moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Point-of-care Low-field Magnetic Resonance Imaging in Acute Stroke (POCS) Study
Trial ID: NCT05816213
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-02-01
Completion date: 2026-11-01
Sponsor: University of L'Aquila
Conditions: Stroke; Ischemic Stroke; Hemorrhagic Stroke; Magnetic Resonance Imaging
Interventions: Portable low-field MRI
Locations: SS Filippo e Nicola Hospital of Avezzano; SS Annunziata Hospital of Chieti; S Salvatore Hospital of L'Aquila; IRCCS Humanitas Research Hospital of Rozzano
Countries: Italy; Italy; Italy; Italy
Enrollment: 300
Age range: 18 Years - None
Sex: ALL

Summary:
Consecutive patients accessing the emergency department with suspected stroke dispatch will be recruited at 3 study units: 1) ASL Abruzzo 1, hospitals of L'Aquila and Avezzano; 2) ASL Abruzzo 2, hospital of Chieti; 3) IRCCS Humanitas Research Hospital of Milan. Anonymized clinical and low-field (LF) MRI data as well as conventional neuroimaging data will be independently assessed by external units (Università Politecnica delle Marche and Policlinico di Messina, respectively). Both units will independently adjudicate the best treatment option, while the latter will also provide historical MRI data of stroke patients to develop artificial intelligence algorithms facilitating LF-MRI images interpretation (Libera Università di Bolzano). Agreement with conventional neuroimaging will be evaluated at different time points (hyperacute, acute -24 h, subacute -72 h, discharge, chronic -4 weeks). Further investigations will include feasibility study to develop an ambulance (mobile stroke unit) equipped with LF-MRI and cost-effectiveness analysis of LF-MRI. This trial will provide necessary data to validate the use of LF-MRI in the acute stroke care.

Eligibility criteria:
Inclusion Criteria:

1. symptoms suggestive of acute stroke (acute onset of a focal neurological deficit which can be referred to the involvement of a specific CNS region);
2. stroke onset \<24 h since when the patient was last known healthy;
3. written informed consent provided by the patient himself or by proxy (for unconscious patients, cognitively impaired or aphasic).

Exclusion Criteria:

1. symptoms not indicative of acute stroke (e.g. syncope, tonic or clonic activity, dizziness or wooziness alone, confusion and amnesia alone, subacute or chronic development of a focal neurological deficit);
2. inability to undergo LF-MRI due to critically impaired vital functions (e.g. hemodynamically unstable patients, need of immediate life-saving maneuvers);
3. implanted ferromagnetic devices potentially interfering with LF-MRI (e.g. cochlear implants);
4. impossibility to achieve written informed consent.

Outcomes:
Accuracy of LF-MRI in the diagnosis of stroke in the acute phase.
Autonomous Artificial Intelligence Versus AI Assisted Human Optical Diagnosis of Colorectal Polyps
Trial ID: NCT06543862
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-11-15
Completion date: 2024-11-15
Sponsor: Centre hospitalier de l'Université de Montréal (CHUM)
Conditions: Colonic Polyp; Artificial Intelligence
Interventions: CADx (AI) system
Locations: Ghislaine Ahoua
Countries: Canada
Enrollment: 540
Age range: 45 Years - 80 Years
Sex: ALL

Summary:
Computer-aided image-enhanced endoscopy can predict the nature of colorectal polyps with over 90% accuracy. This technology uses artificial intelligence (AI) to analyze video recordings of polyps, learning to make diagnoses in real-time. This means that doctors can get immediate predictions about small polyps during the procedure, reducing the need for separate pathology exams and saving costs, ultimately improving patient care.

Human and AI interactions are complex and a framework to reap synergistic effects CADx systems when used by humans to harness optimal performance needs to be established. AI solutions in medicine are usually developed to be used as assistive devices, however, then they rely on humans to correct AI errors. Optical polyp diagnosis is a complex task. Non experts usually achieve diagnostic accuracy in 70-80%. CADx systems have a similar diagnostic accuracy when used autonomously. Clinical evaluation of CADx systems showed that CADx assisted OD performs equally to the operator performance when using non CADx assisted OD. To harness a benefit of clinical CADx implementation we would have to find a way that synergies between human and CADx come into play to eliminate cases in which CADx assisted and/ or human OD results in low diagnostic accuracy and also addresses the problem of serrated polyp recognition.

Eligibility criteria:
Inclusion Criteria:

* Indication for full colonoscopy.

Exclusion Criteria:

* Known inflammatory bowel disease
* Active colitis
* coagulopathy
* familial polyposis syndrome
* poor general health, defined as an American Society of Anesthesiologists class \>3
* emergency colonoscopy

Outcomes:
Accuracy of optical diagnosis, for polyps 1-5mm, compared with an agreed upon CADx-assisted diagnosis
Electrocardiogram-based Artificial Intelligence-assisted Detection of Heart Disease
Trial ID: NCT05442203
Status: ACTIVE_NOT_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2022-09-07
Completion date: 2025-07
Sponsor: Tempus AI
Conditions: Atrial Fibrillation; Structural Heart Disease
Interventions: Zio Patch Monitor; Echocardiogram
Locations: Corewell Health; TriHealth; Geisinger Medical Center
Countries: United States; United States; United States
Enrollment: 1000
Age range: 40 Years - None
Sex: ALL

Summary:
Atrial fibrillation is an abnormal beating of the heart that can lead to stroke or heart failure. Structural heart diseases are conditions that affect the heart valves or heart muscle and can cause permanent heart damage if left untreated. Sometimes people have atrial fibrillation or structural heart disease and do not know it. The purpose of this study is to evaluate two devices that can predict who has or may develop atrial fibrillation or structural heart disease based on the results of an electrocardiogram.

Eligibility criteria:
Inclusion Criteria:

* Retrospective Phase:
* Adults aged 40 or older.
* At least 1 ECG obtained during routine clinical care.
* Prospective Phase:
* AF Cohort:
* Adults aged 65 or older at the time of ECG.
* ECG obtained as part of a clinical care.
* Patient is able to identify a licensed healthcare provider to receive the results of the patch monitor.
* SHD Cohort:
* Adults aged 40 or older at the time of the ECG.
* ECG obtained as part of a clinical care between study start date and the end of study recruitment
* Patient is able to identify a licensed healthcare provider to receive the results of the echocardiogram.

Exclusion Criteria:

* Retrospective Phase:
* Patients who have previously requested that their data not be involved in any secondary use application such as a research study.
* Prospective Phase:
* AF Cohort:
* Any clinical or social factor that would prohibit completing the follow-up studies in a timely fashion.
* Patient currently admitted to the hospital (at time of consent)
* Permanent pacemaker or implanted cardiac defibrillator or implanted loop recorder.
* History of atrial fibrillation or atrial flutter.
* Cardiac surgery within 30 days prior to the index ECG
* Cardiac surgery planned within the next 6 months.
* Allergy to adhesive.
* SHD Cohort:
* Any clinical or social factor that would prohibit completing the follow-up studies in a timely fashion.
* Patient currently admitted to the hospital (at time of consent).
* History of SHD defined as any of the following: severe mitral regurgitation, severe tricuspid regurgitation, moderate or severe aortic stenosis, moderate or severe aortic regurgitation, moderate or severe mitral stenosis, left ventricular systolic dysfunction (LVEF ≤ 40%), or increased septal wall thickness \> 15 mm.
* Allergy to ultrasound gel.

Outcomes:
Positive-predictive value (PPV) of the AF device at six months; Positive-predictive value (PPV) of the SHD device at six months
Intervention of Artificial Intelligence in the Management of Nutritional Therapy Applied to Moroccan Type 2 Diabetics - Al-Khawarizmi Project
Trial ID: NCT07404280
Status: ACTIVE_NOT_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-10-01
Completion date: 2026-09
Sponsor: Avicenna Military Hospital
Conditions: Type 2 Diabetes (T2DM)
Interventions: Moroccan-Adapted Nutritional Plan
Locations: Avicenna Military Hospital of Marrakech
Countries: Morocco
Enrollment: 50
Age range: 18 Years - None
Sex: ALL

Summary:
The goal of this study is to learn if a nutritional intervention adapted to Moroccan dietary habits can improve blood sugar control in adults with Type 2 Diabetes. The main questions it aims to answer are:

* Does a personalized diet based on Moroccan culinary traditions improve long-term blood sugar levels (HbA1c)?
* How does this intervention affect daily blood sugar fluctuations measured as interstitial glucose?
* Does the intervention lead to changes in body measurements, such as weight and Body Mass Index (BMI)?

Participants will:

* Wear a Continuous Glucose Monitor (CGM) to track sugar levels in real-time.
* Keep a detailed food diary of all foods and drinks consumed.
* Follow their usual diet for the first 7 days to establish a baseline.
* Receive personalized dietary advice specifically tailored to Moroccan food habits.
* Return for clinical check-ups at 14, 90, and 180 days for blood tests and body measurements.

Eligibility criteria:
Inclusion Criteria:

* Confirmed diagnosis of T2D by a healthcare professional.
* Age 18 years or older.
* A glycated hemoglobin (HbA1c) level less than 10%.
* Capacity to provide informed written consent and follow the study procedures.

Exclusion Criteria:

* Non-diabetic subjects.
* Type 1 diabetes or secondary diabetes.
* Age younger than 18 years.
* Pregnancy or planned pregnancy during the study period.
* HbA1c level greater than 10%.
* Chronic renal insufficiency or hepatic disease.
* Pathologies of the digestive tract that affect nutrient absorption.
* Known food allergies requiring highly specific restricted diets.

Outcomes:
Change in Interstitial Glucose Levels
Integrating 3D Imaging Technology for Precise Burn Wound Assessment
Trial ID: NCT06938061
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-05-20
Completion date: 2026-06
Sponsor: HealthPartners Institute
Conditions: Burn; 2nd Degree Burn of the Skin; 3rd Degree Burn of the Skin; 1st Degree Burn of the Skin
Interventions: 3D Imaging
Locations: Regions Hospital Burn Clinic
Countries: United States
Enrollment: 75
Age range: 18 Years - 89 Years
Sex: ALL

Summary:
The goal of this prospective cohort study is to determine if the DermaMonitor camera can accurately determine burn size in patients with 1st, 2nd, or 3rd degree burns, with exploratory aim of testing a new Artificial Intelligence (AI) program for determining burn depth. The main aims are:

1. The primary aim of this study is to assess an objective process for the classification of burn size using the DermaMonitor wound camera.
2. An exploratory aim of this study is to begin to train an Artificial Intelligence (AI) program to classify burn depth (1st, 2nd, 3rd degree) using the 3D images from this novel wound camera.

Researchers will compare DermaMonitor size classification and AI depth classification to clinician determinations. Participants will have images taken of their burn. Participation ends when the images are taken.

Eligibility criteria:
Inclusion Criteria:

* Adults sustaining burns
* Outpatients
* No prior surgery on the wounds
* No known infection of the wounds

Exclusion Criteria:

* Burns to the face or sensitive areas (genitals)
* Burns located on or near identifying landmarks such as tattoos or birthmarks
* Non-English speaking patients
* Patients unable to sign their name
* Burn occurred greater than 7 days prior to clinic visit
* Age greater than 90 (Patient age is sent externally to IKO, to ensure no PHI is sent, patients with an age \>90 will not be enrolled)

Outcomes:
Concordance between TBSA based on Clinician measurements and TBSA based on DermaMonitor Camera measurements
Artificial Intelligence-based Activity Recognition and Mortality Prediction Using Circadian Rhythm, Among Cancer Patients in the Hospice Ward
Trial ID: NCT04883879
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2019-12-11
Completion date: 2021-12-31
Sponsor: Taipei Medical University
Collaborators: Ministry of Science and Technology, Taiwan; Taipei Medical University Hospital; National Yang Ming Chiao Tung University
Conditions: End Stage Cancer
Locations: Taipei Medical University
Countries: Taiwan
Enrollment: 80
Age range: 20 Years - None
Sex: ALL

Summary:
The purpose of this study is to develop a novel deep-learning-based survival prediction model employing patient activity data recorded by a wearable device.

Eligibility criteria:
Inclusion Criteria:

* Participants aged 20 years or older admitted to the hospice care unit at Taipei Medical University Hospital
* Participants diagnosed with at least one end-stage solid tumor diseases
* Participants consented to receive hospice care

Exclusion Criteria:

* Participants aged below 20 years of age
* Participants diagnosed with leukemia or carcinoma of unknown primary
* Participants with evident signs of approaching death upon admission
* Participants with no vital signs upon admission
* Participants who continued to receive aggressive treatment despite admission to the hospice care unit

Outcomes:
Specificity and Sensitivity of using Artificial Intelligence based models for prediction of Clinical Outcomes of End-stage Cancer Patients using actigraphy data
Artificial Intelligence-based Techniques to Characterize KIdney Microstructure on Histological ImagEs
Trial ID: NCT06690190
Status: ACTIVE_NOT_RECRUITING
Study type: OBSERVATIONAL
Start date: 2024-11-08
Completion date: 2034-11
Sponsor: Mario Negri Institute for Pharmacological Research
Conditions: End-Stage Renal Disease
Locations: Clinical Research Centre for Rare Diseases Aldo e Cele Daccò
Countries: Italy
Enrollment: 100
Age range: None - None
Sex: ALL

Summary:
The primary aim of this observational exploratory study will be to use fully anonymized histological images of kidney human tissue from patients with any kidney disease and normal kidney tissue to develop novel deep learning-based image processing techniques allowing to characterize kidney microstructure across different pathologies and/or disease stages.

Secondly, the study will aim at validating the novel techniques against gold standard (manual) methods, when available, and at developing novel histological imaging biomarkers that could support differential diagnosis, staging of the disease, monitoring of disease progression and response to therapy, and prediction of the disease progression.

Other exploratory aims will include:

* The use of radiomics techniques to identify disease-specific kidney morphology patterns.
* The implementation of uncertainty quantification techniques, able to increase AI explainability.

Eligibility criteria:
Inclusion Criteria:

* Any kidney disease or
* Healthy kidney

Exclusion Criteria:

* None

Outcomes:
Image processing techniques
Image Mining and ctDNA to Improve Risk Stratification and Outcome Prediction in NSCLC Applying Artificial Intelligence.
Trial ID: NCT06163846
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2020-07-10
Completion date: 2025-06
Sponsor: IRCCS San Raffaele
Conditions: Non Small Cell Lung Cancer
Interventions: Assess the role of baseline image mining, ctDNA data and their combination in patient staging and risk stratification
Locations: Irccs San Raffaele
Countries: Italy
Enrollment: 415
Age range: 18 Years - 70 Years
Sex: ALL

Summary:
Lung cancer is the leading cause of cancer-related death in Europe. Pathological staging is the gold standard, but it can be influenced by neo-adjuvant treatment and number of sampled lymph nodes; it is not feasible in advanced stages and in patients with high-risk comorbidities. Therefore, patients with tumors of the same stage can experience variations in the incidence of recurrence and survival since suboptimal staging leads to inappropriate treatment that result in poorer outcomes. It is still undetermined what are the tumor characteristics that can accurately assess tumor burden and predict patient outcome.Our central hypothesis is that image-derived and genetic characteristics are consistent with disease stage and patient outcome. Combining through artificial intelligence techniques data coming from imaging and circulating cell-free tumor DNA (ctDNA) can provide accurate staging and predict outcome. This hypothesis has been formulated based on preliminary data and on the evidence that image-derived biomarkers by means of image mining (radiomics and deep learning algorithms) are able to provide "phenotype" and prognostic information. On the other hand, the analysis of ctDNA isolated from the plasma of patients has been proposed as an alternative method to assess the disease in the different phases, in particular, at diagnosis and after surgery, for detection of residual disease.

Eligibility criteria:
Inclusion Criteria:

* Patients with new pathological diagnosis of lung cancer, available baseline imaging (CT and FDG-PET/CT), age \> 18 years, and eligibility for surgery will be considered for inclusion.

Exclusion Criteria:

* pregnant or breast- feeding women.

Outcomes:
Artificial intelligence and circulating cell-free tumor DNA (ctDNA) for the staging and predict outcome in patients with with non-small cell lung cancer.
Assessing the Efficacy of Artificial Intelligence in Left Ventricular Function Screening Using Parasternal Long Axis View Cardiac Ultrasound Video Clips
Trial ID: NCT06330103
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2023-05-01
Completion date: 2023-07-31
Sponsor: Rayong Hospital
Collaborators: Chulalongkorn University
Conditions: Heart Failure; Heart Failure With Reduced Ejection Fraction; Cardiac Failure; Echocardiography; Artificial Intelligence
Interventions: Easy EF
Locations: Rayong Hospital
Countries: Thailand
Enrollment: 923
Age range: None - None
Sex: ALL

Summary:
Assessing the Efficacy of Artificial Intelligence in Left Ventricular Function Screening Using Parasternal Long Axis View Cardiac Ultrasound Video Clips

ABSTRACT BACKGROUND: Echocardiography serves as a fundamental diagnostic procedure for managing heart failure patients. Data from Thailand's Ministry of Public Health reveals that there is a substantial patient population, with over 100,000 admissions annually due to this condition. Nevertheless, the widespread implementation of echocardiography in this patient group remains challenging, primarily due to limitations in specialist resources, particularly in rural community hospitals. Although modern community hospitals are equipped with ultrasound machines capable of basic cardiac assessment (e.g., parasternal long axis view), the demand for expert cardiologists remains a formidable obstacle to achieving comprehensive diagnostic capabilities. Leveraging the capabilities of Artificial Intelligence (AI) technology, proficient in the accurate prediction and processing of diverse healthcare data types, offers a promising for addressing this prevailing issue. This study is designed to assess the effectiveness of AI in evaluating cardiac performance from parasternal long axis view ultrasound video clips obtained via the smartphone application.

OBJECTIVES: To evaluate the effectiveness of artificial intelligence in screening cardiac function from parasternal long axis view cardiac ultrasound video clips obtained through the smartphone application.

Eligibility criteria:
Inclusion Criteria:

* Shot 5 second VDO clip of Parasternal long axis heart ultrasound recorded by smartphone Application "Easy EF" without patient identification with result of Ejection fraction that performed by certify cardiologist approved result

Exclusion Criteria:

* Incomplete VDO clip (too much shaking, too shot recording)
* Lighting was inappropriate
* Inappropriate ultrasound framing
* arrhythmia (atrial fibrillation)

Outcomes:
efficiency of AI in screening left ventricular cardiac function by use smartphone application
Application and Effectiveness Evaluation of Artificial Intelligence in Lifestyle Management of Diabetic Patients of Community
Trial ID: NCT06118671
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2023-11-07
Completion date: 2024-11-07
Sponsor: Chongqing Medical University
Conditions: Diabetes
Interventions: personalized lifestyle intervention by AI
Locations: Jinbo Hu
Countries: China
Enrollment: 460
Age range: 18 Years - 80 Years
Sex: ALL

Summary:
The goal of this clinical trial is to learn about the application and effectiveness evaluation of artificial intelligence (AI) in lifestyle management of diabetic patients in community.

Eligibility criteria:
Inclusion Criteria:

* Well informed of the procedures of this trial and informed consent is obtained
* 18-80 years old, gender is not limited
* Diagnosed diabetes (according to WHO1999 diagnostic criteria)
* Well compliance

Exclusion Criteria:

* Pregnant or lactating
* Poor blood glucose control (HbA1c\>11%)
* A history of malignant tumor
* Abnormal liver or renal function \[defined as alanine aminotransferase (ALT)\>2.5 times higher than normal range, or eGFR\<30 mL/min per 1.73 m2\]
* Poor blood pressure control \[systolic blood pressure (SBP)\>180mmHg, or diastolic blood pressure (DBP)\>110mmHg
* With severe heart disease, cardiac function worse than grade II, anemia (Hb\<9.0g/d1)
* Blood routine test indicates that the white blood cell count (WBC) \<3\*109/L
* Body Mass Index (BMI)\<18.5 or ≥35kg/m2
* Drug or alcohol abuse
* Accompanying mental disorder who can't collaborate
* Abnormal digestion and absorption function
* Other endocrine diseases
* Other chronic diseases needed long-term glucocorticoid treatment
* With severe infection, immune dysfunction

Outcomes:
Change of HbA1c
AI-Based Multimodal Multi-tasks Analysis Reveals Tumor Molecular Heterogeneity, Predicts Preoperative Lymph Node Metastasis and Prognosis in Papillary Thyroid Carcinoma: A Retrospective Study
Trial ID: NCT06241092
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2024-04-01
Completion date: 2025-01-20
Sponsor: Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
Conditions: Papillary Thyroid Carcinoma; Molecular Heterogeneity; Multi-model Analysis; Artificial Intelligence; Lymph Node Metastases; Disease-free Survival
Interventions: [None]
Locations: Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
Countries: China
Enrollment: 256
Age range: None - None
Sex: ALL

Summary:
This study involved a comprehensive analysis of 256 PTC patients from Sun Yat-sen Memorial Hospital of Sun Yat-sen University (SYSMH) and 499 patients from The Cancer Genome Atlas. DNA-based next-generation sequencing (NGS) and single-cell RNA sequencing (scRNA-seq) were employed to capture genetic alterations and TME heterogeneity. A deep learning multimodal model was developed by incorporating matched histopathology slide images, genomic, transcriptomic, immune cells data to predict LNM and disease-free survival (DFS).

Eligibility criteria:
Inclusion Criteria:

≥ 18 years of age Diagnosis of Papillary thyroid carcinoma at least one months before trial Willing to return for required follow-up (posttest) visits

Exclusion Criteria:

The patient requires valve or other likely surgery The patient is unable to carry out any physical activity without discomfort The patient had thyroid ache within three months prior to enrollment The patient refuses to give informed consent The patient is a candidate for coronary bypass surgery or something similar

Outcomes:
Lymph node metastasis