance of the CADDIE device and to confirm that the device performs as expected.

Eligibility criteria:
Inclusion Criteria:

1. Screening or surveillance colonoscopy
2. Age 40 years or older
3. Informed consent

Exclusion Criteria:

1. Emergency colonoscopies
2. Inflammatory bowel disease (IBD)
3. Colorectal Cancer (CRC)
4. Previous Colorectal Cancer (CRC)
5. Previous colonic resection
6. Returning for a planned elective therapeutic colonoscopy.
7. Polyposis syndromes (familial adenomatous polyposis)
8. Contraindication for biopsy or polypectomy.

Outcomes:
Number of Adenoma per colonoscopy (APC); Positive percent agreement (PPA)
The Relationship Between Sexual Health Literacy and Artificial Intelligence Literacy in Young Adults: A Cross-Sectional Digital Age Study
Trial ID: NCT07376434
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2026-01-30
Completion date: 2027-02
Sponsor: Istanbul University - Cerrahpasa
Conditions: Sexuality; Artificial Intelligence
Locations: Online study
Countries: Turkey (Türkiye)
Enrollment: 269
Age range: 18 Years - 24 Years
Sex: ALL

Summary:
Young people represent a substantial proportion of the global population, and the experiences gained during adolescence and early adulthood play a critical role in shaping lifelong health behaviors. Health literacy, defined as the ability to access, understand, and use health information, is particularly important during this developmental period.

Sexual health is a key component of overall well-being and quality of life. Risky sexual behaviors among young adults may lead to serious outcomes such as unintended pregnancies, sexually transmitted infections, and sexual violence. Therefore, sexual health literacy is essential for promoting safe behaviors and protecting reproductive health.

In recent years, digital environments have become major sources of health information for young people. With the rapid rise of generative artificial intelligence tools, individuals increasingly rely on AI-based systems for accessing and interpreting health-related content. This highlights the growing importance of artificial intelligence literacy, which refers to the ability to understand, critically evaluate, and effectively use AI technologies.

However, the relationship between artificial intelligence literacy and sexual health literacy has not yet been directly examined. This study aims to investigate the association between these two literacy domains among young adults, contributing to a better understanding of sexual health information-seeking behaviors in the digital age.

Eligibility criteria:
Inclusion Criteria:

* Aged 18-24 years
* Able to read and write Turkish
* Access to the internet
* Voluntarily agreeing to participate

Exclusion Criteria:

* Refusal to provide informed consent
* Visual impairment preventing survey completion
* Cognitive or language impairments preventing survey completion

Outcomes:
Introduction Information Forum; Sexual Health Literacy; Artificial Intelligence Literacy
The Effect of an Artificial Intelligence-Supported Virtual Reality Simulation on Nursing Students' Holistic Care Skills
Trial ID: NCT07518199
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-04
Completion date: 2026-05
Sponsor: Gazi University
Conditions: Nursing Students; Decision-Making; Diagnostic Reasoning
Interventions: AI-VRS; Control
Locations: Gazi University Nursing Faculty
Countries: Turkey (Türkiye)
Enrollment: 80
Age range: None - None
Sex: ALL

Summary:
This study aims to evaluate the effect of artificial intelligence (AI)-supported virtual reality (VR) simulation on nursing students' holistic care skills. The study is a randomised controlled trial involving fourth-year nursing students, divided into an experimental and a control group. Whilst the experimental group will receive AI-supported VR simulation training, the control group will receive traditional case-based training. Outcomes to be assessed include decision-making, symptom identification, nursing diagnosis, simulation design and satisfaction with the training methods.

Eligibility criteria:
Inclusion Criteria:

* Voluntary participation in the study,
* Having enrolled for the first time in the courses HEM 402 Professional Practice I and HEM 404 Professional Practice II in the Department of Nursing, Faculty of Health Sciences,
* Absence of eye conditions affecting depth perception, such as amblyopia (lazy eye), anisometropia(different refractive errors in each eye), and strabismus (squint). (Self- report is accepted.),
* Academic performance score between 2.00 and 4.00.,

Exclusion Criteria:

* Having received training in holistic care skills in addition to their undergraduate nursing degree,
* Having experience with virtual simulation exercises focused on holistic care skills,
* Holding a high school, foundation year or undergraduate degree in a health-related field,
* Having difficulty understanding and speaking Turkish,

Criteria for Exclusion from the Study:

* The participant has not completed or has incompletely completed the required forms and scales,
* The participant in the experimental group had not taken part in or completed the AI-supported virtual reality simulation,
* Students in the control group did not take part in the educational case study,
* Students in the experimental and control groups did not take part in the assessment case study,
* The participant wishes to withdraw from the study,

Outcomes:
Nursing Diagnosis and Symptom Identification within a Holistic Care Framework
Evaluating Game-based Pediatric Diabetes Education
Trial ID: NCT06264258
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-05-22
Completion date: 2026-06-30
Sponsor: Carnegie Mellon University
Collaborators: University of Pittsburgh
Conditions: Diabetes Mellitus, Type 1; Behavior, Health
Interventions: DVx-T1D™ app
Locations: UPMC Children's Hospital of Pittsburgh
Countries: United States
Enrollment: 80
Age range: 6 Years - 12 Years
Sex: ALL

Summary:
The goal of this study is to evaluate the impact of a mobile app video game called Digital Vaccine for Type 1 Diabetes (DVx-T1D)™, proposed as a low-risk, non-invasive, digital therapeutic candidate for behavior change in children 6 to 12 years of age with Type 1 Diabetes (T1D).

The investigators hypothesize that adoption of DVx-T1D™ by T1D patients will positively influence healthy dietary and physical activity behaviors by providing nutrition and lifestyle education through a playful, Artificial Intelligence (AI)-based, cartoon-style medium of mobile gaming, and result in improvements in their T1D control. The investigators propose to conduct a longitudinal randomized controlled trial (RCT) over a 3-month period with a study population of 80 T1D participants (40 each in two arms of the trial) of 6-12 years old. The investigators will quantify the impact of the mobile game app plus standard care vs. standard care (with no exposure to the app) on T1D control (assessed by glycosylated hemoglobin or HbA1c measured during clinic visits and continuous glucose monitoring (CGM) parameters measured at home), physical activity levels and food choices of children (measured using food logs and surveys). Game telemetry, food logs, clinical, anthropometric, demographic, and survey data will be collected to obtain adequately powered, theory-driven evidence of the value of game-based approaches delivered via mobile apps.

Eligibility criteria:
For youth:

* Be between the age of 6 to 12 years.
* Have been diagnosed with type 1 diabetes for at least 1 year.
* Be currently taking insulin through multiple daily injections or an insulin pump.
* Check blood sugar with a meter at least 4 times per day or use a continuous glucose monitor.
* Be willing to comply with all study visits and procedures.

For Parent:

* Biological or adopted parent with custody and medical decision making of the patients
* Living with identified patients
* Over 18 years old
* Resident of Allegheny County

Exclusion Criteria:

-Children with type 2 diabetes, cystic fibrosis related diabetes, or secondary diabetes.

Outcomes:
Change in HbA1c values and and insulin dose adjusted HbA1c before and after the intervention; Change in glucose average and variability
Correlation of Audiovisual Features With Clinical Variables and Neurocognitive Functions in Bipolar Disorder, Mania
Trial ID: NCT03857438
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2016-09-30
Completion date: 2017-07-08
Sponsor: Istanbul Saglik Bilimleri University
Collaborators: Namik Kemal University; Bosphorus University
Conditions: Bipolar Disorder, Manic; Artificial Intelligence; Neurocognition; Treatment Resistant Disorders; Affective Computing; Multi- Modal Analysis
Interventions: Ongoing treatment for bipolar mania; Audiovisual recording during guided presentation
Locations: SBU Erenkoy Mental State Hospital
Countries: Turkey (Türkiye)
Enrollment: 89
Age range: 18 Years - 65 Years
Sex: ALL

Summary:
The aim of this study is to show the physiological changes during manic episode in bipolar mania how much they differentiate from remission and healthy control. Relation of audio-visual features as physiological changes and cognitive functions and clinical variables will be searched. The aim is to find biologic markers for predictors of treatment response via machine learning techniques to be able to reduce treatment resistance and give an idea for personalized treatment of bipolar patients.

Eligibility criteria:
Inclusion Criteria:

* diagnosis of BD type I, manic episode according to DSM-5 \[10\] given by the following doctor,
* being informed of the purpose of the study and having given signed consent before enrollment.

Exclusion Criteria:

* being younger than 18 years or older than 60 years,
* showing low mental capacity during the interview
* expression of hallucinations and disruptive behaviors during the interview,
* presence of severe organic disease,
* presence of any organic disease that may affect cognition
* having less than five years of public education
* diagnosis of substance or alcohol abuse in the last three months (except nicotine and caffeine)
* presence of cerebrovascular disorder, head trauma with longer duration of loss of consciousness, severe hemorrhage and dementia,
* having electroconvulsive therapy in the last one year.

For the healthy control group, the following additional criteria were considered for exclusion

* presence of family history of mood or psychotic disorder,
* presence of psychiatric disorder during interview or in the past.

Outcomes:
Treatment response; Changes in visual features; Changes in audio features; in Stop Signal Test; Changes in Rapid Visual Processing; in Cambridge Gambling Task; Changes in Emotion Recognition Test
Automated Detection and Volumetric Assessment of Intracerebral Hemorrhage Using Artificial Intelligence- Multicenter Study
Trial ID: NCT04733638
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2021-01-26
Completion date: 2023-04-28
Sponsor: Viz.ai, Inc.
Conditions: Intracerebral Hemorrhage
Interventions: Viz ICH VOLUME
Locations: Swedish Medical Center Healthone; Mount Sinai Hospital (Neurosurgery); Presbyterian Medical Center; Forsyth Medical Center; Prisma Health Upstate
Countries: United States; United States; United States; United States; United States
Enrollment: 1000
Age range: 18 Years - None
Sex: ALL

Summary:
To evaluate the performance of the Viz ICH VOLUME algorithm.

Eligibility criteria:
Inclusion Criteria:

* Adult patients (18 and older)
* Presenting with symptoms suspicious for an acute stroke / intracranial hemorrhage
* Having a complete imaging data set including NCCT +/- MRI, CTA and/or MRA

Exclusion Criteria:

* Subjects with poor or incomplete brain imaging.

Outcomes:
Algorithm Performance