moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Atrial Fibrillation Risk Estimation With Single-lead Handheld Electrocardiograms
Trial ID: NCT07468123
Status: ENROLLING_BY_INVITATION
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-07-30
Completion date: 2027-12-31
Sponsor: Massachusetts General Hospital
Conditions: Atrial Fibrillation (AF)
Interventions: 1L ECG screening; Patch monitor
Locations: Mass General Brigham
Countries: United States
Enrollment: 200
Age range: 18 Years - 90 Years
Sex: ALL

Summary:
The goal of this prospective, non-randomized pilot study is to learn whether predictions from a previously validated 12-lead ECG-based artificial intelligence (AI) algorithm (ECG-AI) identify people more likely to have undiagnosed atrial fibrillation (AF).

The main questions it aims to answer are:

Do people predicted to have high risk of AF using ECG-AI have a higher rate of new AF diagnosis using 1L ECG screening compared with people predicted to have a low risk? Do AI-based AF risk estimates from the 12-lead ECG correlate with AF risk estimates from the 1L ECG? Do people find 1L ECG screening for AF acceptable and useful?

Participants will:

Undergo screening with 1L ECG mailed to their home Complete a survey assessing attitudes toward 1L ECG screening Complete a 14-day patch monitor on 1 or 2 occasions depending on 1L ECG results

Eligibility criteria:
Inclusion Criteria:

* Men and women aged 50-90 who are new or established patients in an MGH primary care or ambulatory cardiology practice
* Willing to provide consent to participate in the study to access data from electronic health records (EHR)
* At least 1 12-lead ECG obtained within 5 years prior to study start date for AF risk estimation
* Have access to a smart phone or tablet to use with the AliveCor KardiaMobile 1L ECG device

Exclusion Criteria:

* History of atrial fibrillation or atrial flutter as documented in the patient's current electronic health record medical problem list or self-reported diagnosis
* Implanted cardiac devices (pacemakers, implantable cardiac defibrillators, or cardiac resynchronization therapy, and implantable loop recorders)
* History of allergy to adhesive

Outcomes:
New AF diagnosis (%); Acceptability and usefulness; AI-based AF risk correlation
Deep Learning for Malignant Degeneration Prediction of Pancreatic Cystic Lesions - Beyond High-risk Stigmata
Trial ID: NCT06954753
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-06-01
Completion date: 2026-12-31
Sponsor: Centre Hospitalier Universitaire de Nice
Conditions: Pancreatic Cancer; Pancreatic Cystic Lesions
Interventions: Pancreatic surgery
Enrollment: 250
Age range: 18 Years - None
Sex: ALL

Summary:
This international, multicenter retrospective study aims to develop a deep learning (DL)-based predictive model to identify malignant transformation in pancreatic cystic lesions, improving upon current clinical guidelines. The model will integrate clinical, biochemical, and multimodal imaging data. Several 3D convolutional neural networks will be trained using advanced preprocessing, data augmentation, and hybrid fusion techniques. Model performance will be compared to that of existing international guidelines. The study involves no additional procedures for patients and adheres to strict data anonymization and privacy regulations.

Eligibility criteria:
Inclusion Criteria:

* Patients diagnosed with PCL(s ) who underwent pancreatic surgery in one of the participant centers. Surgical indication must adhere to at least one of current guidelines on PCLs management (6), based on clinical, biochemical, and radiological (MR and/or EUS) features.
* Pancreatic surgery B83performed for supposed increased risk of cyst(s) malignant degeneration following current guidelines on PCLs management (6).
* Absence of clinical, biochemical, radiological, and anatomopathological evidence of pancreatic cancer at pancreatic surgery.
* Non-opposition to the anonymous data processing by the included patients.

Exclusion Criteria:

* Patients presenting with evidence of pancreatic cancer at surgery.
* PCL(s) diagnosis and treatment performed without one between EUS and pancreatic MR. surgery performed in the absence of the criteria proposed by current guidelines.
* Unavailability of both preoperative EUS and pancreatic MR data.
* Unavailability of postoperative PCL(s) anatomopathological analysis results.
* SBO diagnosis performed without CT-scan.

Outcomes:
Prediction of malignant degeneration of pancreatic cystics lesions
The Effectiveness of the Computer-aided Detection/Diagnosis Software, 'WAYMED Endo CL CS', Which Assists Medical Specialists by Automatically Analyzing Colorectal Endoscopic Images With Confirmed Adenoma or Non-Adenoma Lesions, Detecting the Lesion, and Providing Probabilities as to Whether the Lesion Belongs to the Adenoma or Non-Adenoma Group: A Single-center, Single Arm, Retrospective, Superiority, Single-blind, and Pivotal Clinical Trial
Trial ID: NCT07470827
Status: ACTIVE_NOT_RECRUITING
Study type: OBSERVATIONAL
Start date: 2024-09-01
Completion date: 2026-12-01
Sponsor: WAYCEN Inc
Conditions: Colorectal Adenoma; Sessile Serrated Lesion; Hyperplastic Polyp; Carcinoma; Adenoma; Non-Adenoma
Interventions: WAYMED endo CL CS; Reference standard review
Locations: Yonsei University Gangnam Severance Hospital
Countries: South Korea
Enrollment: 1178
Age range: 19 Years - None
Sex: ALL

Summary:
The purpose of this clinical trial is to evaluate the effectiveness of the computer-aided diagnosis (CADx) software, 'WAYMED endo CL CS', which assists medical specialists by automatically analyzing colorectal endoscopic images, identifying lesions, and providing probability values to classify them as Adenoma or Non-Adenoma. This pivotal trial is designed to confirm that 'WAYMED endo CL CS' can support clinicians in diagnostic decision-making by improving the classification of colorectal lesions.

Eligibility criteria:
Inclusion Criteria:

1. Adult patients (≥19 years) with colorectal endoscopic images showing one lesion.
2. Histopathology confirming the lesion as Adenoma (colorectal cancer, adenoma) or Non-Adenoma (sessile serrated lesion, hyperplastic polyp).

Exclusion Criteria:

1. Images previously used for training or internal validation of the investigational software.
2. History of colectomy.
3. Diagnosed with inflammatory bowel disease (e.g., ulcerative colitis, Crohn's disease) or neuroendocrine tumor.
4. Poor image quality (blurred, incomplete lesion capture).
5. Images with multiple lesions or fewer than two images per lesion.
6. Determined by the investigator to be inappropriate for inclusion.

Outcomes:
Clinical Sensitivity in classifying colorectal lesions (%); Clinical Specificity in classifying colorectal lesions (%)
Explore the Relationship Between the Percentage of Colonoscopy Withdrawal Overspeed and the Adenoma Detection Rate: a Prospective, Multicenter, Observational Study
Trial ID: NCT05444166
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2022-07-29
Completion date: 2023-11-29
Sponsor: Renmin Hospital of Wuhan University
Conditions: Colonoscopy; Artificial Intelligence; Gastrointestinal Disease; Deep Learning
Locations: Renmin Hospital
Countries: China
Enrollment: 550
Age range: 18 Years - None
Sex: ALL

Summary:
In this study, the investigators used the optical flow method to measure the colonoscopy withdrawal speed, and doctors were selected from multiple hospitals to collect prospective colonoscopy screening videos, and the percentage of colonoscopy withdrawal overspeed was calculated to explore the relationship between it based on optical flow method and the adenoma detection rate.

Eligibility criteria:
Inclusion Criteria:

1. Male or female ≥18 years old;
2. Able to read, understand and sign an informed consent;
3. The investigator believes that the subjects can understand the process of the clinical study, are willing and able to complete all study procedures and follow-up visits, and cooperate with the study procedures;
4. Patients requiring screening colonoscopy.

Exclusion Criteria:

1. Have drug or alcohol abuse or mental disorder in the last 5 years;
2. Pregnant or lactating women;
3. Patients with known multiple polyp syndrome;
4. patients with known inflammatory bowel disease;
5. known intestinal stenosis or space-occupying tumor;
6. known colon obstruction or perforation;
7. patients with a history of colorectal surgery;
8. Patients with a previous history of allergy to pre-used spasmolysis;
9. Unable to perform biopsy and polyp removal due to coagulation disorders or oral anticoagulants;
10. High-risk diseases or other special conditions that the investigator considers the subject unsuitable for participation in the clinical trial.

Outcomes:
The adenoma detection rate (ADR); The percentage of colonoscopy withdrawal overspeed
Efficacy of Artificial Intelligence Aid-digital Single-operator Cholangioscopy (DSOC) Guided-biopsy Sampling in Suspected Cholangiocarcinoma: A Prospective, Randomized Trial
Trial ID: NCT05374122
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2022-05-01
Completion date: 2024-05-01
Sponsor: Instituto Ecuatoriano de Enfermedades Digestivas
Conditions: Neoplasms; Non-Neoplastic; Bile Duct Neoplasms; Bile Duct Lesions
Interventions: DSOC with AI biopsy guidance; DSOC biopsy without AI guidance
Locations: Carlos Robles-Medranda
Countries: Ecuador
Enrollment: 48
Age range: 18 Years - 99 Years
Sex: ALL

Summary:
Digital single-operator cholangioscopy (DSOC) has emerged as a medical advance with an important role in the evaluation of indeterminate biliary lesions. This technique has demonstrated higher sensitivity in the guidance for tissue acquisition when compared with standard endoscopic retrograde cholangiopancreatography (ERCP). DSOC-guided biopsy is considered technically safe and successful for tissue collection.

Hand in hand with the development of more precise diagnostic techniques, comes the implementation of artificial intelligence (AI) for diagnostic assessment. For the past decade, the role of artificial intelligence (AI) has been increasing at a rapid pace. In the biliary tract, different models have been proposed for the characterization of malignant features. Nevertheless, to date, the discrepancy between the visual impression of the operator and the histological results obtained by cholangioscopy still present, affecting the accuracy the diagnosis.

Based on the above, the investigators aim to assess the diagnostic accuracy of AI for the guidance of tissue acquisition with DSOC compared to DSOC without AI for suspected cholangiocarcinoma. As a secondary aim, the investigators pursue to compare quality of AI-guided biopsies samples vs. DSOC biopsies without AI.

Eligibility criteria:
Inclusion Criteria:

* Patients referred to our center with an indication of DSOC due to suspicion of CBD tumor or indeterminate CBD stenosis.
* Patients who authorized for DSOC-guided biopsy.

Exclusion Criteria:

* Any clinical condition which makes DSOC inviable.
* Patients with more than one DSOC.
* Lost on a six-month follow-up after DSOC.

Outcomes:
Cholangiocarcinoma diagnosis confirmation after biopsy and six-month follow-up
Observational Retrospective and Prospective Study of Infantile Lower Limb Malformations and Deformities
Trial ID: NCT06519175
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2024-10-31
Completion date: 2029-05
Sponsor: Istituto Ortopedico Rizzoli
Collaborators: Ospedale Pediatrico Bambino Gesù; Istituto Giannina Gaslini; Galeazzi Orthopedic Institute; Istituto Nazionale Tumori Regina Elena; Policlinico San Matteo; Istituto Clinico Humanitas
Conditions: Deformity of Limb
Interventions: Conservative treatment; Single or multi-stage surgery; Amputation
Locations: IRCCS Istituto Ortopedico Rizzoli; Istituto Giannina Gaslini; Galeazzi Orthopedic Institute; Policlinico San Matteo; Istituto Nazionale Tumori Regina Elena; Ospedale Pediatrico Bambino Gesù; Istituto Clinico Humanitas
Countries: Italy; Italy; Italy; Italy; Italy; Italy; Italy
Enrollment: 1000
Age range: None - 18 Years
Sex: ALL

Summary:
The skeletal defects of the lower limbs in pediatric age encompass various congenital and acquired conditions that alter bone structure. These alterations can be symmetrical or asymmetrical, with potential consequences such as deformities, short stature, and disability. These condictions may impact not only on the physical and the mental function of the children, but also on their family context.

Management often requires a multidisciplinary approach for early diagnosis and treatment. Deformities are heterogeneous and necessitate a personalized therapeutic plan.

Currently, in pediatric orthopedic surgery there is a lack of an instrument capable of evaluating all these aspects.

The advancement of computer-assisted surgery and artificial intelligence could improve treatment customization and involve patients and families in therapeutic decision-making to prevent complications. This study aims to provide support for clinical practice to promote a systematic, comprehensive, and sequential evaluation of lower limb deformities in developmental age, from which epidemiological data and evidence on treatment approaches used in major referral centers can be extrapolated.

Eligibility criteria:
Inclusion Criteria:

* Age \< 18 years at the time of initial orthopedic evaluation
* Diagnosis of congenital or acquired musculoskeletal deformity to one or both lower limbs
* Presence of informed consent to participate in the study

Exclusion Criteria:

* Refuse to participate in the study by the patient or parent/legal guardian
* Patients with mild paramorphisms not associated with functional limitations

Outcomes:
Pediatric Outcomes Data Collection Instrument; Pediatric Outcomes Data Collection Instrument; Clinical examination with complete assessment of lower limbs; Clinical examination with complete assessment of lower limbs; Mental Health; Mental Health
Characterization of Acute Myocardial Damage With Spectral Computed Tomography. CADAMI-SPECTRAL.
Trial ID: NCT06506071
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2022-09-01
Completion date: 2025-09-01
Sponsor: Salamanca University Hospital
Collaborators: Hospital Universitario Ramon y Cajal
Conditions: Myocarditis; Acute or Subacute; Takotsubo Cardiomyopathy; Infarction, Myocardial; Myocardial Injury
Interventions: Spectral CT
Locations: Complejo Asistencial Universitario de Salamanca. Instituto de Investigación Biomédica de Salamanca
Countries: Spain
Enrollment: 300
Age range: 18 Years - None
Sex: ALL

Summary:
Clinical management of patients with chest pain and elevation of biomarkers of myocardial injury require an accurate diagnosis. Until now, cardiac magnetic resonance imaging (cMRI) is the gold standard for the diagnosis among myocarditis, stress cardiomyopathy, or inapparent ischemic damage. The development of spectral CT has opened up the possibility of characterizing the coronary anatomy and the myocardium in a single procedure. Our aim is to assess the diagnostic ability of differential patterns in first- pass perfusion and delayed iodine enhancement obtained by spectral CT in patients with acute myocardial injury. This study is designed as a prospective multicenter observational study with diagnostic intervention in 150 patients admitted with clinical indication of a cMRI due to suspicion of myocardial infarction with normal coronary arteries (MINOCA), myocarditis or stress cardiomyopathy who will undergo a CT study with double detector technology in two reference centers. A control group (n=150) with an indication for cardiac CT for another cause without coronary or structural heart disease will be included. The iodine maps obtained by spectral CT will be compared with the findings obtained with cMRI, both with conventional techniques and with artificial intelligence algorithms (deep learning). A year follow-up of the cohort will be carried out to assess whether the findings derived from the CT in this group of patients provide prognostic information

Eligibility criteria:
Inclusion Criteria:

* Age ≥ 18 year.
* Hospital admission due to chest pain and acute myocardial injury.
* Clinical indication for cardiac MRI (CMR) due to suspected acute myocardial infarction with coronary angiography showing no significant lesions (\<50%) (MINOCA), acute myocarditis, or stress-induced cardiomyopathy.
* Signed informed consent.

Exclusion Criteria:

* Contraindications to undergo cardiac MRI and CT with administration of gadolinium and/or iodine-based contrast, respectively.
* Pre-existing cardiomyopathy.
* Pre-existing chronic ischemic heart disease.
* Invasive coronary angiography showing coronary lesions ≥50%.

Outcomes:
Correlation between volume of myocardium with abnormal perfusion (measured using spectral iodine density images) and percentage of myocardium with late gadolinium enhacement -LGE- (measured by CMR).
Effects of an Activity Control System in Patients With Diabetes: a Randomized
Trial ID: NCT04380844
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2021-02-01
Completion date: 2021-11-01
Sponsor: Maimónides Biomedical Research Institute of Córdoba
Collaborators: Universidad Católica San Antonio de Murcia
Conditions: Diabetes Mellitus, Type 2
Interventions: Beprevent
Enrollment: 20
Age range: 18 Years - None
Sex: ALL

Summary:
Using a smart device (Beprevent) to monitor activities of daily living improves the lifestyle of patients with DM2

Eligibility criteria:
Inclusion Criteria:

* ≥ 18 years, with the ability to give informed consent
* Patient who has autonomy to carry out activities of daily living (Barthel = 100)
* patient diagnosed with Diabetes Mellitus
* patient in pharmacological treatment
* To live alone or with a person who cannot move on their own
* they do not plan to change their address in the 6 months after inclusion in the study

Exclusion Criteria:

• DM2 patients whose most recent HbA1c value is ≤7%

Outcomes:
Diabetes Knowledge Questionnaire
Artificial Intelligence-Assisted Opportunistic Screening for Breast Cancer Using Non-Contrast Chest CT: A Comparative Study With Mammography and/or Breast MRI
Trial ID: NCT07557654
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2026-05-01
Completion date: 2029-05-01
Sponsor: Fudan University
Conditions: Breast Cancer
Locations: Fudan University Shanghai Cancer Center
Countries: China
Enrollment: 5000
Age range: 18 Years - None
Sex: FEMALE

Summary:
The goal of this observational study is to evaluate the feasibility and effectiveness of using non-contrast chest computed tomography scans for opportunistic breast cancer screening, and to further compare its diagnostic performance with conventional imaging modalities, including mammography and/or breast magnetic resonance imaging.

Eligibility criteria:
Inclusion Criteria:

1. Female patients who have undergone non-contrast chest CT examination;
2. Participants included in the comparative analysis must have undergone at least one comparator imaging modality (mammography and/or breast MRI);
3. If a breast lesion is detected, it must be confirmed by pathology or clinical follow-up at least 12 months;
4. No prior systemic or local therapy before imaging examinations;
5. Imaging data are complete and of sufficient quality for analysis.

Exclusion Criteria:

1. History of other malignancies with potential impact on breast imaging interpretation;
2. Participants with suspected malignant breast lesions but no confirmation;
3. Prior radiotherapy, chemotherapy, or immunotherapy before imaging examinations;
4. Imaging data that are incomplete, of poor quality, or contain significant artifacts preventing reliable analysis;
5. Time interval between non-contrast chest CT and comparator imaging modalities exceeding three months, or with clinical events occurring between examinations that may alter lesion status.

Outcomes:
Screening performance of non-contrast chest CT for detection of breast cancer, with comparison to mammography and/or breast MRI
Using Artificial Intelligence to Improve Data From Danish Cardiac Arrest Registry
Trial ID: NCT05308303
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2022-06-01
Completion date: 2023-06-01
Sponsor: Prehospital Center, Region Zealand
Conditions: Out-Of-Hospital Cardiac Arrest; Trauma; Drug Overdose; Electrocution; Asphyxia; Airway Obstruction
Locations: Prehospital Center
Countries: Denmark
Enrollment: 31200
Age range: None - None
Sex: ALL

Summary:
Out-of-hospital cardiac arrest is a leading cause of death worldwide and patient outcome vary substantially throughout regions suggesting further evaluation and potential for improvement.When focussing on subgroups of OHCA, data in certain areas remains scarce and the need of revised guidelines is evident. Furthermore, enhanced knowledge on these varieties of OHCA's apply to substantial number of patients, also among vulnerable populations. The Danish Emergency Medical System introduced a nationwide registry of electronic medical reports in 2016. This report system allows electronic searches and thereby the opportunity to identify subgroups of OHCA's. Thus, this novel reporting enables the evaluation of new characteristics of cardiac arrests of non-cardiac origin, in cases where an automated external defibrillator (AED) is retrieved but did not recommend defibrillation and finally in OHCA related to foreign body obstruction. With the advantages of artificial intelligence, this project will enhance and strengthen data from the Danish Cardiac Arrest Registry. It may substitute the manual validation of the around 9000 cases per year in Denmark. Further, it proposes improvement of quality and development of observational health research.

Eligibility criteria:
OHCA from non-cardiac origin

Inclusion Criteria:

* OHCA from non-cardiac origin (trauma, asphyxia, electrocution, drowning, overdose)

Exclusion Criteria:

* OHCA from cardiac origin

Outcomes:
Survival; longterm survival
Grenoble Cardiovascular Digital Health Data Observatory
Trial ID: NCT05316025
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2022-05
Completion date: 2025-01
Sponsor: University Hospital, Grenoble
Conditions: Cardiovascular Diseases; Digital; Health Data; Observatory; Coronarography; Heart Failure; Cardiovascular
Enrollment: 5000
Age range: 18 Years - None
Sex: ALL

Summary:
The COVID-19 health crisis has led to a drastic decrease in the rate of myocardial infarction without the causes being completely identified. They are probably multiple, but this crisis has confirmed the need for massive health data from different horizons to better assess coronary disease in order to develop precision medicine. This objective is now achievable thanks to the use of tools such as big data and artificial intelligence (AI). Our team is developing algorithms to analyze medical images and identify people at risk of major cardiovascular events. These algorithms which are developed with retrospective data must be validated on prospective data, which is the objective of the Grenoble cardiovascular digital health data observatory.

The algorithm that will be validated is currently being created as part of a RIPH 3 study "AIDECORO" (NCT: 04598997). It is being developed from clinical, biological and imaging data from 600 patients with ST+ infarction and 1000 "control" patients who have undergone coronary angiography (these data are exported and stored in the PREDIMED health data warehouse via the hospital information system).

Eligibility criteria:
Inclusion Criteria:

* Adult patients who have undergone coronary angiography at CHUGA for whom images are usable.
* No opposition to participation

Exclusion Criteria:

* Coronary image not usable
* Persons referred to in articles L1121-5 to L-1121-8 of the CSP
* Patients living outside the Rhône Alpes region.

Outcomes:
Prospectively validate cardiovascular medical image analysis algorithms capable of identifying patients with poor prognostic criteria using artificial intelligence and big data methods.