moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Caracterización de Subgrupos de Personas Con Diabetes Tipo 1: análisis de características clínicas y glucométricas Utilizando Una aproximación de Inteligencia Artificial
Trial ID: NCT07461805
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-11-04
Completion date: 2028-12
Sponsor: Fundació Institut de Recerca de l'Hospital de la Santa Creu i Sant Pau
Collaborators: Sociedad Española de Diabetes; Associació Catalana de Diabetis
Conditions: Type 1 Diabetes Mellitus
Locations: Hospital de la Santa Creu i Sant Pau, Barcelona, Barcelona 08041
Countries: Spain
Enrollment: 800
Age range: 18 Years - None
Sex: ALL

Summary:
The goal of this observational study is to characterize different subgroups among patients with type 1 diabetes. The main research question is:

Are there distinct subtypes among people with type 1 diabetes?

Participants will be invited to take part in the study by allowing access to their health data. They will not be required to undergo any additional examinations, tests, visits, or interventions.

Eligibility criteria:
Inclusion Criteria:

* Individuals with type 1 diabetes (T1D) aged 18 years or older.
* T1D individuals expected to have regular follow-up at the Endocrinology and Nutrition Department of Hospital de la Santa Creu i Sant Pau.
* Users of continuous glucose monitoring (CGM) systems for at least the last 6 months of 2024.
* Willingness and ability to provide written informed consent to participate in the study (by the patient or his/her representative).

Exclusion Criteria:

* Presence of severe comorbidities or medical conditions that, in the investigator's judgment, could interfere with participation in the study or the interpretation of results. This circumstance is expected to be exceptional, as the study aims to be as inclusive as possible.

Outcomes:
Type 1 diabetes clusters
Application and Effectiveness of a Large Language Model-Based Educational Agent in Medical Education: A Study on the Machine Learning and Data Mining Course
Trial ID: NCT07449182
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-05-01
Completion date: 2026-03-31
Sponsor: Sun Yat-sen University
Conditions: Medical Education; Artificial Intelligence in Medicine
Interventions: KGRAG-based AI Educational Agent System
Locations: North Campus of Sun Yat-sen University
Countries: China
Enrollment: 56
Age range: None - None
Sex: ALL

Summary:
The goal of this interventional study is to evaluate the effectiveness of a Large Language Model (LLM)-based educational AI Agent in graduate students (Masters and PhD) specializing in medicine or nursing who are enrolled in the "Machine Learning and Data Mining" course. The main questions it aims to answer are:

Does the use of an educational AI Agent improve students' academic performance and practical skills in machine learning compared to traditional methods?

Does the AI intervention enhance students' learning confidence, satisfaction, and cognitive engagement?

Researchers will compare students currently using the AI Agent (experimental group) to a historical control group (students from the previous cohort who did not use the AI tool) to see if the AI-assisted learning model leads to significantly higher learning achievements and better educational experiences.

Participants will:

Utilize the Teaching Agent for real-time answers to theoretical questions, personalized study planning, and knowledge reinforcement.

Engage with the Research Agent to assist with literature reviews, research design optimization, and academic writing structure.

Use the Practice Innovation Agent for guidance on coding, algorithm debugging, and applying machine learning models to medical data analysis projects.

Eligibility criteria:
Inclusion Criteria:

1. Medical graduate students from universities in the Guangdong-Hong Kong-Macao Greater Bay Area;
2. Graduate students who have taken the "Machine Learning and Data Mining" course;
3. Have completed the required prerequisite courses: "Medical Statistics" and "Nursing Research";
4. Capable of operating the AI Educational Agent system normally and willing to undergo relevant teaching interventions and assessments during the study period.

Exclusion Criteria:

1. Unwilling to use the AI education agent system, or refusing to allow the research team to collect their relevant data;
2. Students who cannot commit to the full duration of the course or have known scheduling conflicts that would prevent regular attendance;
3. Students who have previously enrolled in or audited this course in prior academic years to avoid learning effect bias

Outcomes:
Composite Academic Performance Score
Tailored Drug Titration Through Artificial Intelligence: an Interventional Study
Trial ID: NCT03397524
Status: WITHDRAWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2018-10-01
Completion date: 2020-03-31
Sponsor: Optima Integrated Health
Collaborators: University of California, San Francisco
Conditions: Hypertension
Interventions: optima4BP
Locations: University of California San Francisco
Countries: United States
Enrollment: 0
Age range: 21 Years - 80 Years
Sex: ALL

Summary:
ARTERY is a randomized clinical trial that investigates the benefit of a predictive modeling artificial intelligence in improving the management of anti-hypertensive medication treatment.

Eligibility criteria:
Inclusion Criteria:

* Two or more blood pressure readings of ≥ 150/95 mmHg during primary care office visits in the Department of General Medicine within the last 6 months
* Therapy with medications from at least 1 anti-hypertensive pharmacological agents at the time of the last office visit
* At least minimally "tech-savvy" defined as Ownership of a compatible smartphone Ability to access the internet

Exclusion Criteria:

* Inability to operate a blood pressure cuff
* Incompatible smartphone device (Galaxy S5 Android 5.0)
* Less than minimally "tech-savvy," defined as inability to use the Internet
* Non-compliance with medical follow-up (frequent "no shows")
* Planned coronary revascularization in the next 12 months
* Myocardial infarction, coronary revascularization, stroke, cardiac or aortic surgery in the previous 90 days
* GFR \< 30 (CKD stage IV/ V)
* Primary care provider rules out the patient due to comorbidities or other factors

Outcomes:
Reduction in systolic BP [SBP] (mmHg) measure in the office
Development and Validation of an Artificial Intelligence System for Automatic Evaluation of the Extent of Intestinal Metaplasia
Trial ID: NCT05459610
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2022-07-01
Completion date: 2023-12-30
Sponsor: Shandong University
Conditions: Intestinal Metaplasia of Gastric Mucosa; Artificial Intelligence; Endoscopy
Locations: Department of Gastrology, QiLu Hospital, Shandong University
Countries: China
Enrollment: 600
Age range: 18 Years - 80 Years
Sex: ALL

Summary:
Gastric intestinal metaplasia(GIM) is an important stage in the gastric cancer(GC). With technical advance of image-enhanced endoscopy (IEE), studies have demonstrated IEE has high accuracy for diagnosis of GIM. The endoscopic grading system (EGGIM), a new endoscopic risk scoring system for GC, have been shown to accurately identify a wide range of patients with GIM. However, the high diagnostic accuracy of GIM using IEE and EGGIM assessments performed all require much experience, which limits the application of EGGIM. The investigators aim to design a computer-aided diagnosis program using deep neural network to automatically evaluate the extent of IM and calculate the EGGIM scores.

Eligibility criteria:
Inclusion Criteria:

* patients aged 18-80 years who undergo the IEE examination

Exclusion Criteria:

* patients with severe cardiac, cerebral, pulmonary or renal dysfunction or psychiatric disorders who cannot participate in gastroscopy
* patients with previous surgical procedures on the stomach
* patients who refuse to sign the informed consent form

Outcomes:
The specificity of AI model to assess the degree of intestinal metaplasia in an endoscopic picture; The accuracy of AI model to assess the degree of intestinal metaplasia in an endoscopic picture; The sensitivity of AI model to assess the degree of intestinal metaplasia in an endoscopic picture
Development and Validation of an Artificial-Intelligence-enabled Portable Colposcopy Device for Optimizing Triage Alternatives for HPV-based Cervical Cancer Screening
Trial ID: NCT06458062
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-02-15
Completion date: 2027-04-30
Sponsor: Duke University
Collaborators: Kenya Medical Research Institute; National Cancer Institute (NCI)
Conditions: Cervical Cancer; HPV Infection
Interventions: Pocket Colposcope
Locations: Kenya Medical Research Institute
Countries: Kenya
Enrollment: 1079
Age range: 25 Years - None
Sex: FEMALE

Summary:
The Pocket colposcope has 510k FDA clearance and has been successfully used in \~2500 unique patients globally in Duke and non-Duke protocols to date. 1054 women who are HPV(+) and planned to undergo treatment at four Ministry of Health-supported outpatient clinics in Kisumu County will be recruited to the study. After providing informed consent, participants will be proceed with colposcopy using the POCKET colposcope.

Eligibility criteria:
Inclusion Criteria:

1. Age ≥ 30 years old and ≤ 64 years old; or Age ≥ 25 years old if women living with HIV (WLWH)
2. Sex: Female
3. Positive HPV test within past 6 months

Exclusion Criteria:

1. Pregnant women (cannot perform a cervical biopsy on a patient who is pregnant unless absolutely indicated)
2. Women with a negative HPV test within the past 9 months
3. Patients incapable of giving informed consent
4. Women with a history of cervical cancer
5. Pelvic exam concerning for cervical cancer or cervical infection
6. History of hysterectomy

Outcomes:
Sensitivity of the Pocket CARE in detecting pre-cancer; Specificity of the Pocket CARE in detecting pre-cancer; Positive Predictive Value (PPV) of the Pocket CARE in detecting pre-cancer; Negative Predictive Value (NPV) of the Pocket CARE in detecting pre-cancer
Evaluation of Repeated, In-Clinic, Self-Imaging by NV-AMD Patients Using the Notal Vision Home OCT
Trial ID: NCT04642183
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2020-11-16
Completion date: 2021-09-30
Sponsor: Notal Vision Inc.
Conditions: Neovascular Age-related Macular Degeneration; Intermediate Age Related Macular Degeneration (Disorder)
Locations: Elman Retina Group, PA
Countries: United States
Enrollment: 70
Age range: 18 Years - None
Sex: ALL

Summary:
this is a single In-Clinic Encounter With the Notal Vision Home OCT study. The study population will include up to 50 Age-related Macular Degeneration patients diagnosed with wet NV-AMD in at least one eye at the time of enrollment. All enrolled subjects should have at least one eye with active (wet) CNV (SRF and/or IRF) at the time of enrollment. All subjects will be enrolled at 1 site in the United States. in this study patients will be placed in a room with the Notal Vision Home OCT device and following a completion of a self tutorial will perform 3 unsupervised self-scans on each study eye (with a rest of \~5 minutes between self-scans). at the end of the testing sessions, the subject will be asked to complete subject user questionnaire.

Eligibility criteria:
Inclusion Criteria:

1. Ability to speak, read and understand English.
2. Ability to understand and agree to contents of informed consent either in writing or verbally.
3. Eighteen (18) years of age or older at the time of Informed Consent.
4. AMD patients diagnosed with wet NV-AMD in at least one eye (ie, with SRF, and/or IRF).
5. Fellow eye with NV-AMD (with or without fluid) or intermediate AMD.
6. Best corrected Visual Acuity of 20/320 or better in eyes participating at the study.

Exclusion Criteria:

1. Subjects with dilated pupils.
2. Any other retinal disease requiring steroidal or anti-VEGF injections.

Outcomes:
Percentage of subjects who completed self-scanning following a self-tutorial.; Percentage of eyes that performed 4 self-scans with the same fluid status; Agreement between the Cirrus OCT and the NVHO device in detecting fluid in the central 3x3mm of the macula.; Summary of subject questionnaire results.
Machine Learning and Artificial Intelligence for Early Detection of Stroke and Atrial Fibrillation. Clinical Cohorts for Validation of New Digital Biomarkers
Trial ID: NCT05855538
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2023-03-02
Completion date: 2025-08
Sponsor: Atrial Fibrillation Network
Conditions: Atrial Fibrillation
Locations: St Vincenz Hospital
Countries: Germany
Enrollment: 600
Age range: 18 Years - None
Sex: ALL

Summary:
The MAESTRIA study is an international, multi-centre, non-interventional, observational registry. The main goal is to enrol a representative group of European patients diagnosed with Atrial Fibrillation (AF) to analyse clinical and relevant parameters (digitalised ECG, echocardiograms, cardiac CTs, MRIs and blood biomarkers) that could be used during clinical practise for the diagnosis of atrial cardiomyopathy. The AF patients will be distributed in 3 groups according to the different manifestation of AF: paroxysmal, persistent and permanent AF.

Eligibility criteria:
Inclusion Criteria:

* Patients with paroxysmal AF, persistent AF or permanent AF.
* Patients (or legally acceptable representative if applicable to country specific regulations) provide written informed consent to participate in the study. The patient has the option to give separate consent to donate extra volume of blood during the routine blood collection, that can be used for biomedical research.
* Patient is at least 18 years old.
* Patient must own a smartphone with Apple iOS version 14.5 (or higher) or with Android version 8.0 (or higher).

Exclusion Criteria:

* Any disease that limits life expectancy to less than 1 year.
* All persons unable to provide informed consent.
* All persons exempt from participation in a study or trial by law.
* Any medical or psychiatric condition which, in the investigator´s opinion, would preclude the participant from adhering to the protocol or completing the study per protocol.

Outcomes:
Physical examination; Clinically relevant changes in CT/MRI; Atrial Arrythmias (AA) burden and vascular stiffness (measured by a measuring bracelet); Cognitive function test (MoCA); EQ-5D-5L Quality of Life questionnaire; Ischaemic events (systemic, myocardial and cerebral); Echo analysis (TEE_transesophageal echocardiography): LAA smoke available; Echo analysis (TEE_transesophageal echocardiography): Flow velocity in LAA; Echo analysis (TEE_transesophageal echocardiography): Antrum diameter; Echo analysis (TTE_transthoracic echocardiography): LVEF; Echo analysis (TTE_transthoracic echocardiography): LA diameter; Echo analysis (TTE_transthoracic echocardiography): LA volume; Echo analysis (TTE_transthoracic echocardiography): Septum thickness; Echo analysis (TTE_transthoracic echocardiography): Degree of mitral valve insufficiency; Echo analysis (TTE_transthoracic echocardiography): Degree of tricuspid valve insufficiency; ECG analysis; MRI analysis: LAEF; MRI analysis: LAVi; MRI analysis: LALRS; MRI analysis: LALBS; CT scan analysis
Impact of AI Visualization on Surgeons' Cognitive Workload in Single-Port Robotic Surgery
Trial ID: NCT07566078
Status: ACTIVE_NOT_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-09-01
Completion date: 2028-09-01
Sponsor: University of Illinois at Chicago
Conditions: Urology
Interventions: Yolo
Locations: UI Health
Countries: United States
Enrollment: 90
Age range: 18 Years - None
Sex: ALL

Summary:
Robotic surgery is now widely adopted in urology, and the da Vinci Single-Port (SP) platform enables complex procedures through a single multichannel incision, with favorable perioperative and outpatient outcomes in selected patients. However, single-port access and AI implementation also introduce unique ergonomic and cognitive challenges for surgeons and operating room staff. Quantifying intraoperative workload has become crucial to understand how new technologies affect performance, safety and training.

The National Aeronautics and Space Administration Task Load Index (NASA-TLX) is a validated multidimensional instrument for subjective workload assessment and has been increasingly applied to surgical and specifically urologic practice. In parallel, augmented reality and artificial intelligence (AI) are emerging as tools to enhance intraoperative visualization and anatomical understanding during robot-assisted urologic procedures. The da Vinci TilePro multi-image display already allows simultaneous viewing of auxiliary imaging, but evidence on how real-time AI overlays integrated via TilePro affect cognitive workload in single-port urologic surgery is lacking. This prospective pilot study evaluates the impact of different TilePro visualization strategies on surgeon and bedside assistant workload, measured by weighted NASA-TLX scores, and explores associations with operative metrics in elective SP urologic procedures.

Eligibility criteria:
Inclusion Criteria: Urology surgeon or urology resident -

Exclusion Criteria: not urology surgeon or not urology resident

\-

Outcomes:
NASA Task Load Index (NASA-TLX) questionnaire
A Prospective, Multi-Center, Randomized Controlled Clinical Study To Measure The Effect Of Use Of Artificial Intelligence (AI) Enabled Computer Aided Detection (CADe) Assistance Software In Detecting Colon Polyps During Standard Colonoscopy Procedures
Trial ID: NCT04555135
Status: TERMINATED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2020-09-28
Completion date: 2021-11-30
Sponsor: EndoVigilant Inc
Conditions: Colon Polyp; Colon Neoplasm; Colon Adenoma
Interventions: EndoVigilant Software
Locations: Pacific Gastroenterology Endoscopy Center; Naugatuck Valley Surgical Center; Greenbelt Endoscopy Center; Dr. Satya Kastuar Gastroenterology Practice
Countries: United States; United States; United States; United States
Enrollment: 769
Age range: 45 Years - None
Sex: ALL

Summary:
EndoVigilant software device augments existing colonoscopy procedure video in real-time by highlighting colon polyps and mucosal abnormalities. It is intended to assist gastroenterologists in detection of adenomas and serrated polyps. The device is an adjunctive tool and is not intended to replace physicians' decision making related to detection, diagnosis or treatment.

This study with an adaptive design measures the clinical benefit (increase in detection of adenomatous and serrated polyps) and increased risk (increased extraction of non-adenomas) during standard colonoscopy procedures when EndoVigilant software device is used.

Eligibility criteria:
Inclusion Criteria:

1. Patient is 45 years old or older.
2. Patient is presenting for colon cancer screening or low-risk surveillance colonoscopy. Low risk surveillance is defined as the patient qualifying for a colonoscopy surveillance interval of 5 years based on US Multi-Society Task Force 2020 Guidelines (i.e., up to 4 tubular adenomas \<1cm, up to 4 sessile serrated polyps \<1cm on most recent colonoscopy).
3. Informed consent document for participating in the study signed by patient or patient's guardian.

Exclusion Criteria:

1. Patient has known history of inflammatory bowel disease (ulcerative colitis, Crohn's disease).
2. Patient has known or suspected polyposis or hereditary colon cancer syndrome (such as familial adenomatous polyposis, hereditary nonpolyposis colorectal cancer).
3. Patient referred for diagnostic colonoscopy to work up symptoms (such as abdominal pain or bleeding), laboratory abnormalities (such as anemia) or imaging findings (such as masses found on imaging).
4. Patient has history of colon resection (not including appendectomy).

Outcomes:
Average Number of Adenomas Per Colonoscopy; Average Number of Adenomas Per Extraction
Artificial Intelligence-Based Prediction of Lymph Node Metastasis and Nodal Station Involvement in Gastric Cancer Using Preoperative Multimodal Imaging and Pathology Data
Trial ID: NCT06957678
Status: ENROLLING_BY_INVITATION
Study type: OBSERVATIONAL
Start date: 2025-01-01
Completion date: 2025-12-31
Sponsor: Qun Zhao
Collaborators: Renmin Hospital of Wuhan University; Nanjing University School of Medicine; Baoding First Central Hospital; Hengshui People's Hospital; No.1 Hospital of Shijiazhuang City; The Second Affiliated Hospital of Xingtai Medical College
Conditions: Gastric Cancer Adenocarcinoma Metastatic; Lymph Node Metastasis; Artificial Intelligence (AI) in Diagnosis
Interventions: Artificial Intelligence-Based Predictive Model for Lymph Node Metastasis
Locations: the Fourth Hospital of Hebei Medical University
Countries: China
Enrollment: 1200
Age range: 18 Years - 80 Years
Sex: ALL

Summary:
This study aims to develop and validate an artificial intelligence (AI) system that can predict whether lymph node metastasis has occurred in patients with gastric cancer before surgery. Using preoperative imaging and pathology data, the AI models will not only predict if metastasis is present but also identify which specific lymph node stations or individual lymph nodes are involved. All lymph nodes will be carefully removed during surgery and examined one by one with detailed pathological methods to ensure accurate diagnosis. The goal is to improve the accuracy of lymph node assessment and assist doctors in making better treatment decisions.

Eligibility criteria:
Inclusion Criteria:

* Age 18 years or older

Histologically confirmed gastric adenocarcinoma

Scheduled for curative-intent gastrectomy with lymphadenectomy

Completed preoperative imaging with contrast-enhanced CT or MRI

Available preoperative biopsy pathology report

Able and willing to provide written informed consent

Exclusion Criteria:

* Evidence of distant metastasis on preoperative imaging

Prior chemotherapy, radiotherapy, or major abdominal surgery

Severe comorbidities contraindicating surgery

Incomplete or poor-quality preoperative imaging or pathology data

Pregnancy or lactation

Outcomes:
Diagnostic Accuracy of the AI Model in Predicting Presence of Lymph Node Metastasis in Gastric Cancer