moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Application of Artificial Intelligence in the Prediction of Clinical Performance, Marginal Fit and Fracture Resistance of Vertical Versus Horizontal Margin Designs Fabricated With Two Ceramic Materials
Trial ID: NCT06164002
Status: ACTIVE_NOT_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2023-09-01
Completion date: 2025-05-15
Sponsor: Faculty of Dental Medicine for Girls
Conditions: Artificial Intelligence
Interventions: change in restoration material and margin design.
Locations: Walaa Tarek El Hefnawy
Countries: Egypt
Enrollment: 40
Age range: 20 Years - 50 Years
Sex: ALL

Summary:
The present study will be performed to evaluate application of artificial intelligence in the prediction of clinical performance, marginal fit and fracture resistance of vertical "feather-edge" versus horizontal "shoulder" margin designs fabricated with two ceramic materials (zirconia and hybrid ceramic)

Eligibility criteria:
Inclusion Criteria:

* 1\. Age range of patients should be 20-50 years old, able to read and sign the informed consent document. 2. Patient with posterior molars indicated for full coverage restorations. 3. Patient should be able to physically and psychologically tolerate conventional restorative procedures. 4. Patients who are willing to return for follow-up examination and evaluation. 5. Indicated molar should have adequate tooth preparation length to ensure proper retention and resistance form. 6. Patient with sound contralateral or adjacent tooth to the selected tooth requiring full coverage restoration.

Exclusion Criteria:

1. Patient in the growth stage with partially erupted teeth.
2. Patient with poor oral hygiene and motivation.
3. Pregnant women.
4. Patient with psychiatric problems or unrealistic expectations.
5. Patients with no opposing occluding dentition in the area intended for restoration.
6. Patients with parafunctional habits. -

Outcomes:
Artificial intelligence application assessment, laboratory marginal fit and fracture resistance
Prevention of Stroke and Sudden Cardiac Death by Recording of 1-Channel Electrocardiograms
Trial ID: NCT04637230
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2021-10-01
Completion date: 2023-06-30
Sponsor: A-Rhythmik GmbH
Conditions: Sinus Rhythm; Atrial Fibrillation; Atrial Premature Complexes; Ventricular Premature Complexes; Ventricular Tachycardia, Nonsustained
Interventions: Electrocardiogram analysis by Artificial Intelligence
Locations: Universitäres Herzzentrum, Lübeck, Germany
Countries: Germany
Enrollment: 10000
Age range: 18 Years - 85 Years
Sex: ALL

Summary:
Single-channel electrocardiograms (lead I of 12-lead surface ECG; 30 seconds) will be collected from subjects/patients at 11 clinical centers in Germany to train an Artificial Intelligence in the automatic diagnosis of regular and irregular heart rhythms. Heart rhythms of interest are normal sinus rhythm (SR), atrial fibrillation (AF), atrial premature beats (APBs), ventricular premature beats (VPBs), and nonsustained ventricular tachycardia (VT). Per diagnosis, 20,000 ECGs are required, for a total of 100,000 ECGs to be obtained from approximately 10,000 subjects/patients.

Eligibility criteria:
Inclusion Criteria:

* Heart rhythm of interest present on ECG

Exclusion Criteria:

* Patient incapable of or not willing to sign informed consent form

Outcomes:
Diagnostic accuracy of AI; ECG R-R interval; ECG QRS-complex duration; ECG QRS-complex fragmentation; ECG QTc interval; ECG T wave inversion
Precision and RElevance of CardIac ultraSound Using Artificial Intelligence for Left Ventricle Ejection Fraction Assessment in the Elderly. ( PRECISE AI)
Trial ID: NCT06478901
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2023-01-14
Completion date: 2024-02-20
Sponsor: Hôpital Broca APHP
Conditions: Heart Failure; Heart Failure Systolic; Elderly; Artificial Intelligence
Interventions: Echocardiography
Locations: Hôpital Broca
Countries: France
Enrollment: 129
Age range: 75 Years - None
Sex: ALL

Summary:
Heart failure (HF) is common in older adults, especially those over 65. It is a leading cause of hospitalization and has high mortality rates. Diagnosing HF in elderly patients can be challenging due to atypical symptoms and multiple other health issues. Echocardiography, an ultrasound of the heart, is crucial for accurate diagnosis and treatment planning.

One problem in geriatric care is the difficulty of accessing echocardiography due to high demand and limited specialized doctors. Recent advancements show that AI-assisted portable ultrasound devices can reliably measure heart function, producing results comparable to traditional methods.

This study aims to evaluate the accuracy and relevance of AI-assisted echocardiography (AutoEF-AI) in elderly patients. It also assesses whether geriatricians, even without specialized training, can capture quality images for AI analysis.

In simple terms, this study investigates if portable ultrasound devices with AI can provide precise heart function diagnostics, making it easier for older adults with heart failure to get the care they need, even without specialists.

Eligibility criteria:
Inclusion Criteria:

* At least 75 years and a clinical presentation of acute heart failure consistent with the criteria of the European Society of Cardiology guidelines

Exclusion Criteria:

* unstable patient

Outcomes:
evaluate the relevance and accuracy of echocardiography assisted by Artificial intelligence in elderly patients
The Effects of Reformer Pilates Exercises on Trunk Endurance, Posture, and Body Awareness in Adolescents
Trial ID: NCT06767228
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-03-01
Completion date: 2025-10-07
Sponsor: Trakya University
Conditions: Adolescent; Healthy
Interventions: Reformer Pilates
Locations: Trakya University
Countries: Turkey (Türkiye)
Enrollment: 26
Age range: 10 Years - 19 Years
Sex: ALL

Summary:
This study aims to investigate the effects of reformer pilates exercises on posture, core endurance, and body awareness in adolescents. The hypothesis is that reformer pilates will improve these outcomes compared to a control group receiving only informational support. Participants will be randomized into two groups and assessed over six weeks.

Eligibility criteria:
Inclusion Criteria:

* Aged between 10-19 years.
* Voluntary participation with signed informed consent.
* Ability to read and write in Turkish.

Exclusion Criteria:

* History of orthopedic injury within the last 3 months.
* Obesity (BMI \> 30 kg/m²).
* Being under follow-up for a scoliosis diagnosis.

Outcomes:
Postural Alignment; Trunk Endurance
Artificial Intelligence for Surgical Care in War-Torn Sudan: Feasibility, Barriers, and Ethical Perspectives From a Conflict Zone
Trial ID: NCT07149012
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2024-10-01
Completion date: 2025-06-01
Sponsor: Sudan Medical Specialization Board
Conditions: Surgical Procedures, Operative; Artificial Intelligence (AI); War Injury; Wounds and Injuries; Disasters; Health Services Accessibility; Developing Countries
Interventions: Artificial Intelligence Awareness and Feasibility Assessment
Locations: Sudan Medical Specialization Board
Countries: Sudan
Enrollment: 185
Age range: None - None
Sex: ALL

Summary:
This study is designed to examine how artificial intelligence (AI) could be applied to support surgical care in Sudan during the ongoing armed conflict. The conflict has disrupted hospital operations, displaced surgical teams, and limited access to specialists and modern technology. The investigators are conducting a survey of Sudanese surgeons working in public, private, military, and conflict-zone hospitals to assess awareness of AI, interest in its application, and perceived challenges. In addition, in-depth interviews with senior surgeons and residents are being performed to further explore perspectives on AI in surgical care.

This study represents one of the first attempts to investigate the role of AI in surgery within an active conflict setting in Africa. Findings from this research are expected to inform the design of AI tools that are tailored for fragile health systems, including offline and low-bandwidth environments.

Eligibility criteria:
Inclusion Criteria:

* Sudanese general surgery residents or consultants.
* Currently working in public, private, military, NGO, or conflict-zone hospitals within Sudan.
* Able to provide informed consent.

Exclusion Criteria:

* Surgeons working outside Sudan.
* Non-surgical medical specialties.
* Inability or unwillingness to complete the survey or participate in interviews.

Outcomes:
Awareness of Artificial Intelligence in Surgical Care Among Sudanese Surgeons
Evaluation of the Success of ChatGPT-4 in Predicting Postoperative Intensive Care Needs and Mortality: Prospective Observational Study
Trial ID: NCT06321328
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2024-03-16
Completion date: 2024-08-01
Sponsor: Kanuni Sultan Suleyman Training and Research Hospital
Conditions: Artificial Intelligence; Intensive Care Unit; PACU Length of Stay
Locations: istanbul Kanuni Sultan Süleyman Education and Training Hospital
Countries: Turkey (Türkiye)
Enrollment: 406
Age range: 18 Years - None
Sex: ALL

Summary:
This is a prospective, observational study to be conducted at Sağlık Bilimleri University Istanbul Kanuni Sultan Süleyman Training and Research Hospital and Başakşehir Çam and Sakura City Hospital. The study aims to record various preoperative and postoperative data of patients who have undergone surgeries, specifically those with ASA scores of III and IV or those indicated to potentially need postoperative intensive care. Data points include patient demographics, type of surgery, ASA score, comorbidities, lab and imaging findings, and both actual and ChatGPT version 4 predicted outcomes regarding postoperative intensive care needs, anesthesia methods, duration of stay in intensive care and the hospital, and 30-day mortality rates. ChatGPT version 4's predictions will be compared with actual outcomes and anesthesiologist decisions.

Eligibility criteria:
Inclusion Criteria:

* Patients 18 years of age and older Patients with ASA scores III and IV who were scheduled for surgery.

Exclusion Criteria:

* Lack of consent of the patient or relatives, Patients undergoing cardiovascular surgery and pediatric cardiovascular surgery.

Patients with ASA score I-II-V-VI. Patients under 18 years of age Emergency cases Operations with surgical complications Operations with anesthetic complications

Outcomes:
Intensive care predictions
Multi-scale Spatio-Temporal Analysis of Research Data
Trial ID: NCT07189312
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-02-02
Completion date: 2026-12-02
Sponsor: Universidad de Granada
Collaborators: University Hospital Virgen de las Nieves
Conditions: Colorectal Cancer
Locations: Virgen de las Nieves Hospital
Countries: Spain
Enrollment: 15
Age range: 18 Years - 90 Years
Sex: ALL

Summary:
MuSTARD is a singular project in the sense that investigators will develop a computational framework that is conceived as a common solution for disparate applications. This framework responds to a common need: the derivation of multi-scale spatio-temporal (ST) models equipped with statistical inference capabilities. Since the sequencing of the human genome, a significant portion of clinical research has shifted to the study of precision medicine (PM), resulting in numerous breakthroughs in both non-communicable and infectious diseases. PM refers to disease treatment and prevention that considers variability in genes, environment, and lifestyle for each person. Main drivers of PM are the omics technologies . Omics data is obtained from new high-throughput instruments that generate massive volumes of data. It is also notoriously complex data to analyze. In the last years, new separation technology has allowed us to measure omics data at single-cell resolution, creating a new (and even more complex) type of data generally referred to as single-cell omics. This allow us to study the spatial distribution of omics in a sample. Spatial and single cell omics represents one of the most powerful approaches today to understand cancer propagation in both time and space. Spatial omics. Unfortunately, there are no computational tools that can readily combine the nature of this new type of data with powerful statistical inference. Furthermore, spatially resolved omics experiments with repeated measures is another form of complex ST data. MuSTARD will provide a new computational framework that can handle ST omics to understand colorectal cancer propagation in both time and space, and leverage it in a new clinical study of reduced sample size (15 patients). The MuSTARD computational framework applied to ST omics will be made available for the general community in the form of open software.

Eligibility criteria:
Inclusion Criteria:

* Diagnosis for colorectal cancer
* Patients with cancer planned surgery
* Absence of instability microsatellites
* Absence of any other associated disease that can affect the cancer pathology

Exclusion Criteria:

* Patients who need an urgent surgery

Outcomes:
To develop a framework of computational tools and services capable of handling multi-scale spatio-temporal structures in massive scientific data and providing interpretable models and visualizations with sound statistical support.
Prospective Validation and Application of an Artificial Intelligence-based Model for Evaluating the Efficacy of Breast Cancer Patients After Neoadjuvant Therapy
Trial ID: NCT06649565
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2024-01-01
Completion date: 2026-12-31
Sponsor: Cancer Institute and Hospital, Chinese Academy of Medical Sciences
Conditions: Breast Cancer
Interventions: no intervention
Locations: Sanhuan Cancer Hospital, Chaoyang District, Beijing(Cancer Hospital, Chinese Academy of Medical Sciences, close medical alliance); Cancer Hospital, Chinese Academy of Medical Sciences
Countries: China; China
Enrollment: 300
Age range: 18 Years - None
Sex: FEMALE

Summary:
Breast cancer has become the world's number one cancer. While its therapeutic efficacy is increasing, how to achieve non-invasive evaluation of the efficacy of neoadjuvant therapy (NAT) for breast cancer patients and thus avoid surgery has become a bottleneck problem that needs to be broken through in clinical diagnosis and treatment. Existing non-invasive evaluation strategies are limited to single-center, single-modality modeling, and have problems such as low performance and poor versatility. Therefore, in the early stage of this study, multi-modality breast cancer patient data from multiple centers across the country were collected and the establishment of an artificial intelligence (AI) efficacy prediction model was preliminarily completed. On this basis, this project intends to further improve the multi-center prospective validation study of the prediction model. The research results will help solve the scientific problem of non-invasive judgment of NAT efficacy in breast cancer patients and provide a new paradigm for the research of high-performance AI diagnosis and treatment auxiliary systems applicable to multiple centers.

Eligibility criteria:
Inclusion Criteria:

* Patients who were treated in the above research centers between January 1, 2024 and October 31, 2025;
* ≥18 years old, female, ECOG score ≤2;
* Pathological biopsy confirmed invasive breast cancer;
* AJCC (8th edition) stage I-III;
* MRI imaging data before and after neoadjuvant therapy;
* Planned mastectomy or breast-conserving surgery after neoadjuvant therapy, and postoperative pathological information obtained.

Exclusion Criteria:

* Bilateral breast cancer, multiple lesions, or occult breast cancer;
* Poor MRI data quality;
* Patients who had received other anti-tumor treatments before enrollment;
* Patients with other malignant tumors

Outcomes:
Breast MRI radiomics characteristics of breast cancer patients during neoadjuvant therapy
A Prospective Cohort Study of Patients Receiving Radiotherapy for Thoracic and Breast Cancer and the Related Cardiotoxicity Following Treatment (RACCOON)
Trial ID: NCT04674501
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2020-12-22
Completion date: 2022-12
Sponsor: Yonsei University
Conditions: Patients Who Receive Thoracic Irradiation
Interventions: Cardiac evaluation and blood sampling
Locations: Yonsei Cancer Center, Yonsei University College of Medicine, Yonsei University Health System
Countries: South Korea
Enrollment: 200
Age range: 20 Years - None
Sex: ALL

Summary:
The purpose of this study is to investigate the risk factors and mechanisms of cardiotoxicity following thoracic radiotherapy and to provide insights in preventing radiation-related cardiotoxicity.

-Condition or disease : Thoracic irradiation -Intervention/treatment : Cardiac evaluation, Blood sampling

Eligibility criteria:
Inclusion Criteria:

1. Patients with solid cancer who are subject to thoracic irradiation.
2. Patients who are 20 years old or older
3. Eastern Cooperative Oncology Group Performance Status Scale 0-2
4. Patients with cognitive abilities to receive virtual reality information

Exclusion Criteria:

1. Patients with a history of previous thoracic irradiation
2. Patients who refused consent
3. Patients with a history of heart failure before radiotherapy

Outcomes:
Cardiotoxicity rate
A Rapid Diagnostic of Risk in Hospitalized Pediatric Patients to Improve Outcomes Using Machine Learning
Trial ID: NCT06771830
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-12-01
Completion date: 2027-12
Sponsor: University of Wisconsin, Madison
Collaborators: AgileMD, Inc.
Conditions: Pediatric ALL; Sepsis
Interventions: Pediatric eCART
Locations: American Family Children's Hospital
Countries: United States
Enrollment: 30000
Age range: None - 17 Years
Sex: ALL

Summary:
This is a study comparing 3 years of retrospective data (pre-implementation) to 2 years of prospective data after the implementation of a pediatric version of Electronic Cardiac Arrest Risk Triage (pediatric eCART), a clinical decision support (CDS) tool that uses electronic health records (EHR) to identify patients with high risk for life threatening outcomes. Up to 30,000 encounters with pediatric patients will be assessed. Acceptability of the pediatric eCART intervention will also be measured from pediatric nurse clinicians.

Eligibility criteria:
Inclusion Criteria (pediatric patients):

* All pediatric patients scored on pediatric eCART (or eligible for scoring on either algorithm in the pre-implementation period) will be screened for study eligibility.
* Patients eligible for pediatric eCART scoring include pediatric (\<18 years of age) patients
* Inpatient locations

Exclusion Criteria (pediatric patients):

* Patients who are ineligible for pediatric eCART scoring
* Neonates and birth encounters will be excluded from the pediatric eCART study

Inclusion Criteria (nurse clinicians):

* UW Health nurses who interact with eCART during patient care

Exclusion Criteria (nurse clinician):

* UW Health nurses no longer employed at UW Health

Outcomes:
In Hospital Mortality; Intensive Care Unit (ICU) free days
A Multi-Centre Prospective Community-Based Observational Study on Prediction of Malignant Progression of Clinically Suspicious Oral Lesions With STRATICYTE
Trial ID: NCT04622462
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2015-03-12
Completion date: 2026-12-31
Sponsor: Proteocyte Diagnostics Inc.
Conditions: Oral Neoplasm
Interventions: Standard of Care Histopathology; STRATICYTE Assessment
Locations: Kingsway Oral & Maxillofacial Surgery; University of Alberta; Western University
Countries: Canada; Canada; Canada
Enrollment: 500
Age range: 15 Years - 90 Years
Sex: ALL

Summary:
The purpose of this observational study is to evaluate the utility of the S100A7 immunohistochemistry signature-based assessment - STRATICYTE - in determining the risk of progression to cancer of clinically suspicious oral lesions.

Eligibility criteria:
Inclusion Criteria:

* Any clinically suspicious lesion biopsied to rule-out oral epithelial dysplasia/oral squamous cell carcinoma

Exclusion Criteria:

* Biopsied lesion with or without dysplasia concomitant with oral squamous cell carcinoma at initial biopsy

Outcomes:
Malignant Transformation Rate: Dysplasia
Evaluation of the Screening Effect of Artificial Intelligence Hand Activity Evaluation System for Cervical Spondylosis
Trial ID: NCT06041542
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2020-12-01
Completion date: 2022-12-30
Sponsor: Guangdong Provincial People's Hospital
Conditions: 10-second G&R Test
Interventions: 10-second hand grip and release test
Locations: Guangdong Provincial People's Hospital
Countries: China
Enrollment: 1380
Age range: 40 Years - 80 Years
Sex: ALL

Summary:
The purpose of this study:

1. Build an artificial intelligence screening tool for cervical spondylosis based on hand function analysis;
2. Verify the accuracy of cervical spondylosis screening tools.

Eligibility criteria:
Inclusion Criteria:

1. Age between 18-80 years old;
2. Patients who have received cervical CT or MR examination within the last month and have been clearly diagnosed or excluded from cervical spondylosis (i.e., those who have been clearly diagnosed before participating in the study);
3. People who are willing to participate in the study with CT/MR examination (i.e., those who have not been diagnosed before joining the study).

Exclusion Criteria:

1. Subjects with cervical spondylosis who have been examined but still cannot be definitively diagnosed or ruled out;
2. Subjects with definite presence of hand trauma;
3. People who do not have access to CT or MR;
4. Subjects who did not comply with the examination, were unable to perform neurological assessment, or were unable to fill out questionnaires.

Outcomes:
mJOA score
The Development and Clinical Application of a Motor Function Assessment System Based on Video Tracking and Artificial Intelligence Technique
Trial ID: NCT06242054
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2021-01-01
Completion date: 2022-12-31
Sponsor: Peking University Third Hospital
Conditions: Cervical Spine Myelopathy
Interventions: Motor Function Assessment System
Locations: Feifei Zhou
Countries: China
Enrollment: 72
Age range: 18 Years - 80 Years
Sex: ALL

Summary:
The goal of this clinical trial is to develop a motion recognition system based on video tracking technology and combine it with artificial intelligence technology to form a motion recognition and function evaluation system in in healthy people and patients with cervical spondylotic myelopathy. The main questions it aims to answer are:

* The development of this motion recognition system,
* In the scenario of hand motor dysfunction, the key parameters of hand movement in healthy people and patients with cervical spondylotic myelopathy were evaluated, and the hand motor function model was established to achieve an objective, highly sensitive, highly specific, repeatable and easy-to-use system in clinical hand motor function evaluation.

Participants will recieved the evaluation of this system and mJOA before the surgery.

If there is a comparison group: Researchers will compare the evaluation results of healthy people to see if this system could recognized the hand motor dysfunction of patients with cervical spondylotic myelopathy.

Eligibility criteria:
Inclusion Criteria:

* Study Group: 1. Patients with cervical spondylotic myelopathy diagnosed by clinical symptoms and cervical spine imaging examination (including cervical flexion and extension X-ray, cervical spine CT and cervical spine MRI).
* Control Group: 1. Healthy people without clinical and imaging manifestations of cervical spondylotic myelopathy.
* Age 18-80 years old.
* Can complete the informed consent process.

Exclusion Criteria:

* Patients with hand dysfunction caused by other reasons (such as rheumatoid arthritis, history of hand trauma, history of hand surgery, etc.).
* Patients who cannot complete the assessment process due to various reasons.

Outcomes:
Motor Function Assessment System; mJOA; 10-Seconds Grab and Release Test