moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
A Prospective Cohort Study of Patients With Inflammatory Bowel Disease: A Trans-Regional Digital Health Effort Unlocking the Potential of Artificial Intelligence and Data Science in Health Care
Trial ID: NCT05733702
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2024-02-01
Completion date: 2029-11
Sponsor: Luxembourg Institute of Health
Collaborators: Luxembourg National Research Fund; Centre Hospitalier du Luxembourg; Hôpitaux Robert Schuman; University of Luxembourg
Conditions: Inflammatory Bowel Diseases
Interventions: All participants will be asked to provide data and samples for collection and analysis.
Locations: Centre Hospitalier de Luxembourg (CHL)
Countries: Luxembourg
Enrollment: 100
Age range: 18 Years - None
Sex: ALL

Summary:
This study is part of the Clinnova program. This is a prospective cohort study including patients with IBD recruited at the time of a treatment change.

At least 800 participants (recruited in France, Germany and Luxembourg) will be enrolled, of which 100 participants are expected to be recruited in Luxembourg with the present study protocol.

The mission of Clinnova is to support the digitalization of healthcare and precision medicine by creating a data-enabling environment for accessing, sharing and analyzing interoperable, high-quality health data.

The main hypothesis is that treatment change decided by clinicians is predictable using objective surrogate markers derived from clinical, epidemiological, and omics data. Identifying these objective markers may facilitate future treatment decisions, provide new insights on the molecular causes for differential treatment response, pathogenesis and progression, and potential pointers for improved personalized therapeutic interventions.

Eligibility criteria:
Inclusion Criteria:

* ≥ 18 years old
* Participants are willing and able to comply with the protocol including undergoing data and sample collection as well as study visits and examinations.
* Signed informed consent form
* Diagnosed with Inflammatory bowel disease, either Crohn's disease or ulcerative colitis, at least 3 months before the enrolment AND occurrence of a significant change in the treatment of the disease (either change of drug dosage OR change of medication within the same treatment class OR change of treatment class OR addition of a drug to a treatment regimen already ongoing). A change of drug dosage or frequency is considered significant if it fulfills the requirements in section 7.1 Note: Patients with ostomy or with short bowel syndrome can be included if they fulfill all the eligibility criteria

Exclusion Criteria:

* Any condition that could potentially hamper the compliance with the study protocol, including study procedures and study visits (such as mental disability that makes it difficult or impossible to answer questionnaires)
* Not fluent in any of the following languages: French, English or German
* Known pregnancy

Outcomes:
Identify clinical, epidemiological and omics characteristics associated with IBD activity triggering a treatment change in patients with UC or CD and allow the phenotyping of patients with similar characteristics
Research on Brain Function of Metabolism-based Artificial Intelligence Assisted Clinical Classification of Obesity
Trial ID: NCT05892224
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2022-01-01
Completion date: 2025-12-31
Sponsor: Shanghai 10th People's Hospital
Conditions: Obesity; Functional Neuroimaging
Interventions: Laparoscopic sleeve gastrectomy
Locations: Shanghai Tenth People's Hospital
Countries: China
Enrollment: 150
Age range: 18 Years - 65 Years
Sex: ALL

Summary:
The purpose of this study was to investigate the differences and similarities of brain function in patients with four subtypes of obesity, and the relationship between brain function changes and complications after weight loss and metabolic improvement. 120 patients with obesity and 30 healthy individuals with normal BMI were enrolled.

Eligibility criteria:
Inclusion Criteria:

* age ranged from 16-65 years old
* BMI over 37.5kg/m2, or BMI over 32.5kg/m2 with diabetes which meets the recommended cut-off bariatric surgery of the Guidelines for surgical treatment of obesity accompanied with or without type 2 diabetes in China.

Exclusion Criteria:

* secondary causes of obesity such as hypothalamic obesity, Cushing syndrome, hypophysis dysfunction, etc
* pregnancy or location
* contraindications for laparoscopic surgery, such as gastrointestinal diseases of intra-abdominal infection, adhesion, etc
* severe heart, liver, and kidney dysfunction
* organic and systemic diseases intolerant of surgery.

Outcomes:
Functional magnetic resonance imaging of brain
Intelligent Medical Assessment for Guiding Ear Infection Treatment
Trial ID: NCT06876259
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-12-04
Completion date: 2027-07-01
Sponsor: Timothy Shope
Collaborators: Merck Sharp & Dohme LLC
Conditions: Acute Otitis Media (AOM); Upper Respiratory Infection
Interventions: Standard Clinical Ear Exam; AI app ear exam and diagnosis
Locations: Children's Community Pediatrics Brentwood; Children's Community Pediatrics Castle Shannon
Countries: United States; United States
Enrollment: 300
Age range: 6 Months - 24 Months
Sex: ALL

Summary:
Ear infections are common in young children with cold symptoms, but they can be difficult to diagnose due to small ear canals, child movement, and limited viewing time. In this study, investigators will take photos of the eardrums of children 6-24 months of age with upper respiratory symptoms. The photos will be reviewed by imaging software enhanced with artificial intelligence (AI app) to determine whether the AI app changes how ear infections are diagnosed and treated. The AI app has undergone rigorous study and was found to be highly accurate; but how using this technology affects the diagnosis and treatment by clinicians has not been studied. This research may help improve diagnostic accuracy for ear infections and ensure antibiotics are prescribed only for those children who have definite ear infections.

Eligibility criteria:
Inclusion Criteria:

* Age 6-24 months
* Presence of upper respiratory infection

Exclusion Criteria:

* No upper respiratory infection
* Otorrhea
* Tympanostomy tubes
* Currently taking antimicrobials

Outcomes:
Antimicrobial prescription rate
The REVEALPLAQUE Study: A pRospEctiVe, multicEnter Study to AnaLyze PLAQUE Using CCTA
Trial ID: NCT05138289
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2021-10-12
Completion date: 2022-11-08
Sponsor: HeartFlow, Inc.
Conditions: Coronary Artery Disease (CAD) (E.G., Angina, Myocardial Infarction, and Atherosclerotic Heart Disease (ASHD))
Interventions: Total Plaque Volume
Locations: HeartFlow Investigative Site
Countries: United States
Enrollment: 258
Age range: 18 Years - None
Sex: ALL

Summary:
This study will evaluate the level of agreement between noninvasive CCTA (coronary computed tomography angiography)-based quantification and characterization of coronary atherosclerosis and invasive IVUS (intravascular ultrasound).

Eligibility criteria:
Inclusion criteria (all must be present):

1. Age ≥18 years
2. Clinically stable patient with known CAD
3. CCTA showing stenosis in at least one major epicardial vessel of stentable/graftable diameter, in whom clinically indicated IVUS is planned within 45 days of the CCTA and FFRct available
4. FFRct successfully processed
5. Willing to comply with all aspects of the protocol
6. Agrees to be included in the study and able to provide written informed consent.

Exclusion criteria (all must be absent):

1. 1\. CCTA showing no stenosis
2. Uninterpretable CCTA by HeartFlow assessment, in which image quality prevents FFRCT from being processed.
3. Acute chest pain
4. CABG (coronary artery bypass graft) prior to CCTA acquisition
5. Prior history of PCI for 3 or more vessels
6. MI (myocardial infarction) less than 30 days prior to CCTA or between CCTA and ICA.
7. Suspicion of acute coronary syndrome (acute myocardial infarction or unstable angina)
8. Known complex congenital heart disease
9. Tachycardia or significant arrhythmia
10. Subject requires an emergent procedure
11. Evidence of ongoing or active clinical instability, including acute chest pain (sudden onset), cardiogenic shock, unstable blood pressure with systolic blood pressure \<90 mmHg, and severe congestive heart failure (NYHA \[New York Heart Association\] III or IV) or acute pulmonary edema
12. Any active, serious, life-threatening disease with a life expectancy of less than 2 months
13. Currently enrolled in another study utilizing FFRCT or in an investigational trial that involves a non-approved cardiac drug or device
14. Persons under the protection of justice, guardianship, or curatorship

Outcomes:
Comparison of Total Plaque Volume
Comparison of the Effects of Traditional and Artificial Intelligence-Assisted Yoga on Quality of Life and Mental Well-Being: A Randomized Controlled Study
Trial ID: NCT06186635
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-02-01
Completion date: 2025-11-30
Sponsor: Gözde Özsezer
Conditions: Healthy People
Interventions: Traditional yoga; AI-Assisted Yoga
Enrollment: 60
Age range: 18 Years - 35 Years
Sex: ALL

Summary:
The goal of the study was to compare the effects of traditional yoga and AI-assisted yoga practice on quality of life and mental well-being in students in a randomized controlled trial. The main question\[s\] it aims to answer are:

1. What is the level of quality of life of students studying at Çanakkale Onsekiz Mart University, Faculty of Health Sciences?
2. What is the level of mental well-being of students studying at Çanakkale Onsekiz Mart University, Faculty of Health Sciences?
3. Does the traditional yoga program applied to students studying at Çanakkale Onsekiz Mart University, Faculty of Health Sciences affect the quality of life?
4. Does the artificial intelligence-supported yoga program applied to students studying at Çanakkale Onsekiz Mart University, Faculty of Health Sciences affect the quality of life?
5. Does the traditional yoga program applied to students studying at Çanakkale Onsekiz Mart University, Faculty of Health Sciences affect mental well-being?
6. Does the artificial intelligence-supported yoga program applied to students studying at Çanakkale Onsekiz Mart University, Faculty of Health Sciences affect mental well-being?

Participants will take part in a traditional and AI-assisted yoga practice. The researchers will compare the effect of traditional yoga and AI-assisted yoga on quality of life and mental well-being.

Eligibility criteria:
Inclusion Criteria:

1. Being a student at Çanakkale Onsekiz Mart University, Faculty of Health Sciences, Department of Nursing, Midwifery, Emergency Aid and Disaster Management.
2. Volunteering to participate in the study
3. Not having any physical dysfunction that would prevent yoga (COPD, septum deviation, etc.)
4. Not having chronic diseases that would prevent yoga (Breast CA, epilepsy, chemotherapy, radiotherapy, stage 3 hypertension \&amp;gt;180/110)
5. Not having done yoga before
6. Being open to communication
7. Having internet access.
8. Having a computer/tablet/phone with a camera.

Exclusion Criteria:

1. Having a physical or mental health problem that will prevent and/or limit practicing yoga.
2. Having had a surgical operation within the last month
3. Having had a cancer-related surgical operation within the last year
4. Not volunteering to participate in the study
5. Not wanting to turn on his camera.
6. Doing yoga regularly

Outcomes:
The Multidimensional Quality of Life Scale; Waarwick-Edinburg Mental Well-Being Scale
Artificial Intelligence (AI)-Assist Sleep Intervention for Insomnia Among Older Adults With Subjective Cognitive Decline: A Pilot Randomized Controlled Trial
Trial ID: NCT07588035
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-04-20
Completion date: 2027-09-01
Sponsor: The Hong Kong Polytechnic University
Conditions: Insomnia; Sleep
Interventions: Artificial Intelligence (AI)-assisted sleep intervention; Usual Care
Locations: Qingyuan Second People's Hospital
Countries: China
Enrollment: 60
Age range: 60 Years - None
Sex: ALL

Summary:
To assess the feasibility and acceptability of Artificial Intelligence (AI)-assisted sleep intervention on improving the insomnia of older adults with subjective cognitive decline.

Eligibility criteria:
Inclusion Criteria:

* older adults aged ≥ 60 years
* Subjective cognitive decline questionnaire 9 (SCD-Q9) ≥ 5
* Montreal Cognitive Assessment (MoCA) ≥ 26. If the number of years of education is less than 12, one point will be added to the total score;
* with insomnia according to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) and Insomnia Severity Index (ISI) ≥ 8
* reachable by phone

Exclusion Criteria:

* having a previously diagnosed primary sleep disorder, such as sleep apnoea
* suffering from a severe chronic illness that could contribute to sleep problems (e.g., emphysema)
* currently undergoing medical, psychological, or psychiatric treatment for sleep disturbances or depression
* having significant visual or hearing impairments.

Outcomes:
Eligibility rate; Recruitment rate; Adherence rate; Attrition rate; Engagement rate; Retention rate; Acceptability
A Single-center, Retrospective, Open Label, Randomized Controlled Trial of Artificial Intelligence Versus Expert Endoscopists for Diagnosis of Gastric Cancer in Patients Who Underwent Upper Gastrointestinal Endoscopy
Trial ID: NCT04040374
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2019-07-01
Completion date: 2019-11-16
Sponsor: Tokyo University
Conditions: Gastric Cancer
Interventions: AI-based diagnosis; The expert endoscopists-based diagnosis
Locations: Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo
Countries: Japan
Enrollment: 500
Age range: 20 Years - None
Sex: ALL

Summary:
Title: A single-center, retrospective randomized controlled trial of artificial intelligence (AI) versus expert endoscopists for diagnosis of gastric cancer in patients who underwent upper gastrointestinal endoscopy.

Précis: this single-center, retrospective randomized controlled trial will include 500 outpatients who underwent upper gastrointestinal endoscopy for gastric cancer screening and will compare the diagnostic detection rate for gastric cancer of AI and expert endoscopists.

Objectives Primary Objective: to evaluate the diagnostic detection rate for gastric cancer of AI and expert endoscopists.

Secondary Objectives: to determine whether AI is not inferior to expert endoscopists in terms of the number of images analyzed for diagnosis of gastric cancer and intersection over union (IOU), and the detection rate of diagnosis of early and advanced gastric cancer.

Endpoints Primary Endpoint: diagnosis of gastric cancer. Secondary Endpoints: image based diagnosis of gastric cancer and IOU. Population: in total, 500 males and females aged ≥ 20 years who underwent upper gastrointestinal endoscopy for screening of gastric cancer at a single hospital in Japan.

Describe the Intervention: AI-based diagnosis of gastric cancer based on upper gastrointestinal endoscopy images.

Study Duration: 3 months.

Eligibility criteria:
Inclusion Criteria:

1. Males or females aged ≥ 20 years who underwent upper gastrointestinal endoscopy at Tokyo University Hospital during 2018.
2. Informed optout consent, obtained from each patient before completion of the study.

Exclusion Criteria:

1. Patients who underwent gastrectomy.
2. Patients who underwent transnasal upper gastrointestinal endoscopy.

Outcomes:
Per patient diagnosis of gastric cancer
Assessment of Brain-heart-muscle Axis Using EEG/MECG/EMG Combo Machine for Evaluating the Severity of Aortic Stenosis, Heart Failure and Ischemic Stroke Through an Artificial Intelligenceassisted System.
Trial ID: NCT05716659
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2023-05-30
Completion date: 2032-08-31
Sponsor: National Taiwan University Hospital
Conditions: CVD
Locations: NTUH
Countries: Taiwan
Enrollment: 750
Age range: 20 Years - None
Sex: ALL

Summary:
The specific objectives and methods of this project are: (1) To test the feasibility and accuracy of integrating EEG, MECG and EMG for detecting the severity of diseases such as aortic stenosis, heart failure and ischemic stroke. (2) Improve the accuracy of this multi-channel brain-heart-muscle device by using an artificial intelligence auxiliary system. (3) Provide tailor-made interdisciplinary treatment strategies for patients with different disease states.

Eligibility criteria:
Inclusion criteria

1. Age≥20 year-old
2. Healthy subjects or patients with A. Metabolic syndrome (MetS) ；B. HF with reduced ejection fractions (HFrEF, defined by left ventricular ejection fraction≤40%) and current or previous symptoms of HF；C. Severe aortic stenosis (defined aortic valve area \< 1.0 cm2 or mean pressure gradient ≥ 40 mmHg), preparing to receive transcatheter aortic valve replacement ；D. Acute ischemic stroke and admitted to stroke intensive care unit (ICU), within 24 hours after onset

The criteria of MetS includes:

1. Abdominal obesity: waist circumference (for Asians) ≥ 90 cm in men and ≥ 80 cm in women;
2. Hyperglycemia: serum fasting glucose level ≥ 100 mg/dl, or receiving drug treatment for elevated blood sugar;
3. Reduced high-density lipoprotein cholesterol (HDL-C): serum HDL-C \< 40 mg/dl in men and \< 50 mg/dl in women or on cholesterol drug treatment;
4. Elevated triglycerides: serum triglyceride ≥ 150 mg/dl or on drug treatment of hypertriglyceridemia;
5. Hypertension: systolic blood pressure ≥ 130 mmHg, diastolic blood pressure ≥ 85 mmHg, or on antihypertensive drug treatment.

Exclusion criteria:

1. Under treatment for malignancy
2. Currently receiving immunosuppressants
3. Modified Rankin Scale (mRS) ≥2 prior to the stroke
4. Symptomatic HF in patients with acute ischemic stroke
5. Unable to perform neurocognitive function test
6. Unable to evaluate the physical activity using questionnaire or 6-minute walk test
7. Unable to cooperate with examinations using 3E combo machine.

Outcomes:
Composite cardiovascular outcome
ABCSG TR Spotlight: Validation of an Artificial Intelligence-based Prognostic Assay in ER+/HER2- Breast Cancer
Trial ID: NCT07541703
Status: ACTIVE_NOT_RECRUITING
Study type: OBSERVATIONAL
Start date: 2026-04-01
Completion date: 2028-03-31
Sponsor: Spotlight Medical
Collaborators: Austrian Breast & Colorectal Cancer Study Group
Conditions: Breast Cancer; Early Breast Cancer; ER-Positive HER2-Negative Breast Cancer
Interventions: Locked AI-based prognostic assay
Locations: Medical University of Vienna, Center of Cancer Research
Countries: Austria
Enrollment: 2200
Age range: 18 Years - 80 Years
Sex: FEMALE

Summary:
This retrospective observational study evaluates the prognostic performance of a locked Artificial Intelligence (AI)-based assay in patients with Estrogen Receptor (ER)-positive, Human Epidermal Growth Factor Receptor 2 (HER2)-negative early breast cancer (EBC) from Austrian Breast \& Colorectal Cancer Study Group (ABCSG)-8, with extended follow-up from ABCSG-16 where available. ABCSG will provide digitized hematoxylin and eosin (H\&E) slides and required baseline clinicopathologic variables to Spotlight Medical without outcome data for blinded assay inference. ABCSG will then perform the prespecified statistical analyses linking assay outputs to clinical outcomes.

Eligibility criteria:
Inclusion Criteria:

* Female patients enrolled in ABCSG-8
* Postmenopausal patients with ER-positive/HER2-negative early invasive breast cancer
* Available archived primary tumor material suitable for H\&E slide digitization
* Available required baseline clinicopathologic variables or variables derivable according to the prespecified statistical analysis plan
* Patients with permission for inclusion in this retrospective translational research study

Exclusion Criteria:

* No suitable archived primary tumor material for H\&E slide digitization
* Missing required baseline clinicopathologic variables not recoverable according to the prespecified statistical analysis plan
* No analyzable follow-up for the endpoint of interest

Outcomes:
Time to distant recurrence
Artificial Intelligence in BreastScreen Norway - a Randomized Controlled Trial
Trial ID: NCT06032390
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-11-25
Completion date: 2033-06
Sponsor: Norwegian Institute of Public Health
Collaborators: Helse Vest; Helse Midt-Norge; Helse Nord; Norwegian Cancer Society
Conditions: Breast Cancer
Interventions: AI assisted mammography screening interpretation; Standard mammography screening interpretation
Locations: Cancer Registry of Norway, Norwegian Institute of Public Health
Countries: Norway
Enrollment: 150000
Age range: 50 Years - 69 Years
Sex: FEMALE

Summary:
The purpose of this randomized controlled trial is to compare the number of breast cancers detected among women who had their screening mammograms interpreted by artificial intelligence (AI) in combination with one or two breast radiologists to the number of breast cancers detected after standard independent double reading in BreastScreen Norway. The aims of the study is to prove that screening interpretation with AI in combination with one or two radiologists (the intervention) is non-inferior to standard interpretation procedure.

Eligibility criteria:
Inclusion Criteria:

* Women attending the population-based mammography screening program BreastScreen Norway providing a signed, informed consent

Exclusion Criteria:

* Women attending the population-based mammography screening program BreastScreen Norway not providing a signed, informed consent

Outcomes:
Screen-detected breast cancers
A Multicenter, Multiomics, Non-interventional, Real-world Study of the Efficacy and Safety of Immune Checkpoint Inhibitors as First-line Therapy for Advanced Malignancies
Trial ID: NCT05862259
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2023-08-10
Completion date: 2025-12-31
Sponsor: Xinqiao Hospital of Chongqing
Conditions: Malignant Tumor; Immunotherapy; Artificial Intelligence
Locations: the second affiliated hospital of Army medical university
Countries: China
Enrollment: 120
Age range: 18 Years - 75 Years
Sex: ALL

Summary:
In this study, we collected the data of immunohistochemistry, gene detection, image, OS, PFS, Orr, and so on. Secondly, the database of immunotherapy for malignant tumor was established, and the predictive model was constructed to verify and establish the rationality and validity of the biomarkers and predictive system of immunotherapy

Eligibility criteria:
Inclusion Criteria:

1. Informed consent has been signed and, in the judgment of the investigator, the patient is able to comply with the study protocol and sign a written informed consent.
2. the advanced malignant tumors (solid tumors of the non-small-cell lung carcinoma, stomach, breast, urinary system, etc.) were diagnosed by histopathology.
3. the stage IV according to the eighth edition of IASLC.
4. PS 0-2, the expected survival \> 3 months.
5. the age of 18-75 years.
6. no contraindication to treatment with immune checkpoint inhibitors.

Exclusion Criteria:

1. the patients' compliance was poor, which violated the rules of the trial;
2. the patients with severe dysfunction of vital organs (heart, liver and kidney) ;
3. the patients with other malignant tumors;
4. the researchers considered that the patients should not participate in other conditions of the trial.

Outcomes:
Progression-free survival