moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Assisted Rehabilitation Care During Post-stroke mANaGement: fEasibiLity Assessment
Trial ID: NCT03787433
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2018-11-23
Completion date: 2020-06-12
Sponsor: Camlin Ltd
Collaborators: Northern Health and Social Care Trust; Azienda Sanitaria Locale 3, Torino
Conditions: Stroke
Interventions: ARC - Assisted Rehabilitation Care
Locations: Azienda Sanitaria Locale 3, Torino; Northern Health and Social Care Trust
Countries: Italy; United Kingdom
Enrollment: 41
Age range: 18 Years - None
Sex: ALL

Summary:
The ARCANGEL study evaluates the feasibility of introducing ARC (Assisted Rehabilitation Care), a new device for home-based post-stroke rehabilitation in the current clinical practise. All the stroke survivors included in the study will received their own equipment to be used at home for 6 months.

Eligibility criteria:
Inclusion Criteria:

* Stroke Diagnosis, with a stable clinical condition
* Age \> 18
* Modified Rankin score lower or equal to 4 or Barthel Index score greater than 10 at the time of enrollment
* Patients must be able to keep the standing position without or with minimum assistance
* Patient giving written consent and engage

Exclusion Criteria:

* Significant cognitive impairment and behavioral disorders - judged by a responsible clinician
* Poor communication or reading skills - judged by a Speech and Language Therapist
* Orthopedic limitation (fractures, amputations, advance osteoarthritis, active rheumatoid arthritis)
* Head trauma
* Epilepsy, not pharmacologically controlled
* Severe spatial neglect
* Neurodegenerative and neuromuscular diseases
* Severe spasticity
* Patient not giving written consent and not engage

Outcomes:
Number of patients successfully completing the study; Ratio between the total number of subjects refusing to participate before training and the total number of subjects screened; Ratio between the total number of subjects refusing to participate after training and the total number of subjects screened; Number of training sessions; Overall training period duration; Assisted Rehabilitation Care (ARC) questionnaire score; Assisted Rehabilitation Care (ARC) questionnaire change; ARC global satisfaction score; Modified version of Adult Carer Quality of Life Questionnaire (AC-QoL) total score
Exploratory Study to Assess Utility of RetiSpec Artificial Intelligence Powered Retinal Imaging Solution in Detecting Amyloid Pathology in United Arab Emirates Nationals With Mild Impairment of Cognition
Trial ID: NCT07254234
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-12
Completion date: 2026-08
Sponsor: Eli Lilly and Company
Conditions: Cognitive Dysfunction; Memory Disorders
Interventions: Retinal scan; Blood test
Locations: Cleveland Clinic Abu Dhabi
Countries: United Arab Emirates
Enrollment: 200
Age range: 55 Years - None
Sex: ALL

Summary:
The purpose of this study is to test the use of a screening tool to detect changes in the eye that are associated with Alzheimer's disease in adult participants with memory issues. For each participant, the study will last up to one month and requires one visit to the research site.

Eligibility criteria:
Inclusion Criteria:

* Participants who are overtly healthy as determined by medical evaluation, including medical history and physical examination.
* Have mild memory complaints or a diagnosis of mild cognitive impairment.
* Have undergone genotyping for apolipoprotein E (APOE) and are willing to make results available to the investigator.
* Montreal Cognitive Assessment (MoCA) score of greater than or equal to 22 and less than or equal to 28.

Exclusion Criteria:

* Have any medical condition that, in the opinion of the investigator, would be a contraindication to participation in the study.
* Have an existing diagnosis of AD or other dementia.
* Have contradictions to or an allergy to the ophthalmic dilating agents.
* Have known diagnosis of severe glaucoma or severe cataracts; have a significant refractive error (more than 12 diopters \[D\] of spherical equivalent refraction); or have any ocular media opacity that prevents imaging of the retina (that is, severe cataract). Note: mild to moderate cataracts or vision correction with glasses or contact lenses are not exclusion criteria.
* Current or previous treatment with anti-amyloid medications.
* Are currently enrolled in any other clinical study involving an investigational product or any other type of medical research judged not to be scientifically or medically compatible with this study.

Outcomes:
Percentage of Participants with the Same Results on the Blood Test and Retinal Scan
Development of Artificial Intelligence-Based Predictive Models to Analyze the Impact of Psychosocial Profile, Fatigue, and Sleep Quality on Disease Severity in Patients With Fibromyalgia
Trial ID: NCT07424534
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2026-02-24
Completion date: 2026-11-30
Sponsor: University of Castilla-La Mancha
Collaborators: Tampere University; Andaluz Health Service
Conditions: Fibromyalgia
Locations: Rafael Velasco Velasco
Countries: Spain
Enrollment: 150
Age range: 18 Years - 65 Years
Sex: ALL

Summary:
The primary goal of this research project is to develop different prediction models in fibromyalgia disease through the application of machine learning techniques and to assess the explainability of the results. As specific objective the research project intends to evaluate the influence of psychosocial variables, fatigue, and sleep quality on the prediction of disease severity in patients with fibromyalgia using an artificial intelligence-based model.

Eligibility criteria:
Inclusion Criteria:

* Age between 18 and 65 years.
* Fullfilled the 2010 American Collegue of Rheumathology criteria for fibromyalgia.
* Understanding of spoken and written Spanish.

Exclusion Criteria:

* Diagnosed psychiatric pathology.
* Rheumatic pathology not medically controlled.
* Neurological pathologies that make evaluations difficult.

Outcomes:
Disease severity
A Prototype Artificial Intelligence Algorithm Versus Liver Imaging Reporting and Data System (LI-RADS) Criteria in Diagnosing Hepatocellular Carcinoma on Computed Tomography: a Randomized Trial
Trial ID: NCT06626087
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2023-11-01
Completion date: 2026-03-31
Sponsor: The University of Hong Kong
Collaborators: Education University of Hong Kong
Conditions: Hepatocellular Carcinoma
Interventions: Prototype artificial intelligence algorithm; LI-RADS
Locations: Department of Medicine and Department of Surgery, The University of Hong Kong, Queen Mary Hospital; Department of Medicine, The University of Hong Kong, Queen Mary Hospital
Countries: Hong Kong; Hong Kong
Enrollment: 300
Age range: 18 Years - None
Sex: ALL

Summary:
This study aims to prospective validate this AI algorithm in comparison with the current standard of radiological reporting in a randomized manner in the at-risk population undergoing triphasic contrast CT. This research project is totally independent and separated from the actual clinical reporting of the CT scan by the duty radiologist. The primary study outcome is to compare the diagnostic performance of the prototype AI algorithm versus LI-RADS criteria in determining HCC on CT in the at-risk population.

Eligibility criteria:
Inclusion Criteria:

* 1\. Age \>=18 years.
* 2\. Defined as the at-risk population requiring regular liver ultrasonography surveillance.

These include:

1. Cirrhotic patients of any disease etiology,
2. Chronic hepatitis B patients of age ≥40 years for men, age ≥50 years for women or with a family history of HCC.

   * 3\. At least one new-onset focal liver nodule detected on liver ultrasonography.

Exclusion Criteria:

* 1\. Liver nodules of \<1 cm. Currently such nodules are not reported using LI-RADS criteria but are recommended for a repeat scan in 3-6 months. In patients with multiple liver nodules, the largest nodule will be assessed.
* 2\. Patients with contraindications for contrast CT imaging, including a history of contrast anaphylaxis and impaired renal function (glomerular filtration rate \<30 ml/min).
* 3\. Patients with prior transarterial chemoembolization or other interventional procedures with intrahepatic injection of lipiodol. Lipiodol is extremely hyperdense on computed tomography and will preclude objective interpretation. Such patients were also excluded in the development of our prototype AI algorithm.

Outcomes:
Diagnostic accuracy for HCC
Artificial Intelligence-driven Avatar-based Video vs. Tell-show-do for Reducing Anxiety and Improving Cooperation in Children During Local Anesthesia: a Randomized Controlled Trial
Trial ID: NCT07449884
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-02-16
Completion date: 2027-05-30
Sponsor: Saint-Joseph University
Conditions: Anxiety; Fear; Local Anesthesia; Pediatric Dentistry
Interventions: AI Avatar-Based video; Tell-Show-Do Technique
Locations: Sint Joseph University of Beirut- Pediatric Dentistry clinic
Countries: Lebanon
Enrollment: 70
Age range: 5 Years - 7 Years
Sex: ALL

Summary:
This randomized clinical trial aims to compare two behavior guidance techniques used to reduce anxiety in children during local anesthesia injections in dental treatment. The study evaluates the traditional Tell-Show-Do (TSD) technique versus an artificial intelligence (AI)-driven, avatar-based video method called the "Mini Dentist."

Children aged 5 to 7 years who require local anesthesia for routine dental treatment will be randomly assigned to one of two groups. The control group will receive the conventional Tell-Show-Do explanation from the pediatric dentist. The experimental group will watch a short animated video featuring an AI-generated avatar that explains and demonstrates the procedure in a child-friendly manner before the injection.

Anxiety and behavior will be assessed using heart rate monitoring, behavioral observation (FLACC scale), and a self-reported pain scale (Wong-Baker FACES). The goal is to determine whether the AI-based method improves cooperation and reduces anxiety compared to the traditional approach.

Participation is voluntary, and all procedures follow ethical research standards.

Eligibility criteria:
Inclusion Criteria:

* Inclusion Criteria:

  * Children aged 5 to 7 years
  * Medically healthy (ASA I)
  * Cooperative behavior during dental examination
  * No known allergy to local anesthetics
  * Require non-urgent dental treatment under local anesthesia
  * First-time experience receiving local anesthesia
  * Able to understand and communicate

Exclusion Criteria:

* History of epilepsy
* Diagnosed anxiety or behavioral disorders
* Cognitive or developmental delay
* Hearing or visual impairment
* Previous experience with dental anesthesia
* Require inferior alveolar nerve block anesthesia

Exclusion Criteria:

\-

Outcomes:
Heart Rate Changes During Local Anesthesia: Physiological Anxiety Response
A Multilevel Approach to Improving Patient-Clinician Relationships and Outcomes in African American Patients With Diabetes Using Artificial Intelligence-Enhanced Motivational Interviewing Training
Trial ID: NCT05696964
Status: WITHDRAWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-02-01
Completion date: 2024-02-01
Sponsor: Wright State University
Collaborators: Five Rivers Health Centers; Kennesaw State University
Conditions: Physician-Patient Relations; Diabetes Mellitus, Type 2
Interventions: Motivational interviewing training enhanced with artificial intelligence
Locations: Five Rivers Health Centers; Wright State University
Countries: United States; United States
Enrollment: 0
Age range: 21 Years - None
Sex: ALL

Summary:
This research aims to optimize patient-clinician relationships through motivational interviewing training in a Federally Qualified Health Center (FQHC) to improve health outcomes for African American patients with Type 2 diabetes.

Eligibility criteria:
Inclusion Criteria:

* Eligible participants are residents in Family Medicine and Internal Medicine residency programs at Wright State University who will be recruited to participate in the study.

Exclusion Criteria:

* None

Note: Patients of these participating residents will be invited to participate in the study by providing survey and health data at specified intervals, but these patients will not be the direct recipients of any intervention in this study. Eligible patients will be self-identifying African American individuals ages 18 and over with a diagnosis of Type 2 diabetes, and are patients of either Family Medicine or Internal Medicine residents participating in this study. The target number of patients is 200.

Outcomes:
Changes in perceptions of the patient-clinician relationship by patients of participating residents.
Covid Radiographic Images Data-set for A.I
Trial ID: NCT04419545
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2020-03-24
Completion date: 2021-03-31
Sponsor: A.O.U. Città della Salute e della Scienza
Collaborators: University of Turin, Italy; Azienda Ospedaliera Ordine Mauriziano di Torino
Conditions: Radiology; Artificial Intelligence
Interventions: Neural network diagnosis algorithm
Locations: Azienda Ospedaliero Universitaria Città della Salute e della Scienza
Countries: Italy
Enrollment: 2500
Age range: None - None
Sex: ALL

Summary:
The possibility to use widespread and simple chest X-ray (CXR) imaging for early screening of COVID-19 patients is attracting much interest from both the clinical and the Artificial intelligence community. In this study we provide insights and also raise warnings on what is reasonable to expect by applying deep learning to COVID classification of CXR images. We provide a methodological guide and critical reading of an extensive set of statistical results that can be obtained using currently available datasets. In particular, we take the challenge posed by current small size COVID data and show how significant can be the bias introduced by transfer-learning using larger public non- COVID CXR datasets. We also contribute by providing results on a medium size COVID CXR dataset, just collected by one of the major emergency hospitals in Northern Italy during the peak of the COVID pandemic. These novel data allow us to contribute to validate the generalization capacity of preliminary results circulating in the scientific community. Our conclusions shed some light into the possibility to effectively discriminate COVID using CXR.

Eligibility criteria:
Inclusion Criteria: chest x ray performed during emergency department or hospital stay

\-

Exclusion Criteria:

* None

Outcomes:
sensibility and specificity of neural network diagnosis
NOrthwestern Tempus AI-enaBLed Electrocardiography (NOTABLE) Trial: A Pragmatic, Real-world Study of an Artificial-intelligence Enabled Electrocardiogram Algorithms to Improve the Diagnosis of Cardiovascular Disease
Trial ID: NCT06511505
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-08-03
Completion date: 2026-02-03
Sponsor: Northwestern University
Conditions: Atrial Fibrillation; Cardiovascular Diseases; Arrhythmia; Valvular Disease
Interventions: TEMPUS AI-enabled ECG-based Screening Tool
Locations: Northwestern University
Countries: United States
Enrollment: 1000
Age range: 40 Years - None
Sex: ALL

Summary:
The goal of this clinical trial is to determine if a machine learning/artificial intelligence (AI)-based electrocardiogram (ECG) algorithm (Tempus Next software) can identify undiagnosed cardiovascular disease in patients. It will also examine the safety and effectiveness of using this AI-based tool in a clinical setting. The main questions it aims to answer are:

1. Can the AI-based ECG algorithm improve the detection of atrial fibrillation and structural heart disease?
2. How does the use of this algorithm affect clinical decision-making and patient outcomes? Researchers will compare the outcomes of healthcare providers who receive the AI-based ECG results to those who do not.

Participants (healthcare providers) will:

Be randomized into two groups: one that receives AI-based ECG results and one that does not.

In the intervention group, receive an assessment of their patient's risk of atrial fibrillation or structural heart disease with each ordered ECG.

Decide whether to perform further clinical evaluation based on the AI-generated risk assessment as part of routine clinical care.

Eligibility criteria:
Inclusion Criteria:

1. Atrial fibrillation algorithm

   1. Age 65 or over
   2. ECG obtained as part of routine clinical care
2. Structural heart disease algorithm

   1. Age 40 or over
   2. ECG obtained as part of routine clinical care

Exclusion Criteria:

1. Atrial fibrillation algorithm

   1. No history of AF
   2. No permanent pacemaker (PPM) or implantable cardioverter defibrillator (ICD)
   3. No recent cardiac surgery (within the preceding 30 days)
2. Structural heart disease algorithm

   1. No history of SHD
   2. No echocardiogram within the past 1 year

Outcomes:
Rate of new CV diagnoses at 6 months
A Prospective Randomized Study Comparing the Adenoma Detection Yield of (i) Standard Colonoscopy ("SC"), (ii) Artificial Intelligence (GI Genius™) ("AI"), and (Iii) Combined Artificial Intelligence (GI Genius™) and G-EYE® Colonoscopy
Trial ID: NCT05317351
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2022-05
Completion date: 2022-12
Sponsor: Smart Medical Systems Ltd.
Conditions: Colorectal (Colon or Rectal) Cancer; Adenoma
Interventions: Standard Colonoscopy; Artificial Intelligence Aided Colonoscopy (GI Genius™); Combined Artificial Intelligence Aided Colonoscopy (GI Genius™) and G-EYE® Balloon Aided Colonoscopy
Locations: Indiana University; NYU Langone Health
Countries: United States; United States
Enrollment: 2160
Age range: 45 Years - None
Sex: ALL

Summary:
This is a multicenter, 3-arm, randomized, open-label study. Subjects referred to colonoscopy for screening or surveillance will be randomized in a 2:5:5 into one of the following arms:

(i) Standard Colonoscopy (ii) Artificial Intelligence Aided Colonoscopy (GI Genius™) (iii) Combined Artificial Intelligence Aided Colonoscopy (GI Genius™) and G-EYE® Balloon Aided Colonoscopy

Eligibility criteria:
Inclusion Criteria:

1. Patients of age ≥ 45 years old
2. Referred to colonoscopy for screening or surveillance colonoscopy (history of adenoma resection)
3. The patient must understand and provide written consent for the procedure.

Exclusion Criteria:

1. Subjects with inflammatory bowel disease
2. Subjects with a personal history of polyposis syndrome
3. Subjects with suspected chronic stricture potentially precluding complete colonoscopy
4. Subjects with diverticulitis or toxic megacolon
5. Subjects with a history of radiation therapy to abdomen or pelvis
6. Pregnant or lactating female subjects
7. Subjects who are currently enrolled in another clinical investigation.
8. Subjects with current oral or parenteral use of anticoagulants, not considered eligible by the investigator.
9. Subjects with recent (within the last 3 mounts) coronary ischemia or CVA (stroke)
10. Any patient condition deemed too risky for the study by the investigator
11. Previous colonic surgery (except for appendectomy)

Outcomes:
Adenoma Per Colonoscopy (APC) (AI compared with AG)
Towards a Smart Blood Culture Bottle: Machine Learning Assisted Electrochemical Profiling to Provide Early In-situ Identification of Bloodstream Infections Pathogens
Trial ID: NCT06853301
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-04
Completion date: 2026-08
Sponsor: University Hospital, Grenoble
Collaborators: CEA - Leti
Conditions: Bacteremia Sepsis
Interventions: Blood culture sampling
Locations: Grenoble University Hospital; Hôpital AVICENNE (AP-HP)
Countries: France; France
Enrollment: 200
Age range: 18 Years - None
Sex: ALL

Summary:
In the context of a bacteremia, although significant progress has been made in speeding up pathogen identification once a blood culture bottle turns positive, few cost-effective solutions have been proposed to improve the earlier stages of the process-specifically, from blood collection to bottle positivity. The investigators propose that transport time could be leveraged to grow and identify bacteria, enabling faster access to actionable results through innovative technologies. This project aims to develop a bacterial identification database by analyzing the electrochemical profile of bacteria growing within the blood culture bottle, using machine learning.

Eligibility criteria:
Inclusion Criteria:

* patient requiring a blood culture sample as standard of care procedure
* body weight \> 50 Kg
* Patient for whom the collection of 2 to 4 additional blood culture bottles is feasible, depending on venous access
* patient who has not objected to participation in the project

Exclusion Criteria:

* Patient protected under the French Public Health Code (pregnant or breastfeeding women, patients under guardianship or curatorship, hospitalized under constraint, or deprived of liberty)
* patients with ongoing antibiotic treatment at the time of sampling

Outcomes:
List of samples with an electrochemical profile
Artificial IntelligenCe Based UlTrasonographic Assessment of IntensiVe CAre UniT-acquired WEakness (ACTIVATE)
Trial ID: NCT06765551
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2024-10-01
Completion date: 2026-06-30
Sponsor: Jena University Hospital
Collaborators: University of Rostock
Conditions: Intensive Care Unit-acquired Weakness; Artifical Intelligence; Ultrasound
Interventions: Neuromuscular Ultrasound
Locations: Department of Anesthesiology and Intensive Care Medicine, Jena University Hospital
Countries: Germany
Enrollment: 50
Age range: 18 Years - None
Sex: ALL

Summary:
The aim of this observational case-control study is to investigate, whether artificial intelligence can detect ultrasound-derived imaging characteristics typical for intensive care unit-acquired weakness. The main questions it aims to answer are:

1. Is the evaluation of specific parameters of neuromuscular ultrasound using AI-based image analysis suitable for detecting and monitoring critically ill ICU patients with ICUAW?
2. Do the results of AI-based ultrasound image analysis correlate with:

(A) the severity of ICUAW (B) the visual grading of muscle echogenicity (C) the 30- and 90-day-outcome?

Eligibility criteria:
Inclusion Criteria:

* Patients aged 18 years or above
* Major elective surgery, e.g. cardiothoracic or abdominal surgery
* Expected ICU stay \>1 day postoperatively
* Healthy, age-machted subjects without ICUAW (recruited from staff of the department of anesthesiology and intensive care medicine)

Exclusion Criteria:

* No informed consent
* Emergency surgery
* Previous participation in the same study
* Preexisting neuromuscular disease
* Preexisting central nervous system disease with residual neuromuscular impairment (e.g. cerebral haemorrhage, stroke, brain tumor)
* High-dose glucocorticoid therapy (\>300 mg hydrocortisone or equivalent per day) before or during particiation in the study

Outcomes:
Incidence of abnormalities of muscle echogenicity assesed by muscular ultrasound