moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Clinical Study of Dynamic Contrast-enhanced Magnetic Resonance Imaging Combined With IVIM-DWI for Early Prediction of Chemosensitivity in Liver Metastasis of Breast Cance
Trial ID: NCT05550090
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2022-09-16
Completion date: 2025-12-30
Sponsor: Zhejiang Cancer Hospital
Conditions: Metastatic Breast Cancer in the Liver
Interventions: Chemotherapy
Locations: Zhejiang Cancer Hospital
Countries: China
Enrollment: 40
Age range: 18 Years - None
Sex: ALL

Summary:
The purpose of this study is to further use DCE-MRI and ivim-dwi to predict the chemotherapy sensitivity of liver metastasis of breast cancer at an early stage, and to predict the treatment response of tumor at an early stage by using the changes of their functional parameters, and to compare the efficacy and advantages of IVIM functional parameters and DCE-MRI parameters in predicting the efficacy.To explore the efficacy of "perfusion" and "diffusion" parameters of magnetic resonance imaging as "biomarkers" for early prediction of chemotherapy response and prognosis of breast cancer patients with liver metastasis. And to provide guidance for optimizing the clinical treatment scheme of breast cancer patients with liver metastasis.

At the same time, this study will use the method of artificial intelligence to deeply mine the images, and further find out the indicators for early prediction of the therapeutic effect of liver metastasis of breast cancer.

Eligibility criteria:
Inclusion Criteria:

1. The primary lesion was pathologically confirmed to be breast cancer, and the patients diagnosed by two imaging methods or pathologically confirmed to be liver metastasis of breast cancer had at least one liver metastasis with the longest diameter ≥ 10mm;
2. No second primary malignant tumor;
3. ECOG score, 0-2 ;
4. The organ function is normal and can tolerate chemotherapy and other anti-tumor treatments;
5. The patient plans to receive systemic chemotherapy or systemic anti-tumor treatment, and the whole process of cooperative treatment. The patient has good compliance with the planned treatment and follow-up, can understand the research process of this study and sign a written informed consent；
6. Contraception during the study period and within 6 months after treatment, non lactation period.

Exclusion Criteria:

1. For patients contraindicated to MR examination, such as built-in metal instruments and allergy to contrast agents;
2. The patient had diffuse liver metastasis or the number of liver metastatic tumors was more than 5;
3. Patients who cannot complete 2 cycles of chemotherapy or systemic anti-tumor treatment;
4. Unable to cooperate with follow-up；
5. Patients who are not suitable for the study according to the investigator.

Outcomes:
Correlation between DCE-MRI parameters combined with IVIM parameters and short-term efficacy of chemotherapy in patients with liver metastasis of breast cancer; Correlation between DCE-MRI parameters combined with IVIM parameters and long-term efficacy of chemotherapy in patients with liver metastasis of breast cancer
E-CLAIR: Efficiency and Cost-effectiveness of Artificial Intelligence Based Diabetic Retinopathy Screening in Flanders
Trial ID: NCT05391659
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2021-06-17
Completion date: 2022-12-01
Sponsor: Universitaire Ziekenhuizen KU Leuven
Conditions: Diabetic Retinopathy
Interventions: deep learning; remote grading of fundus images; gold standard
Locations: UZA; ZNA; AZ sint Jan; AZ Turnhout
Countries: Belgium; Belgium; Belgium; Belgium
Enrollment: 1200
Age range: 18 Years - None
Sex: ALL

Summary:
To evaluate the efficiency and cost-effectiveness of an artificial intelligence based diabetic retinopathy screening program in Flanders

Eligibility criteria:
Inclusion Criteria:

* Diagnosis of diabetes mellitus
* Age \> 18 years old
* Patient is capable of giving informed consent
* Fluent in written and oral Dutch, or interpreter present

Exclusion Criteria:

* \- History of treatment for diabetic retinopathy or diabetic macular edema (laser or intravitreal injections)
* Participant is contraindicated for imaging by fundus imaging systems used in the study

Outcomes:
sensitivity; specificity; AUC
Artificial Intelligence-Based Assessment of Endosseous Lesions: A Prospective Clinical Study
Trial ID: NCT07505485
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-04-01
Completion date: 2026-05-01
Sponsor: University of Bari Aldo Moro
Conditions: Maxillary Cyst; Mandibular Cyst
Interventions: AI assisted Evaluation
Locations: University of Bari Aldo Moro; Dr. Giuseppe D'Albis
Countries: Italy; Italy
Enrollment: 10
Age range: 18 Years - 80 Years
Sex: ALL

Summary:
Despite these advances, CBCT interpretation remains largely qualitative and dependent on the clinician's experience. Conventional evaluation is based on two-dimensional slices and linear measurements, which may underestimate lesion complexity and spatial distribution.

Recent developments in Artificial Intelligence in Medicine have introduced automated image segmentation tools capable of identifying lesion boundaries and calculating volumetric data. These technologies allow a transition from subjective assessment to objective, reproducible quantification.

The potential clinical advantages include:

* Objective measurement of lesion size (volume in mm³)
* Improved surgical planning
* Enhanced prediction of anatomical involvement
* Reduction of diagnostic errors
* Standardization of follow-up and outcome assessment Therefore, the aim of the present study was to evaluate the clinical impact of AI-based segmentation and volumetric analysis of endosseous lesions compared to conventional CBCT interpretation.

Eligibility criteria:
Inclusion Criteria:

* Good health according to the System of the American Society of Anesthesiology
* Aged older than 18 years
* No general medical contraindication for surgery

Exclusion Criteria:

* Smoking more than 15 cigarettes a day

  * Pregnancy
  * Acute infections

Outcomes:
Time required for CBCT interpretation (minutes)
Generative AI Simulation for Diagnostic Communication in Type 2 Diabetes: A Randomized Controlled Trial (DIALOGUE-DM2)
Trial ID: NCT07252193
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-09-22
Completion date: 2025-12-20
Sponsor: Universidad Nacional Autonoma de Mexico
Conditions: Type 2 Diabetes Mellitus; Medical Education; Diagnostic Communication; Artificial Intelligence Simulation
Interventions: AI-Based Simulation Training (DIALOGUE-DM2); Traditional Training
Locations: Universidad Nacional Autónoma de México, Faculty of Higher Studies Iztacala (FES Iztacala)
Countries: Mexico
Enrollment: 120
Age range: 18 Years - 29 Years
Sex: ALL

Summary:
This randomized controlled trial evaluates the effectiveness of a generative artificial intelligence (AI)-based simulation program in improving diagnostic communication skills among medical students. The study is conducted at the Faculty of Higher Studies Iztacala, National Autonomous University of Mexico (UNAM).

A total of 120 medical students are randomized to either an intervention group using the DIALOGUE-DM2 AI simulation platform or a control group following traditional educational methods. Participants complete a pre-test, receive training according to group assignment, and then undergo a post-test evaluation.

The primary outcome is improvement in diagnostic communication skills, measured by standardized patient scenarios and validated rubrics. Secondary outcomes include self-reported confidence, communication domains, and inter-rater agreement between faculty evaluators and AI scoring.

This trial aims to provide high-quality evidence on the potential of generative AI to enhance communication training in medical education, specifically in the context of type 2 diabetes diagnosis.

Eligibility criteria:
Inclusion Criteria:

* Medical students currently enrolled in the Faculty of Medicine (Medical Surgeon Program), UNAM-FES Iztacala.
* Age between 18 and 30 years.
* Able to provide informed consent.
* Willing to participate in all study phases (pre-test, intervention, post-test).

Exclusion Criteria:

* Prior participation in the DIALOGUE pilot study.
* Previous formal training in diagnostic communication beyond the standard medical curriculum.
* Incomplete availability for scheduled sessions.
* Refusal or inability to provide informed consent.

Outcomes:
Change in Diagnostic Communication Performance Score
Age-Related Macular Degeneration Benchmark Imaging Dataset (ABID)
Trial ID: NCT06924021
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-05-12
Completion date: 2026-11
Sponsor: University of Wisconsin, Madison
Conditions: Age-Related Macular Degeneration
Interventions: Retinal Imaging
Locations: Bergstrom Eye Research LLC; Erie Retina Research; Retina Foundation of the Southwest; Valley Retina Institute; Retina Consultants of Texas (Houston); University of Utah - John A. Moran Eye Center; University Station Eye Care Clinic; Charles Research Center; Adelaide Eye and Retina Centre; Centre Hospialier Intercommunal de Paradis (Centre Monticelli Paradis); University of Bonn - Department of Ophthalmology; Sankara Nethralaya, Medical Research Foundation; Aravind Eye Hospital; Aga Khan University
Countries: United States; United States; United States; United States; United States; United States; United States; Argentina; Australia; France; Germany; India; India; Pakistan
Enrollment: 1000
Age range: 50 Years - None
Sex: ALL

Summary:
1000 participants from up to 25 international locations who are at least 50 years old with either healthy eyes or a diagnosis of Age-Related Macular Degeneration (AMD) will be consented to provide images of their eyes for a new dataset. This dataset is an important step in developing an Artificial Intelligence (AI) based screening tool for AMD.

Eligibility criteria:
Inclusion Criteria:

* Participants greater than or equal to 50 years of age at time of signing Informed Consent Form
* Willing to comply with all study procedures and sign the Informed Consent Form (ICF)
* Individuals with normal healthy eyes or diagnosed with any stage of Age-Related Macular Degeneration (treatment naïve patients with early, intermediate, or late AMD). The diagnosis and eligibility review will be confirmed by Central Reading Center.

Exclusion Criteria:

* Therapeutic treatment for any type of AMD, in either eye. Supplements, such as AREDS2 formula, are allowed.
* Unable to acquire adequate quality images, as evaluated by the Central Reading Center
* Severe vision loss requiring urgent surgery
* Contraindicated for acquiring retinal images due to narrow anterior chamber angles or hypersensitivity to light
* A systemic or ocular condition that in the opinion of the Investigator would preclude participation in the study
* Unwilling to sign informed consent form
* Currently or previously enrolled in an interventional AMD clinical trial

Outcomes:
AMD Classification by Participant Count; Geographic Location by Participant Count
A Multicenter, Cluster-randomized, Controlled Study Evaluating the Effectiveness and Safety of Artificial Intelligence(AI)-Assisted Medical Treatment Decision Support System Compared to Conventional Care in HFrEF Patients
Trial ID: NCT07380555
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-01-30
Completion date: 2027-12-31
Sponsor: Shanghai Zhongshan Hospital
Conditions: Heart Failure With Reduced Ejection Fraction (HF-rEF)
Interventions: artificial intelligence (AI) decision support system
Locations: Shanghai Zhongshan Hospital
Countries: China
Enrollment: 1200
Age range: 18 Years - None
Sex: ALL

Summary:
This clinical trial aims to find out if using an artificial intelligence (AI) decision support system can help doctors better manage medications for people with heart failure with reduced ejection fraction (HFrEF). The main goal is to see if this approach can lower the chance of patients being hospitalized for heart failure or dying from heart-related causes compared to standard care.

About 1,200 participants from 60 hospitals will take part in this study. The hospitals, not the individual patients, are randomly assigned to one of two groups:

AI-Guided Group: Doctors at these hospitals will receive medication recommendations from an AI system based on patient health data.

Standard Care Group: Doctors at these hospitals will manage medications as they normally would, without AI assistance.

All participants will have regular check-ups, both remotely every 30 days and in-person at 3, 6, and 12 months. The study will last about 12 months for each participant.

Eligibility criteria:
Inclusion Criteria:

1. Age ≥18 years old;
2. Patients diagnosed with HF in hospital and discharged on medical orders;
3. LVEF≤40% detected by echocardiography within 1 month before enrollment;
4. Signed informed consent is obtained.

Exclusion Criteria:

1. Patients who are unable or unsuitable for follow-up.
2. Patients with poor compliance, presenting a risk of switching study groups.

Outcomes:
Incidence of the composite endpoint of heart failure rehospitalization or cardiovascular death
Log2LoseAI: Reinforcement Learning to Create a Framework for Personalizing Financial Incentives
Trial ID: NCT07225426
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-02-04
Completion date: 2027-02-28
Sponsor: University of Utah
Collaborators: National Heart, Lung, and Blood Institute (NHLBI)
Conditions: Obesity & Overweight
Interventions: personalized financial incentives
Locations: University of Utah
Countries: United States
Enrollment: 80
Age range: 18 Years - None
Sex: ALL

Summary:
The purpose of this study is to determine the feasibility of providing personalized incentives for dietary self-monitoring and/or interim weight loss to people enrolled in a weight-loss program

Eligibility criteria:
Inclusion Criteria:

1. Willing to attend virtual baseline and follow-up data collection visits
2. At least 18 years of age
3. Verified obesity as defined as a BMI ≥30 kg/m2
4. Agree to attend 13 biweekly group classes (virtual) delivered by a registered dietitian
5. Agree to review study materials between classes
6. Regular access to an unshared smart phone
7. Reliable access to internet
8. Able to speak and read English
9. Desire to lose weight
10. Able to connect to a video conference call using a smartphone, tablet or computer with a webcam and microphone
11. Ability to download and use Fitbit app daily
12. Have or be willing to create a Gmail address
13. Physical ability to stand on a scale without support

Exclusion Criteria:

1. Weight loss of at least 10lbs in the month prior to screening
2. Weight \> 380lbs
3. Currently enrolled or enrolled in the previous month in a clinical, research, or community program focusing on lifestyle change that could affect weight
4. New user of weight loss medication
5. Pregnant, lactating or planning on becoming pregnant during the study
6. History of bariatric procedure or planning to have bariatric procedure in the study timeframe
7. Residing in a nursing home, skilled nursing facility or assisted living facility
8. Impaired hearing
9. Significant dementia, drug or alcohol abuse, or unstable psychiatric illness (e.g., schizophrenia, psychosis)
10. Current treatment for cancer or being treated for cancer (besides basal cell carcinoma or squamous cell) in the last 6 months
11. Use of insulin, sulfonylureas, or meglitinides due to increased risk for hypoglycemia
12. Diuretic medication doses higher than hydrochlorothiazide or chlorthalidone 25 mg daily, furosemide 40 mg daily, torsemide 20 mg daily, bumetanide 1 mg daily, or any use of metolazone; use of potassium-sparing diuretics is acceptable
13. Unstable heart disease in the 6 months prior to screening
14. Chronic kidney disease at stage 4 or higher
15. Exertional chest pain
16. Pain, fainting, or other conditions that prohibit mild/moderate exercise
17. History of ascites requiring paracentesis
18. Inducing vomiting to prevent weight gain or counteract the effects of eating an average of 1 time per week or more in the past 3 months\*
19. Not suitable for study participation due to other reasons at the discretion of the investigators

Outcomes:
Screening-to-enrollment ratio; Retention for outcomes
Application of Artificial Intelligence in Colorectal Polypectomy
Trial ID: NCT04485715
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2020-07-21
Completion date: 2021-09-21
Sponsor: Yanqing Li
Conditions: Colonic Polyp
Interventions: AI system
Locations: Department of Gastroenterology, Qilu Hospital, Shandong University
Countries: China
Enrollment: 1000
Age range: 18 Years - 80 Years
Sex: ALL

Summary:
The aim of this study was to assess the performance of AI system in polypectomy

Eligibility criteria:
Inclusion Criteria:

* to undergo colorectal polypectomy;
* agree to give written informed consent

Exclusion Criteria:

* patients with the contraindications to colorectal polypectomy;
* patients with a history of inflammatory bowel disease (IBD), CRC, colorectal surgery;
* patients with high suspicion of polyposis syndromes, IBD and typical advanced CRC;
* patients refused to participate in the trial;
* the procedure cannot be completed due to stenosis, obstruction, huge occupying lesions, or solid stool;
* the procedure have to be terminated due to complications of anaesthesia.

Outcomes:
polypectomy time
Prospective Collection of Clinical and Imaging Data, Radiotherapy Planning and Biomaterials for Integrated Analysis of Therapy Response and Resistance and Detection of Molecular Targets in Adolescents and Young Adults With CNS Tumors
Trial ID: NCT06323408
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-05
Completion date: 2026-03
Sponsor: University Hospital Regensburg
Conditions: Glioma, Malignant; Embryonal Tumor; Medulloblastoma; Ependymoma
Interventions: Tissue, blood, cerebrospinal-spinal fluid
Enrollment: 72
Age range: 15 Years - 39 Years
Sex: ALL

Summary:
The treatment of adolescents and young adults (AYA, 15 to 39 years) with malignant intra-axial CNS parenchymal tumors such as IDH-mutated gliomas, medulloblastomas and ependymomas is still not curative in all cases. The tumor biology and clinical needs to diagnose and treat these tumors are comparable across all age groups, so an integrated treatment environment overseen by adult and pediatric neuro-oncology specialists seems promising to leverage synergisms and advance diagnostic and therapeutic development in these tumors. A comprehensive, prospective and integrated biomaterial and imaging-based pipeline for the multi-faceted evaluation of AYAs has not yet been established for AYA patients with brain tumors in Germany. Current diagnostic platforms neglect the integrative processing of data from MRI and FET-PET imaging, radiotherapy plans, tumor tissue, liquid biopsies and clinical data as well as prognostic markers. A prospective AYA pipeline can therefore enable a better understanding of the aforementioned high-risk CNS malignancies and promises clinical advances for AYA patients and the clinical and scientific research landscape.

Eligibility criteria:
Inclusion Criteria:

1. first diagnosis of high-risk CNS tumors including glioma IDHmutated, medulloblastoma, ependymoma and other intra-axial primary brain tumors
2. adolescents and young adults (AYA; 15 to 39 years)
3. Karnofsky status of 60 or higher
4. resection, extended biopsy or stereotactic biopsy with sufficient tissue volume
5. multimodal therapy planned at the discretion of the treating specialist
6. expected survival time of at least 6 months
7. informed consent from the patient or the patient's legal guardian prior to the first study procedure
8. complete material collection expected.

Exclusion Criteria:

1. synchronous independent tumor disease other than basal cell carcinoma and carcinoma in situ
2. participation in interventional clinical trial except in standard arms of interventional clinical trials.

Outcomes:
AI processing of MRI fand PET or diagnosis; AI processing of MRI and PET for evaluation of response and relapse; Quality control analysis in radiotherapy; Analysis of response in liquid biopsies; Target molecules in tissue and liquid biopsies; Evaluation of progression free survival
Advancing Lung Cancer Screening: Artificial Intelligence, Multimodal Imaging and Cutting-Edge Technologies for Early Detection and Characterization
Trial ID: NCT06531343
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-10-30
Completion date: 2026-08-30
Sponsor: IRCCS San Raffaele
Collaborators: Fondazione Policlinico Universitario Campus Bio-Medico; University of Calabria; University of Salerno
Conditions: Lung Cancer Screening
Interventions: LDCT scan and simultaneous [18F]FDG PET/CT on new-generation long axial field of view scanner; LDTC only
Locations: Irccs San Raffaele
Countries: Italy
Enrollment: 334
Age range: 50 Years - None
Sex: ALL

Summary:
Currently available screening programmes for lung cancer are limited by many challenges including low diagnostic accuracy, radiation exposure and high costs. New technologies in PET/CT scanners can allow cheaper and more sensitive exams with low radiation exposure. AI can be used to denoise LDCT to enhance the accuracy of imaging tests and build riskassessment models. This project aims to develop a new approach exploiting both these revolutionary advancements to bridge the existing gap in lung cancer screening. Patients in a high-risk population will be enrolled into two different cohorts undergoing LDCT scan and simultaneous \[18F\]FDG PET/CT on new-generation long axial field of view scanner (UO1) or screening with low LDCT only (UO2). AI will assist in image enhancement and interpretation and will develop a personalised risk-model guiding the following steps of clinical management, significantly improving early diagnosis of lung cancer, reducing mortality and healthcare costs.

Eligibility criteria:
Inclusion Criteria:

* Age \> 50 years
* PLCOm2012 risk prediction \> 4%
* Be willing to adhere to the study intervention through \[18F\]FDG PET/CT or LDCT imaging
* Signed written informed consent form

Exclusion Criteria:

* Blood glucose levels \>200 mg/dl,
* Ongoing pregnancy and breastfeeding
* Unwillingness to participate,
* Previous diagnosis of lung cancer,
* Previous CT scan within the last 24 months
* Concomitant severe clinical conditions and any condition that preclude the feasibility of the study

Outcomes:
The primary objective is to assess the diagnostic accuracy of low-dose [18F]FDG PET/CT for screening in high-risk individuals or lung cancer Time Frame: