moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Virtual Biopsy Development to Identify Patient's Glioma Grade at the CHU of Poitiers Using Magnetic Resonance Imaging
Trial ID: NCT04313140
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2021-02-22
Completion date: 2029-02
Sponsor: Poitiers University Hospital
Conditions: Development of the Virtual Biopsy; Tumor Grade; Artificial Intelligence Algorithms; Clinical; Radiological
Interventions: Diagnostic test
Locations: CHU De Poitiers
Countries: France
Enrollment: 500
Age range: 18 Years - None
Sex: ALL

Summary:
Glioma is a tumor of the central nervous system. These lesions are sorted with the WHO ranking regarding the tumoral oncotype. The tumoral MRI assessment is the first step before any medical decision. Currently, only anatomical biopsy can give the tumor grade definition and help to define the most adapted treatment.

Eligibility criteria:
Inclusion Criteria:

* Age≥ 18 years,
* Patient with suspected glioma with prior imaging
* Subject free, with legal protection guardianship or curatorship;
* Enrollment in the French Social Security system;
* Informed consent signed by the patient

Exclusion Criteria:

* Any contraindication to an MRI examination
* Legal protection, namely minors, persons deprived of their liberty by a judicial or administrative decision, persons residing in a health or social establishment, adults under legal protection and finally patients in emergency situations
* Pregnant or lactating women, women of childbearing age who do not have effective contraception

Outcomes:
Validation of the virtual biopsy in the context of gliomas through the use of statistical learning algorithms (artificial intelligence) compared to the biopsy.
Evaluating the Efficacy of Artificial Intelligence Models in Predicting Intensive Care Unit Admission Needs
Trial ID: NCT06494748
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2024-07-15
Completion date: 2024-10-02
Sponsor: Kanuni Sultan Suleyman Training and Research Hospital
Conditions: Intensive Care Unit
Interventions: Follow up Decision
Locations: Health Science University İstanbul Kanuni Sultan Süleyman Education and Training Hospital
Countries: Turkey (Türkiye)
Enrollment: 8043
Age range: 18 Years - None
Sex: ALL

Summary:
This study aims to evaluate the efficacy of two artificial intelligence (AI) models in predicting the need for ICU admissions. By comparing the AI models' predictions with actual clinical decisions, we aim to determine their accuracy and potential utility in clinical decision support.

Eligibility criteria:
Inclusion Criteria:

* Patients over the age of 18
* Patients consulted for anesthesia regarding intensive care needs
* Patients with sufficient data in the hospital's electronic health record system

Exclusion Criteria:

* Patients with insufficient data in the hospital records

Outcomes:
Intensive Care Unit Need
Pilot Test for an Early Diagnosis Kit for Breast Cancer Based on Liquid Biopsies
Trial ID: NCT06439940
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2021-07-14
Completion date: 2025-03-31
Sponsor: Oncoliq US Inc
Conditions: Breast Cancer Female; Breast Cancer; Breast Carcinoma
Interventions: Blood sample extraction
Locations: Hospital Nacional Profesor Alejandro Posadas
Countries: Argentina
Enrollment: 364
Age range: 35 Years - None
Sex: FEMALE

Summary:
Oncoliq is a novel early breast cancer detection test based on liquid biopsies and microRNAs. This innovative test aims to improve the accuracy of cancer detection, thereby reducing patient mortality and healthcare costs.

To develop this test, the discovery and validation phases have been completed. In addition, Machine learning and AI were used to determine the algorithm for breast cancer detection.

The overall objective of this protocol is to implement a pilot test to enroll 1,000 women without a previous cancer diagnosis who are attending their annual medical check-up.

To achieve this, we will develop the following specific objectives:

Enroll women over age 35 who attend the gynecological annual check-up. Test the plasma of the enrolled volunteers for specific miRNA biomarkers using RT-qPCR.

Perform an analysis based on artificial intelligence techniques in collaboration with IMAGO Systems (USA) on mammogram images.

Compare the results obtained from the clinical check-up (BiRad from mammogram), IMAGO Systems and RT-qPCR (Oncoliq breast test). Conduct a 5-year follow-up on volunteers with pathological results from Oncoliq breast test.

Participants who meet the inclusion and exclusion criteria and agree to take part in the protocol will be required to sign both pages of the Informed Consent Form and complete the Annex and Survey. Subsequently, a small blood sample of 3-5 mL will be drawn via venipuncture.

Eligibility criteria:
Inclusion Criteria:

* Patients over 35 years old who visit the gynecological clinic for their annual mammographic check-up

Exclusion Criteria:

* Personal history of cancer

Outcomes:
BIRADS mammography
Measuring the Effect of an ePRO-based Clinical Decision Support System in Knee- and Hip Replacement Surgery
Trial ID: NCT04332055
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2020-11-30
Completion date: 2022-12-31
Sponsor: Northern Orthopaedic Division, Denmark
Collaborators: Aalborg University
Conditions: Decision Making, Shared; Arthroplasty, Replacement, Knee; Arthroplasty, Replacement, Hip; Osteoarthritis, Knee; Osteoarthritis, Hip; Artificial Intelligence
Interventions: ERVIN
Locations: Nanna Kastrup
Countries: Denmark
Enrollment: 223
Age range: 18 Years - None
Sex: ALL

Summary:
The randomised clinical trial investigates the effect of using a clinical decision support system (CDSS) aiming to provide the patients and surgeons with greater transparency concerning the obtainable changes in function and health related quality of life (HRQoL) when patients are to decide if they should undergo hip- or knee replacement surgery.

Eligibility criteria:
Inclusion Criteria:

* Patients referred from a general practitioner in the North Denmark Region to the orthopaedic clinic at Aalborg University Hospital
* Patients with painful OA in hip- or knee joint.

Exclusion Criteria:

* Patients unable to answer the questions in danish

Outcomes:
Oxford Hip Score; Oxford Knee Score
Combination of Artificial Intelligence (ENDOAID) and Mucosal Exposure Device (ENDOCUFF) to Enhance Colorectal Neoplasia Detection: a Randomized Controlled Trial
Trial ID: NCT05414448
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2023-03-07
Completion date: 2025-12-31
Sponsor: Chinese University of Hong Kong
Collaborators: University Hospital Augsburg; Changi General Hospital
Conditions: Adenoma; Colorectal Cancer; Colorectal Neoplasms
Interventions: ENDOCUFF
Locations: Prince of Wales Hospital
Countries: Hong Kong
Enrollment: 1726
Age range: 45 Years - 85 Years
Sex: ALL

Summary:
The investigators hypothesize that the combined use of CADe system (ENDOAID) and mucosal exposure device (Endocuff Vision®) would improve the adenoma detection rate when compared to CADe system alone.

Eligibility criteria:
Inclusion Criteria:

1. Aged 45-85 years old;
2. They require elective colonoscopy for colorectal cancer screening, polyp surveillance, or investigation of symptoms such as anemia or altered bowel habit;
3. Written informed consent obtained.

Exclusion Criteria:

1. Contraindication to colonoscopy (e.g. intestinal obstruction or perforation)
2. Contraindication or conditions precluding polyp resection (e.g. active gastrointestinal bleeding, uninterrupted anticoagulation or dual antiplatelets)
3. Staged procedure for polypectomy or biopsy of known unresected lesions
4. Previous surgical resection of colon
5. Personal history of colorectal cancer
6. Personal history of familial polyposis syndrome
7. Personal history of inflammatory bowel disease
8. Advanced comorbid conditions (defined as American Society of Anesthesiologists grade 4 or above)
9. Pregnancy
10. Unable to obtain informed consent

Outcomes:
ADR
Comparison of Artificial Intelligence-Generated and Academician-Developed Multiple True/False Questions in Anesthesiology Education: A Prospective Cohort Study
Trial ID: NCT07522658
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2026-04-14
Completion date: 2026-04-30
Sponsor: Kutahya Health Sciences University
Conditions: Medical Education; Artificial Intelligence; Assessment Methods in Medical Training
Locations: Kütahya Health Sciences University
Countries: Turkey (Türkiye)
Enrollment: 26
Age range: 18 Years - None
Sex: ALL

Summary:
This prospective observational study aims to evaluate the effectiveness and educational value of artificial intelligence (AI)-generated multiple true/false questions compared to those developed by experienced academicians in anesthesiology training.

A total of 27 anesthesiology residents will be included in the study. Question sets consisting of 200 multiple true/false items will be created, with half generated by academicians and the other half generated using an artificial intelligence model (ChatGPT-based system). The questions will be based on standardized educational materials from the anesthesiology training curriculum.

Participants will complete the test in a single session. Each correct answer will be scored as one point, and total scores will be calculated. In addition to test performance, item difficulty, discrimination indices, and test reliability will be analyzed. Furthermore, participants' perceptions regarding question quality will be evaluated.

The study aims to determine whether AI-generated questions can provide a reliable and effective alternative to traditional question development methods in medical education and contribute to more objective and standardized assessment processes.

Eligibility criteria:
Inclusion Criteria:

* Being an anesthesiology resident
* Voluntary participation in the study
* Providing informed consent

Exclusion Criteria:

* Refusal to participate
* Incomplete test responses

Outcomes:
Item Difficulty Index of AI-generated and expert-authored questions
Chinese Atrial Fibrillation Registry
Trial ID: NCT06987825
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2011-08-01
Completion date: 2035-12-31
Sponsor: Beijing Anzhen Hospital
Conditions: Atrial Fibrillation (AF)
Locations: Beijing Anzhen Hospital; Sir Run Run Shaw Hospital, Zhejiang University School of Medicine
Countries: China; China
Enrollment: 100000
Age range: 18 Years - None
Sex: ALL

Summary:
This prospective, observational cohort study aims to establish a long-term registry of adult patients with atrial fibrillation (AF) in China to comprehensively characterize patient profiles and evaluate real-world management and outcomes over time. The objectives of the study are to:

* Describe the demographic, clinical, and treatment characteristics of AF patients across different stages of disease progression.
* Assess the effectiveness and safety of various AF treatment strategies, including oral anticoagulation, catheter ablation, and left atrial appendage closure, in routine clinical practice.
* Identify patient- and treatment-related factors that influence therapeutic outcomes.
* Explore the role of advanced technologies and artificial intelligence in improving procedural performance, risk stratification, and clinical decision-making in AF care.

Participants will undergo standardized baseline evaluation and longitudinal follow-up to collect data on heart rhythm monitoring, treatment exposures, healthcare utilization, clinical events, and patient-reported outcomes. This cohort will generate real-world evidence to inform personalized and evidence-based management of AF.

Eligibility criteria:
Inclusion Criteria:

1. Age ≥ 18 years;
2. Documented atrial fibrillation episode lasting \>30 seconds confirmed by electrocardiogram (ECG), Holter monitor, implantable cardiac monitor, or wearable ECG device
3. Signed informed consent and willingness to participate in follow-up

Exclusion Criteria:

1. Atrial fibrillation due to a reversible cause (e.g., transient secondary AF)
2. Presence of comorbid conditions associated with an expected life expectancy of less than 1 year

Outcomes:
All-cause death
A Prospective Cohort Study of Bicuspid Case Selection and Valve Sizing Using the ABC Bicuspid Sizing Algorithm for Sapien 3 Balloon Expandable Valve
Trial ID: NCT06991517
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-05-13
Completion date: 2027-12
Sponsor: World Health Research Inc.
Collaborators: Edwards Lifesciences
Conditions: Aortic Stenosis
Interventions: The ABC Bicuspid Sizing Algorithm
Locations: John Hunter Hospital; North Shore Private Hospital; Royal North Shore Hospital; The Prince Charles Hospital; St Andrew's War Memorial Hospital; The Alfred; Fiona Stanley Hospital; Foothills Medical Centre; NovaScotia Health Authority- Halifax Infimary; Hamilton General Hospital; Southlake Regional Health Centre; University of Ottawa Heart Institute; St. Michael's Hospital; Institut universitaire de cardiologie et de pneumologie de Québec - Université Laval; Fundación Cardioinfantil - LaCardio; Hospital San Juan de Dios; Centro Médico Nacional Siglo XXI; Instituto Nacional de Cardiología - Ignacio Chávez; Wellington Regional Hospital; National Heart Centre Singapore; Ramathibodi Hospital
Countries: Australia; Australia; Australia; Australia; Australia; Australia; Australia; Canada; Canada; Canada; Canada; Canada; Canada; Canada; Colombia; Costa Rica; Mexico; Mexico; New Zealand; Singapore; Thailand
Enrollment: 290
Age range: None - None
Sex: ALL

Summary:
The goal of this study is to learn what effects the ABC Bicuspid Sizing Algorithm has on the clinical outcomes of patients with bicuspid aortic stenosis after having a transcatheter aortic valve replacement (TAVR) using the Sapien 3 valve. The main questions the study aims to answer are:

1. Does the ABC Bicuspid Sizing Algorithm increase the technical success at exit from the procedure room?
2. Does the ABC Bicuspid Sizing Algorithm increase the device success at 30 days after the procedure?

Participants already being evaluated for a TAVR as part of their regular medical care for bicuspid aortic stenosis will have their diagnostic images assessed using the ABC Bicuspid Sizing Algorithm to help determine their procedure type and valve size. They will have visits 30 days and one year after their procedure.

Eligibility criteria:
Inclusion Criteria:

* Have bicuspid aortic valve disease
* Have severe aortic stenosis or mixed aortic stenosis and regurgitation requiring treatment
* Have no other condition requiring surgical intervention
* Have had a TAVR CT scan (retrospectively gated contrast enhanced acquisition) that is of diagnostic quality and includes multiphase reconstructions of the aortic root at the minimum available slice thickness (with at least three systolic and one diastolic phases)
* Would be treated with a Sapien 3 valve if found to be anatomically suitable for TAVR
* Have a suitable access route for TAVR with a Sapien 3 valve

Exclusion Criteria:

* Are treated with TAVR using a device other than a Sapien 3 valve
* Are unable to be treated with TAVR due to intercurrent illness, clinical instability, or death on waitlist after being accepted for TAVR

Outcomes:
Proportion of cases achieving technical success
Identification of Thoracic CT Scan Biomarkers by Deep Learning for Evaluating the Prognosis of Patients With COVID-19 Disease
Trial ID: NCT04418245
Status: WITHDRAWN
Study type: OBSERVATIONAL
Start date: 2020-03-01
Completion date: 2021-09
Sponsor: Centre Hospitalier Universitaire de Nīmes
Conditions: Covid-19
Interventions: Imaging by thoracic scanner
Locations: CHU la Timone; CHU Montpellier; CHU de Nimes; CHU Poitiers; CHU Strasbourg; CHU Martinique
Countries: France; France; France; France; France; Martinique
Enrollment: 0
Age range: None - None
Sex: ALL

Summary:
The study hypothesis is that low-dose computed tomography (LDCT) coupled with artificial intelligence by deep learning would generate imaging biomarkers linked to the patient's short- and medium-term prognosis.

The purpose of this study is to rapidly make available an early decision-making tool (from the first hospital consultation of the patient with symptoms related to SARS-CoV-2) based on the integration of several biomarkers (clinical, biological, imaging by thoracic scanner) allowing both personalized medicine and better anticipation of the patient's evolution in terms of care organization.

Eligibility criteria:
Inclusion Criteria:

* Patients positive for SARS-CoV-2 according to RT-PCR test between 1st March and 31st May 2020
* Patients undergoing low dose CT scan to establish Covid-19 lung damage
* Available for at least 8 days follow-up

Exclusion Criteria:

• Patients opposing the retrospective use of their data

Outcomes:
Vital status; Patient requiring more than 3 liters of oxygen to maintain a saturation >95% (intensive care unit or resuscitation department); Percentage of lung affected on CT; Percentage of lung affected by ground glass opacity on scan; Percentage of lung affected by condensation on scan
Assessment of Dry Eye Disease and Retinal Nerve Fiber Layer Thickness in Chronic Smokers
Trial ID: NCT07028736
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2023-05-02
Completion date: 2024-05-01
Sponsor: Cairo University
Conditions: Dry Eye Disease; Retinal Nerve Fiber Layer; Chronic Smoking
Interventions: Dry eye assessment
Locations: Cairo University
Countries: Egypt
Enrollment: 50
Age range: 30 Years - 60 Years
Sex: ALL

Summary:
This study aimed to examine the impact of long-term smoking on dry eye parameters and retinal nerve fiber layer thickness in individuals who are chronic smokers.

Eligibility criteria:
Inclusion Criteria:

* Age from 30 to 60 years.
* Both sexes.
* Chronic smokers.

Exclusion Criteria:

* History of ocular surface diseases affecting tear production or secretion (such as Sjogren's syndrome or Stevens-Johnson disease).
* Conjunctivitis.
* Blepharitis.
* Chronic contact lens use or gross lid abnormalities.
* Preexisting glaucoma.
* Retinal diseases.
* Optic nerve pathology.
* Ocular trauma.
* Chemical burns.
* Recent eye surgery (within three months).
* Trachoma-related complications

Outcomes:
Non-invasive tear film breakup time
A Master Research Protocol to Adapt and Evaluate an Artificial Intelligence Based Conversational Agent to Provide Information for Different Health Conditions: the MARVIN Chatbots
Trial ID: NCT05789901
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-03-01
Completion date: 2034-12-31
Sponsor: McGill University Health Centre/Research Institute of the McGill University Health Centre
Collaborators: Centre hospitalier de l'Université de Montréal (CHUM)
Conditions: HIV Infections; Breast Cancer; Pediatric Emergency Medicine
Interventions: MARVIN; MARVIN-Pharma; MARVINA; MARVIN-CHAMP
Locations: Centre hospitalier de l'Université de Montréal (CHUM); McGill University Health Centre (MUHC)
Countries: Canada; Canada
Enrollment: 400
Age range: 14 Years - None
Sex: ALL

Summary:
This research is a continuation of a usability study with the MARVIN chatbot. The investigators aim to adapt the MARVIN chatbot to open it to other health domains (e.g. breast cancer) and populations (e.g. pharmacists). Therefore, this protocol constitutes a master research protocol that will englobe different research projects with individual chatbots. The investigators adopt an adaptive platform trial design, which will allow flexibility in handling multiple interventions adapted to different populations while retaining the characteristics of a platform trial design allowing early withdrawal of ineffective trial arms based on interim data (implementation outcomes) and introduction of new trial arms.

Eligibility criteria:
Inclusion Criteria for all objectives:

* being 14 years or older;
* being fluent in English and/or French;
* being able to understand the requirements of study participation and provide informed consent during the duration of the study;
* having access to a smartphone, tablet, or computer at home/at workplace;
* having access to an internet connection at home or data plan on their device.

Inclusion Criteria Specific to objectives 2 and 3:

* accept to use a Facebook Messenger-based Chatbot;
* accept to use or create a personal Facebook account;
* accept Facebook's privacy and data security policies.

Exclusion Criteria:

* not meeting the inclusion criteria
* any reason, in the opinion of the investigator, which would make the candidate inappropriate for participation in an investigative study involving a chatbot (e.g., cognitive deficit)

Outcomes:
Usability Metric for User eXperience (UMUX-Lite); Acceptability E-Scale (AES); Change in Acceptability E-Scale (AES) over 12 months; Change in Intervention Appropriateness Measure (IAM) over 12 months; Change in Compatibility Subscale over 12 months
Remote Multichannel Monitoring of Patients With Chronic DIseAses Using Speech technoLogies Based On Artificial intelliGence
Trial ID: NCT06644339
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-10-15
Completion date: 2025-04-30
Sponsor: I.M. Sechenov First Moscow State Medical University
Collaborators: PJSC VimpelCom
Conditions: Heart Failure; Diabetes Mellitus; Arterial Hypertension; Lymphoproliferative Disease; Total Knee Replacement
Interventions: Voice assistant
Locations: Federal State Autonomous Educational Institution of Higher Education I.M. Sechenov First Moscow State Medical University of the Ministry of Health of the Russian Federation (Sechenov University), University Clinical Hospital No.1
Countries: Russia
Enrollment: 500
Age range: 18 Years - None
Sex: ALL

Summary:
DIALOG is a study to assess the efficacy and safety of remote patient monitoring using virtual operator voice technologies and a business intelligence (BI) system for timely detection, prevention of early complications, worsening of the condition, and other adverse events in patients who have been discharged from the hospital.

Eligibility criteria:
Inclusion Criteria:

* Disease diagnosed according to the latest Clinical practice guidelines
* Stable condition at the time of discharge from the hospital
* Written informed consent to participate in the study

Non-inclusion criteria:

* Diagnosed dementia or severe cognitive impairment
* The inability to use automatic devices to register blood pressure at home, a blood glucose meter
* Alcohol or drug abuse
* Inability to contact a voice assistant and other study requirements, due to major co-morbidities, social or financial issues, or a history of noncompliance with medical regimens, that might compromise the patient's ability to understand and/or comply with the protocol instructions or follow-up procedures

Exclusion Criteria:

* Unwillingness of the patient to continue participating in the study
* The development of conditions related to the criteria of non-inclusion

Outcomes:
cardiovascular mortality; all-cause mortality; complications, decompensations