moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Improvement of Medical Quality and Outcomes Through Intelligent Management and Decision System of Cerebrovascular Diseases Based on Hospital Information System - A Multicenter, Open-label, Cluster-Randomized Controlled Study
Trial ID: NCT06452888
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-07-01
Completion date: 2026-02-01
Sponsor: Beijing Tiantan Hospital
Conditions: Cerebrovascular Disease
Interventions: Clinical decision support system.; no intervention
Enrollment: 3540
Age range: 18 Years - 80 Years
Sex: ALL

Summary:
This study applied a cerebrovascular disease organizational management and decision system based on hospital information system, aiming to verify the effect of intervention and management after stroke on improving the functional outcomes of acute ischemic cerebrovascular disease through a cluster randomized controlled study.

Eligibility criteria:
Inclusion Criteria:

* Age ≥ 18.
* Diagnosed with ischemic stroke confirmed by magnetic resonance imaging (MRI) or brain computed tomography (CT).
* Within 7 days from onset of symptoms.
* With a NIHSS score of ≥2 at admission (limb score ≥1).
* With a prestroke mRS score of 0-1.
* Admission from the emergency department or outpatient service.
* Voluntary informed consent.

Exclusion Criteria:

* Diagnosis of other cerebrovascular diseases (cerebral hemorrhage, subarachnoid hemorrhage, cerebral venous sinus thrombosis, etc.).
* Diagnosis of non-cerebrovascular disease (central nervous system infection, epilepsy, metabolic encephalopathy, etc.).
* Ischemic stroke with negative diffusion weighted imaging (DWI).
* Being tested for interventions such as drugs or instruments.
* Pregnancy or 6 weeks postpartum.
* With a life expectancy of less than 3 months or who were unable to complete the study follow-up for other reasons.

Outcomes:
The incidence of unfavorable functional outcome
Preliminary Evaluation of the Artificial Intelligence Software VX1+ for the Detection and Automatic Tagging on 3D Mapping of Spatio-temporal Dispersion Areas During Atrial Fibrillation or Atrial Tachycardia Ablation Procedures.
Trial ID: NCT05362656
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2022-05-29
Completion date: 2022-12-09
Sponsor: Volta Medical
Conditions: Atrial Fibrillation; Atrial Tachycardia; Atrial Arrhythmia
Interventions: VX1+ dispersion mapping
Locations: Hôpital Saint-Joseph
Countries: France
Enrollment: 40
Age range: 18 Years - None
Sex: ALL

Summary:
Interventional, prospective, non-randomized, single-center, non-controlled clinical investigation as part of the premarket clinical evaluation of VX1+ medical device, aiming to verify that VX1+ in bidirectional configuration with auto-tagging function is both ergonomic and reliable for dispersed electrograms detection and automatic tagging on 3D-map in real-time.

Eligibility criteria:
Inclusion Criteria:

* Patient aged 18 years or older.
* Patient candidate for catheter ablation of atrial tachycardia, or paroxysmal or persistent atrial fibrillation, de novo or after one or several previous ablation procedures.
* Continuous anticoagulation for more than 4 weeks before ablation.
* Patient giving his signed consent form to participate in the clinical study.
* Patient affiliated to the French social security

Exclusion Criteria:

* Contraindication to AF/AT catheter ablation.
* Major bleeding disorder.
* Contraindication to anticoagulation (Heparin, Warfarin or novel oral anticoagulants (NOAC)) or lack of anticoagulation for 4 weeks prior to the procedure.
* Presence of a left atrium (LA) thrombus on transesophageal echocardiography (TEE) prior to the procedure.
* Patient who is or could potentially be pregnant.
* Person deprived of liberty or under guardianship.
* Patient's refusal to participate in the study.

Outcomes:
Perioperative reliability of VX1+ in the real-time detection of electrograms in AF or AT exhibiting a spatio-temporal dispersion, and auto-tagging of these dispersion areas on 3D electroanatomical maps.
NUTRA- Nutrition in Rheumatoid Arthritis
Trial ID: NCT06831331
Status: ENROLLING_BY_INVITATION
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-02-25
Completion date: 2030-12-31
Sponsor: Vastra Gotaland Region
Collaborators: Göteborg University
Conditions: Rheumatoid Arthritis (RA)
Interventions: REK-diet; RICH-diet
Locations: Klinisk Reumatologiskt Forskningscentrum, Sahlgrenska University Hospital
Countries: Sweden
Enrollment: 150
Age range: 18 Years - 85 Years
Sex: ALL

Summary:
Background: Individuals with Rheumatoid Arthritis (RA) often experience persistent symptoms such as pain and stiffness despite modern pharmacological treatments. They also have an increased risk of cardiovascular disease. Elevated risk factors for cardiovascular disease limit the options for symptom-relieving medications, making a reduction in these risk factors highly desirable. Diet has been shown to reduce several of these factors, but the scientific evidence for rheumatoid arthritis is limited, and thus tailored dietary advice is lacking.

Objective: The purpose of the NUTRA study is to evaluate the effect of a diet that follows the updated Nordic Nutrition Recommendations (2023) on disease activity and cardiovascular risk factors in individuals with newly diagnosed RA. Specifically, the study will analyze the impact of the diet on clinically relevant markers such as blood pressure, lipid profile, inflammatory markers, and body composition.

Study Plan: The study will include about 150 patients, and participants will follow a standardized care protocol where, at their three-month visit, they are randomized into either a diet following the Nordic Nutrition recommendations or a Nutrient rich diet. The intervention diets, will be added as a complementary treatment to the medications the patients are already taking. Participants will receive menus, recipes and some foods for three months, followed by self-maintenance of the diet. Clinical markers will be evaluated before the intervention as well as at three and nine months. Artificial intelligence will be used to predict which factors are crucial for achieving the best possible outcomes from dietary treatment.

Significance: The NUTRA study is highly significant as it aims to generate evidence to guide the design of dietary interventions for patients with rheumatoid arthritis. By understanding the potential role of diet in reducing both disease activity and cardiovascular risk, the study may contribute to improved treatment strategies for this challenging patient group.

Eligibility criteria:
Inclusion Criteria:

* newly diagnosed RA (within 3 months) that have started on Methotrexate and/or cortisone treatment. RA is defined according to the criteria from EULAR/American College of Rheumatology (ACR) 2010.
* BMI 18.5-40 kg/m2
* willing to follow the intervention diet

Exclusion Criteria:

* pregnancy
* lactation
* diagnosis for cancer or other life-threatening diseases
* diabetes type 1 and 2
* inflammatory bowel disease or celiac disease

Outcomes:
Patterns of serum inflammatory markers
A Multimodal Artificial Intelligence Model for Subtyping Diagnosis and Clinical Management of Pancreatic Cystic Lesions Based on Endoscopic Ultrasound and Clinical Information
Trial ID: NCT07463872
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-01-01
Completion date: 2026-06
Sponsor: Huazhong University of Science and Technology
Conditions: Pancreatic Cystic Lesion; Mucinous Cystadenoma of Pancreas; Intraductal Papillary Mucinous Neoplasm of Pancreas; Pseudocyst Pancreas; Serous Cystadenoma; Neuroendocrine Tumors, NET
Interventions: Cyst-AI model
Locations: Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology; Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology
Countries: China; China
Enrollment: 500
Age range: 18 Years - None
Sex: ALL

Summary:
The primary objective is to construct a multimodal AI model (Cyst-AI) based on EUS images and clinical data such as imaging features(CT or MRI) and laboratory tests to assist endoscopists in the diagnosis of pancreatic cystic lesions(PCLs), mainly differentiating mucinous from non-mucinous lesions.

The secondary objective is to evaluate the model's effectiveness in risk stratification and clinical management for patients with PCLs.

Eligibility criteria:
Inclusion criteria:

* Patients whose EUS results indicates pancreatic cystic or cystoid lesions;
* Mucinous lesions: including mucinous cystic neoplasm (MCN), intraductal papillary mucinous neoplasm (IPMN);
* Non-mucinous lesions: including pancreatic pseudocyst, serous cystic neoplasm (SCN), cystic neuroendocrine tumor (cNET).

Exclusion criteria:

* Patients whose age is less than 18 years old;
* Patients who have undergone pancreatic surgery before the EUS examination;
* Patients who have received chemotherapy and radiotherapy for pancreatic tumors before the EUS examination;
* Pathological results indicate that pancreatic lesions are metastatic lesions from other sites;
* Patients whose EUS images or reports are missing;
* EUS image quality does not meet the requirements for review, such as blurry imaging or containing artifacts, biopsy needles, measuring scales, or other additional annotations that are not part of the original EUS image;
* Patients whose final diagnosis is unclear.

Outcomes:
The performance of the diagnostic model in differentiating mucinous from non-mucinous PCLs; The risk stratification performance of the clinical management model for mucinous PCLs
Comparison Between the Health Effects of an AI-driven Model With Those of Human Professional Guidelines for the Treatment of Obesity
Trial ID: NCT07557979
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-05-15
Completion date: 2026-12-31
Sponsor: Texas Tech University
Conditions: Obesity (BMI>30)
Interventions: AI-assisted dietitian group (combination); A dietitian-led group (human); AI-only group using ChatGPT Premium
Enrollment: 21
Age range: 18 Years - 60 Years
Sex: ALL

Summary:
Many non-communicable diseases are diet-related and have a significant impact on public health. It is stated that global dietary shifts are needed to change disease patterns, highlighting the importance of nutrition in addressing public health issues, such as obesity. The field of nutrition has been dependent on clinical and observational studies; however, the emergence of Artificial Intelligence (AI) is transforming these approaches.

ChatGPT may be used to provide dietary recommendations due to its high speed, extensive access to a variety of meal data, and low complexity. However, initial evaluations have shown that ChatGPT may be inaccurate in terms of safety and reliability, and traditional nutrition approaches are highly reliant on experts' knowledge and the validity of nutritional guidelines. It has been suggested that AI in nutrition may be beneficial; however, further investigations are needed. Our proposal aims to fill the represented critical evidence gaps.

The study aims to compare the health effects of an AI-driven model with those of human professional guidelines for the treatment of obesity. Furthermore, investigators seek better strategies to utilize AI, if appropriate, for weight loss and other health benefits.

Eligibility criteria:
Inclusion Criteria:

* Participants with age 18 to 60 years,
* BMI\>30
* Living in Lubbock-TX
* Willing to participate in the study

Exclusion Criteria:

* Pregnant women
* Breastfeeding women
* Recent diagnosis of a severe/acute medical condition within 6 months
* Any other chronic diseases except obesity (e.g., diabetes, chronic kidney diseases, psychiatric conditions, cancer, acute pancreatitis)
* Taking any anti-obesity medications

Outcomes:
Rate of weight change, Fat %, Muscle Mass, and BMI
Deep Learning Enhanced Detection of Aortic Stenosis - The DETECT-AS-Diagnostic Study
Trial ID: NCT06749145
Status: ENROLLING_BY_INVITATION
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-09-16
Completion date: 2028-08-31
Sponsor: Yale University
Collaborators: National Institute on Aging (NIA); Icahn School of Medicine at Mount Sinai; The Methodist Hospital Research Institute
Conditions: Aortic Stenosis
Interventions: Portable 1-lead electrocardiogram; Point-of-care ultrasound; AI-ECG risk algorithm; AI-POCUS
Locations: Yale New Haven Health System; Icahn School of Medicine at Mount Sinai; The Methodist Hospital Research Institute
Countries: United States; United States; United States
Enrollment: 410
Age range: 70 Years - None
Sex: ALL

Summary:
The DETECT-AS Diagnostic Study will assess the performance of artificial intelligence (AI) risk predictions to detect aortic stenosis using results from portable electrocardiogram (ECG) and cardiac ultrasound devices.

Eligibility criteria:
Inclusion Criteria:

* Age 70 years or older
* Attending a routine outpatient primary care clinic at one of the three enrollment sites

Exclusion Criteria:

* Opted out of research studies
* Non-English speaking
* Urgent or emergent visits, defined as a visit for an illness or injury that needs attention quickly or is life-threatening
* Any echocardiogram within 12 months of clinic visit
* Prior history of moderate or severe AS
* Prior history of aortic valve replacement or repair, including transcatheter and surgical AVR with either a bioprosthetic or mechanical valve
* Presence of implantable cardiac devices, including permanent cardiac pacer, implantable cardioverter-defibrillator, or left ventricular assist device
* Prior heart transplant
* History of dementia
* Documented life expectancy of \<1 year or current participation in hospice services.

Outcomes:
Number of participants diagnosed with advanced aortic stenosis (AS) by transthoracic echocardiogram (TTE)
Assessing the Additional Neoplasia Yield of Computer-aided Colonoscopy in Follow-up Patients in a Screening Setting
Trial ID: NCT06160466
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2020-12-16
Completion date: 2024-05-01
Sponsor: Fondazione Poliambulanza Istituto Ospedaliero
Conditions: Colonic Adenoma; Adenoma; Polyp of Colon
Interventions: CADe colonoscopy using GI Genius device; White light
Locations: Fondazione Poliambulanza
Countries: Italy
Enrollment: 1156
Age range: 50 Years - 74 Years
Sex: ALL

Summary:
The goal of this clinical trial is to evaluate the diagnostic yield of CADe in a consecutive population undergoing colonoscopy. The main question it aims to answer is the Adenoma Detection Rate (ADR). Participants undergoing colonoscopy for follow-up in a screening setting will be randomized in a 1:1 ratio to either receive Computer-Aided Detection (CADe) colonoscopy or a conventional colonoscopy (CC). GI Genius is the AI software that will be used in the present trial and is intended to be used as an adjunct to colonic endoscopy procedures to help endoscopists to detect in real time mucosal lesions (such as polyps and adenomas, including those with flat (non-polypoid) morphology) during standard screening and surveillance endoscopic mucosal evaluations. It is not intended to replace histopathological sampling as a means of diagnosis.Researchers will compare the CADe group and the CC-group to see if CAD-e can increase the ADR significantly.

Eligibility criteria:
Inclusion Criteria:

* Patients aged 50 to 74 undergoing colonoscopy examination following a prior colonsocopy were polyps were found (follow-up) performed in the context of a regional mass-screening program.

Exclusion Criteria:

* Patients unwilling or unable to give informed consent.
* Patients reporting use of anti-platelet agents or anticoagulants precluding removal of polyps.

Outcomes:
Adenoma detection rate; Rate of patients detected with 3 or more adenomas.
Automated Detection Methods for Inflammatory Arthritis and Formation of an Image Database
Trial ID: NCT06715488
Status: ACTIVE_NOT_RECRUITING
Study type: OBSERVATIONAL
Start date: 2024-11-15
Completion date: 2027-12
Sponsor: Med2Measure
Collaborators: IISER Pune
Conditions: Rheumatoid Arthritis &Amp; Other Inflammatory Polyarthropathies; Peripheral Spondyloarthritis; Inflammatory Arthritis
Interventions: AI assisted smartphone diagnosis
Locations: Rheumatology Clinic; Poona Superspeciality Clinic
Countries: India; India
Enrollment: 3000
Age range: 18 Years - None
Sex: ALL

Summary:
The investigators are testing the ability of convolutional neural networks (CNNs), that is artificial intelligence, on smartphone photographs in detecting inflammatory arthritis. This promises to be an efficient, accurate, and non-invasive diagnostic tool that will significantly improve early detection and management of inflammatory arthritis.

Eligibility criteria:
Inclusion Criteria:

* Inflammatory arthritis of any etiology

Exclusion Criteria:

* Severe deformity that hampers standardization of photographs

Outcomes:
Accuracy of AI diagnosis against specialist (rheumatologist) opinion
Fluid Optimization in Cancer Surgery With AI-Assisted Management - FOCUS-AFM Trial
Trial ID: NCT07314853
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-02
Completion date: 2029-02
Sponsor: National Cancer Institute, Naples
Conditions: Neoplasm; Cancer; Hemodynamic (MAP) Stability; Cancer Surgery; Artificial Intelligence (AI); Fluid Therapy DURING SURGERY
Interventions: Artificial Intelligence-Assisted Fluid Management; Standard fluid management
Locations: IRCCS Istituto Nazionale Tumori "Fondazione G. Pascale"
Countries: Italy
Enrollment: 176
Age range: 18 Years - None
Sex: ALL

Summary:
The goal of this clinical trial is to learn if using artificial intelligence to guide intravenous fluid therapy during major cancer surgery can help keep blood pressure more stable compared with usual care in adult patients undergoing major cancer surgery. The main questions it aims to answer are:

* Does artificial intelligence-guided fluid therapy reduce hypotensive events during surgery?
* Does this approach improve recovery and reduce complications after major cancer surgery?

Researchers will compare artificial intelligence-guided fluid therapy with standard fluid management to see if the artificial intelligence-guided approach provides better support during surgery.

Participants will:

* Undergo major cancer surgery under general anesthesia
* Receive either artificial intelligence-guided fluid management or standard fluid management during surgery
* Be monitored during and after surgery as part of routine clinical care
* Be followed after surgery to assess recovery and possible complications

Eligibility criteria:
Inclusion Criteria:

* Age ≥ 18 years
* American Society of Anesthesiologists (ASA) Physical Status II-IV
* Undergoing major abdominal oncologic surgery (open or laparoscopic) with an expected duration \>2 hours
* Planned invasive arterial pressure monitoring
* Ability to understand and sign informed consent

Exclusion Criteria:

* Significant arrhythmias (e.g., persistent atrial fibrillation)
* Severe aortic stenosis
* Emergency surgery
* Sepsis
* End-stage renal disease on dialysis
* Pregnancy
* Impossibility to cannulate the radial artery
* Refusal to participate or refusal of data processing consent

Outcomes:
Intraoperative Hypotension Burden
Explore the Value of New Magnetic Resonance Technology in Non-invasive Quantitative Assessment of Renal Dysfunction and Renal Fibrosis
Trial ID: NCT06366529
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2023-09-01
Completion date: 2030-09
Sponsor: Zhen Li
Conditions: Renal Insufficiency, Chronic
Locations: Tongji hospital, NO.1095 jiefang avenue
Countries: China
Enrollment: 500
Age range: None - None
Sex: ALL

Summary:
Currently, renal biopsy is the gold standard for evaluating renal pathology and renal fibrosis, but it is invasive and carries the risk of serious complications; and the sampled tissue is only a small part of the kidney, which is prone to sampling bias. The lack of reliable, comprehensive test results has hindered the research of new anti-fibrotic drugs and delayed the clinical application of effective new drugs. Therefore, the development of a non-invasive dynamic detection method for renal insufficiency and renal fibrosis in vivo is an urgent clinical problem to be solved.

With the continuous development and update of technology, imaging provides a new way to non-invasively evaluate renal fibrosis. Due to the high resolution of soft tissue and the ability to perform multi-parameter analysis, magnetic resonance has developed the diagnosis of renal insufficiency and renal fibrosis from macroscopic simple biomorphological changes to microscopically complex pathophysiological changes. Many imaging techniques measure renal dysfunction and renal fibrosis by assessing the impact of fibrosis on the functional status, physical properties, and molecular properties of the kidney.

In recent years, in the context of precision medicine, artificial intelligence technologies such as radiomics and machine learning are rapidly becoming very promising auxiliary tools in the imaging assessment of renal fibrosis. It can extract and learn features in images with high throughput, make greater use of information in medical images that cannot be recognized by the human eye, and achieve disease diagnosis, prognosis assessment, and efficacy prediction by building models. However, most of the current research is in the preliminary stage, and there are still few studies on the assessment of renal insufficiency and renal fibrosis. I believe that with the continuous improvement of algorithms and the optimization of models, the progress of radiomics and machine learning will be great. To a certain extent, it promotes the development of personalized medicine and precision medicine for patients with renal insufficiency and renal fibrosis.

Eligibility criteria:
Inclusion Criteria:

1. Patients with clinically suspected or confirmed renal insufficiency and prescribed MR examination;
2. Age/gender: no limit;
3. Patients who voluntarily participate in clinical trials and sign written informed consent forms

Exclusion Criteria:

1. Patients with pacemakers of unknown material, metal implants in the body, neurostimulators, and claustrophobia, etc.
2. Patients who cannot tolerate sufficient breath-holding for adequate MR examination

Outcomes:
ESKD
Acceptability and User Perceptions of Artificial Intelligence-based Mobile Applications
Trial ID: NCT05257239
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2022-02-17
Completion date: 2023-06-30
Sponsor: National University Health System, Singapore
Conditions: Weight Management
Interventions: no intervention
Locations: National University of Singapore
Countries: Singapore
Enrollment: 600
Age range: 21 Years - None
Sex: ALL

Summary:
To understand the user perceptions of AI and non-AI-based mobile apps for weight management in people with overweight and obesity.

Eligibility criteria:
Inclusion Criteria:

* Participants will be recruited if they are above 21 years old, have a BMI ≥23kg/m2 and understand English language.

Exclusion Criteria:

* There will be no exclusion criteria to maximize representation of the general population of Singaporean who are overweight and obese.

Outcomes:
Unified Theory of Acceptance and Use of Technology 2 (UTAUT2)