moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Comparison of Distal and Conventional Transradial Access for Coronary Catheterization: A Multicenter, Quasi-Randomized Study
Trial ID: NCT07138170
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-08-15
Completion date: 2026-04-29
Sponsor: Jordanian Research and Artificial Intelligence Group
Conditions: Radial Artery Occlusion Following Coronary Catheterization
Interventions: Conventional Transradial Artery Access; Distal Radial Artery Access
Locations: Specialty Hospital
Countries: Jordan
Enrollment: 350
Age range: 18 Years - None
Sex: ALL

Summary:
Background: Radial artery occlusion (RAO) is a recognized complication of transradial coronary access, with reported incidence rates ranging from 5% to 30%. Distal radial access (DRA), performed at the anatomical snuffbox, has emerged as a promising alternative that may better preserve radial artery patency by maintaining antegrade perfusion through the palmar arch during hemostasis.

Objective: To compare distal radial access with conventional transradial access in terms of radial artery patency and access-site outcomes in a real-world all-comers population undergoing coronary angiography or percutaneous coronary intervention.

Methods: This prospective, multicenter, open-label, quasi-randomized comparative study enrolled 350 patients across three community-based hospitals in Palestine and Jordan between 2024 and 2026. Patients were allocated in a 1:1 ratio to DRA (n=183) or conventional transradial access (n=167) using an alternating sequence. The primary endpoint was radial artery patency assessed by blinded duplex ultrasonography at 24 hours and at 1 to 6 months. Secondary endpoints included access-site pain, bruising, numbness, and crossover to an alternative access site. The 24-hour outcome was analyzed by intention-to-treat and long-term outcomes by per-protocol analysis. Adjusted analyses used Firth penalized logistic regression.

Eligibility criteria:
Inclusion Criteria:

1. Age ≥ 18 years
2. Undergoing diagnostic coronary angiography or percutaneous coronary intervention via an intended radial approach
3. Palpable radial pulse at the intended puncture site
4. Able to provide written or verbal informed consent
5. No contraindication to radial artery access at the intended site, as determined by the treating operator

Exclusion Criteria:

1. Age \< 18 years
2. Inability to provide informed consent
3. Absent or non-palpable radial pulse at the intended access site
4. Active pregnancy
5. Medical condition likely to cause non-compliance with the study protocol or confound data interpretation

Outcomes:
Radial artery occlusion (RAO) assessed by duplex ultrasonography
Prediction of Cognitive Test Performance Using AI-Based Analysis of Narrative Speech
Trial ID: NCT07511270
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-12-01
Completion date: 2026-04-30
Sponsor: Chang Gung Memorial Hospital
Conditions: Cognitive Function Assessment; Mild Cognitive Impairment; Subjective Cognitive Decline
Interventions: AI-Based Storytelling Speech Assessment
Locations: Taoyuan
Countries: Taiwan
Enrollment: 150
Age range: 50 Years - None
Sex: ALL

Summary:
This study aims to evaluate a new artificial intelligence (AI)-based method for measuring cognitive function using speech recordings. Participants will complete a short storytelling task in which they describe a story based on an image while their voice is recorded using a computer or mobile device.

The speech recordings will be analyzed using AI technology to identify patterns in speech that may be related to cognitive function. The system will then estimate scores that correspond to commonly used cognitive tests.

To evaluate the accuracy of this method, the AI-generated scores will be compared with results from standard cognitive assessments administered by trained researchers. These assessments may include tests commonly used to measure memory, attention, and other cognitive abilities.

The goal of this study is to determine whether speech analysis using AI can provide a convenient and efficient approach for cognitive assessment. If successful, this technology may help support early detection of cognitive decline and provide a practical tool for large-scale or remote cognitive screening.

Eligibility criteria:
Inclusion Criteria:

1. Age 50 years or older
2. Able to communicate verbally and understand study instructions
3. Able to hear and speak clearly
4. Willing and able to provide written informed consent

Exclusion Criteria:

1. Severe speech or hearing impairment
2. History of major psychiatric disorders
3. History of stroke, traumatic brain injury, Parkinson's disease, epilepsy, or other neurodegenerative diseases
4. Current use of medications that may affect cognitive performance
5. Unable to complete the study procedures due to severe attention deficits or agitation

Outcomes:
Correlation Between AI-Predicted Cognitive Scores and Standard Cognitive Assessment Scores
Portable Measurement Methods Combined With Artificial Intelligence in Detection of Atrial Fibrillation
Trial ID: NCT04917653
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2021-06-07
Completion date: 2023-12-31
Sponsor: Kuopio University Hospital
Collaborators: University of Eastern Finland
Conditions: Atrial Fibrillation; Heart Rate Fast; Heart Rate Low; Sinus Rhythm
Interventions: Heart rhythm monitoring with wearable device; Blood samples and biomarkers
Locations: Kuopio University Hospital
Countries: Finland
Enrollment: 100
Age range: 18 Years - None
Sex: ALL

Summary:
In Western countries, every sixth person in their lifetime and 15,000 people in Finland have a new stroke each year. About every fourth stroke is based on cardiac embolism. Atrial fibrillation (AF) is the most common arrhythmia that increases the risk of thromboembolic complications, such as stroke. It may cause formation of thrombi in the left atrium with ensuing embolization in the cerebral and peripheral circulation. AF is often asymptomatic and paroxysmal. Thus, the diagnosis of AF is often challenging.

A new onset AF is usually treated with cardioversion (CV), in which the abnormal rhythm is converted back to sinus rhythm (SR). However, a long-lasting AF (\>48 hours) is associated with risk of stroke. Therefore, the duration of AF needs to be known before a CV can be performed. This study evaluates the ability of novel customer-targeted heart measuring devices to detect rhythm change and short AF episodes. Moreover, novel biomarkers will be analyzed from the blood samples of AF patients and their suitability to estimate the duration of AF will be evaluated.

The research will be accomplished in cooperation with the Kuopio University Hospital Emergency Department, the Heart Center, the Department of Applied Physics of the University of Eastern Finland and Heart2Save Ltd.

The results of the research project will be published in the scientific journals of medicine and medical technology and will be presented at scientific conferences of the respective fields. The research results of the project can be utilized by all companies in the medical technology industry, in particular companies that produce ECG measuring instruments and companies that produce rhythm recognition software.

Eligibility criteria:
Inclusion Criteria:

* Patients with early-onset atrial fibrillation treated by cardioversion during the treatment period in the emergency department of Kuopio University Hospital.

Exclusion Criteria:

* Body mass index (BMI) over 35, implanted heart pacemaker device and a medical condition requiring immediate treatment that would be delayed by the study measurements.

Outcomes:
Arrhythmia detection in rhythm change with single-lead ECG and wearable PPG; Evaluation the various blood-based biomarkers in the estimation the duration of atrial fibrillation episode
Prospective Evaluation of Artificial Intelligence ECG With Consumer-Facing ECG Devices for Detection of Hypertrophic Cardiomyopathy and Distinction From Athlete's
Trial ID: NCT06290570
Status: ACTIVE_NOT_RECRUITING
Study type: OBSERVATIONAL
Start date: 2024-05-07
Completion date: 2026-12
Sponsor: Mayo Clinic
Conditions: Hypertrophic Cardiomyopathy
Interventions: 12-Lead ECG; Apple Smart Watch Single Lead ECG; AliveCor KardiaMobile 6-Lead ECG
Locations: Mayo Clinic in Rochester
Countries: United States
Enrollment: 183
Age range: 18 Years - None
Sex: ALL

Summary:
The purpose of this study is to evaluate the AI-ECG algorithm for HCM in detecting HCM and in differentiating it from athlete's using not only the standard 12-lead ECG, but also ECGs obtained with the Apple Watch and Alivecor KardiaMobile devices.

Eligibility criteria:
Inclusion Criteria:

* Patients with clinically validated diagnoses of HCM (n=150) and athlete's (n=150) will be identified by pre-screening of the clinic appointments for each of the specialty HCM and Sports Cardiology clinics or in the CV fellows' clinic (in patients with an established diagnosis and no pending testing). All diagnoses will need to be supported by unequivocal imaging and other ancillary data per our standard of care and at the determination of clinic experts.

Exclusion Criteria:

* Any exception to the above criteria.

Outcomes:
Distribution of AI-ECG probabilities in HCM; Comparative diagnostic performance between tracings obtained from different devices
Comparison of the Hologic Genius Digital Diagnostics System With the ThinPrep Imaging System - a Retrospective Assessment
Trial ID: NCT05000125
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2021-08-20
Completion date: 2021-12-31
Sponsor: Hologic Deutschland GmbH
Conditions: Cervical Cancer Screening
Interventions: Hologic's Genius Digital Diagnostics System
Locations: CytoMol - MVZ für Zytologie und Molekularbiologie Frankfurt GbR
Countries: Germany
Enrollment: 1994
Age range: 35 Years - 90 Years
Sex: FEMALE

Summary:
Quality assurance of the laboratory examinations. Retrospective non-interventional study with ThinPrep slides collected as part of the German Co-Screening Program for routine diagnostic. These slides will retrospectively be used to validate the Hologic Genius Digital Cytology (DC) system vs. the ThinPrep Imaging system (TIS).

Eligibility criteria:
Inclusion Criteria:

* LBC ThinPrep® samples are collected by gynaecologists and sent routinely to cytology laboratory CytoMol as part of the German Cervical cancer screening program.

Outcomes:
Sensitivity and specificity of detection of pre-cancerous lesions using Hologic Genius Digital Cytology Imaging scanner
The Fourth Hospital of Hebei Medical University
Trial ID: NCT06506825
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2024-08-01
Completion date: 2030-12-31
Sponsor: Qun Zhao
Conditions: The Study Focuses on Gastric Cancer
Interventions: Gastric Cancer AI-driven Data Integration Genomic Analysis Biomarker
Locations: Department of General Surgery
Countries: China
Enrollment: 30000
Age range: 18 Years - None
Sex: ALL

Summary:
This clinical trial aims to construct and apply a collaborative network database for gastric cancer in Hebei Province, driven by artificial intelligence (AI) technology. The project seeks to integrate clinical data, genomic information, and treatment outcomes from multiple hospitals and research centers within the region. By leveraging advanced AI algorithms, the database will facilitate comprehensive data analysis to identify novel biomarkers, optimize therapeutic strategies, and improve patient outcomes. This initiative will also support real-time data sharing and collaboration among healthcare providers, researchers, and policymakers, ultimately enhancing the overall management and treatment of gastric cancer in Hebei Province.

Eligibility criteria:
Inclusion Criteria:

All patients with gastric cancer

Exclusion Criteria:

None

Outcomes:
5-year overall survival
A Prospective, Multicenter, Observational Diagnostic Study to Externally Validate an Artificial Intelligence 12-lead Electrocardiogram Analysis Algorithm to Detect Patients With Acute Myocardial Infarction Who Visit Emergency Medical Center
Trial ID: NCT05435391
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2022-03-16
Completion date: 2023-05
Sponsor: CHA University
Collaborators: Medical AI Co., Ltd
Conditions: Myocardial Infarction or Chest Pain
Locations: CHA Bundang Medical Center
Countries: South Korea
Enrollment: 8814
Age range: 18 Years - None
Sex: ALL

Summary:
This study is a prospective multicenter observational study for external validation and model advancement of a deep learning based 12-lead electrocardiogram analysis algorithm targeting adult patients presenting to the emergency department with chest pain and acute myocardial infarction equivalent symptoms.

About 9,000 adult patients will be enrolled at 20 emergency medical centers in Korea. Artificial intelligence algorithms are manufactured by Medical AI Co., Ltd. It is an advanced version based on the model developed and published in 2020. It had the diagnostic performance of area under the receiver operating curve 0.901 and 0.951 for acute myocardial infarction and ST-segment elevation myocardial infarction, respectively. The primary endpoint is a diagnosis of acute myocardial infarction on the day of the emergency center visit, and the secondary endpoint is a 30-day major adverse cardiac event. From March 2022, patient registration will begin at centers that have been approved by the Institutional Review Board.

This is the first prospective multicenter emergency department validation study for a 12-lead electrocardiogram artificial intelligence algorithm to diagnose acute myocardial infarction. This study will give insight into the direction of future development by verifying whether the deep learning algorithm works well for patients visiting the real-world adult emergency medical center.

Eligibility criteria:
Inclusion Criteria:

* Adults over 18 years of age with suspected chest pain and acute myocardial infarction
* The onset or worsening of the symptom occurs within 24 hours

Exclusion Criteria:

* Out-of-hospital cardiac arrest (OHCA): patients with sustained (\>20 minutes) return-of-spontaneous-circulation are not excluded
* Patients in whom acute myocardial infarction can be clearly excluded, such as pneumothorax and traumatic chest pain

Outcomes:
Diagnosis of acute myocardial infarction (Type 1, 2)
Artificial Intelligence-assisted Colonoscopy in the Detection and Characterization of Colorectal Lesions: Randomized Controlled Clinical Trial
Trial ID: NCT07066046
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-02-01
Completion date: 2026-12-01
Sponsor: Instituto do Cancer do Estado de São Paulo
Conditions: Colorectal Cancer (CRC); Adenomatous Polyposis; Colorectal Lesions; Colorectal Cancer
Interventions: Colonoscopy
Locations: Hospital das Clínicas da Faculdade de Medicina da USP
Countries: Brazil
Enrollment: 1000
Age range: 18 Years - None
Sex: ALL

Summary:
The study aims to evaluate the effectiveness of artificial intelligence-assisted colonoscopy in increasing adenoma detection rate and the accuracy in the characterization of colorectal lesions, compared to standard colonoscopy, in a randomized controlled clinical trial setting.

Eligibility criteria:
Inclusion Criteria:

* All patients aged 18 years or older, with an elective indication for colonoscopy who sign the informed consent form agreeing to participate in the study.

Exclusion Criteria:

* History of inflammatory bowel disease.
* History of colorectal cancer.
* Personal history of colorectal surgery.
* Contraindication to endoscopic biopsies.
* History of intestinal polyposis syndromes.
* Urgent or emergency cases.
* Presence of severe, decompensated comorbidities, or with a score of 3 or higher according to the American Society of Anesthesiologists (ASA) classification.
* Incomplete colonoscopy that does not reach the cecum.
* Insufficient or inadequate bowel preparation, with a score lower than 6 on the Boston Bowel Preparation Scale.
* Patients who do not agree to participate in the study and do not sign the informed consent form (ICF).

Outcomes:
Number of patients with at least one adenoma detected, confirmed by histopathological analysis, during colonoscopy, in the AI group vs. control group
EchoNet-LVH-RCT: Blinded, Randomized Controlled Trial of Artificial Intelligence Guided Precision Assessment of Wall Thickness
Trial ID: NCT05791227
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-04-01
Completion date: 2025-11-01
Sponsor: Cedars-Sinai Medical Center
Conditions: Heart Failure
Interventions: Sonographer Evaluation; AI Evaluation
Enrollment: 4000
Age range: 18 Years - 100 Years
Sex: ALL

Summary:
To determine whether an integrated AI decision support can save time and improve accuracy of assessment of echocardiograms, the investigators are conducting a blinded, randomized controlled study of AI guided measurements of wall thickness in parasternal long axis view compared to sonographer measurements in preliminary readings of echocardiograms.

Eligibility criteria:
Inclusion Criteria:

* Sonographers and cardiologists at CSMC Echocardiography Lab
* All transthoracic echocardiogram studies performed in the CSMC echocardiogram lab

Exclusion Criteria:

* transthoracic echocardiogram studies not able to be measured by sonographers in run in period

Outcomes:
Proportion of studies for which the difference in wall thickness between preliminary to overread is greater than 0.3cm for LVIDd; Proportion of studies for which the difference in wall thickness between preliminary to overread is greater than 0.2cm for IVSd; Proportion of studies for which the difference in wall thickness between preliminary to overread is greater than 0.2cm for LVPWd; mean absolute error (MAE) of LVIDd between preliminary to overread between the two arms; mean absolute error (MAE) of IVSd between preliminary to overread between the two arms; mean absolute error (MAE) of LVPWd between preliminary to overread between the two arms
A Multicentric Validation Study on the Effectiveness and Safety of Artificial Intelligence Assisted System in Clinical Application of Retrograde Cholangiopancreatography
Trial ID: NCT04719117
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2020-09-01
Completion date: 2021-12-31
Sponsor: Renmin Hospital of Wuhan University
Conditions: Gastrointestinal Disease; Endoscopy; Artificial Intelligence; Cholangiopancreatography, Endoscopic Retrograde
Locations: Renmin hospital; People's Hospital; Changhai Hospital
Countries: China; China; China
Enrollment: 150
Age range: 18 Years - None
Sex: ALL

Summary:
In this study, the investigators proposed a prospective study about the effectiveness of artificial intelligence system for Retrograde cholangiopancreatography. The subjects would be include in an analyses groups. The AI-assisted system helps endoscopic physicians estimate the difficulty of Endoscopic retrograde cholangiopancreatography for choledocholithiasis and make recommendations based on guidelines and difficulty scores. The investigators used the stone removal times, success rate of stone extraction and Operating time to reflect the difficulty of the operation, and evaluated whether the results of the AI system were correct.

Eligibility criteria:
Inclusion Criteria:

* Who needs ERCP and its related tests are needed to further define the characteristics of digestive tract diseases
* Able to read, understand and sign informed consent
* The investigator believes that the subject can understand the process of the clinical study, is willing and able to complete all the study procedures and follow-up visits, and cooperate with the study procedures
* Patients with a natural duodenal papilla

Exclusion Criteria:

* Has participated in other clinical trials, signed informed consent and is in the follow-up period of other clinical trials
* Has drug or alcohol abuse or mental disorder in the last 5 years
* Women who are pregnant or lactating
* Subjects with previous biliary sphincterotomy
* The investigator determined that subjects were not suitable for ERCP and related tests
* A high-risk disease or other special condition that the investigator considers inappropriate for the subject to participate in a clinical trial
* Patients with known more severe pancreatic head carcinoma
* Patients with acute pancreatitis within 3 days
* Biliary stent replacement or removal did not occur after pancreatic angiography as expected
* Acute cardiovascular and cerebrovascular diseases

Outcomes:
Number of stone removal operations
Stratification of Arrhythmic Risk and/or Heart Failure Risk in Patients With Hereditary Heart Disease
Trial ID: NCT07257289
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2026-02-03
Completion date: 2036-12-12
Sponsor: Nantes University Hospital
Conditions: Hereditary Heart Diseases
Locations: CHU de Bordeaux; CHU de Brest; CHU de Clermont-Ferrand; CHU de Dijon; CHU de La Rochelle; CHU de Limoges; CHU de Montpellier; CHU de Nantes; CHU de Poitiers; CHU de Rennes; CHU de Strasbourg; CHU de Toulouse; CHU de Tours; CHU de la Martinique; CHU de la Réunion
Countries: France; France; France; France; France; France; France; France; France; France; France; France; France; Martinique; Reunion
Enrollment: 1000
Age range: 1 Year - 100 Years
Sex: ALL

Summary:
Sudden cardiac death (SCD) is one of the leading causes of death in developed countries. These deaths (more than 5,000 per year in France) are due to hereditary arrhythmias or cardiomyopathies. Early diagnosis of SCD is often achieved through family screening, but the main challenge is to stratify the risk of SCD in these patients. Indeed, prevention of SCD relies mainly on the implantation of an automatic defibrillator. The challenge is to identify patients who will develop SCD and avoid implanting an implantable cardioverter defibrillator (ICD) in patients who will never develop arrhythmias but who will face complications related to the ICD (inappropriate shocks, infection, lead failure), leading to a reduced quality of life and significant costs for the healthcare system. However, there is a lack of relevant clinical and biological markers for risk stratification, which rules out any possibility of preventive screening. Most of the clinical and ECG (electrocardiogram) parameters identifying an increased risk of SCD have not been reproduced in replication studies.

In this project, the investigator will develop a data processing and analysis pipeline using artificial intelligence methods to assess the individual risk of serious arrhythmic events or heart failure in patients with hereditary arrhythmic diseases or cardiomyopathies through the automated processing of multimodal data (clinical data, electrocardiogram (ECG), imaging (echocardiography, MRI magnetic resonance imaging), genetic data, biomarkers).

Eligibility criteria:
Inclusion Criteria :

I. hereditary heart disease II. All relatives of patients III. Patients referred to the reference centre for suspected hereditary rhythm disorders or cardiomyopathies IV. Written consent V. social security scheme

Exclusion Criteria :

I. Patients participating in a therapeutic trial that may interfere with the research results II. Patients under guardianship or curatorship.

Outcomes:
Arrhythmic and heart failure risk stratification