moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Prospective Longitudinal Evaluation of AI-ECG in a NEwly Diagnosed Heart Failure
Trial ID: NCT05817136
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2023-01-11
Completion date: 2024-06-11
Sponsor: Imperial College London
Conditions: Heart Failure
Interventions: Eko DUO
Locations: Imperial College London
Countries: United Kingdom
Enrollment: 80
Age range: 18 Years - None
Sex: ALL

Summary:
Background:

Heart Failure (HF) is a condition in which the heart can no longer adequately pump blood around the body. The number of patients diagnosed with HF is increasing, consuming 4% of the NHS budget, and deadlier than most cancers. Most patients suffer from HF with reduced Ejection Fraction (HFrEF), where adequate treatment can improve quality of life and survival. Less than 50% of patients receive gold standard NHS guided medication and less than 20% receive appropriate monitoring (via echocardiography surveillance).

This study looks at the use of a 'smart stethoscope' (Eko DUO), a stethoscope that uses information collected from the heart in the form of electrical (ECG) and sounds (phonocardiogram, PCG) waveforms, to predict the pumping function of the heart via artificial intelligence (AI-ECG).

Aims:

By using the smart stethoscope, this study evaluates whether the use of an easy-to-use home self-monitoring programme can:

* Provide a solution for the current poor compliance of NHS echocardiogram surveillance programmes for people with newly diagnosed HF
* Provide real-time assessment of heart function in response to medication changes
* Improve the health economic and health outcomes of HF in the NHS

Methods:

80 participants with newly diagnosed HFrEF, due to pre-existing heart disease and non-heart related causes, will be identified by the clinical team at Imperial College NHS Trust and obtain consent for the research team to approach them. All consented participants will receive a smart stethoscope and instructions for twice-weekly, 15-second self-examination for 3-months. Participants will also be invited for an additional echocardiogram at 6 weeks post-diagnosis, in addition to the routine, standard of care NHS echocardiogram surveillance for HF.

Eligibility criteria:
Inclusion Criteria:

* Age 18 years or above
* Able to give informed consent
* Newly diagnosed with HFrEF (i.e., LVEF below 40%) assessed by a consultant cardiologist within the past two weeks.

Exclusion Criteria:

* Any chest wound, skin pathology or other feature that would prohibit routine Eko DUO examination.
* Participants who have been diagnosed with HF previously

Outcomes:
Descriptive analysis of trends and association of raw AI-ECG signals changes that correlate with HF progression.
ICE Detector-RCT: Blinded, Randomized Controlled Trial of Electrophysiologist vs. Artificial Intelligence Detection of Intracardiac Thrombus
Trial ID: NCT06206187
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-01-05
Completion date: 2025-12-31
Sponsor: Shanghai Chest Hospital
Collaborators: Johnson & Johnson
Conditions: Atrial Fibrillation
Interventions: Automated detection of the intracardiac thrombus through deep learning; Electrophysiologist judgment of the intracardiac thrombus
Locations: Shanghai Chest Hospital
Countries: China
Enrollment: 1500
Age range: 18 Years - 90 Years
Sex: ALL

Summary:
To determine whether an integrated AI decision support can save time and improve the accuracy of detection of intracardiac thrombus, the investigators are conducting a blinded, randomized controlled study of AI-guided detection of intracardiac thrombus to electrophysiologist judgment in preliminary readings of echocardiograms.

Eligibility criteria:
Inclusion Criteria:

1. Aged 18-80 years.
2. Willing to sign informed consent.
3. Patients diagnosed with atrial fibrillation Paroxysmal AF and Persistent AF according to the latest clinical guidelines

Exclusion Criteria:

1. End-stage disease with a mean life expectancy less than 1 year
2. New York Heart Association (NYHA) class III or IV, or last known left ventricular ejection fraction less than 30%
3. Previous surgical or catheter ablation for AF
4. Bradycardia and presence of implanted ICD
5. Uncontrolled hypertension: Systolic blood pressure (SBP) \>180 mmHg or diastolic blood pressure (DBP) \> 110 mmHg
6. Patients with Cardiovascular events including acute myocardial infarction, any PCI, valvular cardiac surgical, or percutaneous procedure within the past 3 months
7. Women of childbearing potential who are, or plan to become, pregnant during the time of the study
8. Have been enrolled in an investigational study evaluating devices or drugs.

Outcomes:
Degree of change from initial (AI vs EP doctor) assessment to final cardiologist assessment
Development of Continuous Glucose Monitoring System Cohort for Personalized Diabetes Prevention and Management Platform Based on Artificial Intelligence Model Using Mathematical Analysis
Trial ID: NCT04369833
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2020-05-11
Completion date: 2020-09-30
Sponsor: Pusan National University Hospital
Conditions: PreDiabetes; Glucose Metabolism Disorders; Diabetes Mellitus
Interventions: continuous glucose monitoring system
Locations: Pusan national university hospital
Countries: South Korea
Enrollment: 130
Age range: 18 Years - None
Sex: ALL

Summary:
The purpose of this study is to collect a variety of clinical data and blood glucose changes using a continuous glucose monitoring device for high-risk diabetes patients (prediabetes) in order to develop a personalized diabetes prevention and management platform based on artificial intelligence model using mathematical analysis.

Eligibility criteria:
Inclusion Criteria:

* Above 18 years old.
* Prediabetes

  1. Fasting plasma glucose : 100\~125mg/dL, fasting is defined as no caloric intake for at least 8 hours.

     OR
  2. 2-hour plasma glucose during 75g oral glucose tolerance test : 140 \~ 199mg/dL

     OR
  3. Glycated hemoglobin(HbA1c) : 5.7\~6.4% (39-47mmol/mol)

Exclusion Criteria:

* with a history of newly diagnosed and treated cancer within the last 5 years
* with a history of hospitalization for active disease within the last 3 months
* with a history of severe cardiovascular disease within the last 3 months
* with a history of steroid treatment in the last 3 months
* people who have had major surgery planned within the last 3 months or who have had surgery within 3 months
* people who are pregnant or have been in the last 3 months after giving birth

Outcomes:
Number of Participants With Insulin Resistance
Effectiveness of a Mobile Telerehabilitation Tool Versus Conventional Treatment in Total Knee Arthroplasty: a Controlled Clinical Trial
Trial ID: NCT06735547
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2023-12-01
Completion date: 2024-10-10
Sponsor: Sanitas University
Collaborators: Biokeralty Research Institute
Conditions: Musculoskeletal Disorders; Knee Osteoarthritis (OA)
Interventions: Telerehabilitation; Conventional therapy group
Locations: Clínica Universitaria Colombia
Countries: Colombia
Enrollment: 216
Age range: None - None
Sex: ALL

Summary:
Knee arthroplasty is one of the most common surgical procedures, and early rehabilitation is essential for patient recovery, pain modulation and a faster reintegration into daily life activities. However, it has been reported that the percentage of patients who continue rehabilitation for the time required to achieve these outcomes is much lower than those who abandon therapy. This is often due to various factors such as office hours, difficult access to appointments, and sometimes insufficient infrastructure and personnel to meet the demand. Currently, due to the global situation, it is crucial to conduct these rehabilitation processes remotely and virtually, making use of various technological tools that enable patients to interact with the healthcare professionals in charge of their rehabilitation. Among these tools are mobile applications on smartphones, which not only facilitate proper physical preparation, but also offer valuable real-time feedback. Furthermore, 8% of the total cost of knee joint replacements corresponds to the rehabilitation program. These costs are significantly reduced with telerehabilitation assisted by mobile tools, which not only improves patient access but also increases adherence and satisfaction levels by allowing the customization of rehabilitation programs based on each patient's specific needs and characteristics. The aim of this study is to evaluate the effectiveness outcomes of an artificial intelligence-guided mobile tool versus conventional rehabilitation during the first three months postoperatively following total knee replacement.

Eligibility criteria:
Inclusion Criteria:

* Patients diagnosed with degenerative osteoarthritis with an indication for primary Total Knee Replacement (TKR) surgery at Clínica Universitaria Colombia or Central de Urgencias de Puente Aranda.
* Patients in the postoperative period following primary outpatient TKR.
* Patients with functional independence.
* Patients who agree to participate in the study and sign the informed consent form.
* Patients with access to a mobile device, tablet, computer, with internet access and the ability to operate it.
* Patients who have an appropriate space to perform therapy at home.
* Patients who will be accompanied during therapy sessions at home.

Exclusion Criteria:

* ASA Class IV (American Society of Anesthesiologists)
* BMI \> 35
* Dementia and cognitive impairment

Outcomes:
Knee Range of Motion; Functionality - WOMAC survey; Functionality - Get Up and Go; Pain - Visual Analogue Scale
Novel Imaging Techniques for the Characterization of Musculoskeletal Tumors II: Texture Analysis and Artificial Intelligence
Trial ID: NCT04394052
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2020-06-01
Completion date: 2030-06-01
Sponsor: Central Hospital, Nancy, France
Collaborators: Lorraine Cancer Institute - ICL; Emille Gallé Surgical Center - CCEG; Diagnostic and Interventional Adaptative Imaging Laboratory - IADI
Conditions: Soft Tissue Neoplasm; Bone Neoplasm
Interventions: MR imaging
Locations: CHU-Nancy
Countries: France
Enrollment: 740
Age range: 18 Years - None
Sex: ALL

Summary:
This study aims at evaluating the value of various artificial intelligence based techniques to improve the characterization and image post-processing for patients with musculoskeletal tumors.

Eligibility criteria:
Inclusion Criteria:

* Patients suspected to have a bone or soft-tissue tumor referred for imaging for initial tumors characterization and staging.

Exclusion Criteria:

* Pregnancy
* Breast feeding patients
* Renal insufficiency
* Contra indications to MRI or CT
* Prior surgery or treatment to the evaluated lesion.

Outcomes:
Lesion benignancy or malignancy
Development and Validation of an Artificial Intelligence System for Anatomic Site Recognition and Lesion Detection Based on Electronic Nasopharyngolaryngoscopic Images: A Prospective Multicenter Study
Trial ID: NCT07326358
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-12-12
Completion date: 2027-03-31
Sponsor: Ruijin Hospital
Conditions: Nasopharyngeal Neoplasms; Laryngeal Disease
Interventions: Diagnostic; Diagnostic
Locations: Ruijin Hospital, Shanghai Jiao Tong University School of Medicine
Countries: China
Enrollment: 500
Age range: 18 Years - None
Sex: ALL

Summary:
An artificial intelligence-assisted system is trained and validated by collecting nasopharyngolaryngoscopy images from patients.

Eligibility criteria:
Inclusion Criteria:

* Age ≥ 18 years;
* Underwent standard electronic nasopharyngolaryngoscopy;
* Patients who underwent biopsy sampling have a clear pathological diagnosis;
* Signed a written informed consent form.

Exclusion Criteria:

* Image quality is substandard with severe motion artifacts;
* Lesion images are unclear and incomplete.

Outcomes:
performance of lesion detection; performance of anatomic site recognition
Assessment of Artificial Intelligence Algorithms for ROTEM Analysis in Coagulation Management
Trial ID: NCT07043556
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-07-01
Completion date: 2025-12-15
Sponsor: Ondokuz Mayıs University
Conditions: Coagulopathy
Interventions: Artificial Intelligence-Based ROTEM Interpretation
Locations: İstanbul Aydın Üniversitesi Sağlık Uygulama ve Araştırma Merkezi Medical Park Florya Hastanesi; Ondokuz Mayis University
Countries: Turkey (Türkiye); Turkey (Türkiye)
Enrollment: 144
Age range: 18 Years - None
Sex: ALL

Summary:
The goal of this observational validation study is to evaluate whether artificial intelligence (AI) models can accurately interpret ROTEM (Rotational Thromboelastometry) data and provide appropriate treatment recommendations in adult patients undergoing elective cardiac or liver transplantation surgery.

The main questions it aims to answer are:

Can AI models (e.g., ChatGPT and Gemini ) accurately determine whether treatment is indicated based on ROTEM parameters? Can AI models correctly identify the type of coagulopathy (e.g., fibrinogen deficiency, platelet dysfunction)? Are the treatment recommendations from AI models concordant with expert clinical consensus? Researchers will compare the decisions made by AI models to a gold standard expert panel to see if AI models can match or approximate expert-level decision-making in interpreting ROTEM outputs.

Participants will:

Undergo elective cardiac or liver transplant surgery. Have standard ROTEM tests performed intraoperatively.

Have their anonymized ROTEM data reviewed independently by:

A panel of 3 clinical experts. AI models (ChatGPT and Gemini) using standardized prompts and ROTEM interpretation guidelines.

Eligibility criteria:
Inclusion Criteria:

* Adult patients undergoing elective cardiac surgery (CABG, valve surgery, aortic procedures)
* Adult patients undergoing liver transplantation
* Availability of complete ROTEM results (EXTEM, INTEM, FIBTEM, +/- HEPTEM, APTEM)
* Informed written consent obtained

Exclusion Criteria:

* Incomplete or technically invalid ROTEM data
* Pediatric patients (\<18 years)
* Refusal to participate or lack of informed consent
* Emergency and redo surgeries

Outcomes:
treatment
Artificial Intelligence Algorithms to Automate the Total Marrow (Lymph-node) Irradiation by VMAT Optimization Using WB-CT/MRI and Synthetic WB-CT - The AuToMI Project
Trial ID: NCT04976205
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2021-06-15
Completion date: 2025-08-08
Sponsor: Istituto Clinico Humanitas
Conditions: Hematological Disease
Interventions: Total Marrow (Lymph-node) Irradiation
Locations: IRCCS Humanitas Research Center
Countries: Italy
Enrollment: 60
Age range: 18 Years - None
Sex: ALL

Summary:
Total Body Irradiation (TBI) was shown to help in providing immunosuppression that facilitates the donor transplant acceptance. Randomized trials demonstrated that conditioning regimens to bone marrow transplantation (BMT) including TBI have produced better survival rates than chemo-only regimens. The TBI target is represented by the whole BM, and eventually the whole lymphatic system, liver, spleen. The increased life expectancy revealed the occurrence of important toxicities because of full doses received by organs at risk (OARs) and this limited the use of TBI. Many groups have explored the possibility of sophisticated techniques for reducing the dose to healthy tissues while increasing the dose to the BM. These newer approaches aim to generate total marrow (lymph-node) irradiation (TMI/TMLI), sparing as much as possible non-skeletal and non-lymphoid structures.

Actually, the time required to optimize a TMI/TMLI plan is 10 days. Therefore, the simulation Computed Tomography (CT) is performed many days before the BMT. Furthermore, the lymph-nodes are defined only on CT images.

Eligibility criteria:
Inclusion Criteria:

* Written, signed informed consent
* Adult patients aged ≥18 years
* Diagnosis of Hematological disease
* Eligibility for allogeneic stem cell transplantation as center guidelines
* TMI/TLI as part of the conditioning regimen

Exclusion Criteria:

\- Conditioning regimen including only chemotherapeutic agents

Outcomes:
Reduction of Lymph nodes target volume thanks to WB-MRI
Real-time Computer-Aided Detection of Colonic Adenomas With NEC WISE VISION® Endoscopy: Prospective, Randomized Clinical Performance Evaluation
Trial ID: NCT05611151
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2023-04-30
Completion date: 2024-09-10
Sponsor: NEC Corporation
Collaborators: Meditrial Europe Ltd.; Meditrial USA Inc.
Conditions: Colorectal Cancer; Adenoma Colon; Polyp of Colon
Interventions: Computer Aided Detection (CADe)
Locations: Kansas City VA Hospital; GastroZentrum Lippe; Humanitas Mater Domini; Humanitas Research Hospital; Portsmouth Hospitals University NHS Trust, Cosham
Countries: United States; Germany; Italy; Italy; United Kingdom
Enrollment: 830
Age range: 45 Years - 75 Years
Sex: ALL

Summary:
This is a prospective, multicenter, randomized study to evaluate the clinical performance of a novel CADe device, WISE VISION® Endoscopy System, in patients undergoing high-definition white light (HDWL) colonoscopy for screening or surveillance of colorectal Cancer (CRC).

Eligible subjects who meet the study inclusion/exclusion criteria will be randomized in a 1:1 ratio to undergo colonoscopy :

* Experimental: CADe colonoscopy procedure with WISE VISION® Endoscopy (CADe Group)
* Control: Standard Colonoscopy without CADe (Standard Colonoscopy Group)

Eligibility criteria:
Inclusion Criteria:

* Patient is presenting to the endoscopy unit for colon cancer screening or surveillance colonoscopy.
* Signed Informed Consent

Exclusion Criteria:

1. Contraindications to colonoscopy
2. Colonoscopy within the previous three (3) years
3. High-risk indications for colonoscopy
4. Antithrombotic therapy that cannot be stopped, precluding polyp resection
5. Inflammatory bowel disease
6. Referred for endoscopic mucosal resection (EMR)
7. Familial adenomatous polyposis syndrome or Serrated Polyposis Syndrome
8. Pregnant or planning a pregnancy during the study period

Outcomes:
Adenomas per Colonoscopy (APC)
A Platform for Multidisciplinary Medical Artificial Intelligence Development
Trial ID: NCT04890847
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2021-03-18
Completion date: 2021-05-31
Sponsor: Sun Yat-sen University
Conditions: Medical Artificial Intelligence; Medical Imaging
Locations: Zhongshan Ophthalmic Center, Sun Yat-sen Univerisity
Countries: China
Enrollment: 200
Age range: None - None
Sex: ALL

Summary:
Biomedical deep learning (DL) often relies heavily on generating reliable labels for large-scale data and highly technical requirements for model training. To efficiently develop DL models, we established an integrated platform to introduce automation to both annotation and model training-the primary process of DL model development. Based on this platform, we quantitively validated and compared the annotation strategy and AI model development with the pure manual annotation method performed on medical image datasets from multiple disciplines.

Eligibility criteria:
Inclusion Criteria:

* have medical imaging record (including ophthalmology, pathology, radiography, blood cells, and endoscopy)

Exclusion Criteria:

* unqualified medical imaging

Outcomes:
annotation accuracy
Development and Application of an AI Model for Accurate Interpretation of Abdominal Enhanced CT Images
Trial ID: NCT07040358
Status: ACTIVE_NOT_RECRUITING
Study type: OBSERVATIONAL
Start date: 2023-12-01
Completion date: 2026-06
Sponsor: First Affiliated Hospital of Zhejiang University
Collaborators: the First Division Hospital of Xinjiang Production and Construction Corps; The First People's Hospital of Yuhang District; Affiliated Hospital of Jiaxing University; Jixi County People's Hospital; Anji County People's Hospital; Zhejiang University; People's Hospital of Beilun District, Ningbo City; Haining People's Hospital; Jingning County People's Hospital
Conditions: Abdominal Diseases
Locations: the First Affliated Hospital, Zhejiang University School of Medicine
Countries: China
Enrollment: 2000000
Age range: 18 Years - None
Sex: ALL

Summary:
This study aims to develop an AI-assisted diagnostic system for abdominal contrast-enhanced CT images using data from multiple inpatient centers. In collaboration with Alibaba DAMO Academy, the project will address key mathematical challenges limiting current automated image interpretation, including feature space alignment, hybrid reasoning, and multimodal report generation. The study includes the following components: (1) construction of a dual-modality foundation model to align abdominal CT features with corresponding radiology reports; (2) development of a model to standardize CT phase variation among patients; and (3) creation of an automated image interpretation and reporting system that integrates multi-source clinical data. The effectiveness of the system will be evaluated through a report quality assessment framework and clinical validation. This project aims to improve the accuracy and clinical applicability of automated abdominal disease interpretation and promote intelligent innovation in healthcare delivery.

Eligibility criteria:
Inclusion Criteria:

* multiphase contrast-enhanced abdominal CT covering the full abdominal region and corresponding radiology reports matched to the CT images

Exclusion Criteria:

* CT images with poor diagnostic quality due to artifacts, including but not limited to: Convolution artifacts caused by improper arm positioning (e.g., arms placed alongside the body instead of above the head),Respiratory motion artifacts due to inadequate breath-holding.

Outcomes:
Performance of AI Model for Lesion Detection on Abdominal Contrast-Enhanced CT