moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
External Validation of Artificial Intelligence-Enabled Electrocardiograms for the Detection of Reduced Left Ventricular Ejection Fraction
Trial ID: NCT07038018
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-08-01
Completion date: 2025-09-30
Sponsor: Tri-Service General Hospital
Conditions: Cardiac Disease
Interventions: AI-ECG Algorithm
Locations: Hualien Armed Forces General Hospital; Kaohsiung Armed Forces General Hospital Gangshan Branch; Kaohsiung Armed Forces General Hospital; Zuoying Armed Forces General Hospital; Tri-Service General Hospital Keelung Branch; Tri-Service General Hospital Penghu Branch; Kaohsiung Armed Forces General Hospital Pingtung Branch; Taichung Armed Forces General Hospital Zhongqing Branch; Taichung Armed Forces General Hospital; Tri-Service General Hospital Beitou Branch; Tri-Service General Hospital Songshan Branch; Taoyuan Armed Forces General Hospital Hsinchu Branch; Taoyuan Armed Forces General Hospital
Countries: Taiwan; Taiwan; Taiwan; Taiwan; Taiwan; Taiwan; Taiwan; Taiwan; Taiwan; Taiwan; Taiwan; Taiwan; Taiwan
Enrollment: 12500
Age range: 18 Years - 100 Years
Sex: ALL

Summary:
This is a multi-center, retrospective study evaluating the performance of an artificial intelligence-enabled electrocardiography (AI-ECG) algorithm in detecting reduced left ventricular ejection fraction (LVEF ≤ 40%). All included patients from participating hospitals must have undergone a digital 12-lead electrocardiogram (ECG) and an echocardiogram with assessment of LVEF within seven days. The AI-ECG algorithm will be applied to evaluate its diagnostic performance, which will be further assessed across subgroups stratified by demographic characteristics and clinical factors.

Eligibility criteria:
Inclusion Criteria:

* patients with ECGs and an echocardiogram within 7 days

Exclusion Criteria:

* Missing ECG signals
* Missing LVEF assessment in echocardiograms

Outcomes:
The Sensitivity and specificity of AI-ECG model for left ventricular ejection fraction ≤ 40%
Clinical Value of Deep Learning Reconstruction Technology in Ankle MRI
Trial ID: NCT07274423
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-08-01
Completion date: 2026-08-01
Sponsor: Lian Yang
Conditions: Ankle Trauma
Locations: Union Hospital，Tongji Medical College，Huazhong University of Science and Technology
Countries: China
Enrollment: 120
Age range: 18 Years - None
Sex: ALL

Summary:
By enrolling patients who underwent ankle MR(Magnetic Resonance) examinations at our center and using randomized allocation, the patients were divided into a study group and a control group. The study group underwent scanning using AI-based(Artificial Intelligence-based) ankle MRI sequences, while the control group was scanned using non-AI ankle MRI sequences.

Eligibility criteria:
Inclusion Criteria:

1. Patients undergoing ankle MRI at Wuhan Union Hospital from August 2025 to August 2026;
2. Aged \> 18 years old;
3. Patients who agree to participate in the study and provide signed informed consent;
4. Presence of a clear history of ankle trauma and related symptoms requiring MRI for diagnosis or evaluation.

Exclusion Criteria:

1 .Patients with MR examination contraindications (e.g., implanted metal devices or severe claustrophobia); 2. Patients with prior ankle surgery; 3. Patients who failed to complete MR examination or whose image quality was insufficient for diagnostic purposes.

Outcomes:
Patient satisfaction
Development and Clinical Application of a Foundational Model for Cardiovascular Disease Diagnosis and Prediction Based on Multimodal Medical Big Data
Trial ID: NCT06591923
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2024-10
Completion date: 2025-04
Sponsor: Shanghai Zhongshan Hospital
Conditions: Coronary Heart Disease (CHD); Heart Failure; Arrhythmias; Valvular Heart Disease
Interventions: No Interventions
Locations: Zhongshan Hospital
Countries: China
Enrollment: 1000000
Age range: 18 Years - None
Sex: ALL

Summary:
The goal of this observational study is to develop and evaluate the efficacy of a foundational model that integrates multimodal medical data to improve the diagnosis and prediction of cardiovascular diseases in patients aged 18 and older, including those with various heart conditions such as coronary artery disease, heart failure, and arrhythmias. The main questions it aims to answer are:

Can a multimodal data-based diagnostic model match or exceed the accuracy of traditional gold-standard methods like coronary angiography, MRI, and echocardiography? Does integrating different types of data (ECG, imaging, biochemical tests) improve diagnostic accuracy and prediction of cardiovascular disease outcomes? Researchers will compare the foundational model with traditional diagnostic methods to see if the model offers better sensitivity, specificity, and prediction accuracy across different heart disease types.

Participants will:

Provide data from past medical records, including ECG, echocardiography, cardiac MRI, and biochemical tests.

Undergo further data collection if necessary, in line with standard clinical procedures for cardiovascular disease management.

Eligibility criteria:
Inclusion Criteria:

Age ≥ 18 years: Patients who are 18 years of age or older. Time period: Patients who were treated or diagnosed between January 1, 2009, and December 31, 2023.

Complete medical records: Patients with comprehensive medical records, including ECG, echocardiography, MRI, CTA, nuclear imaging (SPECT/PET), and biochemical test results.

Cardiovascular diseases: Patients with diagnosed cardiovascular conditions, such as coronary artery disease (CAD), heart failure, arrhythmias, and valvular heart disease (VHD), as well as healthy individuals for comparison.

Willingness to participate: Patients who are able to provide informed consent or their legal representatives.

Exclusion Criteria:

Participation in other clinical trials: Patients who are currently participating in other clinical trials that may affect the study outcomes.

Incomplete medical records: Patients whose medical records lack essential data, such as ECG, echocardiography, MRI, CTA, nuclear imaging (SPECT/PET), or biochemical test results.

Data quality issues: Patients with records that have significant errors, inconsistencies, or incomplete data that cannot be reasonably corrected.

Ethical or legal concerns: Patients whose data cannot be used due to a lack of necessary consent or legal/ethical restrictions.

Outcomes:
Area under ROC
Feasibility of Mobile Game System for Potential Treatment of Developmental Delays
Trial ID: NCT04909710
Status: WITHDRAWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2021-07-15
Completion date: 2026-02-28
Sponsor: Stanford University
Conditions: Autism Spectrum Disorder; Autism
Interventions: Mobile Game System for Potential Treatment of Developmental Delays
Locations: Stanford University
Countries: United States
Enrollment: 0
Age range: 2 Years - 12 Years
Sex: ALL

Summary:
The investigators will test the feasibility and preliminary efficacy of a Mobile game system meant to generate valuable data for measuring progress and building novel artificial intelligence models while delivering impactful education for and treatment of children with developmental delays such as autism. Investigators will enroll children with autism aged 2-8 years old and their neurotypical sibling or an unrelated neurotypical control (aged 2-8 years old).

Eligibility criteria:
Inclusion Criteria:

1. Parent or primary caregiver over 18 of a child with autism, we will enrich for children between the ages of 2 and 8 years old.
2. Autism diagnosis confirmed by a screener such as the Mobile Autism Risk Assessment (MARA) and or similar published tools.

Exclusion Criteria:

(1) Physical, intellectual, language related, or environmental limitations that prevent the ability for the family to play the game as intended.

Outcomes:
Change in parent reported socialization subscale scores of the Vineland Adaptive Behavior Scales,2nd Edition (VABS-2) from baseline to week 12 Vineland Adaptive Behavior Scales, 2d edition (VABS-2) Socialization subscale of the Parent/Caregiver; Mobile Application Usage-Total games started.
Predicting Atrial Fibrillation and Life-threatening Cardiac Arrhythmias and Conduction Disorders in the Perioperative Period in High Cardiac Risk Surgery Using Artificial Intelligence Elements
Trial ID: NCT07348991
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2026-12-31
Completion date: 2028-05-30
Sponsor: I.M. Sechenov First Moscow State Medical University
Conditions: Atrial Fibrillation; Atrioventricular Block, Second and Third Degree; Sinus Node Dysfunction; Ventricular Tachycardia (VT); Ventricular Fibrillation
Interventions: digital single-channel ECG recording
Enrollment: 300
Age range: 18 Years - None
Sex: ALL

Summary:
During the preoperative examination, in addition to the standard examination recommended before high-risk cardiac surgeries, a digital single-lead ECG is recorded using portable CardioQUARK devices. This ECG is analyzed for time, amplitude, and frequency parameters-more than 200 parameters per cardiac cycle. Additionally, the available parameters of a standard 12-lead ECG recording are determined. All parameters of both single-lead and 12-lead ECGs will be included in a multivariate regression analysis to determine the optimal method for predicting atrial fibrillation, life-threatening cardiac arrhythmias, and conduction abnormalities in the perioperative period after high-risk cardiac procedures.

The study will include patients undergoing high-risk cardiac surgeries, such as those on the heart, aorta, esophagus, pancreas, liver, and lungs, as well as high-risk oncological surgeries. A partial analysis will also be performed depending on the type of intervention. Rhythm and conduction disturbances will be recorded during surgery, as well as in the postoperative period, using ECG monitors, 24-hour ECG monitoring, and repeated recordings of a standard ECG when complaints or signs of unstable hemodynamics arise

Eligibility criteria:
Inclusion Criteria:

* Patients over 18 years of age;
* Scheduled surgery with high cardiac risk
* Informed consent was given to participate in the study

Exclusion Criteria:

* Cancellation of the surgery for any reason;
* The patient's unwillingness to continue participating in the study;
* Poor quality of the ECG recording on a single-channel ECG monitor.

Outcomes:
specificity, sensitivity, prognostic significance of a positive and negative result, diagnostic accuracy; specificity, sensitivity, prognostic significance of a positive and negative result, prognostic significance of a positive and positive result, diagnostic accuracy
Validation of the Utility of Myopia Intelligent Diagnostic System: A Clinical Trial
Trial ID: NCT04014725
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2019-07-10
Completion date: 2019-08-01
Sponsor: Sun Yat-sen University
Conditions: Myopia
Interventions: Myopia Intelligent Diagnostic System
Enrollment: 50
Age range: 6 Years - 18 Years
Sex: ALL

Summary:
The screening of myopia via artificial intelligence represents an challenge in computational medicine. Here, the investigators use "deep learning" to create an automatic diagnostic system for myopia screening using ocular appearance images. The investigator also use this system and conduct clinical trial to validate its performance.

Eligibility criteria:
Inclusion Criteria:

* Student aged 6-18

Exclusion Criteria:

\-

Outcomes:
The AUC of the Myopia Intelligent Diagnostic System
Echocardiographic Detection of Cardiotoxicity Using the Ventripoint (VMS) 3D AI Echocardiography System Versus Conventional Echo Analyses and Cardiac MRI in Patients Receiving Chemotherapy
Trial ID: NCT07093918
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2026-04
Completion date: 2030-11
Sponsor: The Royal Wolverhampton Hospitals NHS Trust
Collaborators: VentriPoint Diagnostics Ltd.
Conditions: Cardiotoxicity
Interventions: V-echo; L-echo
Enrollment: 120
Age range: 18 Years - None
Sex: ALL

Summary:
To investigate the clinical utility of both 3D and 2D artificial intelligence reconstruction echocardiography (V-echo and L-echo) assessment against conventional echocardiography and clinical cardiovascular magnetic resonance (CMR) imaging for the characterisation of cardiac function and detection of cardiotoxicity in patients receiving chemotherapy.

Diagnostic accuracy of V-echo and L-echo for quantification of cardiac chamber sizes and systolic function (left ventricular ejection fraction (LVEF) and strain analyses) versus conventional echocardiography (C-echo) and CMR; workflow measures including imaging temporal measures of image acquisition, registration and analysis versus conventional approaches; survey of markers of acceptability by patients and sonographers; cost effectiveness analysis of V-echo and L-echo versus conventional echocardiography; assessment of the relationship between 3D strain parameters derived from V-echo data, 2D strain parameters derived from L-echo and correlation with conventional markers of cardiac function, namely LVEF and global longitudinal strain.

Eligibility criteria:
Inclusion Criteria:

* Aged 18 years or older
* Received potentially cardiotoxic chemotherapy
* Participant requires echocardiography surveillance as standard current practice

Exclusion Criteria:

* Below the age of 18 years old
* Inability to give informed consent
* Implanted cardiac devices
* Not having received cardiotoxic chemotherapy. Ineligible patients and patients not wishing to take part will be excluded, the reasons for this will be recorded and baseline CONSORT demographics noted including age, gender, ethnicity.

Outcomes:
Comparison of LVEF measured by V-echo and L-echo in comparison to CMR at baseline imaging
Effect of Artıfıcıal Intellıgence Based Mobıle Vırtual Assıstant Developed For Indıvıduals Wıth Dıabetes On Cost Effıcıency, Hospıtal Admıssıon Rate, Self-Care And Hypoglycemıa
Trial ID: NCT06079450
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2022-06-10
Completion date: 2023-08-07
Sponsor: Izmir Tinaztepe University
Conditions: Diabetes
Interventions: artificial intelligence based mobile application
Locations: Izmir Katip Celebi University
Countries: Turkey (Türkiye)
Enrollment: 1
Age range: 18 Years - 65 Years
Sex: FEMALE

Summary:
Aim: This study was conducted experimentally to examine the effect of artificial intelligence-based mobile virtual assistant developed for individuals with diabetes on cost, hospitalization rate, self-care and hypoglycemia.

Methods: The research is multi-stage and designed as three stages in itself. According to this; development of the mobile application in the first and second stages and adding artificial intelligence to the application as a project; In the third stage, it was planned to examine the effect of the application on the variables and scales. The data of the study were collected between June 2022 and June 2023 in the Endocrinology Polyclinic of two private hospitals in Izmir and a diabetes association where individuals with diabetes were registered. Power 0.80 was determined by using NCSS PAS statistical software from the population of the research; The minimum number of samples to be included in the study was calculated as n:122 and they were divided into two as intervention and control groups by randomization. The research sample was carried out as intervention (n:60) and control (n:60) lastly due to death and cost. Five data collection tools were used, namely "Individual Introduction Form", "Diabetes Self-Care Scale", "Hypoglycemia Confidence Scale", "Mobile Application Opinion Form" and "Cost Table". An artificial intelligence-based mobile virtual assistant application was applied to the individuals with diabetes in the intervention group, and the data were collected three times, at the 0th, 6th and 12th months, and the costs were recorded. The standard outpatient trainings, which are currently applied, continued to be given to individuals with diabetes in the control group, the data were collected twice, at the beginning (0. month) and 12. months, and the costs were recorded. In the evaluation of the data, number, percentage, arithmetic mean, standard deviation, minimum and maximum median were calculated. Among the variables, chi-square, Kruskal Wallis, Mann Whitney U test and t test were used.

Eligibility criteria:
Inclusion Criteria:

* Having been diagnosed with Type 1 and Type 2 diabetes at least six months ago, according to the criteria of the American Diabetes Association (ADA),
* Using insulin for at least six months,
* Being between the ages of 18- 65,
* Being able to read and write and speak Turkish,
* Having an Android phone and being able to use mobile applications,
* To volunteer to participate in the study.

Exclusion Criteria:

Outcomes:
Diabetes Self-Care Scale; Hypoglycemic Confidence Scale
A RadioPathomics Integrated Artificial Intelligence System to Predict Tumor Regression Grading of Neoadjuvant Treatment in Locally Advanced Rectal Cancer: A Multicenter, Prospective and Observational Clinical Study
Trial ID: NCT04273451
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2020-01-10
Completion date: 2020-12
Sponsor: Sixth Affiliated Hospital, Sun Yat-sen University
Collaborators: The Third Affiliated Hospital of Kunming Medical College.; Sir Run Run Shaw Hospital
Conditions: Rectal Cancer
Locations: the Sixth Affiliated Hospital of Sun Yat-sen University; The Third Affiliated Hospital of Kunming Medical College; Sir Run Run Shaw Hospital
Countries: China; China; China
Enrollment: 100
Age range: 18 Years - 75 Years
Sex: ALL

Summary:
In this study, investigators apply a radiopathomics artificial intelligence (AI) supportive model to predict neoadjuvant chemoradiotherapy (nCRT) response before the nCRT is delivered for the patients with locally advanced rectal cancer (LARC). The radiopathomics AI system predicts individual tumor regression grading (TRG) category based on each patient's radiopathomics features extracted from the Magnetic Resonance Imaging (MRI) and biopsy images. The predictive power to classify each patient into particular TRG category will be validated in this multicenter, prospective clinical study.

Eligibility criteria:
Inclusion Criteria:

* pathologically diagnosed as rectal adenocarcinoma
* defined as clinical II-III staging (≥T3, and/or positive nodal status) without distant metastasis by enhanced Magnetic Resonance Imaging (MRI)
* intending to receive or undergoing neoadjuvant concurrent chemoradiotherapy (5-fluorouracil based chemotherapy, given orally or intravenously; Intensity-Modulated Radiotherapy or Volume-Modulated Radiotherapy delivered at 50 gray (Gy) in gross tumor volume (GTV) and 45 Gy in clinical target volume (CTV) by 25 fractions)
* intending to receive total mesorectum excision (TME) surgery after neoadjuvant therapy (not completed at the enrollment), and adjuvant chemotherapy
* MRI (high-solution T2-weighted imaging, contrast-enhanced T1-weighted imaging, and diffusion-weighted imaging are required) examination is completed before the neoadjuvant chemoradiotherapy
* biopsy H\&E stained slides are available and scanned with high resolution before the neoadjuvant chemoradiotherapy

Exclusion Criteria:

* with history of other cancer
* insufficient imaging quality of MRI to delineate tumor volume or obtain measurements (e.g., lack of sequence, motion artifacts)
* insufficient imaging quality of biopsy slides imaging to delineate tumor volume or obtain measurements (e.g., tissue dissection, color anomaly)
* incomplete neoadjuvant chemoradiotherapy
* no surgery after neoadjuvant chemoradiotherapy resulting in lack of pathologic assessment of tumor response
* tumor recurrence or distant metastasis during neoadjuvant chemoradiotherapy

Outcomes:
The prediction accuracy of the radiopathomics artificial intelligence model
CybeR-human systEms for perSonalIzed mentaL and physIcal wEll-beiNg and Health
Trial ID: NCT06490783
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2024-07-01
Completion date: 2026-05-31
Sponsor: Istituto per la Ricerca e l'Innovazione Biomedica
Conditions: Geriatric Health Services; Health Services for the Elderly
Interventions: Noninvasive wearable devices (smartwatch and heart rate monitor band)
Locations: Institute for Biomedical Research and Innovation (IRIB)-National Reasearch Council (CNR), Messina 98164, Italy
Countries: Italy
Enrollment: 10
Age range: 60 Years - None
Sex: ALL

Summary:
The purpose of the present study is to evaluate the effectiveness of using multi-parameter monitoring devices in the elderly to improve their quality of life not already understood as the absence of disease but in a logic that is intrinsically linked to the body-mind relationship, which is increasingly significant as biological age advances. The study will be conducted on a sample of volunteer elderly subjects who will wear devices capable of constantly monitoring vital parameters such as heart rate, physical activity, sleep quality, stress levels and higher level activities, linked sensory and cognitive aspects ecologically integrated with the elderly person's living environment, in the sense of an evaluative and qualitative focus on relationships within the person's area of action/interaction, possibly supported and stimulated by individualized and easily usable activities. The signals interpreted and returned by the technology to the elderly person who uses it can also act as a reassuring self-assessment of even normal body states, sometimes experienced as threatening and anxiogenic, thus stressful. The collection and management of these data may serve as a reference to the recognition of distress signals and complex experiences (e.g., depressive) that normally have significant effects on mental health, understood as intrinsically linked to the health of the body.

Eligibility criteria:
Inclusion Criteria:

* healthy male and female aged 60 years and older
* signing of informed consent

Exclusion Criteria:

* chronic diseases
* cardiovascular disease
* presence of dementia and/or depression
* presence of confirmed paranoid or psychotic symptoms

Outcomes:
Heart Rate(HR); Heart rate variability (HRV); Facial emotion recognition; Steps taken; Distance traveled; Calories burned