moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Crossover Randomized Controlled Trial to Evaluate the Efficacy and Usability of Artificial Intelligence (ChatGPT) for Health Sciences Students (AIHSS)
Trial ID: NCT05963802
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2023-09-01
Completion date: 2023-12-01
Sponsor: Carleton University
Collaborators: Université de Montréal
Conditions: Educational Activities; AI (Artificial Intelligence)
Interventions: Artificial Intelligence (ChatGPT) Group
Locations: Carleton University
Countries: Canada
Enrollment: 26
Age range: 18 Years - None
Sex: ALL

Summary:
Crossover Randomized Control trial, in which subjects are randomly assigned to one of two groups: one (ChatGPT) receiving the intervention that is being tested, and the control group receiving usual online resources.

Eligibility criteria:
Inclusion Criteria:

* Health Science students
* Must be registered for the course Chronic Health Conditions and Disability (HLTH 3503), Fall 2023.

Exclusion Criteria:

• Unable to provide informed consent

Outcomes:
System Usability Scale (SUS) scores are 0-100,The average SUS score is 68.
Augmented Bladder Tumor Detection Using the Bladder-Portable Artifact Detection System: A Multicentric Prospective Analytic Study Using Real Time Based Artificial Intelligence (IA).
Trial ID: NCT05415631
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2022-05-13
Completion date: 2029-05
Sponsor: Centre Hospitalier Universitaire, Amiens
Conditions: Bladder Cancer; Cystoscopy; Artificial Intelligence
Locations: Amiens University Hospital
Countries: France
Enrollment: 500
Age range: 18 Years - None
Sex: ALL

Summary:
Today the standard for the diagnosis and monitoring of bladder tumors is bladder endoscopy. The performance of this exam is not perfect. With this work, based on artificial intelligence, the investigators wish to combine endoscopy with a complementary diagnostic tool in order to improve patient care. The main objective will be to reduce diagnostic errors / wanderings in patients treated or followed for bladder tumors, by imposing a new standard of diagnostic bladder mapping (high PPV and VPN, high precision)(primary purpose diagnostic). The secondary objective will be to homogenize and systematize the descriptive part of the lesions, and to use AI to better characterize tumor aggressiveness. The final objective being to validate a new precision tool (diagnostic companion) essential for developing and standardizing the therapeutic management of bladder tumors (correcting inter-observer heterogeneity).

In this project, video frame will be first extracted from our dataset of cystoscopy videos hosted in in the Next Cloud Recherche. Selected medical image will be segmented and analyzed using our pre-trained CNN model with a feature detection algorithm to obtain features.

Data will be analyzed on both patient and lesion levels. The study will assess the Bladder-PAD accuracy on the detection of bladder tumors, and its ability to predict tumor risk of recurrence and progression.

Eligibility criteria:
Inclusion Criteria:

* unifocal primary or recurrent suspected bladder cancer with tumor size less or equal than 3 cm
* multifocal primary or recurrent suspected bladder cancer less or equal than 5 lesions and with tumor size less or equal than 3 cm.

Exclusion Criteria:

* Evidence of more than 5 tumors or more than 3 cm
* computed tomography/cystoscopy suspect of muscle-invasive bladder cancer (cT2 or higher)
* computed tomography/magnetic resonance evidence of distant metastases (lymphatic or organic)
* Exclusion criteria will include gross hematuria and bacillus Calmette-Guerin (BCG) treatment or chemotherapy within 3 months of inclusion
* An exception will be made if patients had received only a single course of chemotherapy immediately following TUR
* Patients objecting to the use of their data in the context of research.

Outcomes:
Tumor detection rate of white light cystoscopy; Tumor detection rate of Bladder-PAD cystoscopy; Tumor false detection rate of white light cystoscopy; Tumor false detection rate of Bladder-PAD cystoscopy
Diagnosis of Parkinson's Disease and Prediction of Progression Using Diffusion Weighted Imaging
Trial ID: NCT02837172
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2014-09-25
Completion date: 2019-04-14
Sponsor: University of Alabama at Birmingham
Conditions: Parkinson's Disease
Interventions: Diffusion Weighted Imaging (DWI)
Locations: University of Alabama at Birmingham
Countries: United States
Enrollment: 58
Age range: 19 Years - None
Sex: ALL

Summary:
This project will evaluate the utility of diffusion tensor imaging (DTI) as an adjunctive method to improve early diagnosis of Parkinson's disease (PD). Two populations will be evaluated in this study: 1) Individuals with uncertain PD diagnosis who receive a DaTscan, and 2) individuals with well characterized PD and healthy controls, drawn from the fully enrolled Parkinson's Progression Markers Initiative (PPMI) PD and control cohorts.

Eligibility criteria:
Inclusion Criteria:

* Patients 19 and older
* Referred for clinical DaTscan for possible PD
* Controls from the PPMI dataset.

Exclusion Criteria:

* Pregnant women
* Participants that cannot participate in MRI (metallic artifact or other contraindication(s) to MRI at 3T)

Outcomes:
MRI and DAT scan: Accuracy of diagnosis of Parkinson's disease in a clinically relevant population
AI-Driven Early Detection of Cachexia in Pancreatic Cancer and Feasibility of Diet and Exercise Interventions
Trial ID: NCT07249775
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-10-08
Completion date: 2026-10
Sponsor: H. Lee Moffitt Cancer Center and Research Institute
Collaborators: Florida Cancer Innovation Fund
Conditions: Pancreatic Cancer; Cancer Cachexia
Interventions: Baseline Lifestyle and Symptom Assessment; Extended Lifestyle Monitoring; Feasibility Survey
Locations: Moffitt Cancer Center
Countries: United States
Enrollment: 120
Age range: 18 Years - None
Sex: ALL

Summary:
This observational study aims to (1) validate a multimodal artificial intelligence (AI) model for early detection of cancer-associated cachexia in pancreatic cancer patients and (2) assess the feasibility and acceptability of diet and exercise interventions for cachexia management. The study will use retrospective data from the Florida Pancreas Collaborative and prospective data from newly diagnosed patients at Moffitt Cancer Center.

Eligibility criteria:
Inclusion Criteria:

* Age ≥ 18 years
* Histologically or cytologically confirmed diagnosis of pancreatic exocrine malignancy (e.g.pancreatic ductal adenocarcinoma, acinar cell carcinoma, pancreatic adenosquamous carcinoma, etc.) or clinical features highly suggestive of such malignancy with diagnostic confirmation anticipated during the screening or early study period.
* Presentation consistent with resectable, borderline resectable, local, or locally advanced, or metastatic disease
* May or may not have had surgery
* Treatment plan likely includes systemic therapy for pancreatic cancer
* ECOG performance status 0-2
* Able to tolerate oral intake and not currently receiving enteral or parenteral nutrition
* Able to read and speak English
* Able to provide written informed consent

Exclusion Criteria:

* Pregnant or breastfeeding at the time of enrollment
* Current use of tube feeding (enteral) or total parenteral nutrition
* Presence of ascites or other findings suggestive of decompensated disease
* Evidence or history of bowel obstruction or any gastrointestinal condition that may limit food intake
* Severe or uncontrolled psychiatric illness (e.g., psychosis, dementia) that would interfere with participation
* Presence of other uncontrolled intercurrent illnesses (e.g., infection, heart failure, liver failure) that may interfere with protocol adherence

Outcomes:
AI Model Performance; Survey Completion Rate; Wearable Adherence; Quality of Life Change; Physical Activity Change
Retrospective Observational Study With Real-World Data to Assess Demographic and Clinicopathological Profiles, and Management of Breast Cancer Patients With Positive, Low or Negative Expression of HER2 in Spain
Trial ID: NCT05217381
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2022-02-22
Completion date: 2023-09-20
Sponsor: MedSIR
Collaborators: Daiichi Sankyo; AstraZeneca
Conditions: Early Breast Cancer; Metastatic Breast Cancer; Locally Advanced Breast Cancer
Locations: Hospital del Mar; Hospital Sant Joan Despí - Moisès Broggi; Hospital Universitari Son Espases; Hospital Universitario 12 de Octubre; Hospital Universitario Fundación de Alcorcón; Clínica Universidad de Navarra (CUN); Hospital Universitario Marqués de Valdecilla
Countries: Spain; Spain; Spain; Spain; Spain; Spain; Spain
Enrollment: 18533
Age range: 18 Years - None
Sex: ALL

Summary:
This is a data-driven, retrospective, longitudinal, population- based, observational, multi-centered study using secondary data captured from congruent electronic health records (EHRs).

Eligibility criteria:
Inclusion Criteria:

1. Male or female patients who have at least 18 years of age at enrollment.
2. Patients with initial diagnosis of early BC or locally advanced BC or de novo mBC between the 1st of January 2005 and the 31st of December 2021
3. Pathologically documented BC for:

   * HER2 receptor expression with a validated assay according to American Society of Clinical Oncology - College of American Pathologists (ASCO/CAP) recommendations at the time of diagnosis. HER2 receptor expression can be: o HER2-positive expression: defined as immunohistochemistry (IHC) 3+ or concurrent IHC 2+ with in situ hybridization (ISH) positive or HER2-low expression: defined as IHC 2+ with ISH-negative or IHC 1+ with ISH-negative or untested) or HER2-negative expression: defined as IHC: 0.
   * Estrogen receptor \[ER\]- and/or progesterone receptor \[PgR\] with a validated assay according to ASCO/CAP guidelines at the time of diagnosis during early and/or advanced setting. ER/PgR expression can be: positive: ER or PgR ≥1% or negative: ER and PgR \<1%
4. Electronic Health Records (EHRs), with guaranteed data to meet requisites, about clinicopathological characteristics, type of surgery, treatment management, disease outcomes, and genomic profile. Centers that agree to participate must commit to include all subjects who meet the inclusion criteria, in order to reduce possible selection bias.

Exclusion criteria:

1. Medical charts at Hospital that cannot guarantee reliable and congruent EHRs.
2. If sufficient data cannot be obtained from EHRs.

Outcomes:
Prevalence of HER2 expression change between initial diagnosis versus any of the subsequent BC relapses
A Prospective Study Evaluating Ex-vivo Confocal Imaging of Fluorescent Tagged Monoclonal Antibodies and Proteomic Profiles of Biopsied Tissue to Predict Therapeutic Response Among Children and Adolescents With Inflammatory Bowel Disease
Trial ID: NCT07121920
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-09-01
Completion date: 2028-07-01
Sponsor: Cook Children's Health Care System
Conditions: IBD (Inflammatory Bowel Disease); IBD; IBD - Inflammatory Bowel Disease
Interventions: Confocal Laser Endomicroscopy
Locations: University of Texas at Arlington; Cook Children's Health Care System
Countries: United States; United States
Enrollment: 40
Age range: 2 Years - 21 Years
Sex: ALL

Summary:
This study aims to test the overall hypothesis that the membrane tissue binding capacity of cytokines in the biopsied tissue of patients with Inflammatory Bowel Disease (IBD) is predictive of/strongly correlated to clinical response/outcomes observed.

The key questions under investigation are:

Aim 1: To assess the fluorescent signal intensity at baseline (control antibody with control biopsy and control antibody with IBD biopsy).

Aim 2: To characterize the cellular landscape by surveying surface markers using bar-coded antibodies and performing gene expression profiling on every cell within inflamed tissue of patients with IBD.

Aim 3: Develop algorithm using artificial intelligence to predict responders versus non-responders and to further subclassify IBD patients using phenotype data.

Eligibility criteria:
Inclusion Criteria:

\- Patients must fall into one of the below categories: (i) with suspected and/or established diagnosis of IBD (ii) patients with IBD on treatment with biologics irrespective of treatment response (iii) patients who are diagnosed with IBD but treatment naïve to biologics. (iv) patients diagnosed with IBS and previous endoscopy results were negative for IBD who will serve as controls

Exclusion Criteria:

* Those with previous allergy to fluorescein
* Pregnant and breastfeeding patients

Outcomes:
Number of FITC tagged antibodies
Artificially Intelligent Robot (AIR) Support for Pediatric Asthma Education
Trial ID: NCT07387718
Status: ENROLLING_BY_INVITATION
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-02-10
Completion date: 2027-03
Sponsor: University of Miami
Conditions: Asthma in Children; Artificial Intelligence
Interventions: Standard Asthma Education; Artificially Intelligent Robot (AIR) Support Intervention
Locations: Batchelor Children's Research Institute
Countries: United States
Enrollment: 120
Age range: 3 Years - None
Sex: ALL

Summary:
The purpose of this prevention study is to evaluate the design and usability of a newly developed asthma education protocol with the Human Support Robot (HSR) for children with asthma.

Eligibility criteria:
Inclusion (Eligibility) Criteria for Caregivers or Parents:

* The participant must be at least 18 years old.
* The participant must be willing and able to participate.
* The participant can read English or Spanish and is able to fill out survey instruments by themselves or with assistance.
* The participant cares for a child with asthma.

Exclusion (Eligibility) Criteria for Caregivers or Parents:

* The participant is younger than 18 years old.
* The participant is unwilling to participate in the study.
* The participant is unable to complete survey instruments.
* The participant does not care for a child with asthma.

Inclusion (Eligibility) Criteria for Children:

* The participant must be at least 3 years old - 17 years old with asthma.
* The participant must assent to participation.
* The participant's guardian must have consented.

Exclusion (Eligibility) Criteria for Children:

* The participant is younger than 3 years old.
* The participant does not have asthma.
* The participant is unwilling to participate in the study.
* The participant's guardian did not consent.

Inclusion (Eligibility) Criteria for Healthcare Providers:

* The participant must be at least 18 years old.
* The participant must be willing and able to participate.
* The participant can read English and is able to fill out survey instruments by themselves.
* The participant must view a robotic education session.

Outcomes:
Feasibility of the Human Support Robot: Enrollment Rate; Feasibility of the Human Support Robot: Completion Rate; Feasibility of the Human Support Robot: Session Length; Feasibility of the Human Support Robot: Number of Technical Issues; Acceptability of the Human Support Robot or Acceptability of the Robot-Supported Education Session
Deep Learning for Detection of Pulmonary Hypertension and Reduced Left Ventricular Ejection Fraction Using a Combined Digital Stethoscope and Three-lead Electrocardiogram
Trial ID: NCT07087613
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-06-15
Completion date: 2026-08-31
Sponsor: Eko Devices, Inc.
Conditions: Hypertension, Pulmonary; Heart Failure With Reduced Ejection Fraction
Interventions: Eko CORE 500 Digital Stethoscope
Locations: Prairie Cardiovascular; Prairie Education & Research Cooperative; St Johns Hospital, Springfield
Countries: United States; United States; United States
Enrollment: 3850
Age range: 18 Years - None
Sex: ALL

Summary:
This is a prospective, observational study evaluating whether heart sounds (phonocardiograms) and three-lead electrocardiograms (ECGs) recorded using the Eko CORE 500 digital stethoscope can help detect pulmonary hypertension (PH) and low left ventricular ejection fraction (EF ≤ 40%). PH is a condition characterized by high blood pressure in the pulmonary arteries, which can lead to heart failure and carries significant risks if undiagnosed. Low EF, which indicates reduced pumping ability of the heart, is also associated with increased risk of severe cardiac events but can remain undetected because patients often have no symptoms or only nonspecific symptoms.

In this study, adults undergoing clinically indicated echocardiograms at outpatient sites will be invited to participate. Participants will complete a single study session lasting about 20 minutes, during which heart sounds and a three-lead ECG will be collected using the Eko CORE 500 device. If participants have had a clinical 12-lead ECG within 30 days of their echocardiogram, those data may also be used for analysis. The echocardiogram performed as part of routine care within seven days before or after the Eko CORE 500 recording will serve as the reference standard to confirm the presence or absence of PH and low EF.

Up to 3,850 participants may be enrolled across multiple sites to ensure that approximately 3,500 complete the study. The data collected will be used to develop and validate artificial intelligence (AI) algorithms that aim to detect PH and identify low EF, potentially enabling earlier and simpler screening for these conditions in clinical practice.

Eligibility criteria:
Inclusion Criteria:

* Adults aged 18 years and older
* Able and willing to provide informed consent
* Completed a clinical echocardiogram within 7 days before or after study procedures

Exclusion Criteria:

* Unwilling or unable to provide informed consent
* Patients who are hospitalized
* Patients undergoing echocardiography with a limited echocardiogram

Outcomes:
Sensitivity and specificity of the deep-learning algorithm for detecting pulmonary hypertension (PH)
Evaluation of Deep Learning Reconstruction Algorithms in Dual Low-dose CT Vascular Imaging
Trial ID: NCT06372756
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2023-06-01
Completion date: 2026-03
Sponsor: Hao Tang
Conditions: Deep Learning
Interventions: Deep learning image reconstruction
Locations: Tongji Hospital Affiliated to Tongji Medical College of Huazhong University of Science and Technology
Countries: China
Enrollment: 1200
Age range: 18 Years - 90 Years
Sex: ALL

Summary:
The goal of this observational study is to evaluate the impact of deep learning image reconstruction on the image quality and diagnostic performance of double low-dose CTA. The main question it aims to answer is to explore the feasibility of deep learning image reconstruction in double low-dose CTA.

Eligibility criteria:
Inclusion Criteria:

* Patients with head and neck CTA, coronary artery CTA, and abdominal CTA due to stroke, coronary heart disease and abdominal inflammatory disease, and abdominal tumors.

Exclusion Criteria:

* Age \<18 years, pregnancy, allergic reaction to iodine contrast agent, renal insufficiency, and severe hyperthyroidism.

Outcomes:
The specificity and sensitivity calculated through the optimal cutoff value of the receiver operating characteristic curve.
Study on the Application of Multi-omics in the Assessment of Efficacy and Prediction of Side Effects in Cervical Cancer
Trial ID: NCT06254729
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2024-02-16
Completion date: 2030-02-16
Sponsor: First Affiliated Hospital Xi'an Jiaotong University
Collaborators: Gansu Maternal and Child Health Hospital; Hanzhong Central Hospital
Conditions: Uterine Cervical Neoplasms
Interventions: Observational study
Enrollment: 4000
Age range: 18 Years - None
Sex: FEMALE

Summary:
The main objectives of this study are to construct a multi-omics-based prognostic and side-effect prediction model for cervical cancer based on pre-treatment imaging, digital pathology, genomics, proteomics, molecular biology, metabolomics, and intestinal flora characteristics data of cervical cancer patients, combined with patients' clinical information, to guide the precise treatment of cervical cancer patients; and to deeply excavate the characteristics related to recurrent cervical cancer based on time-series multi-omics data. Construct an artificial intelligence auxiliary model for dynamic monitoring of cervical cancer recurrence based on longitudinal multi-omics. To provide a real-time and timely tool for clinical early prediction, early identification, early diagnosis and early intervention of cervical cancer, to prolong the survival time and improve the quality of patients' survival.

1. To realize multi-omics feature extraction of cervical cancer patients before treatment, and build a prognosis and side-effect prediction model of cervical cancer to guide accurate treatment;
2. To make iterative, comprehensive, real-time assessment of the risk of recurrence of cervical cancer based on time-series multi-omics data, and to build an early warning model for early identification and early diagnosis of recurrent cervical cancer;
3. To establish a prognostic and side-effect prediction and risk dynamic assessment model for cervical cancer, to build an intelligent decision support system, to implement the application of prognostic and side-effect prediction and dynamic monitoring model, to further assist in the precise diagnosis and treatment of cervical cancer, and to provide an accurate prognostic tool for identifying, diagnosing, and intervening in cervical cancer during the follow-up process.

Eligibility criteria:
Inclusion Criteria:

* Pathology: patients with pathologically confirmed cervical cancer
* Location: primary tumor of the cervix

Exclusion Criteria:

* Patients with no prior radiation therapy
* Patients without treatment
* Patients without regular follow-up

Outcomes:
Number of Circulating Tumor Cell Count; Concentration of Alpha-fetoprotein (AFP); Concentration of Carcinoembryonic Antigen (CEA); Concentration of carbohydrate antigen 199 (CA199); Concentration of Squamous Epithelial Cell Carcinoma Antigen (SCC-Ag); Concentration of carbohydrate antigen 125(CA125); Count of Bacteria in urine; Count of bacteria in stool; 5-year overall survival rate of Participants; disease-free survival of Participants; progression-free survival of Participants