moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Multicenter Evaluation Study of the CADe System ENDOMIND in Surveillance Endoscopy for Colorectal Cancer in German Outpatient Cohort
Trial ID: NCT05006092
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2021-09-01
Completion date: 2022-11-30
Sponsor: Wuerzburg University Hospital
Collaborators: Klinikum Stuttgart
Conditions: Surveillance Endoscopy for Colorectal Cancer; Artificial Intelligence; Polyps of Colon; CADe
Interventions: ENDOMIND
Locations: Universitätsklinikum Würzburg
Countries: Germany
Enrollment: 1149
Age range: 18 Years - None
Sex: ALL

Summary:
Evaluation of an artificial intelligence system for polyp detection (CADe)

Eligibility criteria:
Inclusion Criteria:

* indication CRC surveillance endoscopy
* indication post-polypectomy surveillance endoscopy
* positive fecal immunochemical test in patients \>=50 years

Exclusion Criteria:

* reasonable suspicion of inflammatory bowl disease
* reasonable suspicion of familiar polyposis Syndrome
* Patient after radiation/resection of colonic parts

Outcomes:
adenoma detection rate
Prediction of Abdominal Aortic Aneurysm Shrinkage After Endovascular Repair With Artificial Intelligence
Trial ID: NCT06250998
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2024-04-03
Completion date: 2024-06
Sponsor: Rijnstate Hospital
Collaborators: University Medical Center Groningen; Amsterdam UMC, location VUmc; Medisch Spectrum Twente; Karolinska Institutet
Conditions: AAA - Abdominal Aortic Aneurysm
Locations: Rijnstate Hospital; Amsterdam UMC; Medisch Spectrum Twente; Universitair Medisch Centrum Groningen; Karolinksa Institutet
Countries: Netherlands; Netherlands; Netherlands; Netherlands; Sweden
Enrollment: 1000
Age range: 18 Years - None
Sex: ALL

Summary:
The goal of this observational study is to find out what factors contribute to a good treatment outcome in patients who have received a stent for their dilation of the main abdominal artery (abdominal aortic aneurysm or AAA). The primary goal of this study is to build a prediction model for abdominal aortic aneurysm shrinkage one year after the treatment.

Eligibility criteria:
Inclusion Criteria:

* Age ≥ 18 years;
* Elective, initially technically successful (as described by the ESVS guidelines11), infrarenal EVAR for unruptured infrarenal AAA;
* Implanted endograft is a Gore Excluder, Medtronic Endurant, Cook Zenith, Cordis Incraft, Endologix AFX, Jotec E-Tegra, or Vascutek Anaconda endograft;
* Follow-up of the AAA of at least one year with imaging of the same modality as the preoperative imaging;
* Axial reconstructions of preoperative CT angiography available, preferably in the arterial phase and with 3 mm or smaller slice thickness.

Exclusion Criteria:

* Patients with ruptured or symptomatic AAA;
* Patients that were treated with fenestrated or branched endografts, iliac branched devices, non-CE marked devices within trial setting, other endografts (EVAS cases) or chimney procedures;
* Acute procedures;
* Reintervention before one year follow-up;
* Revision cases after previous open or endovascular aneurysm repair of an AAA;

Outcomes:
Prediction model for AAA shrinkage
A Single-Center, Retrospective, Rater-Blinded, Crossover Study to Evaluate the Efficacy of the Medical Image Analysis Software 'SCALE PET' for Quantitative Analysis of Brain PET Images in Patients With Alzheimer's Disease
Trial ID: NCT07417540
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-06-02
Completion date: 2025-11-17
Sponsor: NEUROPHET
Collaborators: Ajou University Hospital, Suwon, South Korea
Conditions: Alzheimer's Disease (AD); Mild Cognitive Impairment (MCI)
Interventions: Neurophet SCALE PET
Locations: Ajou University Hospital
Countries: South Korea
Enrollment: 190
Age range: 50 Years - None
Sex: ALL

Summary:
The goal of this study is to learn if medical image analysis software, Neurophet SCALE PET, can enhance readers' amyloid beta positivity performance in people with Alzheimer's Disease. The main question it aims to answer is:

Does the use of medical image analysis software assistance result in a statistically significant difference in readers' sensitivity and specificity for the classification of amyloid-β (Aβ) positivity?

Four readers will interpret amyloid PET images in two sessions (with and without software assistance), separated by a 4-week washout period, and will record Aβ positivity and diagnostic confidence using predefined rating scales.

Eligibility criteria:
Inclusion Criteria: Medical imaging datasets meeting all of the following criteria will be eligible for inclusion in this study.

1. Age ≥ 50 years at the time of brain magnetic resonance imaging (MRI)
2. Participants who underwent amyloid brain PET imaging using one of the following tracers: 18F-Florbetaben, 18F-Florbetapir, 18F-Flutemetamol
3. Availability of 3D T1-weighted brain MRI.
4. The interval between amyloid PET and 3D T1-weighted MRI is within 1 year.
5. Participants meeting one of the following predefined groups based on prior visual interpretation of amyloid PET:

   * Aβ-positive group: Prior clinical interpretation of amyloid PET reported as Aβ positive.
   * Aβ-negative group: Prior clinical interpretation of amyloid PET reported as Aβ negative.

Exclusion Criteria: Medical imaging datasets meeting any of the following criteria will be excluded from the study.

1. Poor image quality due to artifacts, motion, shading, or other technical issues that make visual interpretation difficult or prevent proper software processing.
2. Presence of prior brain surgery resulting in significant structural changes, or presence of intracranial metallic implants.
3. Contrast-enhanced 3D T1-weighted brain MRI.
4. Presence of structural brain lesions other than cerebral infarction.
5. Cases deemed inappropriate for study participation at the investigator's discretion.

Outcomes:
Difference in readers' sensitivity in SW-assisted vs. unassisted classification of Aβ positivity; Difference in readers' specificity in SW-assisted vs. unassisted classification of Aβ positivity
Effect of Predictive Model on Total Double-Balloon Enteroscopy Rate: a Randomized Controlled Trial
Trial ID: NCT07288346
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-01-12
Completion date: 2027-08-31
Sponsor: Shandong University
Collaborators: Qilu Hospital of Shandong University (Qingdao); Dezhou People's Hospital; Yantai Affiliated Hospital of Binzhou Medical University; Zaozhuang Municipal Hospital
Conditions: Double-balloon Enteroscopy
Interventions: Predictive model
Locations: Dezhou People's Hospital; Qilu Hospital of Shandong University (Qingdao); Yantai Affiliated Hospital of Binzhou Medical University; Zaozhuang Municipal Hospital; Qilu Hospital of Shandong University
Countries: China; China; China; China; China
Enrollment: 338
Age range: 18 Years - 80 Years
Sex: ALL

Summary:
The aim of this study is to assess the effect of predictive model on total enteroscopy rate in double-balloon enteroscopy.

Eligibility criteria:
Inclusion Criteria:

1. patients aged between 18 and 80 years who were planned to undergo an attempt at total enteroscopy for suspected small-bowel disease
2. CT scan of the abdomen was performed within two weeks before enteroscopy

Exclusion Criteria:

1. cases terminated upon the target lesion (strictures, masses, hemorrhagic or other lesions) and not the maximal insertion
2. poor quality of bowel preparation
3. high risk esophageal varices
4. the insertion route is not oral or anal because of changes in anatomical structure
5. unsuitable for general anesthesia
6. pregnant women
7. unable to provide informed consent

Outcomes:
Total enteroscopy rate
Real-time Artificial Intelligence-based Speech Enhancement Methods for Hearing Aid Improvement
Trial ID: NCT05985473
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-03-06
Completion date: 2026-09-15
Sponsor: Institut Pasteur
Conditions: Deafness Neurosensory
Interventions: audiological measurements; Objective and subjective audiological measurements; Evaluation of the denoising methods REFINED
Locations: CEntre de Recherche et d'Innovation en Audiologie Humaine
Countries: France
Enrollment: 40
Age range: 18 Years - None
Sex: ALL

Summary:
Individuals with Auditory Neuropathy Spectrum Disorders (ANSD) represent 1-10% of adults with hearing loss. These individuals have little or no benefit from current hearing aids because ANSD is a continuum of hearing impairments due to synaptic or neural dysfunction in the peripheral and central parts of the auditory pathways, which impairs temporal information processing without necessarily affecting auditory sensitivity. There is a need to find ad-hoc denoising methods, based on the expert knowledge of audiologists, to improve the noise comprehension performance of these patients. Implemented denoising methods, based on artificial intelligence, will also greatly benefit more standard hearing loss cases.

Eligibility criteria:
Inclusion Criteria:

For all participants:

* Age greater than or equal to 18 years,
* French mother tongue,
* Have an average tonal hearing loss (calculated according to the BIAP method) \< 30 dB HL, on headphones, for each ear,
* Be professionally active, not be pre-retired and not be unemployed
* Be affiliated with a social security plan,

For participants with TSNA:

\- Have degraded comprehension in noise (threshold of Intelligibility in Noise \> 3 dB compared to the norm).

For controls:

\- Have normal comprehension in noise (Intelligibility in Noise threshold ≤ 3 dB from the norm).

Exclusion Criteria:

For all participants:

* Have a conductive or mixed hearing loss, which is when the hearing loss affects both the outer and/or middle ear and the inner ear.
* Have an asymmetric hearing loss, i.e. a difference in average hearing loss between the left and right ear greater than 30 dB,
* Have sequelae of ear infections and/or a history of ENT disease that permanently affects hearing (vestibular schwannoma, Meniere's disease, sudden or fluctuating deafness, congenital hypoacusis)
* Being under guardianship,
* Being deprived of liberty by judicial or administrative decision, or being subject to legal protection.

Outcomes:
Comparison of percentage of words correctly repeated by participants (ANSD and controls)
Utilizing Artificial Intelligence to Optimize Chest Compression Region During Cardio-pulmonary Resuscitation for Patients With Out-of-hospital Cardiac Arrest.
Trial ID: NCT07431710
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-01-06
Completion date: 2027-12-31
Sponsor: Far Eastern Memorial Hospital
Collaborators: National Health Research Institutes, Taiwan
Conditions: Out-of-hospital Cardiac Arrest (OHCA); Cardiopulmonary Resuscitation (CPR); Aortic Valve Compression; Precision Resuscitation
Interventions: Device: AI-Enhanced Arterial Waveform Monitor (AIR-CPR App); AI-Guided Chest Compression Repositioning; Transesophageal Echocardiography (TEE)
Locations: Far Eastern Memorinal Hospital
Countries: Taiwan
Enrollment: 255
Age range: 20 Years - None
Sex: ALL

Summary:
The AIR-CPR project aims to improve survival rates for patients with Out-of-Hospital Cardiac Arrest (OHCA) by utilizing Artificial Intelligence (AI) to optimize chest compression locations. Current guidelines recommend a standardized compression point (the lower half of the sternum), yet recent research indicates that this position can compress the aortic valve in approximately 48.7% of patients, significantly reducing the chances of successful resuscitation.

This study will develop a deep learning model based on YOLO v8 to analyze real-time arterial pressure waveforms to identify proper aortic valve opening and closing. By identifying specific waveform features that humans cannot easily distinguish, the AI will guide rescuers to adjust the compression site-typically toward the left ventricle-to ensure optimal blood output. The project seeks to transform CPR from a standardized "one-size-fits-all" approach into a personalized, precision medicine intervention.

Eligibility criteria:
Inclusion Criteria:

1. Adults aged 20 years or older.
2. Patients with out-of-hospital cardiac arrest (OHCA) undergoing 3.cardiopulmonary resuscitation (CPR) in the emergency department.

Cardiac arrest caused by non-traumatic factors.

Exclusion Criteria:

1. Pregnant patients.
2. Patients with obvious signs of death.
3. Patients with a signed "Do Not Resuscitate" (DNR) order.
4. Patients requiring extracorporeal cardio-pulmonary resuscitation (ECPR).
5. Patients requiring Resuscitative Endovascular Balloon Occlusion of the Aorta (REBOA).
6. Cardiac arrest caused by massive hemorrhage, aortic emergencies, tension pneumothorax, cardiac tamponade, or pulmonary embolism.
7. History of severe aortic valve disease or previous aortic valve surgery.
8. Patients for whom TEE or femoral arterial catheterization is contraindicated.
9. Situations where the medical team is unable to perform TEE or femoral arterial catheterization during CPR.

Outcomes:
AI Identification Accuracy of Aortic Valve Compression
MC250406 Feasibility Study: Automated Risk Stratification, Serial AI-Augmented Imaging, and Biobanking for Early Detection of Sporadic Pancreatic Cancer (AI-PACED)
Trial ID: NCT07324096
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-03-24
Completion date: 2029-03-24
Sponsor: Mayo Clinic
Conditions: Pancreatic Ductal Adenocarcinoma
Interventions: Biospecimen Collection; Computed Tomography with Contrast; Electronic Health Record Review
Locations: Mayo Clinic in Rochester
Countries: United States
Enrollment: 100
Age range: 50 Years - 85 Years
Sex: ALL

Summary:
This clinical trial studies a new screening program to improve the early detection of sporadic pancreatic cancer in individuals with a high risk of developing pancreatic cancer. Pancreatic cancer remains one of the deadliest solid tumors, characterized by a long phase without symptoms followed by rapid progression once clinically evident. Despite advancements in treatment, the survival rate for pancreatic cancer remains low. Research has helped to identify a subset of individuals with a markedly high short-term risk for developing pancreatic cancer, which includes adults aged 50 and older with glycemically-defined new-onset diabetes and an Enriching New-Onset Diabetes for Pancreatic Cancer (ENDPAC) score ≥ 3. However, current practice guidelines do not provide clear pathways for surveillance or early detection. The screening program in this trial combines repeated contrast-enhanced computed tomography (CT) scans using artificial intelligence (AI) and blood draws. Contrast-enhanced CT is an imaging technique which creates a series of detailed pictures of areas inside the body; the pictures are created by a computer linked to an x-ray machine and a contrast agent is used to enhance the images. The images are then reviewed using AI, which may make it easier to spot cancer earlier on the CT scans than with the human eye. Studying samples of blood in the laboratory from high-risk individuals may help doctors understand more about why they may develop pancreatic cancer. This may be an effective way to screen high-risk individuals and improve the early detection of sporadic pancreatic cancer.

Eligibility criteria:
Inclusion Criteria:

* Age ≥ 50 and ≤ 85 years
* Glycemically-defined new-onset diabetes (gNOD) with onset ≤ 180 days preceding enrollment
* Enriching New-onset Diabetes for Pancreatic Cancer (ENDPAC) score ≥ 3, based on validated risk stratification models
* Provide written or remote informed consent

Exclusion Criteria:

* Prior diagnosis of pancreatic ductal adenocarcinoma (PDA)
* Known hereditary cancer syndromes (e.g., BRCA1/2, Lynch syndrome, Peutz-Jeghers)
* Prior history of pancreatic surgery
* Pancreatic cyst surveillance at time of registration
* Contraindications to contrast-enhanced CT imaging per standard clinical practice at time of registration

Outcomes:
Recruitment yield (Feasibility); Imaging adherence rates (Feasibility); Blood collection success rates (Feasibility); Completeness of electronic medical record (EMR)-based follow-up (Feasibility)
A Feasibility and Pilot Trial of Gamification and AI-based Health Coaching Intervention Components for Diabetes Management Using the GODART Platform
Trial ID: NCT05344859
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2023-05-15
Completion date: 2025-10-31
Sponsor: University of Alabama at Birmingham
Collaborators: National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)
Conditions: Diabetes Mellitus, Type 2
Interventions: Weekly Automated Health Coaching; Weekly Human Health Coaching; Adapted Reward Level; Fixed Gamified Reward Level
Locations: Department of Family and Community Medicine, University of Alabama at Birmingham
Countries: United States
Enrollment: 88
Age range: 18 Years - None
Sex: ALL

Summary:
The purpose of this study is to pilot and assess the feasibility of implementing an artificial intelligence-assisted individualized lifestyle modification intervention for glycemic control in rural populations, which can be delivered even with regular landline phone service. This study will provide us with the knowledge to plan a well-powered optimization trial in the future to develop an optimal (low-cost) intervention package that can be delivered in a sustainable manner to the rural portions of America.

Eligibility criteria:
Inclusion Criteria:

1. a diagnosis of T2DM
2. HbA1C ≥7% to ≤ 10.5% for phase 1- 14 days and phase 2 of the study- 6 months.
3. ≥18 years of age
4. the ability to converse in and read English.

Exclusion Criteria:

1. Present or soon-planned pregnancy
2. Current enrollment in any structured lifestyle intervention study for diabetes or weight management.
3. Patients currently on insulin treatment
4. Major cardiac event in the past 6 months
5. Renal failure in the past 6 months
6. Listening and Speaking Impairment

Outcomes:
Change in Hemoglobin A1C level between screening and 6 months
Impact of Ostracism on Prospective Memory in ADHD Children With or Without Emotional Dysregulation/ Impact de l'Ostracisme Sur la mémoire Prospective Chez Des Enfants Atteint de TDA/H Avec ou Sans dysrégulation émotionnelle.
Trial ID: NCT05520996
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2021-12-15
Completion date: 2022-06-21
Sponsor: University Hospital, Rouen
Conditions: ADHD
Interventions: Cyberball
Locations: CHU - Rouen
Countries: France
Enrollment: 24
Age range: 8 Years - 12 Years
Sex: ALL

Summary:
Assess the effect of exposure to a situation of social exclusion or ostracism on the prospective memory of children with ADHD

Eligibility criteria:
Inclusion Criteria:

* Child with attention deficit disorder with or without hyperactivity (ADHD) according to the following criteria: score greater than 27 on the ADHD-RS scale or score greater than 65 on the Conners scale, and after clinical staff discussion
* Social security
* non opposition of parents and children to the study

Exclusion Criteria:

* Subject presenting elements in favor of a depressive disorder according to a clinical evaluation and on an evaluation by the CBCL scale
* Minor and/or parents who do not understand French
* Person under legal protection, adult guardianship or curatorship.

Outcomes:
difference between prospective memory tests performed before and after cyberball task
A Study to Evaluate Accuracy and Validity of the Chang Gung ECG Abnormality Detection Software
Trial ID: NCT05903313
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2023-10-06
Completion date: 2024-02-28
Sponsor: Chang Gung Memorial Hospital
Conditions: Long QT Syndrome; Sinus Bradycardia; Sinus Tachycardia; Premature Atrial Complexes; Premature Ventricular Complexes; Atrial Flutter; Right Bundle-Branch Block; Left Bundle-Branch Block; Left Ventricular Hypertrophy; Myocardial Infarction
Interventions: Chang Gung ECG Abnormality Detection Software
Locations: Chang Gung memorial hospital
Countries: Taiwan
Enrollment: 4306
Age range: 20 Years - None
Sex: ALL

Summary:
"Chang Gung ECG Abnormality Detection Software" is a is an artificial intelligence medical signal analysis software that detect whether patients have abnormal ECG signals of 14 diseases by static 12-lead ECG. The 14 diseases were

* Long QT syndrome
* Sinus bradycardia
* Sinus Tachycardia
* Premature atrial complexes
* Premature ventricular complexes
* Atrial Flutter, Right bundle branch block
* Left bundle branch block
* Left Ventricular hypertrophy
* Anterior wall Myocardial Infarction
* Septal wall Myocardial Infarction
* Lateral wall Myocardial Infarction
* Inferior wall Myocardial Infarction
* Posterior wall Myocardial Infarction

The main purpose of this study is to verify whether "Chang Gung ECG Abnormality Detection Software" can correctly identify abnormal ECG signals among patients of 14 diseases. The interpretation standard is the consensus of 3 cardiologists. The results of the software analysis will be used to evaluate the performance of the primary and secondary evaluation indicators.

Eligibility criteria:
Inclusion Criteria:

* Equal or greater than twenty years old.
* Static 12-lead electrocardiogram of General Electric MUSE XML format file.
* The data comes from the static 12-lead electrocardiogram device of General Electric (model MAC5500).
* The electrocardiogram signal is 500 Hz.
* The Alternating current (AC) filter of the electrocardiogram signal is 60 Hz.
* The resource of original diagnosis was a cardiologist.

Exclusion Criteria:

* Cases used in the model development process.
* Lacks any electrode.
* Contain any electrode lacks a segment.

Outcomes:
Sensitivity and Specificity