moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Synergistic Effect of G-Eye Balloon for Behind the Folds Visualization With Artificial Intelligence Assisted Polyp Detection (Discovery System) on Adenoma Detection Rate. 'Discovery III Study'
Trial ID: NCT05220345
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2022-05-12
Completion date: 2024-09-26
Sponsor: Radboud University Medical Center
Collaborators: PENTAX Europe GmbH
Conditions: Colorectal Polyp; Colonoscopy; Artificial Intelligence; Adenoma
Interventions: colonoscopy assisted by both balloon-Behind The Folds visualizing and Computer assisted detection (CADe)
Locations: Radboud university medical center Nijmegen
Countries: Netherlands
Enrollment: 196
Age range: 18 Years - None
Sex: ALL

Summary:
Colonoscopy is the gold standard for colorectal screening. The diagnostic accuracy of colonoscopy highly depends on the quality of inspection of the colon during the procedure. To increase detection new polyp detection systems based on artificial intelligence (AI) have been developed. However, these systems still depend on the ability of the endoscopist to adequately visualize the complete colonic mucosa, especially to detect smaller and more subtle lesions, or lesions hidden behind folds in the colon. With this study we want to combine a device to flatten the folds in the colon combined with an artificial intelligence system to further improve the detection rate of lesions during colonoscopy.

Eligibility criteria:
Inclusion Criteria:

* Adult patients (\>18 years)
* Referred and scheduled for diagnostic, screening (non-iFOBT based), or surveillance colonoscopy.

Exclusion Criteria:

* Inflammatory bowel disease (IBD)
* Known polyp or tumor upon referral
* Therapeutic procedure (e.g., endoscopic mucosal resection)
* Prior surgical resection of any portion of the colon
* American Society of Anesthesiologists score of ≥3
* Inadequately corrected anticoagulation disorder or anticoagulation medication use
* inability to provide informed consent.

Outcomes:
Adenoma Detection Rate (ADR)
AI-based Quantification of Liver Fat Fraction Using Two-energy Ultra-low Dose CT Compared to MRI
Trial ID: NCT05676177
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2023-01-29
Completion date: 2025-12-31
Sponsor: Sheba Medical Center
Conditions: Non-Alcoholic Fatty Liver Disease; Steatosis of Liver
Interventions: Ultra Low Dose CT; Fat quantification MRI
Locations: Prof. Noam Tau
Countries: Israel
Enrollment: 150
Age range: 18 Years - None
Sex: ALL

Summary:
Our aim is to develop an AI based tool to use ultra-low dose CT in two separate energy levels using a single-energy CT machine to quantify liver fat in individuals at risk for having non-alcoholic fatty liver disease (NAFLD), compared to MRI which serves as the standard of reference.

Secondary aim of our study is to validate the developed artificial intelligence (AI)-based model on a second group of participants ("external validation").

Eligibility criteria:
Inclusion Criteria:

Adult patients (age ≥18 years)

* At risk for hepatic steatosis (defined as at least one of the followings: age \>50 years, over weight (BMI\>25), impaired fasting glucose or impaired glucose tolerance, T2DM, gestational diabetes, hyperlipidemia, hypertension, elevated liver enzymes, family history of steatosis or cirrhosis, increased liver span per medical examination, increased ferritin levels and the patatin-like phospholipase domain-containing 3 polymorphism), as decided by the treating endocrinologist in our institute's Medical screening department. 12-14
* No history of malignancy involving the liver.
* No known risk factors for hepatic iron deposition (multiple prior blood transfusions, known hemochromatosis).
* Subjects able to understand study procedures and provide informed consent.
* Subjects able to hold their breath during CT and MRI scans.

Exclusion Criteria:

Patients younger than 18 years.

* Patients with risk factors from hepatic iron deposition (multiple prior blood transfusions, known hemochromatosis).
* Patients with known malignancy that involves the liver.
* Patients unable to hold their breath for both CT and MRI.
* Patients with severe claustrophobia.
* Patients with implanted devices of shrapnel.
* Pregnant people

Outcomes:
Developing AI model of liver fat fraction assessment on data obtained from ultra-low dose CT, using MRI data as a standard of reference
Intervention Combining Interactive Communication Technologies and Nicotine Replacement Therapy Sampling for Proactively Recruited Smokers in Smoking Hotspots: a Pragmatic Randomized Controlled Trial
Trial ID: NCT04001972
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2019-08-19
Completion date: 2021-09-30
Sponsor: The University of Hong Kong
Collaborators: Food and Health Bureau, Hong Kong
Conditions: Smoking Cessation; Tobacco Use Cessation
Interventions: Brief advice (AWARD model); NRT-S; IM Apps and Chatbot; Regular SMS message
Locations: School of Nursing, The University of Hong Kong
Countries: Hong Kong
Enrollment: 664
Age range: 18 Years - None
Sex: ALL

Summary:
Brief advice combined with active referral to smoking cessation (SC) services increases quitting. We aim to strengthen the effect of combined intervention by using interactive communication technologies and nicotine replacement therapy sampling (NRT-S).

Eligibility criteria:
Inclusion Criteria:

1. Adult smokers aged 18+ years who smoke cigarette(s) daily.
2. Exhaled carbon monoxide (CO) level of ≥ 4ppm.
3. Having smartphones and willing to install IM Apps and a Chatbot.
4. Hong Kong residents able to read and communicate in Chinese (Cantonese or Putonghua).

Exclusion Criteria:

1. Smokers who have psychiatric/psychological diseases/on regular psychotropic medications.
2. Smokers who are using SC medication, NRT, other SC services or projects.
3. Smokers who have contraindication for NRT use: severe angina, arrhythmia, myocardia infraction, pregnancy (or intended to become pregnant \<6 months) or breastfeeding.

Outcomes:
Biochemically validated abstinence of NRT-S +IM Apps and Chatbot vs. regular SMS; Biochemically validated abstinence of NRT-S +IM Apps and Chatbot vs. regular SMS
A Multicenter Prospective Study of Artificial Intelligence Predicting Duodenal Stump Leakage After Laparoscopic Radical Gastrectomy for Gastric Cancer
Trial ID: NCT06807372
Status: ACTIVE_NOT_RECRUITING
Study type: OBSERVATIONAL
Start date: 2024-09-11
Completion date: 2026-12
Sponsor: Jichao Qin
Collaborators: Second Affiliated Hospital, School of Medicine, Zhejiang University; Jinhua Central Hospital; Second Affiliated Hospital of Nanchang University
Conditions: Gastric Cancers
Locations: Department of Gastrointestinal Surgery, The First Affiliated Hospital, Zhejiang University School of Medicine
Countries: China
Enrollment: 1200
Age range: 18 Years - 85 Years
Sex: ALL

Summary:
This study aims to validate a machine learning model for predicting duodenal stump leakage after laparoscopic radical gastrectomy for gastric cancer.

Eligibility criteria:
Inclusion Criteria:

1. Aged older than 18 years and younger than 85 years
2. Primary gastric carcinoma confirmed by preoperative pathology result
3. Expected curative resection via laparoscopic distal or total gastrectomy and reconstruction via Billroth-II or Roux-en-Y anastomosis
4. American Society of Anesthesiologists (ASA) class I, II, or III
5. With full documents of preoperative examinations such as blood test and abdominal CT scanning
6. Written informed consent

Exclusion Criteria:

1. Pregnant or breastfeeding women.
2. Severe mental disorder or language communication disorder.
3. Other surgical procedures of gastrectomy is performed.
4. Interrupted of surgery for more than 30 minutes due to any cause.
5. Malignant tumors with other organs
6. Performed gastrectomy in the past

Outcomes:
Incidence of duodenal stump leakage
Validation of an Artificial Intelligence Enabled Diagnostic Support Software (ArtiQ.Spiro) in Primary Care Spirometry Datasets - a Retrospective Analysis
Trial ID: NCT05648227
Status: ACTIVE_NOT_RECRUITING
Study type: OBSERVATIONAL
Start date: 2022-07-01
Completion date: 2025-02-28
Sponsor: Royal Brompton & Harefield NHS Foundation Trust
Collaborators: The Hillingdon Hospitals NHS Foundation Trust; University of Leicester; Southern Health NHS Foundation Trust; Imperial College London; Queen Mary University of London; King's College London; Papworth Hospital NHS Foundation Trust; King's College Hospital NHS Trust
Conditions: Respiratory Disease
Locations: Harefield Hospital
Countries: United Kingdom
Enrollment: 1000
Age range: 18 Years - None
Sex: ALL

Summary:
A retrospective study to evaluate the diagnostic performance of an Artificial Intelligence enabled software (ArtiQ.Spiro) in UK primary care spirometry datasets.

Eligibility criteria:
Inclusion Criteria:

* Adult aged 18 years or over
* At least one of the following respiratory symptoms: cough, wheeze, shortness of breath, reduced exercise tolerance
* Spirometry performed for clinical purposes in a non-hospital lung function setting (such as a community clinic, a GP practice, or at home)
* Spirometry was supervised by a doctor or non-medical allied health professional

Exclusion Criteria:

* Aged 17 or under
* No respiratory symptoms
* Spirometry performed for pre-operative assessment
* Spirometry performed exclusively as part of a research study
* Spirometry performed at home without supervision.

Outcomes:
Evaluate diagnostic performance of an Artificial Intelligence enabled software (ArtiQ.Spiro) in UK primary care spirometry datasets.
AI Rivals Traditional Bite Wing Radiography in Detecting Proximal Secondary Caries in A Group of Egyptian Patients at Cairo University, Faculty OF Dentistry Hospital (Diagnostic Accuracy Study)
Trial ID: NCT06667986
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2024-11-15
Completion date: 2026-02-15
Sponsor: Cairo University
Conditions: Caries,Dental
Interventions: artificial intelligence models (YOLO and Mask-RCNN)
Enrollment: 322
Age range: 22 Years - 60 Years
Sex: ALL

Summary:
This study uses digital bitewing radiography as a standard for diagnosing proximal secondary caries. Patients will undergo imaging with a parallel technique and fixed settings to ensure high-quality, consistent images. Radiographs are interpreted by experienced dental professionals to maintain diagnostic accuracy. Machine learning models YOLO and Mask-RCNN will analyze these images in three phases: pre-analytical, analytical, and post-analytical. A dataset of 322 labeled images, annotated by experts, is used to train these models. Data augmentation methods enhance model performance, and accuracy is assessed against radiographic results to confirm reliability.

Eligibility criteria:
Inclusion Criteria:

1. Adult Patients Aged 22-60 Patient
2. Males or females.
3. Patients have proximal restorations.
4. Co-operative patients who show interest in participating in the study.

Exclusion Criteria:

1. Patients with orthodontic appliances, or bridge work that might interfere with evaluation
2. Patients with no caries.
3. Systematic disease that may affect participation.
4. Patients not willing to be part of the study or ones who refuse to sign the informed consent.

Outcomes:
two deep learning models, YOLO and Mask-RCNN, will be trained on this dataset to accurately detect and classify images showing signs of secondary caries
Understanding the Acceptability of Artificial Intelligence as a Support for Healthcare Providers in the Diagnosis of Prostate Cancer - the Patient at the Heart of His Care
Trial ID: NCT07074405
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-12-02
Completion date: 2025-12-31
Sponsor: Centre Hospitalier Universitaire de Liege
Conditions: Prostate Cancer
Interventions: Questionnaire and Physical Exam
Locations: CHU Liege
Countries: Belgium
Enrollment: 51
Age range: 18 Years - None
Sex: MALE

Summary:
This study investigates the acceptability of artificial intelligence (AI) as a diagnostic support tool among patients with localized prostate cancer and healthcare providers, as well as their willingness to share health data for AI development.

Background AI tools in healthcare show promising potential, especially in improving diagnosis accuracy and personalizing treatment. However, successful implementation depends not only on technical performance but also on the acceptability of AI among its users-both patients and professionals. Prior research has shown varied acceptability depending on context, disease severity, task performed by AI, and user population.

Objectives Assess patients' acceptability of AI as a diagnostic support in prostate cancer.

Explore patients' willingness to share health data for developing clinical AI.

Assess healthcare providers' acceptability of AI in this diagnostic context.

Methodology Design: A cross-sectional, mixed-method, multinational study (Belgium, Italy, Spain).

Quantitative Phase: Online questionnaire, using adapted theoretical frameworks (Value Perception Model, NASSS-AI, TFA).

Qualitative Phase: Will follow based on quantitative findings.

Participants: Adults diagnosed with localized prostate cancer. Recruitment via hospitals, social media, and patient associations.

Data Collected: Personal and health information, attitudes toward AI, willingness to share data.

Ethics Approved by ethics committees in each participating country.

Informed consent obtained digitally before participation.

Data anonymized and GDPR-compliant.

Eligibility criteria:
Inclusion Criteria:

* over 18 years;
* with diagnose of localized prostate cancer;
* consent to the study.

Exclusion Criteria:

* not speaking French;
* diagnosed with metastatic cancer from the outset;
* terminally ill;
* people suffering from mental retardation, dementia or altered state of consciousness.

Outcomes:
Perceived Benefits of using AI in the diagnosis of prostate cancer; Perceived Risks of using AI in the prostate cancer diagnosis; Intention to use AI; Willingless to share personal data
Creating New Models of Laparoscopic Surgery Skills Acquisition and Assessment: Randomised-controlled Trial of Laparoscopic Skills Acquisition Using an AI-enhanced Game-based Simulation Tool Compared With a Laparoscopic Simulator Box Trainer
Trial ID: NCT07276022
Status: ACTIVE_NOT_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-11-19
Completion date: 2026-07-31
Sponsor: University of Edinburgh
Conditions: Laparoscopic Surgery
Interventions: AI-enhanced game-based simulation tool (Laptitude); Laparoscopic simulator box trainer (SurgTrac)
Locations: Hospital General San Juan de Dios; Christian Medical College (CMC), Ludhiana; Obafemi Awolowo University (OAU)
Countries: Guatemala; India; Nigeria
Enrollment: 157
Age range: None - None
Sex: ALL

Summary:
The goal of this clinical trial is to learn whether an AI-enhanced, game-based laparoscopic simulation tool can improve laparoscopic skills training and help increase surgical capacity in surgical trainees and other healthcare professionals learning laparoscopic surgery. The main questions it aims to answer are:

* Does an AI-enhanced game-based simulator lead to faster and/or higher quality acquisition of laparoscopic technical skills than a standard box trainer?
* Is AI-enhanced game-based simulation a feasible and scalable model for laparoscopic skills training across diverse healthcare settings?
* Researchers will compare training with the Laptitude AI-enhanced game-based simulator to training with a standard laparoscopic box trainer to see if the AI-enhanced approach results in better performance on validated laparoscopic skills assessments and more efficient training.

Participants will:

* Be randomly assigned to train using either the Laptitude AI-enhanced game-based simulator or a standard box trainer.
* Complete a structured programme of laparoscopic training tasks.
* Undergo standardized assessments of laparoscopic skills performance during and/or after the training period.

Eligibility criteria:
Inclusion Criteria:

* Novice medical students: no previous experience previous simulation training and/or operative experience.
* Novice medically-qualified doctors (MDs): no previous experience previous simulation training and/or operative experience.
* Intermediate experience medical students: previously used laparoscopic simulator box trainer.
* Intermediate experience medically-qualified doctors (MDs): previously used laparoscopic simulator box trainer.

Exclusion Criteria:

* Inability to provide informed consent.
* Inability to attend for training and assessment.

Outcomes:
Composite measure of flow, handling and respect across five laparoscopic simulator tasks
Artificial Intelligence (AI) Mobile Application Versus Health Care Provider (HCP) for Bodyweight Squats: A Randomized, Blinded, Controlled Clinical Trial
Trial ID: NCT04624594
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2019-10-15
Completion date: 2019-12-30
Sponsor: Columbia University
Collaborators: National Medical Fellowships
Conditions: Squat Form
Interventions: Artificial Intelligence Feedback; Physical Therapist Feedback
Locations: Columbia University Medical Center
Countries: United States
Enrollment: 30
Age range: 20 Years - 35 Years
Sex: ALL

Summary:
To assess if an artificial intelligence (AI) mobile application can identify and improve bodyweight squat form in adult participants when compared to a Physical Therapist (PT).

Eligibility criteria:
Inclusion Criteria:

* Columbia University affiliate
* Aged 20 to 35 years
* Able to perform moderate bodyweight exercise for 10 minutes

Exclusion Criteria:

* Unable to provide consent

Outcomes:
Number of correct squats
Bayesian Network to Predict Transfer in Intensive Care Units or Death in Newly Covid-19 Hospitalized Patient
Trial ID: NCT04412031
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2019-11-11
Completion date: 2020-05-15
Sponsor: Hospices Civils de Lyon
Conditions: to Predict an Unfavorable Evolution of Covid-19 at the Hospital Admission of Patients
Interventions: non applicable
Locations: Hôpital E Herriot - Hospices Civils de LYON
Countries: France
Enrollment: 802
Age range: 18 Years - None
Sex: ALL

Summary:
The project is a part of the "Protocol for a multicentre study of nosocomial SARS-CoV2 transmission: The NOSO-COR Project" registered in ClinicalTrials (NCT04290780).

In the current pandemic context, medical resources have often been exceeded. Developing, using artificial intelligence techniques, an algorithm capable of detecting patients at risk of acute respiratory distress following Sars-Cov2 infection could help physicians to optimize the treatment of patients and health decision-makers to optimize resources. Thus, the goal of this project is to create a prediction model using artificial intelligence to predict an unfavorable evolution of Covid-19 at the hospital admission of patients

Eligibility criteria:
Inclusion Criteria:

* Participant must be 18 years or older at the time of signing the informed consent
* Volunteer patients, care givers and healthcare professionals in France and hospitals affiliated with the GABRIEL network. Demographic and clinical data will be collected using case-report forms designed especially for the purpose of the project.
* A nasopharyngeal swab will be collected and tested for SARS-CoV2 by RT-PCR.
* Admission to hospital

Exclusion Criteria:

* Age lower than 18 years old
* Patients protected by the law

Outcomes:
Need of mechanical ventilation, transfer to an intensive care unit or death within 21 days of admission.
Diabetic Retinopathy Screening Point-of-Care Artificial Intelligence -DRES-POCAI: AI - Clinical Intervention at San Ysidro Health
Trial ID: NCT06721351
Status: ACTIVE_NOT_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-07-27
Completion date: 2026-04-30
Sponsor: Centro De Salud La Comunidad De San Ysidro Inc DBA: San Ysidro Health
Collaborators: Eyenuk, Inc.; University of California, San Diego
Conditions: Diabetic Retinopathy (DR)
Interventions: Diabetic Retinopathy screening Point of Care Artificial Intelligence
Locations: San Ysidro Health Chula Vista; San Ysidro Health - Comprehensive Health Center - Ocean View; San Ysidro Health King-Chavez Health Center
Countries: United States; United States; United States
Enrollment: 548
Age range: 22 Years - None
Sex: ALL

Summary:
This research study is being conducted to improve eye care by using artificial intelligence (AI) to make diabetic eye screenings faster and more accessible. AI technology mimics human decision-making, enabling computers and systems to analyze medication information. Specifically for this screening, AI examines digital images of the eye and based on that information, may identify if a participant has diabetic retinopathy. It can assist doctors in making decisions about a participant's diagnosis, treatment or care plans to improve patient care. This is a collaboration between San Ysidro Health (SYHealth), University of California, San Diego (UC San Diego), and Eyenuk. The Kaiser Permanente Augmented Intelligence in Medicine and Healthcare Initiative (AIM-HI) awarded SYHealth funds to demonstrate the value of AI technologies in diverse, real-world settings.

Eligibility criteria:
Inclusion Criteria:

1. Capacity to provide informed consent. Individuals must have the capacity to understand the study information, risks, and benefits and voluntarily provide informed consent.
2. Stated willingness to comply with all study procedures and availability for the duration of the study.
3. Established and active patient of SYHealth-CV and KC (having a medical appointment in the last 18 months).
4. Person aged 22 and older.
5. Established diagnosis of DM.
6. Medical appointment(s) (in-person or telehealth) scheduled during the intervention period.
7. Has not completed a dilated eye exam or retinal exam in the last 11months.

Exclusion Criteria:

1. have a prior diagnosis of DR, macular edema, or retinal vascular occlusion;
2. have persistent visual Impairment in one or both eyes;
3. history of ocular injections, laser treatment of the retina, or intraocular surgery (excluding cataract surgery);
4. pregnant women; and
5. diagnosis of mental or degenerative disease that prevents self-consent for the study.

Outcomes:
DR screening completion and result; DR screening efficiency; Knowledge and attitudes about DR; DM self-efficacy; DM self-management; DR Screening Satisfaction Survey (Intervention Group); Demographic and Clinical Data; Social Determinants of Health
Tongue Image Database and Diagnostic Model for Digestive Tract Disease Diagnosis
Trial ID: NCT05182983
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2020-11-15
Completion date: 2022-04-25
Sponsor: First Affiliated Hospital of Wenzhou Medical University
Conditions: Digestive Tract Diseases; Liver Diseases; Colorectal Disease; Gastric Diseases; Small Bowel Disease
Interventions: Tongue images，coating on the tongue and clinical data
Locations: Gang Chen
Countries: China
Enrollment: 10690
Age range: 18 Years - None
Sex: ALL

Summary:
The tongue images of patients with gastrointestinal diseases and healthy people will be collected and the tongue image database will be established. Through deep learning and artificial intelligence, early screening models of various gastrointestinal tumors based on tongue images were constructed.

Eligibility criteria:
Inclusion Criteria:

1. Be older than 18 years old;
2. Clinical or pathological diagnosis of Digestive tract diseases;
3. Patients before receiving surgery, chemotherapy, radiotherapy, targeted therapy, local therapy, immunotherapy and other treatment measures；
4. The patient had no tongue disease
5. Patients understand and are willing to sign written informed consent.

Exclusion Criteria:

1. Patients unable to complete the process as required (mental disorder or poor compliance, etc.);
2. The patient or guardian is unwilling to participate in this study;
3. Combined with other serious systemic diseases (heart failure, renal failure,hematopoietic system disease or Uncontrollable acute infection);

Outcomes:
Diagnosis