moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
An Artificial Intelligence System for Rapid Onsite Cytologic Pathology Evaluation（ROSE） of Endoscopic Ultrasound-guided Fine-needle Aspiration (EUS-FNA) Sample: a Prospective, Multicenter, Diagnostic Study.
Trial ID: NCT06718725
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2024-09-01
Completion date: 2026-04
Sponsor: Qilu Hospital of Shandong University
Collaborators: Taian City Central Hospital; Qilu Hospital of Shandong University (Qingdao); Shandong Provincial Hospital; Liaocheng People's Hospital; The Affiliated Hospital of Qingdao University; Binzhou People's Hospital; Binzhou Medical University; 960th Hospital of PLA; Yidu Central Hospital of Weifang
Conditions: The Malignant Lesions and Non-malignant Lesions of Pancreas, Bile Duct, Liver and Lymph Node
Interventions: ROSE-AI system
Locations: Qilu Hospital of Shandong University
Countries: China
Enrollment: 236
Age range: 18 Years - None
Sex: ALL

Summary:
This is an observational study with a prospective, multicenter, disgnostic design. An artificial intelligence system named ROSE-AI system was developed using cytopathological slide images taken by microscope camera or smartphone of pancreas, bile duct, liver and lymph node, collected retrospectively from patients who underwent EUS-FNA and ROSE, and the performance of ROSE-AI system was validated in the datasets collected prospectively.This study aims to assist endoscopists in conducting rapid on-site cytopathology evaluations during EUS-FNA without the presence of cytopathologists. In addition, the diagnostic field was compared between the cytopathologists and ROSE-AI system, endoscopists with or without ROSE-AI system.

Eligibility criteria:
Inclusion Criteria:

1. the patient age ≥18 years accepted EUS-FNA+ROSE.
2. agree to participate in the research and be able to sign written informed consent.

Exclusion Criteria:

1. uncorrectable coagulopathy (PTT \>50 seconds or INR \>1.5) and/or uncorrectable thrombocytopenia (platelet count \<50 × 109 /L).
2. patients who were too clinically ill to undergo an EUS examination.
3. lesions that were deemed inaccessible for EUS-guided sampling.
4. unsuccessful EUS-FNA (e.g., failure to obtain an adequate specimen, patient intolerance, intraoperative accidents, etc.).
5. Patients with unqualified ROSE smear.
6. Patients who underwent biopsy during EUS-FNA but did not receive a definitive pathological diagnosis or pathological report.
7. pregnancy.

Outcomes:
the accuracy, sensitivity and specificity of the ROSE-AI system in identifying malignant/non-malignant ROSE samples
Application of New Algorithm ISR-Net in Measuring CT-FFR of Coronary In-stent Stenosis.
Trial ID: NCT05131191
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2022-01-01
Completion date: 2024-06-01
Sponsor: Beijing Hospital
Conditions: Coronary Stent Occlusion
Interventions: CT-FFR measurement; invasive FFR
Locations: Beijing Hospital
Countries: China
Enrollment: 150
Age range: 18 Years - 90 Years
Sex: ALL

Summary:
CT-FFR(CT-derived flow reserve fraction) usually could not been measured accurately for in-stent lesions due to the serious interference with the metal structs. ISR-Net is a new algorithm in assessing the flow of coronary in-stent stenosis. We compare the CT-FFR value of in-stent lesions with the invasive FFR measured by pressure wire to evaluate the accuracy of ISR-Net algorithm. The research results are of great significance to solve the bottleneck problem of CT-FFR and expand its application scope.

Eligibility criteria:
Inclusion Criteria:

General Inclusion Criteria:

* Over 18 years old;
* Be able to understand the purpose of the test and sign the informed consent form;
* Previous intracoronary stent implantation;
* According to the comprehensive clinical evaluation, coronary angiography and FFR were proposed;

CTA image Inclusion Criteria:

* The coronary CT angiography images showed that the reference vessel diameter of the stenosis segment in the stent was ≥ 2mm;
* The stenosis degree of coronary stent diameter ≥ 30% and ≤ 90% by visual inspection.

Exclusion Criteria:

General exclusion criteria:

* Previous coronary artery bypass grafting (CABG), artificial heart valve implantation, pacemaker or implantable defibrillator implantation;
* There are persistent or active symptoms of clinical instability, including acute chest pain (sudden onset), cardiogenic shock, unstable blood pressure state (systolic blood pressure less than 90 mmHg), severe congestive heart failure (NYHA cardiac function grade III or IV) or acute pulmonary edema;
* Acute myocardial infarction occurred within 7 days before enrollment;
* Patients with other severe diseases are not suitable for clinical trials, such as complex congenital heart history, sick sinus syndrome, long QT syndrome, severe arrhythmia or tachycardia, severe asthma, severe or very severe chronic obstructive pulmonary disease, chronic renal function impairment (serum creatinine value \> 1.5 mg / dL or creatinine clearance rate \< 45ml / kg · 1.73m2);
* There are contraindications to the use of disodium adenosine triphosphate;
* Allergic to iodized contrast media;
* Pregnancy or unknown pregnancy status;
* The expected life is less than 2 months;
* There are any other factors that the researchers believe are not suitable for inclusion or completion of this study.

CTA image exclusion criteria:

* The coronary artery image was obviously misplaced;
* Coronary artery occlusion.

Outcomes:
To predict the sensitivity, specificity and accuracy of CT-FFR in the functional sense of in stent lesions based on ISR-Net algorithm.
LEARN: Learning Environment for Artificial Intelligence in Radiotherapy New Technology
Trial ID: NCT05184790
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2023-02-28
Completion date: 2026-01-31
Sponsor: University of Sydney
Collaborators: Princess Alexandra Hospital, Brisbane, Australia; Calvary Mater Newcastle, Australia; Western Sydney Local Health District; Austin Health; Peter MacCallum Cancer Centre, Australia
Conditions: Arrhythmias, Cardiac; Breast Cancer; Prostatic Cancer; Brain Cancer; Kidney Cancer; Head and Neck Cancer; Liver Cancer; Pancreatic Cancer; Spinal Neoplasm
Locations: Royal North Shore Hospital; Princess Alexandra Hospital; Alfred Health; Peter MacCallum Cancer Centre
Countries: Australia; Australia; Australia; Australia
Enrollment: 300
Age range: 18 Years - None
Sex: ALL

Summary:
This study will develop a whole-of-body markerless tracking method for measuring the motion of the tumour and surrounding organs during radiation therapy to enable real-time image guidance.

Routinely acquired patient data will be used to improve the training, testing and accuracy of a whole-of-body markerless tracking method. When the markerless tracking method is sufficiently advanced, according to the PI of each of the data collection sites, the markerless tracking method will be run in parallel to, but not intervening with, patient treatments during data acquisition.

Eligibility criteria:
Inclusion Criteria:

* Will receive radiation therapy for brain, breast, head and neck, kidney, liver, pancreas, prostate, spine cancer treatment or cardiac arrhythmia treatment at a participating centre.
* Will receive CT planning, and a cone beam CT scan for at least one fraction of radiation therapy.
* Will receive intrafraction x-ray imaging for the liver, pancreas, prostate, spine cancer treatment or cardiac arrhythmia treatment. As intrafraction imaging is not common standard of care for brain, breast, head and neck and kidney cancer treatments there is no requirement to have intrafraction x-ray imaging data for these anatomical sites.
* Provides written informed consent.

Exclusion Criteria:

* Less than 18 years of age

Outcomes:
Accuracy of markerless tracking
Pilot Study of Strepic® Device for the Diagnosis of Group A Streptococcal Pharyngitis
Trial ID: NCT03777098
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2019-01-02
Completion date: 2021-12-30
Sponsor: David A. Talan
Collaborators: Light AI Inc.
Conditions: Group A Streptococcal Infection
Interventions: Strepic® Device
Locations: Olive View-UCLA Medical Center
Countries: United States
Enrollment: 426
Age range: 7 Years - 64 Years
Sex: ALL

Summary:
The objective of this pilot study is to acquire images using the Strepic® device, a clinical prototype that has been designed specifically as a viable, low-cost, commercially realizable autofluorescence-based diagnostic test, using (1) fluorescence and (2) white light image data, as well as other clinical data points. By acquiring and analyzing the images of pharyngeal bacterial fluorescence and white light patterns in patients with Group A Streptococcus (GAS)-associated pharyngitis and comparing them with those observed in non-GAS pharyngitis, it is believed an algorithm can be developed such that the device will improve the ability of clinicians to quickly and accurately identify GAS infections.

Eligibility criteria:
Inclusion Criteria:

1. All patients aged 7-64 years of age with suspected GAS pharyngitis with modified Centor (McIsaac) score ≥2; and
2. Patient agrees to participate in study and patient or parent/guardian sign consent/assent form.

Exclusion Criteria:

1. Pharyngeal swab performed immediately prior to study team approach;
2. Prior enrollment in this study;
3. Pharyngeal symptoms \>7 days;
4. Peritonsillar abscess;
5. Antibiotic use within one week of index visit;
6. Inability to open mouth to visualize the pharynx; and
7. Inability to understand English or Spanish, as study forms including consent and assent forms will be written in English and Spanish;
8. Woman who is pregnant (self-reported, pregnancy test will not be performed) or nursing;
9. Current prisoner (define as an individual involuntarily confined in a penal institution, including persons: (1) sentenced under a criminal or civil statue; (2) detained pending arraignment, trial, or sentencing; and (3) detained in other facilities (e.g., for drug detoxification or treatment of alcoholism) under statutes or commitment procedures providing such alternatives to criminal prosecution or incarceration in a penal institution).

Outcomes:
Strepic Device Image; Throat culture or PCR result
Prospective Validation Study of Artificial Intelligence-based Prediction Algorithm for the Prediction of Paroxysmal Atrial Fibrillation
Trial ID: NCT05725187
Status: ENROLLING_BY_INVITATION
Study type: OBSERVATIONAL
Start date: 2022-10-14
Completion date: 2025-12-31
Sponsor: Ewha Womans University Mokdong Hospital
Collaborators: Ewha Womans University Seoul Hospital
Conditions: Atrial Fibrillation Paroxysmal
Interventions: MobiCare
Locations: Chonnam National University Hospital; Yongin Severance Hospital; Gachon University Gil Medical Center; Kyung Hee University Hospital; Korea University Anam Hospital; Hanyang University Seoul Hospital; Chung-Ang University Hospital; Ewha Womans University Seoul Hospital; Ewha Womans University Mokdong Hospital; Korea University Guro Hospital; Chungbuk National University Hospital
Countries: South Korea; South Korea; South Korea; South Korea; South Korea; South Korea; South Korea; South Korea; South Korea; South Korea; South Korea
Enrollment: 600
Age range: 20 Years - None
Sex: ALL

Summary:
The purpose of this study is to predict the occurrence of paroxysmal atrial fibrillation by finding high-risk group from normal sinus rhythm ECG through artificial intelligence-based prediction algorithm.

Eligibility criteria:
Inclusion Criteria:

* Participants must be above 20 in age
* Participants are patients with symptom of arrhythmia who visited outpatient clinic or who have been hospitalized

Exclusion Criteria:

* Excluding patients with cardiac implantable electronic device such as pacemakers, implantable defibrillators (ICD), or cardiac resynchronization therapy (CRT).
* Excluding pregnant women and lactating women.

Outcomes:
Occurrence of paroxysmal AF
Chest X-Ray Abnormality Detection Using Artificial Intelligence: Retrospective Study of Carebot AI CXR Performance in Preclinical Practice
Trial ID: NCT05594485
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2022-08-15
Completion date: 2022-10-20
Sponsor: Carebot s.r.o.
Conditions: Artificial Intelligence; Lung Diseases; Pneumothorax; Pleural Effusion; Cardiomegaly; Lung Cancer; Pulmonary Edema; Consolidation; Pneumonia; Atelectasis; Hilar Calcification; Fracture Rib
Interventions: Carebot AI CXR
Locations: Nemocnice Havířov, p. o.
Countries: Czechia
Enrollment: 127
Age range: 18 Years - None
Sex: ALL

Summary:
The purpose of this study is to describe the design, methodology and evaluation of the preclinical test of Carebot AI CXR software, and to provide evidence that the investigated medical device meets user requirements in accordance with its intended use. Carebot AI CXR is defined as a recommendation system (classification "prediction") based on computer-aided detection. The software can be used in a preclinical deployment at a selected site before interpretation (prioritization, display of all results and heatmaps) or after interpretation (verification of findings) of CXR images, and in accordance with the manufacturer's recommendations. Given this, a retrospective study is performed to test the clinical effectiveness on existing CXRs.

Eligibility criteria:
Inclusion Criteria:

* Hospital patients who were referred for chest radiography between August 15 and 17, 2022.

Exclusion Criteria:

* Pediatric CXR images (under 18 years of age)
* Scans with technical problems (poor image quality, rotation)
* Images in lateral projection

Outcomes:
Primary objective
sAItisfACES - Impact of an Artificial Intelligence Tool on the Satisfaction of Professionals and Patients in Ambulatory Consultations: A Randomized Cluster Study
Trial ID: NCT06726447
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-12-01
Completion date: 2026-12-01
Sponsor: Universidad Autonoma de Madrid
Conditions: General Health Status
Interventions: Artificial Intelligence Tool
Locations: ACES Centers; CSEU La Salle - UAM
Countries: Spain; Spain
Enrollment: 148
Age range: 18 Years - 80 Years
Sex: ALL

Summary:
This multicenter cluster-randomized study evaluates the impact of an artificial intelligence (AI) tool on the satisfaction of healthcare professionals and patients in outpatient consultations, measuring its effect on perceived satisfaction (through a visual analog scale), the duration of consultations, and the quality and quantity of clinical data recorded. Adult patients (18-80 years) seen in outpatient centers will participate, comparing those using the AI tool with centers following the usual procedure. The tool is expected to reduce the administrative burden, improve user satisfaction and increase the efficiency and quality of the clinical registry. Recruitment will take place between December 2024 and May 2025, with final analysis planned for the end of 2025.

Eligibility criteria:
Inclusion Criteria:

* Patients between 18 and 80 years of age.
* Patients consulting for any health reason in the outpatient clinics of the centers participating in the study.
* Patients who sign the informed consent to participate in the study.

Exclusion Criteria:

* Patients who are unable to understand or complete the questionnaires, due to:
* Language barriers.
* Cognitive disabilities.
* Any other reason that prevents their adequate participation.
* Patients who are currently participating in other clinical trials or research studies that may interfere with the results of this study.

Outcomes:
Satisfaction with the consultation
A Multi-Reader Multi-Case (MRMC) Study for Assessing the Impact of Legit.Health Plus on the Clinical Assessment of Generalized Pustular Psoriasis and Other Skin Conditions by Healthcare Professionals.
Trial ID: NCT07428915
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2024-06-01
Completion date: 2024-09-15
Sponsor: AI Labs Group S.L
Conditions: Pustular Psoriasis (PP); Generalized Pustular Psoriasis (GPP); Eczema Atopic Dermatitis; Acute Generalized Exanthematous Pustulosis; Acne; Acne Conglobata; Seborrheic Keratoses; Seborrheic Dermatitis; Palmoplantar Pustulosis; Plaque Psoriasis; Pemphigus Vulgaris (PV); Impetigo; Hidradenitis Suppurativa (HS); Tinea Corporis
Interventions: AI-based medical device for aided diagnosis in Dermatology
Locations: AI Labs Group S.L.
Countries: Spain
Enrollment: 15
Age range: 18 Years - None
Sex: ALL

Summary:
This study aims to determine if an artificial intelligence (AI) medical device can help healthcare professionals more accurately diagnose rare and complex skin conditions. Dermatological issues are common in primary care, but there is often a gap in diagnostic accuracy between general practitioners and specialists, which can lead to treatment delays for serious conditions like Generalized Pustular Psoriasis (GPP) and Hidradenitis Suppurativa (HS).

The researchers hypothesized that the AI device would enhance the diagnostic accuracy of healthcare professionals for GPP and other dermatological conditions. To test this, the study followed a prospective observational design involving 15 practitioners, including both general practitioners and dermatologists.

During the study, participants were asked to evaluate 100 clinical images. For each case, they first provided a diagnosis based on the image and patient history alone. They were then shown the AI's analysis-which included the top five suggested diagnoses and confidence levels-and asked if they would like to adjust their initial assessment.

The primary question the study sought to answer was whether the information provided by the AI device could significantly increase the number of correct diagnoses made by these professionals, particularly for rare diseases that are often difficult to identify in a standard clinical setting

Eligibility criteria:
Inclusion Criteria:

* Board-certified general practitioners and dermatologists, regardless of their professional experience.
* Good quality images of patients with GPP.
* Good quality images of patients with HS.
* Good quality images of patients with pathologies that can be confused with GPP or HS, leading to a wrong diagnosis.

Exclusion Criteria:

* Images of patients with pathologies different from GPP or HS that can be easily identified.

Outcomes:
Diagnostic Accuracy for Generalized Pustular Psoriasis (GPP) with and without Artificial Intelligence Support.; Diagnostic Accuracy for different skin conditions with and without Artificial Intelligence Support
Predicting Disease Progression in Atrial Fibrillation: A Multiparametric Approach for Prognostic Marker Identification and Personalized Patient Management
Trial ID: NCT06647914
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-09-03
Completion date: 2026-08-31
Sponsor: IRCCS Policlinico S. Donato
Collaborators: Federico II University; Irccs Sdn; Marche Polytechnic university, Ancona, Italy
Conditions: Atrial Fibrillation (AF)
Enrollment: 322
Age range: 18 Years - None
Sex: ALL

Summary:
This project leverages artificial intelligence (AI) to decipher Atrial Fibrillation (AF) progression and optimize treatment strategies. By recruiting a diverse cohort of 322 AF patients, we will gather a robust multiparametric dataset including clinical, genetic, electrocardiographic, and echocardiographic data. Harnessing AI, we will extract and correlate hidden components within ECG-obtained P-wave data and echocardiographic studies with atrial fibrosis, culminating in an atrial fibrosis score (AFS). The AFS will non-invasively predict fibrosis extent and AF clinical progression, including metrics like rehospitalization, cardiac morbidity, and mortality. Ultimately, this endeavor aims to improve AF patient management, significantly reducing healthcare costs, and enhancing patient quality of life.

Eligibility criteria:
Inclusion Criteria:

* History of paroxysmal or persistent atrial fibrillation
* Clinical indication for Atrial Fibrosis (AF) ablation according to the 2020 ESC Guidelines

Exclusion Criteria:

* Age below 18 years old
* Refusal to sign consent
* Noncompliance with the study protocol

Outcomes:
A composite clinical point used to evaluate the Atrial Fibrosis Score prognostic capability
Evaluating AI-Generated Plain Language Summaries on Patient Comprehension of Ophthalmology Notes Among English-Speaking Patients
Trial ID: NCT06859216
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-03-01
Completion date: 2026-06-01
Sponsor: University of California, Los Angeles
Conditions: Ophthalmic Disease; Artifical Intelligence; Large Language Model
Interventions: AI-Generated Plain Language Summaries
Locations: UCLA
Countries: United States
Enrollment: 460
Age range: 18 Years - None
Sex: ALL

Summary:
This clinical trial is testing whether plain language summaries made by artificial intelligence help people understand their eye doctor's notes better. Adults receiving eye care at the Jules Stein Eye Institute will get either the usual medical notes or a note with the addition of an AI-generated summary that explains the information in simple, everyday words. Participants will then answer a short survey and receive a follow-up call to share how clear the information was, how well they understood their diagnosis and treatment, and whether they feel more confident about their care. The goal is to find out if these plain language summaries can make it easier for people to understand their eye care and improve communication between patients and health care providers.

Eligibility criteria:
Inclusion Criteria:

* Age ≥ 18 years English-speaking Receiving ophthalmology care at the Jules Stein Eye Institute Able to provide informed consent

Exclusion Criteria:

* Known cognitive impairments (e.g., dementia, intellectual disability) that would affect comprehension Prisoners or wards of the state Unable to provide informed consent

Outcomes:
Patient Comprehension Score (Immediate Post-Visit)
Research on Precision Diagnosis and Treatment Decision of Common Eye Diseases Based on Artificial Intelligence
Trial ID: NCT07078552
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2024-01-01
Completion date: 2025-05-01
Sponsor: Second Affiliated Hospital, School of Medicine, Zhejiang University
Collaborators: Shanghai Jiao Tong University School of Medicine; Tianjin Eye Hospital
Conditions: Congenital Ptosis
Interventions: ptosis
Locations: 2 nd Affiliated Hospital, School of Medicine, Zhejiang University, China
Countries: China
Enrollment: 368
Age range: None - 18 Years
Sex: ALL

Summary:
The goal of this observational study is to investigate the clinical features of congenital ptosis in children by analyzing eyelid movement captured through smartphone videos. The main question it aims to answer is:

Can video-based eyelid measurements and levator muscle function grading provide useful guidance for the treatment of congenital ptosis in children?

Participants are children diagnosed with congenital ptosis. Their blinking behavior will be recorded using a smartphone, and eyelid morphology and muscle strength will be assessed from the video data.

Eligibility criteria:
Inclusion Criteria:

* participants aged ≤18 years with compliance during video recording. Congenital ptosis diagnosis mandated documented symptom onset within the first year of life and bilateral or unilateral MRD1 of \< 2.5 mm

Exclusion Criteria:

* acquired ptosis, myasthenia gravis, Marcus Gunn syndrome, eyelid deformities, or prior ophthalmic surgery

Outcomes:
levator function testing