moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
PRE3 PRE-CUBE. PREdiction, PREvention, PREdisposition: Applications in Neurology and Endocrinology
Trial ID: NCT06227364
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2023-03-08
Completion date: 2025-01-21
Sponsor: IRCCS Centro Neurolesi Bonino Pulejo
Conditions: Dementia; Mild Cognitive Impairment
Interventions: MCI patients; metabolic patients
Locations: Irccs Centro Neurolesi Bonino Pulejo
Countries: Italy
Enrollment: 200
Age range: 18 Years - 80 Years
Sex: ALL

Summary:
This project aims to introduce solutions for strengthening the management capabilities of two types of patients, in terms of care and prevention. Specifically, it is aimed at neurological patients with mild or minor neurocognitive decline (Mild Cognitive Impairment, MCI) and endocrinological patients diagnosed with obesity. The study want to enhance the predictive capacity of the care process management system through an Artificial Intelligence (AI) system. All patients diagnosed with MCI and obesity who, within 30 months, will be referred to the Neurology and Endocrinology clinics of the IRCCS Centro Neurolesi Bonino-Pulejo in Messina will be evaluated.

Study design

Patients will not be subjected to any procedure that goes beyond normal clinical practice; the clinical and neuropsychological variables that will be collected for the study are those that are commonly collected in normal clinical practice. All patients diagnosed with MCI and obesity who, within 30 months, will be referred to the Neurology and Endocrinology clinics of the IRCCS Centro Neurolesi Bonino-Pulejo in Messina will be evaluated.

All patients will undergo clinical, neuropsychological and psychological evaluation at the beginning (baseline T0), at the end (T1) and after ... months from the end of treatment (follow up T2).

Eligibility criteria:
Inclusion Criteria:

For MCI patients:

Patients aged between 18 and 80 years; Clinical and instrumental diagnosis of MCI, according to the normal clinical practice adopted; Written informed consent obtained from the patient;

For Metabolic patients:

Age: \>18 years BMI \> 30 kg/m²

Willingness to follow requests as per protocol, including:

Signing an informed consent. Participation in scheduled follow-up visits. Carrying out instrumental and laboratory tests

Exclusion Criteria:

For MCI patients:

age under 40 other concomitant neurological and/or psychiatric condition drug abuse unstable clinical conditions requiring acute hospitalization.

For Metabolic patients:

unstable clinical conditions requiring acute hospitalization. associated serious pathologies and/or with reduced life expectancy, including renal failure under dialysis treatment, liver cirrhosis, malignant neoplasms, NYHA class III-IV heart failure, chronic respiratory failure under oxygen therapy.

psychological or psychiatric conditions that may reduce study cooperation.

Outcomes:
the creation of predictive algorithms capable of calculating the risk of onset and/or staging of neurodegenerative and metabolic pathologies
Multicenter Real-World Evidence (RWE) Study: Evaluation of Platelet-Rich Plasma (PRP) Treatment in Tendinopathies
Trial ID: NCT07558564
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2026-04
Completion date: 2028-03
Sponsor: ReSport Clinic
Collaborators: BioSmartData
Conditions: Patellar Tendinopathy; Achilles Tendinopathy (AT); Epicondylitis, Lateral; Plantar Fasciitis, Chronic
Interventions: Platelet rich plasma (PRP)
Locations: Instituto Médico Arriaza; Instituto Cugat; ReSport Clinic; Ripoll y dePrado Medical Group; Hospital MIKS
Countries: Spain; Spain; Spain; Spain; Spain
Enrollment: 2000
Age range: 18 Years - None
Sex: ALL

Summary:
This multicenter, prospective, observational study, based on real-world evidence (RWE), aims to evaluate the efficacy and safety of Platelet-Rich Plasma (PRP) treatment across four types of tendinopathies: patellar, Achilles, epicondylar, and plantar fasciosis. Data collection and analysis will be conducted using the BioSmartData® platform, including hematology analyzers for PRP characterization, clinical variables, treatment protocols, and longitudinal assessment of therapeutic response.

Eligibility criteria:
Inclusion Criteria:

* Clinical and/or ultrasound diagnosis of chronic tendinopathy (duration \>6 weeks)
* Age \>18 years
* Provision of written informed consent
* Willingness and ability to complete follow-up assessments for 12 months

Exclusion Criteria:

* Surgical treatment of the affected tendon within the previous 6 months
* Corticosteroid injection within the last month
* Systemic inflammatory disease (e.g., rheumatoid arthritis)
* Coagulopathies or ongoing anticoagulant therapy
* Complete blood count showing a platelet concentration \<100 × 10⁹/L (to exclude patients with potential thrombocytopenia)
* Pregnancy or breastfeeding

Outcomes:
Visual Analog Scale; VISA-P; VISA-A; Patient Rated Tennis Elbow Evaluation Scale; Foot Function Index
SOPHIE: Standardized Online Patient for Healthcare Interaction Education
Trial ID: NCT07409233
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-06-01
Completion date: 2025-01-01
Sponsor: University of Rochester
Collaborators: James P. Wilmot Cancer Center
Conditions: Clinician Communication Skills Training
Interventions: SOPHIE Simulation Training; Control Educational Materials
Locations: University of Rochester
Countries: United States
Enrollment: 51
Age range: 18 Years - None
Sex: ALL

Summary:
This study evaluated an online simulation based educational program designed to improve clinician communication skills. Participants were clinicians and clinical trainees who completed simulated patient interactions and communication assessments as part of an educational evaluation. Participants were randomly assigned to receive either an AI based simulation training program or standard educational materials. Communication performance was assessed using standardized rating methods in simulated scenarios. The study did not involve patients or health related clinical interventions.

Eligibility criteria:
Inclusion Criteria:

* Participants are clinicians or clinical trainees involved in patient communication as part of their professional or educational role
* Age 18 years or older
* Ability to participate in online educational activities and simulated patient encounters
* Ability to provide informed consent

Exclusion Criteria:

* Prior participation in this study
* Inability to complete the required online activities or simulated encounters
* Any condition that in the opinion of the investigator would interfere with participation or data quality

Outcomes:
Mean Communication Performance Improvement Score in Simulated Patient Encounters
Evaluation of the Accuracy of Intensive Care Unit (ICU) Admission Indications in Emergency Department Patients: A Comparison Between Clinical Decisions and ChatGPT-4o Prediction
Trial ID: NCT06726733
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2024-09-01
Completion date: 2025-02-28
Sponsor: Bursa Yuksek Ihtisas Training and Research Hospital
Conditions: Intensive Care Unit (ICU) Admission; Emergency Department Patient; Artificial Intelligence (AI); Clinical Decision-making
Locations: Bursa Yuksek Ihtisas Training and Research Hospital
Countries: Turkey (Türkiye)
Enrollment: 624
Age range: 18 Years - None
Sex: ALL

Summary:
This retrospective study evaluates the accuracy of ICU admission indications by comparing clinical decisions with predictions from ChatGPT-4. Patient data, including demographics, vital signs, laboratory results, imaging findings, and clinical decisions, will be retrospectively collected and documented systematically using Case Report Forms. The model will be trained using ICU admission guidelines and tasked to predict ICU needs based on collected patient data. This study aims to systematically assess the alignment between AI-based predictions and clinical decisions for ICU admissions.

Eligibility criteria:
Inclusion Criteria:

* Patients aged 18 years and older who are consulted to the anesthesiology and reanimation clinic for ICU indications from the emergency department will be included in the study.

Exclusion Criteria:

* Patients consulted to the anesthesiology and reanimation clinic for ICU indications from inpatient services.
* Patients consulted to the anesthesiology and reanimation clinic from the emergency department for reasons other than ICU indications.
* Patients consulted to the anesthesiology and reanimation clinic for ICU indications from the emergency department but with insufficient recorded data (patients with data loss)

Outcomes:
Accuracy rate of ChatGPT-4 in predicting ICU indications; False positive rate; False negative rate
Establish an Artificial Intelligence Clinical Decision Support System for Patients With Carotid/Vertebral Artery Stenosis.
Trial ID: NCT06978348
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-05-10
Completion date: 2025-06-10
Sponsor: Shanghai Jiao Tong University School of Medicine
Conditions: Ischemic Cerebrovascular Disease; Artificial Intelligence; Prediction Model
Locations: Model
Countries: China
Enrollment: 244296
Age range: None - None
Sex: ALL

Summary:
Establish an artificial intelligence clinical decision support system for patients with carotid/vertebral artery cerebrovascular stenosis, early identification of patients who may have cerebral infarction. With the support of this project, it is expected that a secondary prevention clinical decision support system for chronic stroke will be established, which is likely to become an important auxiliary tool for the management of cerebrovascular diseases in the future.

Eligibility criteria:
Inclusion Criteria:

Patients undergoing vascular (carotid/vertebral artery) B-ultrasound

Exclusion Criteria:

Patients with missing clinical data such as medical history, cerebrovascular ultrasound results and biochemical data

Outcomes:
Establish an artificial intelligence clinical decision support system for patients with carotid/vertebral artery cerebrovascular stenosis，Early identification of patients who may have cerebral infarction.
A Prospective, Observational, Single Centre Study to Assess the Feasibility of the Data Harvested by the Acorai Intracardiac Pressure Monitoring (ICPM) System in Predicting Prognosis in Patients Discharged From Hospital After Treatment for Acute Decompensated Heart Failure
Trial ID: NCT06397833
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2024-04-29
Completion date: 2027-02-01
Sponsor: Royal Brompton & Harefield NHS Foundation Trust
Conditions: Heart Failure; Decompensated Heart Failure
Interventions: Acorai ICPM system
Locations: Harefield Hospital
Countries: United Kingdom
Enrollment: 150
Age range: 18 Years - None
Sex: ALL

Summary:
Acorai is developing the Acorai Heart Monitor, a handheld electronic device for monitoring pressures inside the heart in a non-invasive manner, by placing the device on the chest of a patient. Currently these intracardiac pressures are measured in an invasive way, during a right heart catheterisation procedure. This procedure carries risks to the patient. There is a clinical need for a non -invasive, easy to use, tool to monitor patients that suffer from heart failure. The study aim is to use the Acorai device to collect the intracardiac pressure measurements from patients admitted to hospital with heart failure and who are awaiting discharge home. Using the data, we will assess the feasibility of the Acorai derived cardiac output measurements, and assess what data best predicts survival, death, or major adverse cardiac events (MACE)

Eligibility criteria:
Inclusion Criteria: Subjects must meet all the following criteria to be eligible for participation in the clinical investigation

1. Subjects medically fit for discharge following an admission to Hospital with decompensated heart failure requiring intravenous diuretic therapy
2. Subject is willing and physically able to comply with the specified evaluations as per clinical investigation plan, as assessed by the investigator.
3. Subject is able to provide written consent to participate in the study

Exclusion Criteria:

1. Pregnancy
2. Discretionary exclusion when, in the opinion of the investigator, the inclusion of a potential subject is not in their best interest or not in the interest of compliant performance of the clinical trial

Outcomes:
To assess the feasibility of the Acorai ICPM system to predict survival and hospitalisation at 1 year in hospitalised heart failure patients deemed medically fit for discharge.
Identification of Objective Metabolomics-based Biomarkers of Yogurt Consumption
Trial ID: NCT07491887
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-03-23
Completion date: 2026-06-30
Sponsor: Laval University
Conditions: Diet
Interventions: Yogurt-free diet; Low-yogurt diet; Moderate-yogurt diet; High-yogurt diet
Locations: Institute on nutrition and functional foods, Laval University
Countries: Canada
Enrollment: 16
Age range: 18 Years - 70 Years
Sex: ALL

Summary:
Substantial evidence links yogurt consumption to a lower risk of type 2 diabetes (T2D). However, the existing evidence is derived exclusively from prospective cohort studies relying on self-reported dietary questionnaires, which-despite being validated-are subject to random and systematic errors that may compromise evidence quality and hinder regulatory approval.

The project aims to discover and validate biomarkers of yogurt intake.

The investigators will conduct a randomized, crossover, dose-response feeding trial involving 16 generally healthy adult participants (8 females, 8 males). The trial will include four 7-day diet periods, each separated by a 1-week washout. All diets will be based on a dairy-free background and supplemented with one of the following: (a) 1 serving of soy-based pudding (no yogurt), (b) 0.5 serving of yogurt, (c) 1 serving of yogurt, or (d) 2 servings of yogurt per 2,380 kcal/day.

On the 7th day of each diet, participants will complete a mixed-meal test at INAF, consuming a smoothie containing the same yogurt dose as during the preceding days. Plasma samples collected in the postprandial state will be used to profile over 20,000 metabolites. Using artificial intelligence-based approaches, potential biomarkers of yogurt intake will be identified. These candidates will then be filtered using standard statistical methods to assess dose- and time-responsiveness, robustness, and biological plausibility. Biomarkers that meet all criteria will be considered validated indicators of yogurt intake.

Eligibility criteria:
Inclusion criteria:

* age: 18-70 years;
* BMI: 18-40 kg/m²;
* willingness to consume plain yogurt daily for 3 weeks;
* for coffee/tea consumers, willingness to consume these beverages without added milk, cream or sugar for 4 weeks.

Exclusion criteria:

* lactose intolerance; blood donation within 2 months; severe chronic illness or ongoing treatment for conditions such as infections, autoimmune diseases, cancer, type 2 diabetes, Crohn's disease, celiac disease, hepatic or renal diseases;
* history of severe cancer, cardiovascular disease or bariatric surgery;
* unstable body weight for 3 months;
* illicit drug use or alcoholism; tobacco/cannabis smoking/vaping (ensuring no day-to-day fluctuations and objective reporting of consumption is too complex);
* allergies or aversions to components of the diets.

Outcomes:
Biomarker of yogurt intake
Artificial Intelligence Based on Machine Learning in Computed Tomography for Quantifying Lung Changes of Bronchiectasis Patients
Trial ID: NCT05751538
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2023-03-01
Completion date: 2023-12-30
Sponsor: Ruijin Hospital
Conditions: Bronchiectasis
Interventions: No intervention
Locations: Ruijin Hospital, Medical School of Shanghai Jiaotong University
Countries: China
Enrollment: 730
Age range: None - None
Sex: ALL

Summary:
Bronchiectasis is a chronic respiratory disease characterized by permanent bronchiectasis.The incidence and prevalence of bronchiectasis have assumed continuously grows in global. Chest computed tomography (CT) remains the imaging standard for demonstrating cystic fibrosis (CF) airway structural disease in vivo. However, visual scoring systems as an outcome measure are time consuming, require training and lack high reproducibility. Our objective was to validate a fully automated artificial intelligence (AI)-driven scoring system of CF lung disease severity.

Eligibility criteria:
Inclusion Criteria:

* Patients diagnosed with bronchiectasis (according to the Chinese consensus, patient's previous chest CT examination must show bronchiectasis)

Exclusion Criteria:

* Patients with CT data and medical records missing

Outcomes:
Correlations of the artificial intelligence-driven scores with manual scores
Effect of the Thrive AI Health App on Lifestyle Behaviors and Quality of Life
Trial ID: NCT07376655
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-04
Completion date: 2027-04
Sponsor: Yale University
Collaborators: Thrive AI Health
Conditions: Diabetes Type 2; Overweight or Obesity
Interventions: App
Locations: Yale University
Countries: United States
Enrollment: 20
Age range: 40 Years - None
Sex: ALL

Summary:
The goals of this randomized trial is to learn:

1. If the Thrive AI Health app will help adults improve their everyday habits (diet, exercise, and sleep).
2. How often participants will use the Thrive AI Health app to which they will have free access

The Thrive AI Health app uses artificial intelligence (AI) to give personalized advice. It is designed to help people eat better, exercise more, manage stress, and sleep well.

Researchers will compare changes in diet, exercise and sleep in participants using the app to those participants not using the app.

Participants will complete study questionnaires and an in-person visit at the beginning and end of the study.

Eligibility criteria:
Inclusion Criteria:

* Be at high risk for chronic disease defined as:

Body mass index (BMI)\>25

BMI\<=25 who are prediabetic or have type 2 diabetes

* Aged 40 years or older

Exclusion Criteria:

* Unable to walk

Outcomes:
Physical activity
An Exploratory Clinical Study for the Development of a Novel Ultrasound Transducer Technology Intended for Non-invasive Monitoring of Cardiac Outputs
Trial ID: NCT05734664
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2022-10-19
Completion date: 2023-01-23
Sponsor: Pulsify Medical
Collaborators: KU Leuven; Universitaire Ziekenhuizen KU Leuven
Conditions: Cardiac Output Measurements; Healthy Subjects
Interventions: Pulsify sensor; State-of-the-art ultrasound equipment
Locations: UZ Leuven
Countries: Belgium
Enrollment: 8
Age range: 18 Years - None
Sex: ALL

Summary:
Pulsify Medical aims at developing a sensor designed for the non-invasive, continuous and real-time monitoring of cardiac hemodynamics in patients at risk based on new transducer technology and artificial intelligence. It relies on real-time 3D greyscale ultrasound reconstruction of the LV myocardium. This medical device is still under development and the main objective of this clinical investigation is to gather data on the accuracy of the current version of the sensor developed by Pulsify Medical to further guide the development of the device. The data generated will not be used for conformity assessment and this single-center prospective study on 8 patients is therefore deemed appropriate for this purpose.

During the pretreatment visit, eligibility of the subjects for the investigation will be assessed. Once subjects are considered eligible and they have consented to participate in the investigation, the visit to perform the monitoring will be scheduled. An operator will then perform on each patient two scans with the Pulsify sensor. For accurate 3D reconstruction, the ECG and breathing cycle during the acquisition will also be recorded. Immediately after acquisition with the Pulsify sensor, an echocardiographist will record a complete 2D and 3D ultrasound data set using regular ultrasound equipment used for medical examinations in the hospital. The data generated will then be used to compare the accuracy of the Pulsify sensor for cardiac output measurements with state-of-the-art ultrasound. The data will also enable to acquire a dataset of ultrasound images taken with the Pulsify sensor technology in a manner that is very close to the image capture mechanism of Pulsify's final product.

Eligibility criteria:
Inclusion Criteria:

Healthy volunteers can participate in this study if they:

* are aged over 18 years old
* have no known cardiovascular diseases
* are willing and able to comply with all investigation related procedures

Exclusion Criteria:

* Volunteers with arrhythmias, PM or ICD
* Volunteers with life supporting electronic devices (e.g. pacemakers)
* Volunteers with bad echocardiographic windows
* Volunteers post recent thorax surgery and open chest wounds
* Volunteers with abnormal thorax configuration (COPD, scoliosis, pectus excavatum, etc.) on criteria for the selection of topics
* Women in gestation
* Women that are breastfeeding

Outcomes:
Comparison Cardiac Output
Development of the Aiberry AI Mental Health Screening Platform in a Diverse Population
Trial ID: NCT04994899
Status: TERMINATED
Study type: OBSERVATIONAL
Start date: 2022-09-22
Completion date: 2023-04-06
Sponsor: Aiberry, Inc
Conditions: Depression; Anxiety; Healthy
Locations: University of Arizona; Georgetown University; Hapworth Research Inc.
Countries: United States; United States; United States
Enrollment: 800
Age range: 13 Years - 79 Years
Sex: ALL

Summary:
Aiberry is creating a multi-modal artificial intelligence (AI) platform that analyzes facial, audio and text features to screen for mental illness. This multicenter study will be used to collect data to validate the platform's ability to detect depression and anxiety in a diverse patient population.

Eligibility criteria:
Inclusion Criteria:

* Age 13 to 79 years old
* English-speaking

Exclusion Criteria:

* Previous participant in the study, within the last three months
* Individuals unable to verbally respond to standardized questions
* Individuals unable to participate in a virtual visit as they do not have the right hardware (phone/laptop) or no internet connectivity
* Individuals unable to provide consent

Outcomes:
Prediction of depression score from a self-report instrument
A Non-interventional Study Evaluating Samples From Patients With Suspected Non-small Lung Cancer or Breast Cancer to Describe Pathology Practices and to Evaluate Computational Pathology Plus Artificial Intelligence Algorithms.
Trial ID: NCT06827132
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-10-25
Completion date: 2027-05-31
Sponsor: AstraZeneca
Conditions: Lung Cancer; Breast Cancer
Locations: Research Site
Countries: Kenya
Enrollment: 600
Age range: None - None
Sex: ALL

Summary:
A multinational observational study to evaluate the current pathology practices and the utilization of computational pathology plus artificial intelligence algorithms in patients with suspected lung and breast cancer.

Eligibility criteria:
Inclusion Criteria:Sample from adult patients (≥ 18 years) with suspected non-small cell lung cancer or invasive breast cancer or ductal carcinoma in situ.

\-

Exclusion Criteria:

* Samples with the inadequate technical quality of slides (pre-analytics quality) or images, e.g., broken slides, large out-of-focus areas, slides with fixation artefacts.

  * Samples from cases that were included in the training or technical validation.
  * Sample taken by fine needle aspiration.
  * Sample sent for cytological evaluation.

Outcomes:
primary objective; Primary Objective; exploratory objective