moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
The Effect of Artificial Intelligence-Generated Audiovisual Education on Osteoporosis Knowledge and Digital Health Literacy
Trial ID: NCT07436000
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-01-01
Completion date: 2026-02-01
Sponsor: Kars State Hospital
Conditions: Osteoporosis
Interventions: Audio-Based Digital Education (Podcast); Visual Digital Education (Video)
Locations: Gaziantep City Hospital
Countries: Turkey (Türkiye)
Enrollment: 60
Age range: 45 Years - None
Sex: ALL

Summary:
This study aims to enhance osteoporosis awareness and health literacy among individuals through audiovisual digital educational content generated using the NotebookLM artificial intelligence platform, based on educational materials developed by the International Osteoporosis Foundation. In addition, the effects of these audiovisual educational materials on osteoporosis knowledge and digital health literacy will be evaluated.

Eligibility criteria:
Inclusion Criteria:

* Individuals aged 45 years and older who have been diagnosed with osteoporosis or are at risk for osteoporosis
* Ability to read and understand Turkish
* Ability to use digital devices such as a smartphone, tablet, or computer

Exclusion Criteria:

* Patients with severe cognitive impairment, a diagnosis of dementia, or neurological disorders that limit effective communication
* Patients unable to follow audio or video-based educational content due to visual or hearing impairment
* Patients with serious systemic diseases other than osteoporosis (e.g., malignancy or advanced heart failure)
* Individuals who have previously participated in an osteoporosis education program
* Individuals without access to digital platforms

Outcomes:
Change in Osteoporosis Knowledge Level
Effect of Artificial Intelligence Based Gamification Training on Cognition and Functional Outcomes in Patients With Stroke
Trial ID: NCT07209592
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-10-15
Completion date: 2026-06-30
Sponsor: Cairo University
Conditions: Stroke
Interventions: AI gamification; conventional therapy
Enrollment: 40
Age range: 45 Years - 60 Years
Sex: ALL

Summary:
this study will be conducted to investigate the effect of artificial intelligence based gamification training on cognition and functional outcomes in patients with stroke

Eligibility criteria:
Inclusion Criteria:

* Forty hemiplegic patients with mild post stroke cognitive impairment.
* The patients age will be 45 to 60.
* Chronic stroke (6 months post-stroke).
* Score of 1 to 1+ to points on the Modified Ashworth Scale (MAS) on upper limb (shoulder , elbow ,hand).
* Stage of recovery 4-5 brunnstrom
* Essential educational level ( at least high school level)
* BMI will be ranged from 18,5 -24.9 kg\\m2

Exclusion Criteria:

* Moderate and severe post stroke cognitive impairment
* Excluding cognitive impairment for any other psychological or neurological disease such as depression and dementia
* history of recent upper limb surgery
* Any musculoskeletal disorders of upper extremity (e.g fractures or injuries)
* Having a heart pacemaker, pregnancy
* Other neurological diseases or cancer
* Hormonal disturbances

Outcomes:
cognitive disability; upper limb function; handgrip strength; kinetic motion analysis
Artificial Intelligence in Endoscopic Ultrasound
Trial ID: NCT06564571
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2024-01-19
Completion date: 2027-12
Sponsor: Orlando Health, Inc.
Conditions: Pancreatic Cancer; Pancreas Disease; Pancreatic Cyst
Interventions: Patients undergoing endoscopic ultrasound procedures
Locations: Orlando Health
Countries: United States
Enrollment: 310
Age range: 18 Years - 100 Years
Sex: ALL

Summary:
The objective of the study is to determine if this artificial intelligence system is capable of detecting abnormalities in the pancreas that are identified by an endoscopist at endoscopic ultrasound procedures.

Eligibility criteria:
Inclusion Criteria:

* Age ≥ 18 years
* Any patient undergoing endoscopic ultrasound examination

Exclusion Criteria:

* Age \< 18 years

Outcomes:
Rate of detection pancreatic abnormalities by AI
Statewide System and Organizational Strategy for Evidence-Based Practice Implementation and Sustainment in Substance Use Disorder Treatment
Trial ID: NCT05344534
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2021-08-30
Completion date: 2025-01-30
Sponsor: University of California, San Diego
Collaborators: University of Central Florida; Pacific Institute for Research and Evaluation; Chestnut Health Systems
Conditions: Control; Experimental
Interventions: Leadership and Organizational Change for Implementation
Locations: UC San Diego
Countries: United States
Enrollment: 479
Age range: 18 Years - None
Sex: ALL

Summary:
This project will test an implementation strategy that seeks to align system, organization, and clinic leadership strategies to implement and sustain the Lyssn artificial intelligence (AI) quality assurance platform to support implementation of Motivational Interviewing (MI) and evidence-based practice (EBP) for substance use disorders. The Leadership and Organizational Change for Implementation - System Level (LOCI-SL) strategy seeks to engage policymakers and funders at the system level to improve implementation leadership, climate, provider attitudes and behaviors at the provider organization level to enhance implementation climate for EBP. LOCI-SL will be tested in a statewide addiction service system through clinics in community based behavioral health organizations.

Eligibility criteria:
1. Stakeholders from Oregon Health Authority (OHA), Oregon Alcohol and Drug Policy Commission (ADPC), staff at provider/payer advocacy organizations (e.g., CCO Oregon, Oregon Council for Behavioral Health), and relevant managed care organizations (Coordinated Care Organizations, CCOs), Indiana Department of Mental Health and Addiction (DMHA), or Indiana Department of Child Services (DCS).
2. Executives and/or managers at participating substance use disorder and/or mental health treatment agencies.
3. Clinic managers/supervisors in clinics that provide substance use disorder and/or mental health treatment.
4. Counselors (i.e., service providers) employed at participating agencies that provide substance use disorder and/or mental health treatment.

Outcomes:
Change on Implementation Leadership Scale; Change on Implementation Climate Scale
Identification of Neuroinflammation and Neuroimaging Biomarkers Through Data Driven Artificial Intelligence Techniques for Unraveling the Heterogeneity of Aged Subjects at Risk of Dementia and to Better Inform Prevention Strategies
Trial ID: NCT06746909
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2022-02-01
Completion date: 2026-06-30
Sponsor: IRCCS Fondazione Stella Maris
Collaborators: Humanitas Research Hospital IRCCS, Rozzano-Milan; Azienda Ospedaliero, Universitaria Pisana; Istituto di Neuroscienze Consiglio Nazionale delle Ricerche
Conditions: MCI; SCD; Healthy
Interventions: Multidomain cognitive training and physical training
Locations: Azienda Ospedaliera Universitaria Pisana
Countries: Italy
Enrollment: 115
Age range: 65 Years - 89 Years
Sex: ALL

Summary:
SCD and MCI are very heterogeneous conditions, which can be prodromal to different types of dementia. The application of data driven clustering methods on neuroimaging and inflammatory data aims at identifying the features characterizing subgroups of subjects at high risk of developing overt dementia. This approach promises to develop tailored early interventions for subjects with profiles of cognitive decline correlating with high risk of progression. To this end in this project, we propose to examine in these subjects both brain structure and cerebral blood flow (CBF) through MR T1-weighted and pCASL (pseudo-continuous arterial spin labeling) imaging and the neuroinflammatory status. The latter will be done by measuring the populations of innate and adaptive immune cells, the chemokines that are required to attract these cells to their potential sites of action in the CNS, and the cytokines by means they exert their function. The analyses performed in aged subjects with MCI and in SCD, will allow identifying common mechanisms predisposing to an increased susceptibility to progression to overt cognitive decline.

Eligibility criteria:
Inclusion Criteria:

* age between 65 and 89 years; 5 years of schooling;
* diagnosis of Mild Cognitive Impairment (Croisile et al., 2011) or
* diagnosis of Subjective Cognitive Decline (Jessen et al., 2014).

Exclusion Criteria:

* neurological pathologies; dementia;
* psychiatric disorders;
* pathologies or orthopedic problems limiting participation to the aerobic physical training program and to the MR imaging.

Outcomes:
Short term training effects
To Build and Evaluate a Precise Diagnosis, Therapy Assessment and Prognosis Prediction Model of Breast Cancer Based on Artificial Intelligence
Trial ID: NCT04270032
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2020-02-01
Completion date: 2024-09-01
Sponsor: The First Affiliated Hospital of the Fourth Military Medical University
Collaborators: Seoul National University Bundang Hospital; Xidian University; Shenzhen University
Conditions: Breast Cancer
Interventions: ABUS and HHUS
Locations: The First Affiliated Hospital of Fourth Military Medical University
Countries: China
Enrollment: 10000
Age range: 18 Years - None
Sex: FEMALE

Summary:
The purpose of this study is using a deep learning method to analyze the automated breast ultrasound (ABUS) and hand-held ultrasound(HHUS) images, establish and evaluate a diagnosis, therapy assessment and prognosis prediction model of breast cancer. The model would provide important references for further early prevention, early diagnosis and personalized treatment.

Eligibility criteria:
Inclusion Criteria:

1. Female patients over 18 years old who come to the two centers for physical examination or treatment;
2. Complete basic information and image data

Exclusion Criteria:

1. There is no complete ABUS and HHUS images data;
2. The image quality is poor;
3. In multifocal breast cancer, the correlation between the tumor in the image and the postoperative pathological examination is uncertain.

Outcomes:
sensitivity; false-positive per volume; area under curve; overall survival(OS) time; Disease-free survival (DFS) time
Virtual Intelligence for Transformative Lifestyle Solutions in Pain
Trial ID: NCT07124598
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-02
Completion date: 2026-07
Sponsor: Montefiore Medical Center
Collaborators: National Institute on Drug Abuse (NIDA); AugMend Health
Conditions: Chronic Pain
Interventions: VR Headset Device - Meta Quest 3
Locations: Montefiore Multidisciplinary Pain Medicine Program (MMPP) at Montefiore Einstein Hutchinson Campus
Countries: United States
Enrollment: 15
Age range: 18 Years - None
Sex: ALL

Summary:
This pilot study aims to evaluate the feasibility and acceptability of a VR-based chronic pain management intervention with a virtual AI coach for patients with Opioid misuse and opioid use disorder (OM/OUD). The intervention is a single-day 45-minute VR intervention which is subdivided into three smaller sessions:

Session 1: A 15-minute AI check-in to ask questions about biopsychosocial health, Session 2: A 20-minute Pain Coping Skills Training (PCST) session offering psychoeducation on managing chronic pain Session 3: A 10-minute stress reduction exercise.

The VR sessions will be conducted using hardware (VR Headset Device - Meta Quest 3) and software developed by AugMend Health Company.

The study will be conducted in a clinical setting at the Montefiore Multidisciplinary Pain Medicine Program (MMPP), a Pain Medicine outpatient specialty practice within a major urban medical center. MMPP providers see thousands of patients every month, some of which have concurrent opioid misuse or OM/OUD.

Eligibility criteria:
Inclusion Criteria:

* Patients with opioid misuse or International Classification of Diseases 10th revision (ICD-10) diagnosis of opioid use disorder
* Patients with Chronic pain of at least moderate intensity (\>4 Pain Intensity Score) with no pain medication changes in 14 days
* Ability to understand written/spoken instruction and provide informed consent in English
* Ability and willingness to participate in all components of the study

Exclusion Criteria:

* History of severe motion sickness, cybersickness, or conditions that could make participation in VR hazardous or cause adverse effects
* Conditions that could prevent proper use of VR headset (such as vision problems that cannot be corrected by contact lenses or glasses that fit in VR, Significant hearing impairments that cannot be corrected by a hearing device)
* History of seizures or seizure disorder
* Acute exacerbation of psychiatric conditions that preclude the ability to participate in the study

Outcomes:
Feasibility - Recruitment Rate; Feasibility - Completion; Feasibility - Virtual Reality Experience and Feasibility Survey (VREFS); Acceptability - Acceptability of Intervention Measure (AIM); Satisfaction - Change; Satisfaction - Degree of Change
Lung Nodule Characterization by Artificial Intelligence Techniques
Trial ID: NCT03843164
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2019-03-12
Completion date: 2021-03
Sponsor: University Hospital, Strasbourg, France
Conditions: Incidental Lung Nodule
Locations: Service de Radiologie B - NHC
Countries: France
Enrollment: 50
Age range: 18 Years - None
Sex: ALL

Summary:
Management of incidental lung nodule is difficult and mainly based on simple morphometric characteristics such as maximum size and shape. Radiomics could play a role in simplifying this management by orientating towards a benign or a malignant origin, by comparing advanced characteristics to a large database of lung nodules.

The primary purpose is to evaluate the performances of a novel tool based on radiomics to characterize incidental lung nodules, discovered on computed tomography.

The secondary objectives are:

* to evaluate the variation in the performances of the software based on various technical aspects of the CT, such as radiation dose, reconstruction algorithm, type of scanner,…
* to compare the performances of this software to those of expert readers,
* to analyze the potential impact of this software on patient's management.

Eligibility criteria:
Inclusion Criteria:

* Adult patient (at least 18 years old);
* With a chest CT acquired between Jan 1 2012 and Oct 1 2018 at the Strasbourg University Hospital;
* CT being available over the PACS and exhibiting at least one lung nodule;
* Patient having given its authorization for the exploitation of his medical data for this research.

Exclusion Criteria:

* Patient having expressed direct opposition to participation in this study;
* Patient under juridical protection;
* Patient under tutelage or guardianship.

Outcomes:
Evaluate the performances of a novel tool based on radiomics to characterize incidental lung nodules, discovered on computed tomography.
Development of Artificial Intelligence Prediction Model for Uveitis Progression and Prognosis
Trial ID: NCT04705103
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2021-01-10
Completion date: 2022-12-30
Sponsor: Sun Yat-sen University
Conditions: Uveitis
Enrollment: 450
Age range: None - None
Sex: ALL

Summary:
Uveitis is a recurrent inflammation of the eye that can lead to vision loss with various and complicated etiologies. There are many difficulties in uveitis management due to the requirement of long-term monitoring and treatment. This study intends to establish a longitudinal cohort of uveitis patients to colloect multimodal clinical data including visual acuity, intraocular pressure, anterior segment photography, fundus photography, fluoroscopy and other ophthalmic examinations, and final diagnosis in order to use artificial intelligence and deep learning technology to extract ocular features, explore the association with the progression and prognosis of uveitis and finally establish a disease management model for uveitis.

Eligibility criteria:
Inclusion Criteria:

* Participants who were diagnosed as uveitis, including the initial stage or relapse, or remission of uveitis.

Exclusion Criteria:

* Participants who cannnot cooperate with ocualr examination.

Outcomes:
uveitis progression
The Development of Artificial Intelligence Dry Eye Screening and Referral System
Trial ID: NCT04413370
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2020-01-06
Completion date: 2022-08-01
Sponsor: Sun Yat-sen University
Conditions: Dry Eye
Interventions: Dry eye diagnostic test
Locations: Zhongshan Ophthalmic Center
Countries: China
Enrollment: 518
Age range: 18 Years - None
Sex: ALL

Summary:
Dry eye is one of the most common ocular surface diseases. Its pathogenic factors are related to multiple etiology. Because of the complexity of the pathogenesis of dry eye, the diversity of related examinations, and the inconsistency of symptoms and signs of dry eye patients, the diagnosis of dry eye has higher requirements on the professional technology and examination equipment of ophthalmologists.

The purpose of this study is to establish a case-control cohort of dry eye patients. Multimodal data will be collected from participants, including medical history information, ocular surface disease index scale (OSDI), anterior segment photography, and treatment outcome of dry eye patients. The correlation between the characteristics of anterior segment images and dry eye diagnosis will be explored by artificial intelligence algorithms. The purpose of this study was to develop an artificial intelligence dry eye screening and referral system.

Eligibility criteria:
Inclusion Criteria:

1. Subjects whose age are greater than or equal to 18 years old;
2. Subjects who can cooperate with the inspection;
3. Subjects who agree to participate in the study and sign the consent form.

Exclusion Criteria:

1. Subjects who cannot do the inspection.
2. Subjects who suffer from diseases that compromise the inspection.

Outcomes:
Area under the curve (severe)
Application of Artificial Intelligence for Young-onset Colorectal Cancer Screening Based on Electronic Medical Records
Trial ID: NCT06342622
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2023-12-01
Completion date: 2024-01-25
Sponsor: Renmin Hospital of Wuhan University
Conditions: Colorectal Cancer
Interventions: Using routine clinical data and machine learning models.
Locations: Renmin Hospital of Wuhan University
Countries: China
Enrollment: 11000
Age range: 18 Years - 49 Years
Sex: ALL

Summary:
In this study, we aimed to develop, internally and temporally validate the machine learning models to help screen YOCRC bansed on the retrospective extracted Electronic Medical Records (EMR) data.

Eligibility criteria:
Inclusion Criteria:

* Newly diagnosed with CRC (YOCRC group)
* Age at 18-49 when diagnosis (YOCRC group)
* Never received any CRC-related treatment (YOCRC group)
* No CRC confirmed by colonoscopy or pathology (non-YOCRC group)
* Age at 18-49 (non-YOCRC group)

Exclusion Criteria:

* Hospital stay less than 24 hours or with incomplete Complete Blood Count
* Patients with inflammatory bowel disease or hereditary CRC syndromes
* History of other types of primary malignant tumor and other reasons that made them unsuitable for enrollment

Outcomes:
The performance of machine learning screening models
Clinical Characteristics and Prognostic Factors of Patients With COVID-19 Using Big Data and Artificial Intelligence Techniques (BigCoviData)
Trial ID: NCT04569851
Status: TERMINATED
Study type: OBSERVATIONAL
Start date: 2020-01-01
Completion date: 2024-02-28
Sponsor: Savana Research
Collaborators: Castilla-La Mancha Health Service
Conditions: COVID-19 Infection
Locations: Hospital Universitario de Guadalajara; Hospital Universitario La Princesa
Countries: Spain; Spain
Enrollment: 100000
Age range: None - None
Sex: ALL

Summary:
This is a multicenter, non-interventional, retrospective study using data captured in the EHRs (Electronic Health Records) of the participating hospital sites to determine factors that predict disease prognosis and outcomes in COVID-19 patients, specifically: Hospitalization/Off-site monitoring, transfer to ICU and/or need for medical mechanical ventilation (both invasive and non- invasive), length of ICU stay, and outcome (cure/ hospital discharge, in-hospital death)

Eligibility criteria:
Inclusion Criteria:

* All patients with suspected COVID-19 infection, as captured in the patients' EHRs of the participating sites.

Exclusion Criteria:

* Not suspected COVID-10 infection

Outcomes:
Factors that predict disease prognosis and outcomes in COVID-19 patients - Hospitalization; Factors that predict disease prognosis and outcomes in COVID-19 patients - ICU
Phonokardiographie Bei Erwachsenen
Trial ID: NCT03600051
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2015-12-10
Completion date: 2017-01-31
Sponsor: CSD Labs GmbH
Conditions: Aortic Insufficiency; Aortic Stenosis; Mitral Insufficiency; Mitral Insufficiency and Aortic Stenosis; Tricuspid Regurgitation; Insufficiency, Pulmonary; Insufficiency, Tricuspid
Interventions: Automated Heart Murmur Detection AI
Locations: University Hospital
Countries: Austria
Enrollment: 90
Age range: 18 Years - None
Sex: ALL

Summary:
Background: Computer aided auscultation in the differentiation of pathologic (AHA class I) from no- or innocent murmurs (AHA class III) via artificial intelligence algorithms could be a useful tool to assist healthcare providers in identifying pathological heart murmurs and may avoid unnecessary referrals to medical specialists.

Objective: Assess the quality of the artificial intelligence (AI) algorithm that autonomously detects and classifies heart murmurs as either pathologic (AHA class I) or as no- or innocent (AHA class III).

Hypothesis: The algorithm used in this study is able to analyze and identify pathologic heart murmurs (AHA class I) in an adult population with valve defects with a similar sensitivity compared to medical specialist.

Methods: Each patient is auscultated and diagnosed independently by a medical specialist by means of standard auscultation. Auscultation findings are verified via gold-standard echocardiogram diagnosis. For each patient, a phonocardiogram (PCG) - a digital recording of the heart sounds - is acquired. The recordings are later analyzed using the AI algorithm. The algorithm results are compared to the findings of the medical professionals as well as to the echocardiogram findings.

Eligibility criteria:
Inclusion Criteria:

* Adults with a heart defect verified by echocardiography

Outcomes:
Sensitivity for pathological heart murmur detection