moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Validation and Extension Study of the Predictive Model for Intraoperative Hemodynamic Instability in Pheochromocytoma/Paraganglioma Resection - a Single-center, Prospective, Observational Cohort Study
Trial ID: NCT07516275
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2026-04
Completion date: 2028-02
Sponsor: Peking Union Medical College Hospital
Conditions: Pheochromocytoma; Paraganglioma; Hemodynamic Instability; Intraoperative Complications
Enrollment: 200
Age range: 18 Years - 99 Years
Sex: ALL

Summary:
The study will enroll patients scheduled for PPGL removal surgery at Peking Union Medical College Hospital. Before surgery, researchers will use a 6-variable model to predict the patient's risk of experiencing severe blood pressure swings during the operation. During surgery, a real-time early warning tool will be tested for its ability to accurately predict blood pressure changes 60 seconds in advance. The study will also explore the value of continuous glucose monitoring (CGM) in understanding blood pressure fluctuations and evaluate the performance of an artificial intelligence (AI) agent for preoperative anesthesia assessment, comparing its accuracy, consistency, and efficiency against that of human anesthesiologists.

Participation involves no changes to the patient's standard surgical or medical care. It includes collecting clinical data, wearing a CGM sensor from the day before to the day after surgery, and having the preoperative assessment performed by both the AI agent and anesthesiologists.

Eligibility criteria:
Inclusion Criteria:

* Age ≥ 18 years.
* Diagnosed with pheochromocytoma/paraganglioma by imaging and laboratory tests and scheduled for elective resection.
* Planned intraoperative continuous invasive arterial pressure monitoring.
* Willing and able to provide written informed consent.
* Able to comply with preoperative CGM monitoring, AI agent assessment, and postoperative follow-up.

Exclusion Criteria:

* Intraoperative hemodynamic data missing ≥20%.
* Postoperative histopathology excludes PPGL diagnosis.
* Cardiac paraganglioma or metastatic PPGL.
* Severe cardiac disease (e.g., severe valvular disease, severe heart failure) that could independently cause intraoperative HDI.
* Pregnancy or breastfeeding.
* Mental illness, cognitive impairment, or communication barriers that prevent compliance with study procedures.
* Refusal to undergo CGM monitoring or AI agent assessment.
* Major illness (e.g., acute myocardial infarction, stroke, severe infection) within 3 months prior to surgery.
* Severe hepatic or renal insufficiency (Child-Pugh class C, eGFR \<30 ml/min/1.73 m²).

Outcomes:
Predictive Model Discrimination (AUROC)
Detection of Reduced Left Ventricular Ejection Fraction With Three-Lead ECG Using Artificial Intelligence
Trial ID: NCT07270692
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-08-01
Completion date: 2026-11-01
Sponsor: Eko Devices, Inc.
Conditions: Low Ejection Fraction
Interventions: Eko digital stethoscopes
Locations: P. D. Hinduja Hospital and Medical Research Centre
Countries: India
Enrollment: 500
Age range: 18 Years - None
Sex: ALL

Summary:
The main objectives of this study are to train and evaluate an algorithm that predicts whether an individual has an ejection fraction ≤ 40%, using heart sounds and a 3-lead ECG as inputs, as well as determine the impact of gender, age, and race on algorithm performance.

Eligibility criteria:
Inclusion Criteria:

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

Exclusion Criteria:

* Unwilling or unable to provide informed consent
* Patients who are hospitalized

Outcomes:
Primary Objective: Collection of Heart Sounds and 3-lead ECG Recordings to Train Algorithm
Eyecontrol coMmunication Platform for dEliRium manaGemEnt in Intensive Care Units (EMERGE) : A Multicenter Randomized Controlled Trial
Trial ID: NCT06029244
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-02-25
Completion date: 2025-11-18
Sponsor: Beth Israel Deaconess Medical Center
Collaborators: BIRD (Israel-United States Binational Industrial Research and Development) Foundation- Funding agency; Rabin Medical Center; Assuta Medical Center; Eyefree Assisting Communication Ltd
Conditions: Delirium; Delirium in Old Age; Critical Illness; Intensive Care Unit Delirium
Interventions: EyeControl-Pro; Control
Locations: Beth Israel Deaconess Medical Center; Assuta Ashdod Medical Center; Rabin Medical Center
Countries: United States; Israel; Israel
Enrollment: 160
Age range: 50 Years - None
Sex: ALL

Summary:
The purpose of this research is to investigate whether addition of the EyeControl-Pro platform as an adjunct to standard guideline-based intensive care unit management of critically ill patients is effective in reducing delirium incidence and severity.

Eligibility criteria:
Inclusion Criteria:

* Mechanically ventilated patients aged \>=50 years
* RASS score of -3 to +1 and
* Anticipated to require \>=24 hours of mechanical ventilation

Exclusion Criteria:

* Not expected to survive \>=24 hours
* Have limitations in care (Do Not Resuscitate, or comfort-focused care orders)
* Receiving paralytic neuromuscular blocking agent (NMBA) infusion or anticipated need for NMBA use
* Have advanced dementia or cognitive impairment including post-concussive syndrome.
* Have severe uncorrected psychiatric disorders.
* Have uncorrected hearing or visual impairment.
* Acute or subacute neurological disorder hindering communication or ability to participate in CAM ICU assessments
* Enrolled in a clinical trial which prohibits co-enrollment.
* Incarcerated
* Have no identified legally appointed representative (LAR)
* Are unable to communicate in the predominant local language (English at US site and English/Hebrew/Arabic/Russian in Israel)
* Refusal of treating clinical team.

Outcomes:
Prevalence of any delirium occurrence defined as CAM ICU 7 score (>=2) during the first 7 days in ICU post-randomization or ICU discharge/death (whichever is sooner)
A Medico-Economic Evaluation of a Telehealth Platform for Elective Outpatient Surgeries: a Trial Study
Trial ID: NCT04948632
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2022-01-21
Completion date: 2024-02-28
Sponsor: Centre hospitalier de l'Université de Montréal (CHUM)
Collaborators: HEC Montreal
Conditions: Surgery--Complications; Patient Satisfaction; Quality of Life
Interventions: Complete LeoMed application; Basic LeoMed application
Locations: Centre hospitalier de l'Université de Montréal (CHUM)
Countries: Canada
Enrollment: 1000
Age range: 18 Years - None
Sex: ALL

Summary:
The main objective of this study is to carry out a medico-economic evaluation of a new platform for outpatient surgical care, by comparing the effectiveness and utility of the deployment of this trajectory on patients and the health system to a control group.

Eligibility criteria:
Inclusion Criteria:

* Patients over 18 years old undergoing elective outpatient surgery under general or regional anesthesia
* Internet access for the patient or their caregiver from home
* Written, oral and spoken comprehension of French or English

Exclusion Criteria:

* Patient's or caregiver's inability to learn and use digital technologies
* Conversion of outpatient to inpatient hospitalization on the day of surgery
* Patient's refusal.

Outcomes:
Cost-effectiveness analysis of the LeoMed® telecare platform
Epidemiologic Liver Outcomes Retrospective Study to Confirm The Prognostic Value of the FibroNest Digital Pathology Fibrosis Biomarker (Ph-FCS) in Patients With MASLD (DPAILO-1)
Trial ID: NCT06344364
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2025-09-01
Completion date: 2026-03-15
Sponsor: PharmaNest, Inc
Collaborators: Chinese University of Hong Kong; University of Seville; Fundacio Clinic Barcelona; Sorbonne University
Conditions: Metabolic Dysfunction-associated Steatotic Liver Disease
Interventions: Digital Pathology FibroNest Phenotypic Fibrosis Composite Score (Ph-FCS)
Locations: The Chinese University of Hong Kong; Fundació de Recerca Clinic Barcelona; University of Seville
Countries: Hong Kong; Spain; Spain
Enrollment: 1241
Age range: 18 Years - None
Sex: ALL

Summary:
The aim of this multi-center, retrospective epidemiologic study is to confirm the prognostic performance of the Digital Pathology (DP) FibroNest Phenotypic Fibrosis Composite Score (Ph-FCS), derived from standard digital pathology liver biopsy images, in predicting clinical hepatic decompensation events in patients with metabolic dysfunction-associated steatohepatitis (MASH).

Eligibility criteria:
Inclusion Criteria:

* Adult pts ( \>=18 years old) with MASLD defined histologically.
* Liver biopsy with fibrosis stains available for digitization or already digitized.
* Clinical follow-up \>1 year available recording liver-related outcomes either through hospitalization ICD-10 codes or through clinical observation

Exclusion Criteria:

* Liver diseases other than MASLD Note: no exclusion based on bariatric surgery, significant weight loss or enrollment in NASH clinical studies, but data is collected for data analysis / competing effects (see data analysis plan)

Outcomes:
Performance of Hepatic Decompensation Event predictive value of the FibroNest Ph-FCS
Safety Incident Reporting System for Students During Their Clinical Internship (SAFEST)
Trial ID: NCT05350345
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2022-04-09
Completion date: 2023-11-08
Sponsor: Fundación para el Fomento de la Investigación Sanitaria y Biomédica de la Comunitat Valenciana
Conditions: Patient Safety
Interventions: Root-cause analysis
Locations: Grupo ATENEA
Countries: Spain
Enrollment: 105
Age range: 16 Years - None
Sex: ALL

Summary:
Patient safety is a priority in Europe. The World Health Organization's World Alliance for Patient Safety has included incident reporting systems as indispensable tools for patient safety. These systems are widespread in healthcare facilities throughout Europe. While in some countries trainees in healthcare disciplines are able to report incidents, in others they are unable to do so. In many cases, they do not have adequate information about the reporting systems, there is low motivation to report, or there is a fear that reporting may lead to problems in their studies.

Until now, there have been no interventions designed and validated to achieve the objective of promoting incident reporting among students of health disciplines. Nor there were tools for these students to participate in the analysis of the causes of these incidents and in the identification of barriers to prevent their recurrence.

Researchers currently have tools from the digital world (artificial intelligence and gamification) whose application in this area can be useful for improving patient safety.

In this context, the investigators have developed an incident notification system aimed at students and trainees in order to familiarize this group with the notification process and thus contribute to improving patient safety. Students will be encouraged to participate with the incentive of earning Miguel Hernández University nanocourse credits or direct prizes. Once the notification is made, their role will be to evaluate and give feedback to notifications made by other peers, so they will get points. After finishing, those students with the most points will be rewarded with the prizes mentioned above.

Eligibility criteria:
Inclusion Criteria:

* Students with Clinical Practices.

Exclusion Criteria:

\-

Outcomes:
Root-cause analysis
The Recovering Strategy on Forward Head Posture in Chinese Adolescents With Explainable Artificial Intelligence: Tai Chi and Manual Therapy
Trial ID: NCT05804539
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2021-03-01
Completion date: 2021-07-20
Sponsor: Northeast Normal University
Conditions: Forward Head Posture
Interventions: Manual therapy; Tai Chi; Tai Chi and Manual therapy
Locations: Chinese Center of Exercise Epidemiology
Countries: China
Enrollment: 65
Age range: 10 Years - 19 Years
Sex: ALL

Summary:
Forward head posture (FHP) is a prevalent deformity that can cause various health issues in adolescents. The programs combining manual therapy (MT) and stability exercises (SE) have shown better effectiveness than stability exercises and home exercises in recovering FHP. However, the effectiveness of the therapy program consisting of Tai Chi and MT for recovering FHP remains unclear. Therefore, this study aimed to investigate the effects of Tai Chi with MT on FHP recovery. Meanwhile, with a particular focus on personalized medicine, we utilized explainable artificial intelligence (XAI) to predict if individuals would reverse to healthy posture based on different interventions.

Eligibility criteria:
Inclusion Criteria:

1. who was diagnosed as FHP;
2. whose age was between ten to nineteen years old.

Exclusion Criteria:

1. who had nervous system disease or musculoskeletal system disease;
2. who had congenital scoliosis or congenital malformation disease;
3. who participated in other sports training;
4. who participated in other training program in the past of three months before the beginning of the study.

Outcomes:
Using photogrammetry to calculate the cranial vertebral angle of participants before and after the intervention.; Using universal goniometer to calculate the cervical range of motion of participants before and after the intervention.
Predicting Immunotherapy Response and Survival of Liver Cancer Patients Using Artificial Intelligence and Radiomics (Radiology-AI-Liver)
Trial ID: NCT07059936
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-07
Completion date: 2026-09
Sponsor: Union Hospital, Tongji Medical College, Huazhong University of Science and Technology
Conditions: Hepatocellular Carcinoma (HCC)
Locations: Union Hospital，Tongji Medical College，Huazhong University of Science and Technology
Countries: China
Enrollment: 350
Age range: 18 Years - None
Sex: ALL

Summary:
CT imaging data and MR imaging data from liver cancer (hepatocellular carcinoma) patients prior to the initiation of immunotherapy (and possibly also after treatment) will be collected. The processing pipeline includes automatic tumor segmentation, radiomics feature extraction, feature selection, and construction of a classification model.

Eligibility criteria:
Inclusion Criteria:

1. Patients were treated for hepatocellular carcinoma in Wuhan Union Hospital from July 2025 to July 2026;
2. Aged \> 18 years old;
3. At least one CT scan or MR scan before treatment;
4. Tissue biopsy pathological examination confirmed the diagnosis of the above tumors.

Exclusion criteria:

1. Poor image quality;
2. Incomplete clinical data or loss of follow-up;
3. Presence of another primary malignancy other than liver cancer;
4. Unclear pathological diagnosis.

Outcomes:
disease-free survival; Progression-free survival; overall survival
Developement of Prognosis Predictive Precision Medicine Based on a Digital Twin Reflecting the Motor Patterns in Stroke Patients
Trial ID: NCT06035198
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2023-10-05
Completion date: 2025-12-31
Sponsor: Seyoung Shin
Conditions: Stroke
Interventions: Stroke
Locations: Bundang CHA Medical Center
Countries: South Korea
Enrollment: 1192
Age range: 20 Years - None
Sex: ALL

Summary:
The purpose of this study is to realize customized precision medicine for stroke patients and the visual digitization of patient medical information required in the era of digital transformation.

This study will collect large-scale, high-quality clinical data and daily life exercise pattern data covering the subacute to chronic stages of stroke patients. Based on this, we will use artificial intelligence analysis technology to predict the functional status of stroke patients in the chronic stage after their disability is fixed. We aim to develop the next generation of personalized digital twins by creating a prognostic prediction model.

Eligibility criteria:
Inclusion Criteria:

* Adults over 20 years of age with a history of stroke onset between 2018.01.01 to 2023.07.31.
* Patients who exist rehabilitation medical evaluation after stroke onset
* Patients who voluntarily decide to participate in this study

Exclusion Criteria:

* Those who have severe internal diseases such as unstable conditions of the cardiovascular system, digestive system, respiratory system, endocrine system, etc. and are in poor overall condition.
* Those who have impaired ability to consent (MMSE score less than 10 points) and not accompanied by a care-giver.
* In any other cases where the researcher determines that participation in this study is not appropriate.

Outcomes:
Modified Barthel Index (MBI)
Towards Bridging Generalists to Subspecialists With Large Language Models
Trial ID: NCT06935253
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-01-10
Completion date: 2025-12
Sponsor: Stanford University
Collaborators: Google LLC.
Conditions: Hypertrophic Cardiomyopathy (HCM); Cardiomyopathy; Genetic Disease; Cardiology
Interventions: Large Language Model
Locations: Stanford
Countries: United States
Enrollment: 12
Age range: 18 Years - None
Sex: ALL

Summary:
This study evaluates the impact of large language models (LLMs) versus traditional decision support tools on clinical decision-making in cardiology. General cardiologists will be randomized to manage real patient cases from a cardiovascular genetic cardiomyopathy clinic, with or without AI assistance. Each case will be assessed by two cardiologists, and their responses will be graded by blinded subspecialty experts using a standardized evaluation rubric.

Eligibility criteria:
Inclusion Criteria:

* Board certified or board eligible Cardiologist.

Exclusion Criteria:

* Not currently practicing clinically

Outcomes:
Subspecialist Preference
Artificial Intelligence Based Rapid Identification of ST-elevation Myocardial Infarction Using Electrocardiogram (ARISE)
Trial ID: NCT05118009
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2022-05-01
Completion date: 2023-05-31
Sponsor: National Defense Medical Center, Taiwan
Conditions: Myocardial Infarction, Acute
Interventions: AI-enabled ECG-based Screening Tool
Locations: National Defense Medical Center
Countries: Taiwan
Enrollment: 43176
Age range: 18 Years - None
Sex: ALL

Summary:
This is a randomized controlled trial (RCT) to test a novel artificial intelligence (AI)-enabled electrocardiogram (ECG)-based supporting tool for improving the diagnosis and management of ST-elevation myocardial infarction.

Eligibility criteria:
Inclusion Criteria:

* Patients in emergency department or inpatient department.
* Patients recieved at least 1 ECG examination.

Exclusion Criteria:

* The patients recieved ECG at the period of inactive AI-ECG system.

Outcomes:
E2C (ECG to coronary angiography) time
Clinical Research on Navigation and Quality Control System of Pancreatic Ultrasound Endoscopy Based on Deep Learning
Trial ID: NCT05792267
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2021-01-01
Completion date: 2025-06-30
Sponsor: The Third Xiangya Hospital of Central South University
Conditions: Pancreatic Disease
Interventions: artificial intelligence system
Locations: The Third Xiangya Hospital of Central South University
Countries: China
Enrollment: 200
Age range: 18 Years - 80 Years
Sex: ALL

Summary:
The goal of this clinical trial is to develop and verify the auxiliary role of the artificial intelligence system in pancreatic ultrasound endoscopic scanning.The main questions it aims to answer are as follows: 1.The comparison of the image recognition accuracy between the artificial intelligence system and the ultrasound endoscopist; 2. Whether the artificial intelligence system can improve the efficiency of the pancreatic scanning for the ultrasound endoscopist. Participants will undergo pancreatic EUS with or without the assistance of the artificial intelligence system.

Eligibility criteria:
Inclusion Criteria:

* 1\. Age ≥18 years old, \<80 years old 2.Patients who need endoscopic ultrasonography of pancreas; 3. Agree to participate in this study and sign the informed consent form.

Exclusion Criteria:

* Subjects who meet any of the following criteria cannot be selected for this trial:

First. The patient's physical condition does not meet the requirements of conventional endoscopic ultrasonography:

1. Poor physical condition, including hemoglobin ≤8.0g/dl, severe cardiopulmonary insufficiency, etc.
2. Anesthesia assessment failed
3. Pregnancy or breastfeeding
4. In the acute stage of chemical and corrosive injury, it is very easy to cause perforation
5. Recent acute coronary syndrome or clinically unstable ischemic heart attack
6. Heart disease patients with right-to-left shunt, patients with severe pulmonary hypertension (pulmonary artery pressure\> 90mmHg),patients with uncontrolled systemic hypertension and patients with adult respiratory distress syndrome.

Second. Disagree to participate in this study.

Third. There are other problems that do not meet the requirements of this research or that affect the results of the research:

1. Pancreatic disease has undergone surgery or radiotherapy and chemotherapy beforehand;
2. Mental illness, drug addiction, inability to express themselves or other diseases that may affect follow-up.

Outcomes:
Accuracy; The completeness for standard station scanning
Electrocardiogram-Based Deep Learning for Time-Resolved Prediction of Heart Failure With Reduced Ejection Fraction: A Multinational Study
Trial ID: NCT07519434
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2014-02
Completion date: 2023-12
Sponsor: Shanghai Zhongshan Hospital
Collaborators: Shanghai 10th People's Hospital; Beth Israel Deaconess Medical Center
Conditions: Heart Failure; Heart Failure With Reduced Ejection Fraction; Left Ventricular Dysfunction; Cardiac Remodeling
Locations: [None]
Countries: China
Enrollment: 286709
Age range: 18 Years - None
Sex: ALL

Summary:
This study aims to develop and validate a deep learning-based electrocardiogram (ECG) model for predicting the future risk of heart failure with reduced ejection fraction (HFrEF). The model is trained using raw 12-lead ECG data and generates individualized, time-resolved risk estimates over a 5-year period.

Data are obtained from multiple cohorts, including Zhongshan Hospital, Shanghai Tenth People's Hospital, and Beth Israel Deaconess Medical Center, representing diverse populations across China and the United States. The model is designed to identify individuals at elevated risk of developing HFrEF before the onset of overt clinical disease.

The performance of the model is evaluated using multiple complementary metrics, including discrimination, calibration, and clinical utility. In addition, interpretability analyses are conducted to explore the physiological relevance of ECG features associated with predicted risk.

This study seeks to provide an accessible and scalable tool for early risk stratification of heart failure, with the potential to support timely clinical decision-making and improve patient outcomes.

Eligibility criteria:
Inclusion Criteria:

Adults aged ≥18 years Underwent standard 12-lead electrocardiography (ECG) Underwent transthoracic echocardiography with available LVEF measurement Availability of paired ECG-echocardiography data Data available for follow-up assessment

Exclusion Criteria:

Missing or incomplete ECG or echocardiography data Poor-quality ECG recordings unsuitable for analysis Missing key clinical variables required for model development

Outcomes:
Incident Heart Failure With Reduced Ejection Fraction (HFrEF)