moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Application of the Belle.AI Comparative Image Reference System for Describing Chronic Ear Infections in Pediatric Patients From Low-Resource Communities in Remote, At-Home Settings
Trial ID: NCT07025473
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-06-30
Completion date: 2025-09-30
Sponsor: BelleTorus Corporation
Conditions: Ear Infection
Interventions: Smartphone-enabled otoscopy
Locations: Urban Health Plan, Inc.
Countries: United States
Enrollment: 20
Age range: 1 Year - 17 Years
Sex: ALL

Summary:
The purpose of this research is to examine the clinical effectiveness of an investigational AI-powered smartphone app that describes the physical characteristics of an otoscopy image. It is not intended to make any diagnosis. Only healthcare professionals will make the diagnosis. The investigators will compare the diagnosis made by physicians or physician extenders (healthcare professionals) against the description provided by the support AI app tool to determine the clinical relevance of the system and examine its use within a clinical setting.

Eligibility criteria:
Inclusion Criteria:

* Patient must be aged 1 to 17 years old.
* Patient must present with a primary middle ear complaint.
* Parent of the pediatric patient must have the ability and willingness to provide informed consent and comply with study procedures and visits.
* Participants must have access to the required technology (e.g., smartphone with internet access) and be capable of using the provided off-the-shelf otoscope for the required image capture.

Exclusion Criteria:

• Patients who are unable to comply with study procedures due to physical or mental health limitations (as assessed by study coordinator).

Outcomes:
Accuracy of Belle AI Image References; Provider-Rated Usefulness
LESTONNAC Study: Observed ST Elevation Does Not Require Acute Cardiac Necrosis
Trial ID: NCT05689970
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2022-07-25
Completion date: 2024-07-24
Sponsor: Idoven 1903 S.L.
Collaborators: Hospital General Universitario Gregorio Marañon; Spanish Society of Cardiology
Conditions: STEMI - ST Elevation Myocardial Infarction
Interventions: AI platform to detect ST elevation in ECG
Locations: Hospital Universitario de Canarias; Hospital de Basurto; Hospital Vall D' Hebron; Idoven; Servicio Cardiología Hospital Universitario Gregorio Marañón; Hospital Clínico San Carlos; Hospital Clínico Universitario de Valladolid
Countries: Spain; Spain; Spain; Spain; Spain; Spain; Spain
Enrollment: 420
Age range: 18 Years - None
Sex: ALL

Summary:
Patients with chest pain and persistent ST segment elevation (STE) may not have acute coronary occlusions or serum troponin curves suggestive of acute necrosis. Our objective is the validation and cost-effectiveness analysis of a diagnostic model assisted by artificial intelligence (AI). Our hypothesis is that an AI analysis of the surface electrocardiogram allows a better distinction of patients with STE due to acute myocardial ischemia, from those with another etiology. This is a prospective multicenter study with two groups of patients with STE: I) coronary arteries without significant lesions and without serum troponin curve suggestive of acute necrosis, II) myocardial infarction with acute coronary occlusion. A manual centralized electrocardiographic analysis and another by AI algorithms will be performed.

Eligibility criteria:
Inclusion Criteria:

* Age≥18 years.
* Chest pain or symptoms suggestive of myocardial ischemia.
* STE at point J in the12-lead electrocardiogram prior to activation of the infarction code in two contiguous leads ≥0.1 mV, in V2 and V3 ≥0.2 mV.
* Signature of informed consent.

Exclusion Criteria:

* Left bundle branch block.
* Acute cardiac necrosis in the absence of significant epicardial coronary artery stenosis \>70% (vasospasm, takotsubo stress cardiomyopathy, myocarditis, coronary artery dissection, acute myocardial infarction without obstructive coronary lesions - MINOCA).
* STE≤0.1 mV with pathologic Q wave suggestive of previous chronic infarction.
* Severe anemia (hemoglobin \<8.0 g/dl).

Outcomes:
Clinical validation of a screening model assisted by AI
Deep Learning OCT and OCTA Quantification in Neovascular Age Related Macular Degeneration
Trial ID: NCT05969418
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2023-07-01
Completion date: 2023-12-21
Sponsor: Federico II University
Conditions: Macular Degeneration Choroidal Neovascularization
Interventions: Diagnostic test: using artificial intelligence to evaluate the changes OCT and OCTA parameter
Locations: Federico II University
Countries: Italy
Enrollment: 50
Age range: 50 Years - 90 Years
Sex: ALL

Summary:
To evaluate the activity of neovascular macula degeneretion as assessed by SD-OCTand OCT-A using a split-person study design and deep-learning quantification.

Eligibility criteria:
Inclusion Criteria: age older than 50 years

* diagnosis of neovascular macular degeneration
* absence of previous retinal surgery and congenital eye diseases.
* absence of errors of refraction
* absence of lens opacities
* absence of low-quality OCT and OCTA images

Exclusion Criteria:• age younger than 50 years

* previous retinal surgery and congenital eye diseases
* errors of refraction
* lens opacities
* low-quality OCT and OCTA images

Outcomes:
The measurements of retinal and choriocapillary vessel density in patients with macular degeneration complicated by nvc
Screening for Significant Coronary Artery Stenosis Using Single-channel Electrocardiogram Analysis With Artificial Intelligence Elements
Trial ID: NCT07579624
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2026-05-01
Completion date: 2027-12-01
Sponsor: I.M. Sechenov First Moscow State Medical University
Conditions: Ischemic Heart Disease; Coronary Artery Stenosis; Stable Angina Pectoris; Acute Coronary Syndromes
Interventions: single-channel electrocardiogram
Locations: 1 University Hospital
Countries: Russia
Enrollment: 400
Age range: 18 Years - None
Sex: ALL

Summary:
It is a prospective, controlled, single-center, non-randomized, observational study. Two patient groups are planned for inclusion: the first - 200 patients with significant coronary artery stenosis confirmed by coronary angiography (CAG) or multislice computed tomography (MSCT) results; the second - a control group consisting of 200 patients without significant stenosis according to CAG or MSCT data.

All study subjects will have a date of coronary artery imaging via CAG or MSCT with assessment of myocardial perfusion.

Stress echocardiography tests or fractional flow reserve (FFR) assessment will be conducted as indicated.

All patients included in the study will undergo ECG recording within 1 month before or after CAG or MSCT in standard lead I for 1 minute, followed by spectral analysis of the obtained data, which will be stored at the remote monitoring center of Sechenov University without being linked to the personal data of patients. A spectral analysis of the electrocardiogram will be performed using a continuous wavelet transform.

The result of this study will be the identification of ECG parameters that correlate with significant coronary artery stenosis.

Eligibility criteria:
Inclusion Criteria:

* Presence of written informed consent from the patient to participate in the study.
* Age 18 years and older.
* Outpatient visit and/or hospitalization at the research center with coronary visualization performed.

Non-inclusion criteria:

* Absence of sufficient data on coronary anatomy and stenosis significance.
* Any conditions impairing the quality of single-channel ECG recording (Parkinson's disease, essential tremor, and others).
* Absence of written informed consent from the patient to participate in the study.

Exclusion Criteria:

* Patient's unwillingness to continue participation in the study.
* Inability to perform full analysis of single-channel ECG digital characteristics.
* Refusal of coronary visualization methods for any reason.

Outcomes:
Sensitivity, specificity, positive/negative predictive values, and overall accuracy
Effects of Expert Arbitration on Clinical Outcomes When Disputes Over Diagnosis Arise Between Physicians and Their Artificial Intelligence Counterparts: a Randomized, Multicenter Trial in Pediatric Outpatients
Trial ID: NCT04011761
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2019-11-01
Completion date: 2021-04-30
Sponsor: Guangzhou Women and Children's Medical Center
Conditions: Pediatric Outpatients Encountered in Three Specialty Clinics, i.e. Respirology, Gastroenterology, and Genito-urology
Interventions: expert arbitration over discordant diagnoses made by AI diagnostic system and human doctors, respectively
Locations: Guangzhou Women and Children's Medical Center
Countries: China
Enrollment: 10000
Age range: None - 18 Years
Sex: ALL

Summary:
We have recently developed an artificial intelligence (AI) framework to diagnose common pediatric diseases. This randomized controlled clinical trial aims to investigate the effects of expert arbitration on clinical outcomes in the situation where the AI-based diagnosis differs from the diagnosis made by pediatricians.

Eligibility criteria:
Inclusion Criteria:

1. Outpatients who visits the respirology clinics or the gastroenterology clinics during the recruitment period.
2. Written informed consent is provided by parents/guardians

Exclusion Criteria:

1\. Patients with any conditions that require immediate diagnosis and treatment.

Outcomes:
Hospital revisit; Hospitalization in the next 3 months after the initial visit; Average total outpatient cost
Enhancing Health Literacy Through Artificial Intelligence-Powered Chatbot: a Randomized Controlled Trial on Addressing Human Papillomavirus Vaccination Misinformation Among Caregivers in Japan
Trial ID: NCT06702423
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-11-25
Completion date: 2025-12-31
Sponsor: The University of Hong Kong
Collaborators: London School of Hygiene and Tropical Medicine; Nagasaki University
Conditions: Healthy
Interventions: AI-based chatbot; Leaflet
Locations: London School of Hygiene and Tropical Medicine
Countries: United Kingdom
Enrollment: 844
Age range: 20 Years - None
Sex: FEMALE

Summary:
The goal of this study is to learn if an innovative digital artificial intelligence (AI)-based communication tool works to enhance health literacy among Japanese caregivers regarding Human Papillomavirus (HPV) vaccination.

Participants will:

* Answer a pre-intervention survey, get information regarding HPV vaccination from an AI-based chatbot or leaflet, and answer a post-intervention survey.
* Continue interacting with the chatbot or leaflet for two weeks.
* Answer a follow-up survey.

Eligibility criteria:
Inclusion Criteria:

* A nationally representative sample of female caregivers with daughters aged 12-18 who have not received any HPV vaccine.
* Those who have access to a smartphone or computer with internet connectivity to interact with experiment materials (chatbot or online leaflet).
* Those who provide informed consent and agree to participate in the study.

Exclusion Criteria:

* Those who need help reading through the participant information sheet, providing consent online, accessing experiment materials (chatbot or online leaflet), or answering surveys will be excluded. This may include people who do not have access to an online environment or people with severe cognitive impairments who cannot interact with the experiment materials. Also, those who have participated or are participating in similar studies regarding vaccination or experiments that utilize chatbots will be excluded.

Outcomes:
health literacy
Development and Pre-validated Multiple Variable Prediction Model Using Machine Learning for Early Functional Recovery After Joint Replacement Surgery.
Trial ID: NCT07333560
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2026-02
Completion date: 2027-12
Sponsor: Istituto Ortopedico Rizzoli
Conditions: Artificial Intelligence (AI); Machine Learning; Joint Replacement; Predictive Model
Interventions: Predictive Model for Early Mobility Recovery and Length of Stay
Locations: SAITeR IRCCS Istituto Ortopedico Rizzoli
Countries: Italy
Enrollment: 943
Age range: 18 Years - None
Sex: ALL

Summary:
The goal of this observational study is to develop and pre-validate a machine learning algorithm to predict early recovery of mobility in patients undergoing hip or knee joint replacement surgery. The primary research question is:

Can a machine learning model accurately classify patients with faster versus slower recovery of autonomous mobility in the first days after joint replacement surgery?

Patients who have undergone elective hip or knee arthroplasty and received post-operative physiotherapy will have their clinical and perioperative data collected retrospectively (2020-2023) and prospectively (March 2026-December 2027). The algorithm will be trained on retrospective data and tested prospectively to evaluate its predictive performance for early mobilization and length of hospital stay.

Eligibility criteria:
Inclusion Criteria:

* Adults aged 18 years or older
* Patients underwent elective hip or knee arthroplasty.
* Patients for whom postoperative physiotherapy was initiated.

Exclusion Criteria:

* Patients who underwent surgery for oncologic disease, femoral fracture, or revision joint arthroplasty.
* Patients for whom postoperative physiotherapy was not provided due to postoperative complications
* clinical data are unavailable.

Outcomes:
Area under the receiver operating characteristic curve (AUROC) for discrimination ability of the machine learning predictive model
Using Artificial Intelligence (AI) to Improve Social Conversation in Autistic Adolescents and Adults
Trial ID: NCT05987774
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2023-10-15
Completion date: 2050-06-30
Sponsor: Stanford University
Conditions: Autism Spectrum Disorder
Interventions: AI Modules for Improving Social Conversation
Locations: Stanford University School of Medicine
Countries: United States
Enrollment: 540
Age range: 11 Years - 35 Years
Sex: ALL

Summary:
The purpose of this study is to identify whether researched and commonly used face-to-face interventions can be effectively implemented through artificial intelligence (AI) using an application on the phone or computer. The investigators plan to recruit verbal individuals diagnosed with autism spectrum disorder who demonstrate challenges with socialization. Modules focusing on various difficulties experienced by autistic individuals will provide practice and feedback using voice recognition and feedback. If effective, this intervention can be scaled up to provide cost-effective accessible assistance to individuals, particularly those who do not have access to care or prefer to secure services in the comfort of their own homes.

Eligibility criteria:
Inclusion Criteria:

* Primary diagnosis of autism spectrum disorder (ASD)
* Age between 11 to 35
* Ability to engage in social conversation using full sentences for a 20 minute period
* Verbal communication difficulties in an individual targeted area (e.g., empathy) as measured on the conversation sample

Participants with a co-occuring diagnosis of ADHD, depression, or social anxiety will not be excluded since these are very common in this population and are often related to or a bi-product of difficulties with social conversation.

Exclusion Criteria:

* Non-documented or self-diagnosis of ASD
* At or above 60% correct responding on targeted area of social conversation during conversation probe
* No access to phone or computer for intervention sessions
* Inability to carry on a conversation during the conversation probe
* Apparent intellectual impairment that interferes with the ability to carry on a conversation (e.g., responds only in single words or does not respond to conversation, lack of understanding of questions or content during conversation probe)
* Non English speaking
* Serious medical or psychiatric issues that may interfere with conversation or the ability to complete the program
* Lack of interest in participating.

Outcomes:
Social Conversation Sample
Evaluation of Artificial Intelligence-Integrated Hearing Aids for Individuals with Hearing Loss
Trial ID: NCT06792110
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-02-01
Completion date: 2026-07-01
Sponsor: University of Washington
Conditions: Hearing Loss; Hearing Loss, Sensorineural
Interventions: Artificial Intelligence Integrated Hearing Aid Use; Phonak Infineo Hearing Aid; Oticon Real Hearing Aid
Enrollment: 100
Age range: 18 Years - None
Sex: ALL

Summary:
The goal of this clinical trial will be to evaluate the efficacy of artificial intelligence-integrated hearing aids in individuals with hearing loss. The main questions to answer are:

1. How effective is an artificial intelligence integrated hearing aid in improving speech perception in noise.
2. How does an artificial intelligence integrated hearing aid compare to currently available commercial hearing aids.

Eligibility criteria:
Inclusion Criteria:

* Minimum Age of 18 yo
* Bilateral sensorineural hearing loss amenable to hearing aids
* Able to give informed consent
* Native English speaker
* Able to wear a standard pair of hearing aids

Exclusion Criteria:

* Unable to participate in informed consent
* Use of a cochlear implant
* Hearing loss not amenable to hearing aid use
* Conductive hearing loss

Outcomes:
Speech Reception Threshold; Awareness of Environmental Marker
Applying Artificial Intelligence to the 12 Lead ECG for the Diagnosis of Pulmonary Hypertension: an Observational Study
Trial ID: NCT05942859
Status: ENROLLING_BY_INVITATION
Study type: OBSERVATIONAL
Start date: 2023-10
Completion date: 2027-08
Sponsor: Royal United Hospitals Bath NHS Foundation Trust
Collaborators: Liverpool John Moores University
Conditions: Pulmonary Hypertension (Diagnosis)
Interventions: Artificial Intelligence and Machine Learning technology
Locations: Royal United Hospital Bath NHS Trust
Countries: United Kingdom
Enrollment: 600
Age range: 18 Years - None
Sex: ALL

Summary:
The goal of this observational study is to apply Artificial Intelligence (AI) and machine learning technology to the resting 12-lead electrocardiogram (ECG) and assess whether it can assist doctors in the early diagnosis of Pulmonary Hypertension (PH). Early and accurate diagnosis is an important step for patients with PH. It helps provide effective treatments early which improve prognosis and quality of life. The main questions our study aims to answer are:

1. Can AI technology in the 12-lead ECG accurately predict the presence of PH?
2. Can AI technology in the 12-lead ECG identify specific sub-types of PH?
3. Can AI technology in the 12-lead ECG predict mortality in patients with PH?

In this study, the investigators will recruit 12-lead ECGs from consenting participants who have undergone Right heart Catheterisation (RHC) as part of their routine clinical care. AI technology will be applied to these ECGs to assess whether automated technology can predict the presence of PH and it's associated sub-types.

Eligibility criteria:
Inclusion Criteria:

1. prospective cohort: From July 2023, all patients aged 18 or over who are referred to the Bath Pulmonary Hypertension shared care service with clinical suspicion of PH and, who through their routine clinical care, undergo a RHC and 12-lead ECG.
2. Retrospective cohort: All patients aged 18 or over who were referred to the local Pulmonary Hypertension shared care service between 2007 and June 2023, and through their routine clinical care, have undergone RHC within a year of a 12-lead ECG. This cohort will also include patients who are deceased.

Exclusion Criteria:

* Patient's less than 18 years-old
* Patients who do not give valid consent (except deceased patients; REC approved)
* Patients who have not undergone RHC to assess for PH
* Patients who have not had an ECG within 12 months of their RHC

Outcomes:
Pulmonary Hypertension diagnosis
Correlation of Dermatologist Made Biopsy Decisions With Artificial Intelligence (AI) Assisted Total Body Photography Detection of Outlier Lesions
Trial ID: NCT05940090
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2023-07-03
Completion date: 2023-10-08
Sponsor: Halcyon Dermatology
Conditions: Skin Lesion
Interventions: SkinIO
Locations: Halcyon Dermatology
Countries: United States
Enrollment: 23
Age range: 18 Years - 85 Years
Sex: ALL

Summary:
This research study will determine how a smart phone application (app) for total body photography outlier analysis compares to a dermatologist's decision to biopsy skin lesions.

Eligibility criteria:
Inclusion Criteria:

* Males and females participants in overall good health ages 18 to 85 who have skin lesions

Exclusion Criteria:

* Those who are under 18 or over 85

Outcomes:
Dermatologist made biopsy decision compared to AI outlier lesions
Intraoperative Nomogram to Predict Breast Cancer- Related Lymphedema Based in Artificial Intelligence Image Recognition: a Randomized Controlled Trial
Trial ID: NCT04665882
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2020-12-11
Completion date: 2027-12-31
Sponsor: Wuhan University
Conditions: Breast Cancer; Axillary Lymph Node Dissection; Breast Cancer Related Lymphedema; Axillary Reverse Mapping
Interventions: Axillary surgery based on lymphedema prediction nomogram
Locations: Zhongnan Hospital of Wuhan University
Countries: China
Enrollment: 600
Age range: 18 Years - 80 Years
Sex: FEMALE

Summary:
It has been hypothesized that damaged arm lymphatic drainage is associated with the arm lymphedema after axillary lymph node dissection (ALND). However, the majority of breast cancer patients with complete ALND has not suffered from arm lymphedema, which appears to be due to the residual arm lymph nodes that has not been removed in the axillary dissection. With the compensation of the residual arm lymph flow above the level of axillary vein, the arm lymphatic drainage could keep balance and remain normal function.

This arm lymphedema prediction model that included the protective factor, the proportion of arm lymph flow above the level of axillary vein, allows intraoperative intervention to be performed for the high-risk group. The arm lymphatics of these distinguished patients would be preserved to eliminate the occurrence of arm lymphedema in this study.

Eligibility criteria:
Inclusion Criteria:

* Patients aged 18 years or older with T1-3 invasive breast cancer;
* Clinically node-positive breast cancer, defined as positive on preoperative axillary palpation, ultrasound examination, and computed tomography scan with contrast;
* Patients who underwent mastectomy with a positive sentinel lymph node (SLN);
* Patients who underwent breast-conserving surgery containing more than two positive SLNs.

Exclusion Criteria:

* Neoadjuvant chemotherapy;
* Previous history of breast cancer.

Outcomes:
Rate of arm lymphedema; Rate of locoregional recurrence; Rate of distant metastasis