moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Machine-Learning Prediction and Reducing Overdoses With EHR Nudges
Trial ID: NCT06806163
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-03-10
Completion date: 2026-05-31
Sponsor: University of Pittsburgh
Collaborators: National Institute on Drug Abuse (NIDA)
Conditions: Opioid Overdose; Opioid Use; Opioid Use Disorder; Opioids
Interventions: EHR-Embedded Elevated-Risk Flag; EHR-Embedded Elevated-Risk Flag with Behavioral Nudges; Usual Care
Locations: University of Pittsburgh
Countries: United States
Enrollment: 1350
Age range: 18 Years - None
Sex: ALL

Summary:
The goal of this cluster randomized clinical trial is to test a clinician-targeted behavioral nudge intervention in the Electronic Health Record (EHR) for patients who are identified by a machine-learning based risk prediction model as having an elevated risk for an opioid overdose.

The clinical trial will evaluate the effectiveness of providing a flag in the EHR to identify individuals at elevated risk with and without behavioral nudges/best practice alerts (BPAs) as compared to usual care by primary care clinicians.

The primary goals of the study are to improve opioid prescribing safety and reduce overdose risk.

Eligibility criteria:
Inclusion Criteria:

* Received an opioid prescription within the past year
* Age 18 years or older at the time of the opioid prescription
* At least one visit to an internal medicine or family care practice within the past year

Exclusion Criteria:

* Diagnosis of malignant cancer within the past year
* Enrollment in hospice care

Outcomes:
Prescribing Practices Composite Score
Model Study on Cervical Cancer Screening Strategies and Risk Prediction
Trial ID: NCT06204133
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2023-11-01
Completion date: 2024-06-30
Sponsor: Fujian Maternity and Child Health Hospital
Conditions: Cervical Cancer Screening; Risk Assessment; Artificial Intelligence; Machine Learning
Interventions: Artificial intelligence model building
Locations: Fujian Maternity and Child Health Hospital; Ningde maternal and child health hospital; Gansu Provincial Maternity and Child-care Hospital; Shunde Women's and Children's Hospital of Guangdong Medical University; Shenzhen Maternal and Child Health Hospital; Guiyang maternal and child health care hospital; Hubei Maternal and Child Health Hospital
Countries: China; China; China; China; China; China; China
Enrollment: 1112846
Age range: 25 Years - 64 Years
Sex: FEMALE

Summary:
By collecting non-image medical data of women undergoing cervical screening in multiple centers in China, including age, HPV infection status, HPV infection type, TCT results, and colposcopy biopsy pathology results, a multi-source heterogeneous cervical lesion collaborative research big data platform was established. Based on artificial intelligence (AI) machine learning, cervical lesion screening features are refined, a multi-modal cervical cancer intelligent screening prediction and risk triage model is constructed, and its clinical application value is preliminarily explored.

Eligibility criteria:
Inclusion Criteria:

* Age 25-64 years old;
* There was no history of precancerous lesions or cervical cancer;
* No previous cervical surgery or cervical removal;

Exclusion Criteria:

* HPV test results are not available;
* Pregnant or lactating women;
* There is a serious immune system disease, and the disease is active;

Outcomes:
Cervical histopathology; colposcopy
Community-based Lung Cancer Screening With Low-dose CT in China (CLUS Study) Version 2.0
Trial ID: NCT03975504
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2018-08-01
Completion date: 2023-07-31
Sponsor: Shanghai Chest Hospital
Conditions: Lung Neoplasms
Interventions: Low Dose Computed Tomography; artificial intelligence (AI); autofluorescence imaging (AFI)
Locations: Shanghai Chest hospital
Countries: China
Enrollment: 6000
Age range: 45 Years - 75 Years
Sex: ALL

Summary:
Our previous study, china lung cancer screening study version 1.0, had proven that LDCT led to a 74.1% increase in detecting early-stage lung cancer compare to usual care (NCT02898441). The present one arm study is performed to evaluate the efficacy of new techniques in improving the implementation of lung cancer screening and validate our previous findings. 6000 high-risk subjects (age 45-75) were recruited to take LDCT screening. (Baseline + 2 biennial repeated LDCT screening). Follow-up for lung cancer incidence, lung cancer mortality and overall mortality was performed. Blood samples were stored in a Biobank. Management of positive screening test was carried out by a pre-specified protocol.

Eligibility criteria:
Inclusion Criteria:

* Eligible participants were those aged 45-75 years, and with either of the following risk factors:

  1. history of cigarette smoking ≥ 20 pack-years, and, if former smokers, had quit within the previous 15 years;
  2. malignant tumors history in immediate family members;
  3. personal cancer history;
  4. professional exposure to carcinogens;
  5. long term exposure to second-hand smoke;
  6. long term exposure to cooking oil fumes.

Exclusion Criteria:

1. Had a CT scan of chest within last 12 months
2. History of any cancer within 5 years

Outcomes:
The mortality rate of lung cancer; The attendance rate of high-risk individuals; Diagnostic accuracy rate of lung cancer
Tumor-Infiltrating Lymphocytes in Endometrial Cancer: Correlations With Tumor Grade, Stage, and Subcellular CD133, WNT-1, and mTOR Expression
Trial ID: NCT06976333
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2024-12-01
Completion date: 2025-05-08
Sponsor: Jagiellonian University
Conditions: Endometrium Cancer; Tumor Infiltration
Interventions: Tumor-infliltrating lymphocyte (TIL) percentage; Grey level co-occurrence matrix (GLCM); Fractal dimension (FD)
Locations: Jagiellonian University
Countries: Poland
Enrollment: 52
Age range: 18 Years - None
Sex: FEMALE

Summary:
Endometrial cancer (EC) is a leading cancer among women globally. The tumor microenvironment in EC is characterized by complex interactions between cancer cells and immune components. Among these proteins, CD133, WNT-1, and mTOR have emerged as key molecular markers with potential prognostic and therapeutic implications in EC. Understanding the association between these molecular alterations and the immune contexture of EC can provide valuable insights into EC biology and lead to the identification of novel therapeutic targets.

In this study, the spatial organization of tumor-infiltrating lymphocytes (TILs) in EC and their correlations with tumor grade, stage, and subcellular CD133, WNT-1, and mTOR expression were investigated. Artificial intelligence-assisted image analysis was performed to quantify TIL metrics, including TIL percentage, grey level co-occurrence matrix (GLCM M1 and M2) parameters, and fractal dimension (FD).

Eligibility criteria:
Inclusion Criteria:

* confirmed diagnosis of EC
* adequate quality of archival material
* absence of prior neoadjuvant treatment
* complete medical documentation

Exclusion Criteria:

* none

Outcomes:
TIL percentage [%]; GLCM M1 (×10⁶); FD; GLCM M2 (×10³)
"Contribution of Metagenomic Analysis of Faecal Microbiota Combined With Artificial Intelligence for the Prediction of Colorectal Cancer Risk; Study in Patients Scheduled for Colonoscopy".
Trial ID: NCT06174233
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2024-06-26
Completion date: 2028-06-25
Sponsor: Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement
Collaborators: Assistance Publique - Hôpitaux de Paris
Conditions: Patients Aged 50-75 and Requiring Colonoscopy
Locations: Hopital Avicenne, Centre de Recherche sur Volontaire
Countries: France
Enrollment: 2500
Age range: 50 Years - 75 Years
Sex: ALL

Summary:
Colorectal cancer (CRC) is the second most common type of cancer worldwide. The European Union recommends national CRC screening for people aged between 50 and 74. Generally, an immunological test called FIT (Fecal Immunochemical Test), based on the quantitative detection of human haemoglobin, is performed on a stool sample. If the haemoglobin level is above the recommended threshold, a colonoscopy is recommended to detect colorectal lesions. Recent studies have identified potential microbiota signatures associated with colon cancer. In this study, we will analyze the microbiota of a population aged 50 to 75 years undergoing colonoscopy as part of routine care in order to confirm the presence of microbiota signatures associated with the presence of adenomas, advanced adenomas and CRC.

Eligibility criteria:
Inclusion Criteria:

* Between the ages of 50 and 75
* Requiring colonoscopy
* Able to provide a faecal sample and to perform a FIT test prior to colonoscopy
* Without known inflammatory bowel disease (chronic inflammatory bowel disease (IBD), chronic ulcerative colitis (CUC) and Crohn's disease (CD))
* No medical condition that, in the opinion of the investigator, should preclude inclusion in the study
* Not having participated in any other clinical research study in which an investigational drug has been administered within 60 days prior to and including the date of informed consent or is likely to be administered during the period of colonoscopy
* Participation agreement signed electronically

Exclusion Criteria:

* Persons not living in France (declaratory);
* Persons subject to a protective measure, in particular guardianship or trusteeship, or unable to express their consent (declarative);
* Persons who have had a colectomy (declaratory);
* Person with a digestive stoma (declaration);
* Person who did not sign the consent form;
* Person who did not answer the entry questionnaire;
* Person who did not send a compliant stool sample;
* Antibiotics taken in the 3 months prior to inclusion (self-report);
* Person aged under 50 or over 75.

Outcomes:
Measurement of true-negative, true-positive, false-negative and false-positive rates after colonoscopy, associated with the new test based on metagenomic analysis and clinical data.
Dietary Intake and Eating Habits During the COVID-19 Pandemic
Trial ID: NCT04407533
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2020-05-25
Completion date: 2022-08-31
Sponsor: McGill University
Conditions: COVID-19
Interventions: Diet tracking and survey
Locations: McGill University - School of Human Nutrition
Countries: Canada
Enrollment: 2020
Age range: 18 Years - None
Sex: ALL

Summary:
The COVID-19 pandemic affects eating habits, diet quality and physical activity differently among individuals; it is unclear how these behaviours will evolve as the pandemic continues. In this observational study, dietary intake will be frequently collected using an artificial intelligence (AI)-enhanced mobile application combined with real-time analysis, and lifestyle behaviours from online questionnaires, to provide timely, relevant data for public health decision making.

Eligibility criteria:
Inclusion Criteria:

* Men and women aged 18 years and over, living in Canada
* Owning a smartphone and having access to internet
* Able to read and understand English or French

Exclusion Criteria:

* Any active and uncontrolled acute disease that interferes with usual food intake
* Being hospitalized
* Pregnancy or lactation

Outcomes:
Dietary intake
Implementation of High Definition Screening Using Handheld Imaging and Digital Health Technologies Within a Learning Health System to Identify Cardiovascular Disease at the Point-of-care: The ASE-INNOVATE Program
Trial ID: NCT03713333
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2018-10-20
Completion date: 2020-03-20
Sponsor: Scripps Health
Collaborators: West Virginia University
Conditions: Cardiovascular Diseases; Hypertension; Heart Failure; Atrial Fibrillation; Metabolic Syndrome; Genetic Disease
Interventions: Digital Health Device Diagnostics
Locations: West Virgina University
Countries: United States
Enrollment: 374
Age range: 18 Years - None
Sex: ALL

Summary:
The need for new models of integrated care that can improve the efficiency of healthcare and reduce the costs are key priorities for health systems across the United States. Treatment costs for patients with at least one chronic medical or cardiovascular condition make up over 4-trillion dollars in spending on healthcare, with estimations of a population prevalence of 100-million affected individuals within the next decade. Therefore, the management of chronic conditions requires innovative and new implementation methods that improve outcomes, reduce costs, and increase healthcare efficiencies. Digital health, the use of mobile computing and communication technologies as an integral new models of care is seen as one potential solution. Despite the potential applications, there is limited data to support that new technologies improve healthcare outcomes. To do so requires; 1) robust methods to determine the impact of new technologies on healthcare outcomes and costs; and 2) evaluative mechanisms for how new devices are integrated into patient care. In this regard, the proposed clinical trial aims to advance the investigator's knowledge and to demonstrate the pragmatic utilization of new technologies within a learning healthcare system providing services to high-risk patient populations.

Eligibility criteria:
Inclusion Criteria:

* All participants of the ASE 2018 Outreach Event who are at least 18 years old who are referred for a cardiac evaluation

Exclusion Criteria:

* Those not willing to consent

Outcomes:
Health Economic Outcomes
Prevention and Screening Intervention Project - Towards Elimination of Cervical Cancer: Validation of Artificial Intelligence as Decision Support System in VIA
Trial ID: NCT06452004
Status: ACTIVE_NOT_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2022-10-01
Completion date: 2025-12-31
Sponsor: University Medical Center Groningen
Collaborators: Uganda Cancer Institute; Female Cancer Foundation; International Centre for Diarrhoeal Disease Research, Bangladesh; Manipal Academy for Higher Education; Friendship Bangladesh
Conditions: HPV Infection; Cervix Cancer; Cervical Dysplasia
Interventions: Validation of AI-DSS in Bangladesh; Validation of AI-DSS in Uganda; Validation of AI-DSS in India
Locations: Mpasana
Countries: Uganda
Enrollment: 600
Age range: 30 Years - 60 Years
Sex: FEMALE

Summary:
The research project is a component of another research project that applies the protocol of the World Health Organization for screening of cervical cancer, with testing of high-risk Human Papilloma Virus (hrHPV) as first screening. In the screen, triage and treat approach women who tested positive for hrHPV are undergoing Visual Inspection of the cervix with Acetic Acid (VIA). This procedure is applied in Uganda, India and Bangladesh. However the quality of VIA by lower-trained staff is variable because Low and Middle Income Countries face limited numbers of qualified health care professionals. Artificial intelligence (AI) might be a solution to improve consistency of VIA assessment. This research validates an AI decision support system (AI-DSS) under field conditions.

Eligibility criteria:
Inclusion Criteria:

* Female in eligible age group
* Women who tested HPV positive and are eligible for VIA
* Ability to give informed consent and participate in study

Exclusion Criteria:

* Clinical signs of cervical carcinoma
* Menstruation or other vaginal blood loss
* HPV negative women

Outcomes:
Accuracy, sensitivity, specificity of AI-DSS
Short-term and Long-term Effects of Skin-to-skin Contact in Healthy Term Infants
Trial ID: NCT06777524
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-02-10
Completion date: 2030-01-31
Sponsor: Korea University Anam Hospital
Conditions: Skin-to-skin Contact
Interventions: skin-to-skin contact education
Locations: Korea University Anam Hospital NICU; Korea University Anam Hospital NICU
Countries: South Korea; South Korea
Enrollment: 130
Age range: 1 Hour - 72 Hours
Sex: ALL

Summary:
This is a prospective, double-blind, randomized controlled clinical trial study to investigate short- and long-term effects of mother-infant skin-to-skin contact in healthy term infants, in order to provide supporting data for emphasizing mother-infant skin contact and family-centered care in South Korea.

Eligibility criteria:
Inclusion Criteria:

* Babies born at Korea University Anam Hospital
* Term infants (37 weeks or more)
* Infants with a birth weight of 2,500g or more
* Healthy infants with an Apgar score of 7 or higher at 5 minutes
* Mothers and infants without severe acute complications, such as requiring intensive care treatment immediately after delivery

Exclusion Criteria:

* Preterm infants born at 37 weeks of less gestation
* Low birth weight infants weighing less than 2500g at birth
* Mothers under the age of 20
* Unmarried mothers
* Multiple pregnancies (twins or triplets)
* Parents do not consent to participation in the study

Outcomes:
Percentage of any breast feeding (combined with formula and/or solid or not)
Multicenter Randomized Controlled Trial of a Multimodal AI Agent for Ophthalmology Clinical Decision Support
Trial ID: NCT07401459
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-03-01
Completion date: 2026-12-31
Sponsor: The Hong Kong Polytechnic University
Conditions: Ophthalmology; Large Language Models; AI Agent; Eye Disease; Retinal Disease
Interventions: EyeAgent AI system
Locations: The Hong Kong Polytechnic University
Countries: China
Enrollment: 300
Age range: 6 Years - 75 Years
Sex: ALL

Summary:
This study is a multicenter randomized controlled trial evaluating the effectiveness and safety of EyeAgent, a multimodal artificial intelligence (AI) agent designed to assist ophthalmologists in clinical decision-making. Participants will be recruited from ophthalmology clinics and hospitals in Hong Kong and mainland China. The AI agent acts as a digital co-pilot, analyzing patient images and clinical history to provide diagnostic and management recommendations. The trial aims to determine whether the use of the AI agent improves diagnostic accuracy, treatment decision-making performance, report generation, workflow efficiency, and user satisfaction compared to standard clinical practice.

Eligibility criteria:
Inclusion Criteria:

1. Outpatient participants aged 6 to 75 years.
2. Participants who undergo ophthalmic examinations for medical purposes during the study period.
3. Participants who can produce clear ophthalmic images in both eyes.
4. Agree to participate in this study with written informed consent:

   1. Participants aged 18 years or older provide their own consent.
   2. Participants aged 6-17 years require consent from a parent or legal guardian.

Exclusion Criteria:

1. Participants who are reluctant to participate in this study.
2. Participants presenting with acute or emergency ocular conditions requiring immediate intervention.
3. Participants with poor quality of ophthalmic images, including blurriness, artifacts, underexposure, or overexposure.
4. Other unsuitable reasons determined by the evaluators.

Outcomes:
Diagnostic accuracy rate; Rate of inappropriate treatment decisions
AI-Enabled Electrocardiogram-Guided Guideline-Directed Medical Therapy on Incident Left Ventricular Dysfunction: A Target Trial Emulation Study
Trial ID: NCT07355023
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-01-01
Completion date: 2026-01-15
Sponsor: Tri-Service General Hospital
Conditions: Left Ventricular (LV) Systolic Dysfunction
Interventions: Guideline-directed medical therapies
Locations: Tri-Service General Hospital
Countries: Taiwan
Enrollment: 5000
Age range: 18 Years - None
Sex: ALL

Summary:
This multicenter retrospective study evaluates whether artificial intelligence-enabled electrocardiography (AI-ECG) can identify individuals at high risk for left ventricular dysfunction and whether targeted guideline-directed medical therapy can mitigate subsequent risk. Using a large multicenter cohort of patients with preserved left ventricular systolic function, the investigators applied an AI-ECG-based risk stratification approach and emulated a target trial to examine the association between guideline-directed therapies and the risk of incident left ventricular functional decline.

Eligibility criteria:
Inclusion Criteria:

* With an ECG followed by an echocardiogram within a 90-day interval
* With preserved left ventricular ejection fraction (LVEF ≥ 50%)

Exclusion Criteria:

* missing essential variables or ECG lead data
* any prior LVEF \< 50%
* loss to follow-up or death during the 90-day assessment window

Outcomes:
Rate of left ventricular ejection fraction (LVEF) ≤40%
Artificial Intelligence to Optimize Early-stage Hepatocellular CarcinomaTreatment Based on Multi-modal Image
Trial ID: NCT05627297
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2022-12-01
Completion date: 2025-11-01
Sponsor: The Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School
Conditions: Hepatocellular Carcinoma
Interventions: CEUS and CEMRI
Locations: Nanjing Drum Tower Hospital
Countries: China
Enrollment: 200
Age range: None - None
Sex: ALL

Summary:
This study intends to establish two prognostic models based on contrast-enhanced ultrasound (CEUS) and dynamic enhanced magnetic resonance (DE-MRI) multimodal images: prognostic model of liver cancer patients after hepatectomy and prognostic model of liver cancer patients after radiofrequency ablation. Combined with artificial intelligence imaging omics, traditional imaging omics and clinical information, to predict and compare the prognosis of two different treatment methods for early liver cancer, so as to realize the individual selection of treatment methods for early liver cancer patients

Eligibility criteria:
Inclusion Criteria:

* Primary single hepatocellular carcinoma confirmed by histology or cytology, with a maximum diameter ≤5.0 cm;

  * Good liver function, Child-Pugh grade A;

    * No serious dysfunction of heart, lung, kidney and other important organs ④ Liver resection or radiofrequency ablation was performed in our hospital, and the study protocol and follow-up procedure were followed.

Exclusion Criteria:

* Patients who cannot perform DCE-MRI examination due to metal implants in the body ② Patients who had received other treatment before DCE-MRI or CEUS;

  * Invisible lesions, diffuse lesions or poor DCE-MRI/CEUS image quality under CEUS; ④ Drug abuse, clinical or psychological or social factors that make informed consent or study implementation affected.

Outcomes:
Good/Poor overall survival