moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Feasibility and Diagnostic Relevance of Recording an ECG Trace With a Connected Watch
Trial ID: NCT06272396
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-03-26
Completion date: 2028-03-26
Sponsor: University Hospital, Bordeaux
Conditions: Arrhythmias, Cardiac
Interventions: Smart watch
Locations: Hôpital Haut-Lévèque
Countries: France
Enrollment: 3000
Age range: None - None
Sex: ALL

Summary:
Over the past few years, various systems have been developed to record ECG traces on an ambulatory basis. The latest connected watch models feature 2-electrode ECG recording. This single (1D) or six derivations (6D) can be used to derive a wealth of information about the heart's rhythm. The information that can be derived from an ECG recording with a derivation goes far beyond̀ simple differentiation between atrial fibrillation and sinus rhythm. In contrast, in various clinical situations, a tracing restricted to an intracardiac defibrillator (ICD) may prove falsely normal, wrongly reassuring a patient and delaying management. The primary objective of the study is to evaluate the sensitivity of ECG-1D or 6D recordings from a connected watch in measuring electrical parameters, compared with a standard ECG-12D, also to obtain a bank of tracings, to create and validate an artificial intelligence algorithm for the automatic analysis of ECG tracings recorded with a connected watch and also to validate the feasibility and sensitivity of recording an ECG tracing with a connected watch in children.

Eligibility criteria:
Inclusion Criteria:

* Patients hospitalized or seen in consultation in one of the departments of the Haut Lévêque cardiology hospital,
* Patient of both sexes, minor (from birth to 18 years) or adult,
* Free, informed and written consent from the patient or, in the case of minors, from the legal parent (at the latest on the day of inclusion and before any examination required for the research),
* Subject affiliated to or benefiting from a social security regime.

Exclusion Criteria:

* Person incapable of giving consent,
* Person subject to a legal protection measure (safeguard of justice, tutorship or curatorship),
* A person deprived of liberty by judicial or administrative decision.

Outcomes:
Comparison of electrical parameters measured using the 2 ECG methods: P-wave amplitude; Comparison of electrical parameters measured using the 2 ECG methods: P-wave duration; Comparison of electrical parameters measured using the 2 ECG methods: QRS amplitude; Comparison of electrical parameters measured using the 2 ECG methods: QRS duration; Comparison of electrical parameters measured using the 2 ECG methods: QT/QTc
Metabolic Characteristics Analysis and Precise Prognostic Model Establishment for Patients With Acute Coronary Syndrome Complicated With Obstructive Sleep Apnea
Trial ID: NCT07398430
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2026-02-01
Completion date: 2027-12-31
Sponsor: Beijing Hospital
Conditions: Sleep Disordered Breathing (SDB)
Interventions: Biological Sample Collection
Enrollment: 1200
Age range: 18 Years - 85 Years
Sex: ALL

Summary:
Obstructive sleep apnea (OSA) significantly increases the risk of cardiovascular events in patients with acute coronary syndrome (ACS), yet the underlying metabolic mechanisms remain unclear. This study aims to analyze the metabolic characteristics of ACS patients with OSA using metabolomics based on a prospective cohort. The study intends to construct an artificial intelligence-based risk stratification model to improve prognosis prediction and facilitate precision medicine for this population.

Eligibility criteria:
Inclusion Criteria:

* Age 18-85 years.
* Diagnosed with ACS, including ST-segment elevation myocardial infarction, non-ST-segment elevation myocardial infarction, and unstable angina.
* Willing to participate and sign the informed consent form.

Exclusion Criteria:

* Cardiogenic shock or cardiac arrest.
* Central sleep apnea or other types of sleep disordered breathing.
* Previous or current treatment with continuous positive airway pressure.
* Inability to complete polysomnography or invalid data.
* Malignancy or life expectancy \< 1 year.

Outcomes:
Incidence of Major Adverse Cardiovascular and Cerebrovascular Events (MACCE)
A Multi-center Study of Differential Diagnosis Breast Nodules by Ultrasound Artificial Intelligence and Ultrasound Elastography
Trial ID: NCT03887598
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2019-01-18
Completion date: 2020-02-18
Sponsor: Xin-Wu Cui
Collaborators: Xinhua Hospital, Shanghai Jiao Tong University School of Medicine; Taizhou Hospital; Wuhan Hospital of Traditional Chinese Medicine; Macheng People's Hospital; Huangshi Central Hospital; Affiliated Hospital of Jiangsu University; The First People's Hospital of Yichang; Yichang Second People's Hospital; Xiangyang Central Hospital; The Second Hospital of Anhui Medical University; Anqing Municipal Hospital; Huainan People's Hospital; Wenzhou Central Hospital; Xuzhou First People's Hospital; The Central Hospital of Lishui City; Huai'an First People's Hospital; WISCO General Hospital; First People's Hospital of Jiangxia District, Wuhan City; Enshi State Central Hospital; Lianyungang Third People's Hospital; First People's Hospital of Xianyang
Conditions: Breast Cancer
Interventions: Ultrasound diagnosis
Locations: Xin-Wu Cui
Countries: China
Enrollment: 2000
Age range: 18 Years - None
Sex: FEMALE

Summary:
The application of computer-aided diagnosis (CAD) technology "S-Detect" enables qualitative and quantitative automated analysis of ultrasound images to obtain objective, repeatable and more accurate diagnostic results. The Elastic Contrast Index (ECI) technique, unlike conventional strain-elastic imaging techniques, can evaluate the elastic distribution in the region of interest. The purpose of the study was to evaluate the differential diagnosis value of ultrasound S-Detect technology for benign and malignant breast nodules and evaluate the differential diagnosis consistency of the ultrasound S-Detect technique and the examiner for benign and malignant breast nodules and explore the differential diagnosis value of Samsung ultrasound elastic contrast Index (ECI) technique for benign and malignant breast nodules.

Eligibility criteria:
Inclusion Criteria:

1. Had breast lesions detected by ultrasound
2. Age 18 or older
3. Upcoming FNAB or surgery
4. Signing informed consent

Exclusion Criteria:

1. Patients who had received a biopsy of breast lesion before the ultrasound examination
2. Can not cooperate with the test operation
3. Patients who were pregnant or lactating
4. Patients who were undergoing neoadjuvant treatment.

Outcomes:
Benign or malignant lesions as determined by pathology; Elastic ratio
Intraoperative Confocal Laser Scanning Microscopy and Artificial Intelligence for Optimized Surgical Excision of Basal Cell Carcinoma
Trial ID: NCT06600165
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-03-01
Completion date: 2027-12-31
Sponsor: LMU Klinikum
Conditions: Basal Cell Carcinoma
Interventions: Ex vivo confocal microscopy
Locations: Clinic and Policlinic of Dermatology and Allergy, LMU Munich
Countries: Germany
Enrollment: 1000
Age range: None - None
Sex: ALL

Summary:
The aim is to use AI to assist surgeons in analyzing CLSM tissue slide images obtained during BCC surgeries with the aim to integrate it in real time. We plan to use AI to analyze CLSM images of BCCs and distinguish between tumor tissue, inflammatory tissue, and non-tumor/non-inflammatory tissue. This approach would provide surgeons with real-time feedback and automated image analysis, leading to a more targeted and efficient approach to tissue analysis. By improving the accuracy and speed of tissue analysis, our proposal could ultimately improve operative patient outcomes and benefit healthcare professionals.

Eligibility criteria:
Inclusion Criteria:

* Patients with Basal Cell Carcinoma

Exclusion Criteria:

* Patient unable to consent

Outcomes:
Ex vivo confocal laser scanning microscopy
Research on Automatic Detection of Ovarian Mass and Intelligent Auxiliary Diagnosis System Based on Multimodal Ultrasound Images
Trial ID: NCT06528236
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2024-07-30
Completion date: 2029-07-30
Sponsor: Zhejiang Provincial People's Hospital
Collaborators: The Second Affiliated Hospital of Harbin Medical University; Sichuan provincial maternity and child health care hospital; The Affiliated Hospital of Qingdao University; Aksu First People's Hospital; Women's Hospital School Of Medicine Zhejiang University
Conditions: Ovarian Neoplasms; Adnexal Mass
Interventions: Artificial intelligence model
Enrollment: 100000
Age range: None - None
Sex: FEMALE

Summary:
Research on automatic detection of ovarian mass and intelligent auxiliary diagnosis system based on multimodal ultrasound images.

Eligibility criteria:
Inclusion Criteria:

1. During gynecological ultrasound examination, at least one patient with persistent ovarian tumor was found.
2. The patient underwent surgical treatment and the histopathological results.

Exclusion Criteria:

1. Histopathological analysis confirms non-ovarian tumor;
2. Histopathological results are inconclusive;
3. Issues with image quality: the ovarian mass is incomplete and does not show some surrounding tissues (but the mass is too large to exclude completely); the images are overly blurry, making it difficult to determine the characteristics of the ovarian mass (possible reasons include hardware quality issues with the ultrasound machine, motion blur, focusing problems, presence of intestinal gas in the patient); gain settings make it difficult to judge the characteristics of the ovarian mass (such as low contrast, excessively dark images, or saturation); the presence of artifacts affects the assessment of ultrasound characteristics of the ovarian mass and should be excluded.

Outcomes:
Area under the curve
Early REmote Rehabilitation to Improve Health of the Elderly After CARDiac Surgery. (RECARD Trial). A Bi-center, Prospective, Randomized, Open-label, Blinded Evaluation (PROBE) Trial
Trial ID: NCT06370611
Status: ENROLLING_BY_INVITATION
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-05-01
Completion date: 2026-09-30
Sponsor: Ivy susanne Modrau, MD
Collaborators: Aalborg University Hospital
Conditions: Cardiac Rehabilitation
Interventions: Early mobile health intervention supplemented by weekly calls from a physiotherapist
Locations: Aarlborg University Hospital; Aarhus University Hospital
Countries: Denmark; Denmark
Enrollment: 120
Age range: 65 Years - None
Sex: ALL

Summary:
An increasing number of older patients undergo heart surgery. Despite seemingly successful outcomes, these vulnerable patients may face prolonged decreased functional capacity, reduced self-efficacy, and impaired quality of life after discharge. Early engagement in physical activity and education plays a key role in health and well-being after heart surgery The goal of this clinical trial is to evaluate the effectiveness of an app-based exercise program and weekly calls from a physiotherapist in the early stages after open heart surgery.

Eligible patients will be randomly assigned to either a control group receiving standard care or an intervention group, which will be introduced to an individually tailored exercise program as an addition to standard care.

Follow-up is planned with an out-patient visit six weeks after discharge and a telephone interview six months post-surgery.

All participants will undergo assessments at enrollment and during follow-up. Physical performance will be evaluated through physical tests. General well-being, quality of life, self-reported physical activity, and cost-effectiveness of the intervention will be assessed through questionnaires. Muscle health will be examined through blood sampling, CT scans, and muscle biopsies.

The primary endpoint is the 30-second Chair Stand Test serving as a measurement of physical function.

Eligibility criteria:
Inclusion Criteria:

* Provision of signed and dated informed consent form
* Scheduled first-time heart surgery through median sternotomy
* Male or female, aged ≥ 65 years (The age criterion implies the exclusion of women of childbearing potential)

Exclusion Criteria:

* Medical conditions requiring referral to specialized rehabilitation (e.g., severe heart or lung failure, need for blood pressure monitoring during exercise)
* Emergent procedure
* Anticipated inability to perform intervention, study assessment procedures, or follow-up
* Participation in any other interventional clinical that may interfere with the outcome measures

Outcomes:
Change (Δ) 30-second chair stand test (30CST) baseline vs. 6-week FU
Usefulness of the Endo-AID Artificial Intelligence System in the Detection of Colorectal Adenomas. a Randomized Controlled Trial
Trial ID: NCT04945044
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2021-11-15
Completion date: 2022-01-31
Sponsor: Hospital Universitario de Canarias
Conditions: Adenoma Detection Rate
Interventions: Computed adenoma detection system (CADe); Control group (regular colonoscopy)
Locations: Department of Gastroenterology
Countries: Spain
Enrollment: 370
Age range: 18 Years - None
Sex: ALL

Summary:
The main purpose of the study to evaluate the usefulness of the Endo-AID artificial intelligence system in the detection of colorectal adenomas in consecutive patients for outpatient colonoscopy.

The secondary aims were:

* To evaluate the benefit of Endo-AID in adenoma detection rate by comparing endoscopists with high and low adenoma detection rate.
* To evaluate serrated detection rate, advanced adenoma detection rate, adenoma detection rate according to the size (\<= 5mm, 6-9mm,\> = 10mm) and number of adenomas by colonoscopy. Stratification by location and morphology.

Eligibility criteria:
Inclusion Criteria:

* Age ≥ 18 years.
* Patients referred for outpatient colonoscopy

Exclusion Criteria:

* Colonic resection
* Taking anticoagulants or antiagregants that contraindicate the performance of therapy
* Patients with a recent colonoscopy (\<6 months) of good quality (e.g. cited for endoscopic therapy)
* Inflammatory bowel disease
* Patients with incomplete colonoscopy
* Patients with inadequate preparation using the Boston Colonic Preparation Scale (BBPS). A cleaning quality of less than 2 points in any of the 3 colonic sections will be considered inadequate.
* Patients with polyposis syndromes
* Refusal to participate in the study.

Outcomes:
Adenoma detection rate
Multinational, Multicenter, Retrospective Study to Evaluate an AI/ML Technology-Based End-to-End CADe/CADx SaMD, Which Allows Detection, Localization and Characterization of Pulmonary Nodules (REALITY)
Trial ID: NCT06576232
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2022-09-21
Completion date: 2024-08-21
Sponsor: Median Technologies
Conditions: High Risk Cancer
Interventions: Median LCS
Locations: University of Pennsylvania - Penn Center for Innovation; Baptist Clinical Research Institute; The University of Texas M.D. Anderson Cancer Center; Fundacion instituto de investigacion sanitaria de la fundacion jimenez diaz (FJD); Universidad de Navarra
Countries: United States; United States; United States; Spain; Spain
Enrollment: 1147
Age range: 50 Years - 80 Years
Sex: ALL

Summary:
This is a Multinational, Multicenter, retrospective study for the evaluation of the standalone efficacy and safety of an Artificial Intelligence/Machine Learning (AI/ML) technology-based end-to-end Computer assisted Detection/Computer Assisted Diagnosis (CADe/CADx) Software as a Medical Device (SaMD) developed to detect, localize and characterize malignant, and suspicious for lung cancer nodules on Low Dose Computed Tomography (LDCT) scans taken as part of a Lung Cancer Screening (LCS) program.

LDCT Digital Imaging and Communications in Medicine (DICOM) images of patients who underwent lung cancer screening were selected and included into the study. Selected scans will then be analyzed by the CADe/CADx SaMD and compared to radiologist generated reference standards including lesions localization and lesion cancer diagnosis.

Figures of merit at patient level and lesion level detection and diagnostic efficacy will be calculated as well as sub-class analysis to ensure algorithm performance generalizability.

Eligibility criteria:
Inclusion Criteria:

* ≥50-80 Years of age;
* Current or ex-smoker (\>=20 pack years);
* Patient screened and surveilled for lung cancer screening following lung cancer screening guidelines (equivalent to United States Preventive Services Task Force (USPSTF) 2021 Criteria);
* Received LDCT due to inclusion in high-risk category for lung cancer.

Exclusion Criteria:

* Prior lung resection;
* Pacemaker or other indwelling metallic medical devices in the thorax that interfere with CT acquisition;
* Patients/images used during AI model development;
* Patients with only hilar and/or mediastinal cancer(s);
* Patients with only ground glass cancer(s);
* Patients with nodules, solid or part-solid \>30mm (masses);
* Patients that are not accompanied with the required clinical information;
* Patients with imaging with any of the following: missing slices, slice thickness \>3mm;
* Partial cover of the lung.

Outcomes:
AUROC (Area under ROC curve) at patient level
Human Factors Validation Study of Artificial Intelligence Ultrasound Navigation Software for Cardiac Imaging
Trial ID: NCT05660044
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2022-07-20
Completion date: 2022-08-03
Sponsor: UltraSight
Conditions: Cardiac Disease
Locations: Sheba medical center
Countries: Israel
Enrollment: 34
Age range: 18 Years - 100 Years
Sex: ALL

Summary:
The Product is a machine learning software, that utilizes AI to provide real-time guidance to acquire diagnostic-quality ultrasound views of the heart.

Eligibility criteria:
Inclusion Criteria:

* Age 18 and older
* Novice users or medical professionals who do not perform echocardiographs as part of their routine job responsibilities.
* Have the capacity to understand study risks and consent to be in the study
* Be available and willing to participate in the study
* Not be employed by the Sponsor or another medical device or pharmaceutical company

Exclusion Criteria:

\-

Outcomes:
Usability of UltraSight AI guidance software
Patient and Endoscopists' Experiences and Perceptions of Colonoscopy and New Technologies in Colonoscopy
Trial ID: NCT04747665
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2021-02-01
Completion date: 2021-12-31
Sponsor: The Leeds Teaching Hospitals NHS Trust
Collaborators: Cancer Research UK
Conditions: Bowel Cancer; Colonoscopy
Interventions: Patient and endoscopist reported experience
Locations: Leeds Teaching Hospitals NHS Trust
Countries: United Kingdom
Enrollment: 100
Age range: 18 Years - None
Sex: ALL

Summary:
Bowel cancer is the 3rd most common cancer in males and females. Most bowel cancers develop from precancerous polyps in the bowel. Colonoscopy can detect and remove these polyps, thereby reducing the risk of developing bowel cancer. However, colonoscopy is an invasive procedure with inherent risk, and can be both uncomfortable and embarrassing to undergo. The risks and numerous other barriers often mean colonoscopy, and cancer preventing polyp removal, is not performed.

The investigators aim to start the first in-human feasibility clinical investigation of a robotic magnetic colonoscope in 2021. The new device aims to reduce the discomfort and risk associated with colonoscopy. As part of the patient and end user involvement aspect of the project the investigators aim to perform a mixed methodology inductive type research project exploring experiences of colonoscopy for patient and endoscopists.

The study will follow an exploratory mixed methodology format with two parallel work-streams for patients and endoscopists. Each work-stream will consisting of a focus groups (1-2 for each patients and endoscopists), followed by 20 endoscopist and 30 patient in-depth interviews, then an online questionnaire. Each stage will be thematically analysed to form the semi-structured questions for the next stage. The target population will be endoscopists who perform colonoscopy and people who have undergone, or refused to undergo, a colonoscopy. Focus groups will be performed in groups of 6-10 participants, and both interview and focus groups will be performed via video/teleconferencing.

Eligibility criteria:
Inclusion Criteria:

One of:

* undergone a colonoscopy
* offered a colonoscopy but declined
* perform or training to perform colonoscopy

Exclusion Criteria:

* Non-English speaking
* Unable to comply with any study requirements
* New or ongoing diagnosis of bowel cancer

Outcomes:
Patient participant reported experience of colonoscopy - focus group; Patient participant reported experience of colonoscopy - interviews; Patient participant reported barriers to colonoscopy - focus group; Patient participant reported barriers to colonoscopy - interviews; Colonoscopist participant perceptions of the patient experience of colonoscopy - focus group; Colonoscopist participant perceptions of the patient experience of colonoscopy - interviews; Colonoscopist participant perceptions of the patient barriers to colonoscopy - focus group; Colonoscopist participant perceptions of the patient barriers to colonoscopy - interviews; PREMs patient participant questionnaire; PREMs colonoscopist participant questionnaire
The Cost-effectiveness of Artificial Intelligence Acute Kidney Injury Prediction Auxiliary Software (Acura AKI)
Trial ID: NCT06685367
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-10-17
Completion date: 2025-09-15
Sponsor: Huede Healthtech Co., Ltd.
Conditions: Acute Kidney Injury; Intensive Care; Renal Replacement Therapy
Interventions: Acura AKI
Locations: Taichung Veterans General Hospital (TCVGH)
Countries: Taiwan
Enrollment: 3600
Age range: 20 Years - None
Sex: ALL

Summary:
"Huede" AI Aided AKI Prediction Software, Acura AKI, uses machine learning algorithms to predict the risk of AKI within the next 24 hours and provide a ranking of feature importance. By using Acura AKI, physicians can assess the risk of AKI, focusing on high-risk patients to provide care decisions. This study will be conducted in a prospective randomized clinical trial in adult ICUs, implementing the Acura AKI system for predicting AKI. The study aims to determine whether early prediction and intervention using the Acura AKI system can improve the outcomes of critically ill patients with adverse kidney conditions. The study endpoint is to evaluate the cost-effectiveness of using Acura AKI, including the incidence of AKI, dialysis rates, mortality rates, length of hospital stay, and treatment costs.

Eligibility criteria:
Inclusion Criteria:

* Over 20 years old
* Admitted to adult ICU
* Hospital stay of more than 30 hours

Exclusion Criteria:

* Known to have acute kidney injury at enrollment
* Currently undergoing hemodialysis treatment
* No available blood or urine test data
* Pregnant women
* HIV-positive patients
* Those who have not provided informed consent form
* Regarded as unsuitable for inclusion in the trial by the researcher

Outcomes:
Acute kidney injury (AKI) incidence