moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
New 3D-echocardiography-based Motion Integration and mAnagement for subsTrate characteRisation and Stereotactic Arrhythmia radIoablation tX in Ventricular Tachycardias- the MATRIX-VT Study
Trial ID: NCT05964660
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2023-08-14
Completion date: 2025-03
Sponsor: University of Luebeck
Collaborators: Institute for Robotics and Cognitive Systems, University of Luebeck, Germany
Conditions: Ventricular Arrythmia
Locations: Klinik für Rhythmologie
Countries: Germany
Enrollment: 20
Age range: 18 Years - 100 Years
Sex: ALL

Summary:
Prospective, observational, single center, pilot study to analyze the feasibility of motion and structural data integration in patients with ventricular arrhythmia by means of artificial intelligence for improved arrhythmogenic substrate characterization and motion management during stereotactic arrhythmia radioablation.

Eligibility criteria:
Inclusion Criteria:

* Age ≥ 18 years
* Indication for catheter ablation of ventricular arrhythmias (ventricular fibrillation, ventricular tachycardia, premature ventricular contractions)
* Indication for preprocedural contrast-enhanced cardiac computed tomography for cardiac structure characterization

Exclusion Criteria:

* Platelet count \< 100,000 cells/mm3
* BMI \> 45 kg/m2 or \< 18 kg/m2

Outcomes:
Feasibility of algorithm training to achieve improved arrhythmogenic substrate characterization; Feasibility of algorithm training to improve the planning target volume definition for stereotactic arrhythmia radioablation therapy
Reference Values and Clinical Screening Test of Diffuse Noxious Inhibitory Controls (DNIC) Using Deep Learning and Artificial Intelligence
Trial ID: NCT04896827
Status: SUSPENDED
Study type: OBSERVATIONAL
Start date: 2022-04-05
Completion date: 2025-12
Sponsor: Université de Sherbrooke
Collaborators: Lucine; Centre for Research of CHUS (CRCHUS)
Conditions: Chronic Pain
Interventions: Conditioned pain modulation test
Locations: Université de Sherbrooke
Countries: Canada
Enrollment: 244
Age range: 18 Years - 79 Years
Sex: ALL

Summary:
Chronic pain (CP) is disabling for people triggering important costs for society. A deficit of diffuse noxious inhibitory controls (DNIC) is one of the CP mechanisms. DNICs are evaluated in research setting using a CPM protocol (conditioned pain modulation). There is a lack of reference values on the effectiveness of DNICs. Wider research on DNIC will help to understand CP and to develop a clinical screening test evaluating DNICs. This study aims more specifically to determine whether it is possible to develop a facial recognition system to automate pain measurement and the effectiveness of pain control mechanisms.

Eligibility criteria:
Healthy participants

Inclusion Criteria:

* 18-79 years old
* No chronic pain
* Able to provide consent

Exclusion Criteria:

* Cardiovascular disease (arrhythmia, cerebrovascular accident, infarction...)
* Raynaud syndrome
* Severe psychiatric disease (dementia, schizophrenia, psychosis, major depression)
* Injuries or loss sensitivity to their forearms or hands
* Pregnant women or in post-partum period (\<1 year)

Participants with chronic pain

Inclusion Criteria:

* 18-79 years old
* Chronic pain (chronic pain is defined by any regular pain for more than 6 months)
* Able to provide consent

Exclusion Criteria:

* Cardiovascular disease (arrhythmia, cerebrovascular accident, infarction...)
* Raynaud syndrome
* Severe psychiatric disease (dementia, schizophrenia, psychosis, major depression)
* Injuries or loss sensitivity to their forearms or hands
* Pregnant women or in post-partum period (\<1 year)
* Chronic pain caused by cancer or migraine

Outcomes:
Conditioned pain modulation (CPM) profiles; Temporal summation profiles
Effectiveness of Artificial Intelligence (AI) in the Prevention, Diagnosis, and Triage of Respiratory Diseases: A Multicenter, Randomized Controlled Study
Trial ID: NCT06931782
Status: ENROLLING_BY_INVITATION
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-01-01
Completion date: 2025-06-30
Sponsor: The First Affiliated Hospital of Guangzhou Medical University
Conditions: Respiratory Diseases; Artificial Intelligence (AI); Randomized Controlled Trial
Interventions: AI
Locations: the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong 510120
Countries: China
Enrollment: 2400
Age range: 18 Years - 75 Years
Sex: ALL

Summary:
This study will evaluate the impact of using the GPT-4o compared to traditional online tools in the field of respiratory disease prevention, focusing on the dissemination of knowledge and behavior changes among the general public. We will explore the effectiveness of GPT-4o in enhancing public awareness and management capabilities regarding respiratory diseases and promoting appropriate preventive behaviors.

Eligibility criteria:
Inclusion Criteria:

* No medical background
* Aged from 18 to 75 years old

Exclusion Criteria:

* Had a medical background
* Exceeds the age criteria
* Failed to comply with the survey requirements

Outcomes:
the accuracy of participants in answering questions related to triage, diagnosis, and risk factor identification of respiratory diseases using artificial intelligence versus internet-based information retrieval assessed by questionnaire survey
Integrated Multi-omics and Machine Learning-driven Personalized Treatment of Obesity-associated Fatty Liver Disease
Trial ID: NCT05554224
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2008-06-25
Completion date: 2028-12-31
Sponsor: Institut Investigacio Sanitaria Pere Virgili
Collaborators: Hospital Universitari Sant Joan; La Caixa Foundation; Instituto de Salud Carlos III; University of Barcelona
Conditions: NAFLD; Obesity, Morbid; Comorbidities and Coexisting Conditions
Interventions: To propose diagnostic tests for liver diseases before surgical decisions.
Locations: Hospital Universitari Sant Joan
Countries: Spain
Enrollment: 1104
Age range: 18 Years - None
Sex: ALL

Summary:
The investigators seek to analyze the samples provided by patients with obesity-associated fatty liver disease at the multi-omics level and to integrate the results with clinical information, genotypic variants, and factors influencing inter-organ crosstalk. The main aim is to improve the interpretation of fatty liver disease associated with obesity and diabetes by developing predictive models built with algorithms from artificial intelligence. The challenge is to decipher the flow of information by exploring contributing factors, proximate causes of regulatory defects, and maladaptive responses that may promote therapeutic approaches.

Eligibility criteria:
Inclusion Criteria:

* Body mass index greater or equal to 40 kg/m\^2.
* Body mass index between 35 and 40 kg/m\^2 with high-risk comorbidities (diagnosis or treatment for hypertension, dyslipidemia, or type 2 diabetes mellitus).
* Positive psychiatric evaluation.
* Age greater or equal to 18 years old.

Exclusion Criteria:

* Legal or illegal drug consumption, including alcohol.
* Diagnosis of Hepatitis.
* Current cancer diagnosis or treatment.
* Clinical or analytical evidence of severe illness.
* Clinical or analytical evidence of chronic or acute inflammation.
* Clinical or analytical evidence of infectious diseases.
* Clinical or analytical evidence of terminal illness.

Outcomes:
Weight change; Type 2 diabetes mellitus incidence; Hypertension incidence; Chronic liver diseases incidence; Dyslipidemia incidence
Validation of 3D Stroke Models to Differentiate Stroke Subtypes and Predict Source in Acute Ischaemic Stroke
Trial ID: NCT05055960
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2021-11-12
Completion date: 2024-09-01
Sponsor: University of Leicester
Collaborators: Open University
Conditions: Stroke
Interventions: Stroke simulation and machine learning
Locations: University of Leicester
Countries: United Kingdom
Enrollment: 100
Age range: 18 Years - None
Sex: ALL

Summary:
Stroke is a common condition which results in significant disability for patients. There are different causes of stroke, but around one quarter are as a result of clots or other material from the heart lodging in blood vessels in the brain, stopping the blood supply to that area. Atrial fibrillation is a common cause of blood clots which go to the brain and can be easily treated with blood thinning medications, which significantly reduce the risk of further strokes. However, at the moment, atrial fibrillation is difficult to identify, and heart monitoring can be needed for up to one year. This significantly delays starting blood thinning medications and leaves patients at risk of stroke during this time. Therefore, better ways of picking up strokes caused by atrial fibrillation are needed. One such method may be to use brain scans which are routinely taken at the time a patient presents with an acute stroke. By using mathematical models to work out the source of stroke, we may be able to determine which strokes are caused by atrial fibrillation at the time the patient presents with their stroke. This would reduce the number of investigations patients under-go, saving money for the NHS, and reducing the number of tests patients have. Therefore, the aim of this project is to create an anonymised database of brain scans from patients who have presented to hospital with a stroke to develop and test these recently developed models to see if they can accurately identify which strokes are caused by atrial fibrillation, and which ones are not. This project has the potential to improve patient outcomes by reducing treatment delays and improving the accuracy of the diagnosis of the stroke source.

Eligibility criteria:
Inclusion Criteria:

* Patients presenting with acute ischemic stroke to the emergency department, transient ischaemic attack clinic, or hyperacute stroke unit
* Adults aged over 18 years
* Patients receiving a magnetic resonance imaging (MRI) scan

Exclusion Criteria:

* Insufficient clinical demographic and outcome data (\<75% of all variables)
* No acute stroke diagnosis or identifiable lesion on MRI
* Patients with a computed tomography scan only

Outcomes:
A database of anonymous magnetic resonance imaging scans from patients presenting with acute ischaemic stroke for the development and validation of stroke simulation models.
A Randomized Controlled Study of Including a Deep Learning-based Analysis of Chest Computed Tomography as an Aid to Decision Making of Multidisciplinary Team Meetings for Lung Cancer Screening in Eligible Patients
Trial ID: NCT05704920
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-04-08
Completion date: 2030-10-01
Sponsor: Centre Hospitalier Universitaire de Nice
Conditions: Lung Cancer
Interventions: IA; Not IA
Locations: CHU de Nice - Hôpital de Pasteur
Countries: France
Enrollment: 2722
Age range: 18 Years - 80 Years
Sex: ALL

Summary:
Lung cancer (LC) screening using low-dose chest CT (LDCT) has already proven its efficacy.

The mortality reduction associated with LC screening is around 20%, much higher than the reduction in mortality associated with screening for breast, colon or prostate cancers.

Implementing lung cancer screening on a large scale faces two main obstacles:

1. The lack of thoracic radiologists and LDCT necessary for the eligible population (between 1.6 and 2.2 million people in France);
2. The high frequency of false positive screenings: in the NLST trial, more than 20% of the subjects screened were found to have at least one nodule of an indeterminate lung nodule (ILN) whereas less than 3% of ILNs are actually LC.

The gold standard for determining on the benign or malignant nature of a nodule is definitive histology. Otherwise, the evolution of the nodule on serial thoracic imaging is a good alternative. The period of indeterminacy of a nodule can be as long as 24 months in many cases, which can be a source of prolonged and sometimes unjustified anxiety for screening candidates.

The purpose of this randomized controlled study that focuses on LC screening in patients aged 50 to 80 years, who smoked more than 20 packs/ year or stopped smoking less than 15 years ago. Its objective is to determine whether assisting multidisciplinary team (MDT) meetings with an AI-based analysis of screening LDCT accelerates the definitive classification of nodules into malignant or benign.

Eligibility criteria:
Inclusion Criteria:

* Age between 50 and 80 years old
* active smoker or ex-smoker who quit smoking less than 15 years ago
* smoking history of at least 20 pack-years
* signature of the informed consent
* affiliation to French social security

Exclusion Criteria:

* clinical signs suggestive of cancer
* recent chest scan (\<1 year) for another cause
* radiological abnormality requiring follow-up or additional investigations
* health problem significantly limiting life expectancy from the clinician's point of view
* health problem limiting ability or willingness to undergo lung surgery
* Patients with active neoplasia, except basal cell carcinoma of the skin.
* vulnerable people: adults under guardianship, adults under curatorship medical and/or psychiatric problems of sufficient severity to limit full adherence to the study or expose patients to excessive risk

Outcomes:
Diagnosis of lung disease
Data Collection Study to Develop an Artificial Intelligence-Based Ultrasound Guidance Software (Curious) for Musculoskeletal Examinations
Trial ID: NCT06025279
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2023-08-03
Completion date: 2023-11-29
Sponsor: Smart Alfa Teknoloji San. ve Tic. A.S.
Collaborators: Ankara University
Conditions: Ultrasound Imaging of Anatomical Structures; Musculoskeletal Diseases
Interventions: Ultrasound Scan
Locations: Ankara University School of Medicine
Countries: Turkey (Türkiye)
Enrollment: 300
Age range: 18 Years - None
Sex: ALL

Summary:
The primary objective of this observational study is to acquire ultrasound images (raw data) encompassing various planes within the musculoskeletal system. This data will be instrumental in the development of artificial intelligence-guided software. The study aims to enlist 300 volunteers, comprising individuals with both healthy musculoskeletal systems and those presenting pathologies. These participants will undergo ultrasound scans administered by two experienced professionals, employing FDA-cleared ultrasound devices.

The main question it aims to answer is:

-Are the collected ultrasound images of diagnostic quality?

Eligibility criteria:
Inclusion Criteria:

* Volunteers over the age of 18
* Able to accept and sign the Informed Consent Form before participating in the study

Exclusion Criteria:

* Volunteers below the age of 18
* Unwilling to accept or having psychiatric or neurological diseases to sign an Informed Consent Form before participating in the study
* Inability to lie flat
* Anatomical deformity in the area to be scanned

Outcomes:
Collecting ultrasound data for artificial intelligence software that highlighted structures
Surgical Skill Labs for Robotic Mastectomy Using Cadaver And Porcine Models and Educational Program Using a Surgical Guide by Artificial Intelligence
Trial ID: NCT06380803
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2021-04-29
Completion date: 2025-01-26
Sponsor: Severance Hospital
Collaborators: National Research Foundation of Korea
Conditions: Breast Neoplasms; Surgery; Educational Problems; Surgical Procedure, Unspecified
Interventions: A educational program
Locations: Yonsei University College of Medicine
Countries: South Korea
Enrollment: 64
Age range: None - None
Sex: ALL

Summary:
The goal of this prospective study is to investigate the satisfaction, NASA-Task Load Index (TLX), and surgical proficiency of a robotic breast surgery education program using cadaver or porcine models, as well as to develop an AI-based surgical guide for utilization within the educational program in trainees for robotic breast surgery.

The main question\[s\]it aims to evaluate :

* Satisfaction questionnaire of a educational program
* NASA-TLX of a educational program
* global evaluative assessment of robotic skills (GEARS) of a robotic breast surgery for surgical proficiency

Participants will participate the educational program and fill out a satisfaction questionnaire and NASA-TLX.

The tutor evaluates GEARS for 15 minutes at the beginning and end of training.

After the development of the surgical guide based on AI, researchers will compare a training group with or without surgical guide to see the effect of the surgical guide.

Eligibility criteria:
Inclusion Criteria:

* A breast surgeon who completed the basic dry lab for surgical robot platform before the educational program
* A trainee who signed a informed consent

Exclusion Criteria:

* A trainee who did not sign a informed consent
* A trainees judged by the researcher to be inappropriate for participation in this study

Outcomes:
Satisfaction questionnaire of a educational program; NASA-Task Load index of a educational program; Global Evaluative Assessment of Robotic Skills of a robotic breast surgery for surgical proficiency
Artificial Intelligence-assisted Optical Diagnosis for the Resect-and-discard Strategy in Clinical Practice in Non-academic Hospitals: a Randomized Controlled Study
Trial ID: NCT07362524
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-02-15
Completion date: 2026-11-15
Sponsor: Shanghai Jiao Tong University School of Medicine
Conditions: Colorectal Polyps
Interventions: AI-Assisted Real-time Endoscopic Diagnosis System
Locations: Nantong First People's Hospital; Wuxi Ninth People's Hospital
Countries: China; China
Enrollment: 363
Age range: 18 Years - None
Sex: ALL

Summary:
To validate the feasibility of promoting colorectal diminutive polyp "predict, resect, and discard" and "diagnose and leave" strategies for non-experts in general hospitals through CADx system.

Eligibility criteria:
Inclusion Criteria:

1. Agree to participate in this prospective randomized controlled study;
2. Age ≥18 years for colonoscopy;
3. At least one colorectal diminutive polyp (size \<=5mm) found during examination;

Exclusion Criteria:

1. Already participating in other clinical trials, having signed informed consent and being in the follow-up period of other clinical trials;
2. Already participating in drug clinical trials and being in the washout period of experimental or control drugs;
3. History of drug or alcohol abuse or psychological disorders in the past five years;
4. Pregnant or lactating patients;
5. Known polyposis syndromes;
6. Patients with gastrointestinal bleeding;
7. Previous history of inflammatory bowel disease, colorectal cancer, or colorectal surgery;
8. Patients with contraindications to tissue biopsy;
9. Previous history of allergic reactions to bowel cleansing agent components;
10. Suffering from intestinal obstruction or perforation, toxic megacolon, heart failure (Grade III or IV), severe cardiovascular disease, severe liver failure, or renal insufficiency, etc.;
11. Patient's bowel preparation BBPS score \<6 for this colonoscopy;
12. Researchers consider the patient unsuitable for trial participation.

Outcomes:
Agreement between optical- and histology-based post-polypectomy surveillance intervals according to United States Multi-Society Task Force (USMSTF) guidelines; Negative Predictive Value for Diagnosing Neoplastic Lesions in Rectosigmoid Diminutive Polyps under High Confidence
An Alternative Remote Rehabilitation Strategy for the Improvement of Sarcopenia in the Elderly: Tai Chi as an Exercise Form
Trial ID: NCT05767710
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2023-04-10
Completion date: 2023-07-20
Sponsor: Northeast Normal University
Conditions: Sarcopenia
Interventions: AI telerehabilitation - Tai Chi; general telerehabilitation - Tai Chi; traditional rehabilitation - Tai Chi; AI telerehabilitation - Yi Jin Jing; general telerehabilitation - Yi Jin Jing; traditional rehabilitation - Yi Jin Jing; AI telerehabilitation - Ba Duan Jin; general telerehabilitation - Ba Duan Jin; traditional rehabilitation - Ba Duan Jin
Locations: Chinese Center of Exercise Epidemiology
Countries: China
Enrollment: 225
Age range: 60 Years - 75 Years
Sex: ALL

Summary:
Sarcopenia is an age-related disease characterized by a progressive decrease in skeletal muscle content and loss of muscle function. Tai Chi exercises have been shown to improve skeletal muscle mass in elderly people suffering from sarcopenia. However, offline interventions are a very labor- and financial-intensive approach. Therefore, researchers designed an artificial intelligence-assisted program and Tai Chi exercise format to investigate whether a remote rehabilitation approach could replace face-to-face interventions

Eligibility criteria:
Inclusion Criteria:

(1) Patients between the ages of 60-75 years; (2) who meet the screening criteria for sarcopenia in Asia established by AWGS 2019; (3) who have a computer at home (4) who can use a computer or was accompanied by family number who can use a computer.

Exclusion Criteria:

(1) participants taking medications that significantly impact musculoskeletal function (2) participants suffering from respiratory failure or other bodily problems; (3) participants with mental disorders or neurological disorders; (4) patients who participate in other training programs on a regular basis.

Outcomes:
Using quantitative CT to investigate the change of skeletal muscle area in sarcopenic elderly.; Using a Jamar hydraulic hand dynamometer to investigate change of handgrip in sarcopenic elderly.; Using a bioelectrical impedance body composition analyzer with a multifrequency device to investigate the change of relative skeletal muscle mass index in sarcopenic elderly.
Development and Pilot Evaluation of an Artificial Intelligence-Assisted System for Automated Oxygen Flow Regulation: The Saturista Device
Trial ID: NCT07576946
Status: ACTIVE_NOT_RECRUITING
Study type: OBSERVATIONAL
Start date: 2026-03-15
Completion date: 2026-12
Sponsor: Koç University
Collaborators: Health Institutes of Turkey; Istinye University
Conditions: Oxygen Therapy
Locations: Istinye University
Countries: Turkey (Türkiye)
Enrollment: 7
Age range: 18 Years - None
Sex: ALL

Summary:
This study aims to evaluate the functional performance and feasibility of the Saturista system, a novel device designed for automated oxygen flow regulation based on continuous monitoring of oxygen saturation (SpO₂). The system integrates real-time physiological monitoring, wireless data transmission, and centralized visualization to support timely detection of oxygen desaturation and clinical decision-making.

A pilot feasibility study will be conducted in healthy volunteers using a scenario-based protocol, including baseline monitoring, controlled desaturation, probe disconnection, and signal recovery. The primary objective is to assess the system's ability to generate alarms in response to changes in SpO₂ and to display data in real time. Secondary objectives include evaluation of response time, signal management, and overall system stability.

Eligibility criteria:
Inclusion Criteria:

* Healthy adult volunteers aged 18 years or older
* Resting oxygen saturation (SpO₂) within the normal range at baseline
* Ability to understand the study procedures and provide written informed consent
* Willingness to participate in all planned testing scenarios

Exclusion Criteria:

* History of chronic respiratory disease, including asthma, chronic obstructive pulmonary disease, or interstitial lung disease
* Known cardiovascular disease or any condition that may affect oxygen saturation or hemodynamic stability
* Current respiratory symptoms, acute infection, or fever at the time of participation
* Anemia, peripheral circulatory disorders, or any condition that may interfere with pulse oximetry measurements
* Skin lesions, deformity, or injury at the intended probe placement site
* Pregnancy
* History of syncope, intolerance to short-duration breath-holding, or any condition that may increase risk during testing
* Inability or unwillingness to comply with study procedures

Outcomes:
Real-Time Data Display Performance