moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Development of an Artificial Intelligence-Based Motion Analysis System for the Detection of Chronic Obstructive Pulmonary Disease (COPD)
Trial ID: NCT07010211
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-08-01
Completion date: 2026-03-01
Sponsor: Burcin Celik
Collaborators: Ondokuz Mayıs University
Conditions: Chronic Obstructive Pulmonary Disease (COPD)
Interventions: Gait Video Recording and Analysis
Enrollment: 56
Age range: 40 Years - 80 Years
Sex: ALL

Summary:
This study aims to develop a non-invasive and contact-free diagnostic system that uses artificial intelligence (AI) to detect Chronic Obstructive Pulmonary Disease (COPD) by analyzing walking patterns.

Participants in this study will include individuals with a diagnosis of COPD and healthy volunteers. All participants will undergo a 6-minute walk test (6MWT), during which their movements will be recorded using video. In addition, they will complete a breathing test (spirometry) and a short questionnaire about symptoms.

The recorded videos will be analyzed using an AI model based on motion tracking software. This model will evaluate walking-related parameters such as step count, step length, walking time, and total walking distance. The goal is to determine whether walking patterns can be used to detect COPD with high accuracy, especially in situations where traditional lung function tests may not be available or feasible.

This study is observational and does not involve any experimental drug or treatment. The results may help to create new diagnostic tools that are easy to use, safe, and accessible for early detection of COPD.

Eligibility criteria:
Inclusion Criteria:

* Aged between 40 and 80 years
* Ability to provide informed consent
* For COPD group: Previously diagnosed with COPD based on GOLD criteria (FEV1/FVC \< 0.70)
* For control group: No history of pulmonary disease and normal spirometry results
* Physically able to perform the 6-minute walk test
* Willingness to participate in video recording during gait analysis

Exclusion Criteria:

* Younger than 40 or older than 80 years
* Acute respiratory tract infection or other active infections
* Severe heart failure, advanced arrhythmias, or other serious cardiovascular conditions
* Physical disability preventing completion of the 6-minute walk test
* Neurological or orthopedic conditions causing major gait disturbance
* Inability to perform spirometry due to physical or cognitive limitations
* Pregnant or breastfeeding women Diagnosed with other serious pulmonary diseases (e.g., interstitial lung disease, active tuberculosis) Refusal to give informed consent or to be video recorded

Outcomes:
Diagnostic Accuracy of AI-Based Gait Analysis for Detection of COPD
Impact and Sequelae of High Ventilatory Drive in Critically Ill COVID-19 Patients With Acute Respiratory Failure Requiring High Flow Oxygen or Mechanical Ventilation: Mechanistic and Genomic Characterization Using Artificial Intelligence
Trial ID: NCT05363332
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2021-11-15
Completion date: 2024-11-15
Sponsor: Corporacion Parc Tauli
Collaborators: Althaia Xarxa Assistencial Universitària de Manresa; Hospital Universitario Central de Asturias
Conditions: COVID-19; Critical Illness; Hypoxemic Respiratory Failure; Neurocognitive Dysfunction; Mechanical Ventilation Complication
Locations: Candelaria De Haro; Fundació Althaia; Hospital Universitario Central de Asturias
Countries: Spain; Spain; Spain
Enrollment: 126
Age range: 18 Years - None
Sex: ALL

Summary:
Critically ill COVID-19 patients with acute respiratory failure, in the intensive care unit (ICU), often feature high respiratory drive, determining large inspiratory efforts resulting in high pressures and global and regional over-distention, leading to lung injury. SARS-CoV-2 neurotropic-penetration in control centers in medulla oblongata might contribute to dysregulation and to excessively high respiratory drive observed in these patients. These pathophysiological conditions may often lead to the development of patient-ventilator asynchronies in aptients under mechanical ventilation, again leading to high tidal volumes and increased lung injury. These phenomena can contribute to prolonged duration of mechanical ventilation and ICU length of stay, but also can result in long term adverse outcomes like emotional/psychological and cognitive sequelae. All them compromising the quality of life of critically ill survivors after ICU discharge.

The investigators will conduct a multicenter study in adult critically ill COVID-19 patients with hypoxemic respiratory failure, aiming to: 1) characterize incidence and clustering of high respiratory drive by developing algorithms, 2) apply artificial intelligence in respiratory signals to identify potentially harmful patient-ventilator interactions, 3) characterize cognitive and emotional sequelae in critically ill COVID-19 survivors after ICU discharge and 4) identify sets of genes and transcriptomic signatures whose quantified expression predisposed to asynchronies and cognitive impairment in critically ill COVID-19 patients.

Eligibility criteria:
Inclusion Criteria:

* Adults patients with hypoxemic respiratory failure.
* Admitted to ICU.
* Mechanical ventilation or high flow nasal cannula

Exclusion Criteria:

* Neurologic patients with brainsteam affection.

Outcomes:
Respiratory drive
Instructor-led Artificial Intelligence-Based Emergency Triage Education Tool in Enhancing Clinical Critical Thinking and Triage Practice: Randomized Control Trial
Trial ID: NCT06811987
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-03-06
Completion date: 2026-08-30
Sponsor: Hong Kong Metropolitan University
Conditions: Nursing Teaching Pedagogy; Conversational AI Nursing Education Serious Game
Interventions: Instructor-led AI-Triage educational serious game; Self play AI-Triage educational serious game
Enrollment: 350
Age range: None - None
Sex: ALL

Summary:
The study aims to develop an artificial intelligence (AI) based emergency triage education tool for providing critical thinking and questioning skills training to nursing students. It also aims to contribute valuable implications by collecting the knowledge, attitudes, and compassion aspects of nursing students toward AI-based education

The objectives of the study:

1. To evaluate the effectiveness of instructor-led and self-play pedagogy approaches in enhancing students' critical thinking by an AI-based emergency triage education tool.
2. To evaluate the effectiveness of instructor-led and self-play pedagogy approaches in enhancing students' triage skill retention by an AI-based emergency triage education tool.
3. To evaluate students' learning motivation, simulation-based experience, and usability towards AI-based technology learning.

Participants will

1. play the education tool in different pedagogy approaches
2. answer the questionnaires and receive a qualitative interview

Eligibility criteria:
Inclusion Criteria:

1. Aged 18 or above;
2. Currently studying the NURS 225F of the Bachelor of Nursing (General) at Hong Kong Metropolitan University;
3. Able to use electronic devices to accomplish an online survey and participate in the AI-based emergency triage education tool

Exclusion Criteria:

* Currently working in emergency department

Outcomes:
Triage skill retention
Artificial Intelligence Guided Echocardiographic Screening of Rare Diseases (EchoNet-Screening)
Trial ID: NCT06664866
Status: ENROLLING_BY_INVITATION
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-10-28
Completion date: 2026-11-01
Sponsor: Cedars-Sinai Medical Center
Collaborators: Palo Alto Veteran Affairs Hospital; Providence Heart & Vascular Institute; Northwestern Medicine
Conditions: Cardiac Amyloidosis
Interventions: EchoNet-LVH Assessment
Locations: Cedars Sinai Medical Center; Palo Alto Veteran Affairs Hospital; Northwestern Medicine; Providence Heart and Vascular Institute
Countries: United States; United States; United States; United States
Enrollment: 500
Age range: 22 Years - None
Sex: ALL

Summary:
Recent advances in machine learning and image processing techniques have shown that machine learning models can identify features unrecognized by human experts and accurately assess common measurements made in clinical practice. Echocardiography is the most common form of cardiac imaging and is routinely and frequently used for diagnosis. However, there is often subjectivity and heterogeneity in interpretation. Artificial intelligence (AI)'s ability for precision measurement and detection is important in both disease screening as well as diagnosis of cardiovascular disease.

Cardiac amyloidosis (CA) is a rare, underdiagnosed disease with targeted therapies that reduce morbidity and increase life expectancy. However, CA is frequently overlooked and confused with heart failure with preserved ejection fraction. Some estimates suggest that CA can be as prevalence as 1% in a general population, with even higher prevalence in patients with left ventricular hypertrophy, heart failure, and other cardiac symptoms that might prompt echocardiography.

AI guided disease screening workflows have been proposed for rare diseases such as cardiac amyloidosis and other diseases with relatively low prevalence but significant human impact with targeted therapies when detected early. This is an area particularly suitable for AI as there are multiple mimics where diseases like hypertrophic cardiomyopathy, cardiac amyloidosis, aortic stenosis, and other phenotypes might visually be similar but can be distinguished by AI algorithms. The investigators have developed an algorithm, termed EchoNet-LVH, to identify cardiac hypertrophy and identify patients who would benefit from additional screening for cardiac amyloidosis.

Eligibility criteria:
Inclusion Criteria:

* Patients receiving an echocardiogram that is determined to be suspicious by EchoNet-LVH

Exclusion Criteria:

* Patients that decline consent
* Patients receiving an echocardiogram that is determined to be not suspicious by EchoNet-LVH

Outcomes:
Positive Predictive Value
Artificial Intelligence-Assisted Expressive Writing Intervention to Improve Adolescent Emotional Distress: A Cluster Randomized Controlled Trial
Trial ID: NCT07105137
Status: ACTIVE_NOT_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-05-19
Completion date: 2025-11
Sponsor: Peking University
Conditions: Emotional Disturbance; Emotional Regulation; Insomnia
Interventions: AI-GNW; NF-GNW; NWG
Locations: Peking University
Countries: China
Enrollment: 900
Age range: 10 Years - 19 Years
Sex: ALL

Summary:
This cluster-randomized controlled trial aims to evaluate the efficacy of an AI-assisted Guided Narrative Writing (AI-GNW) in alleviating emotional distress among adolescents. Classes will be randomized in a 1:1:1 ratio to one of three arms-AI-GNW, a no-feedback GNW (NF-GNW), or a Neutral Writing Group (NWG)-and will complete a three-day writing intervention. On the first day, participants in both GNW conditions will recount a negative experience and reflect on their thoughts and feelings; on the second day, they will focus exclusively on the negative aspects of that experience; and on the third day, they will explore its positive dimensions. In contrast, NWG participants will objectively document their previous day's daily routine. After each session, those in the AI-GNW arm will receive individualized, AI-generated feedback, whereas NF-GNW participants will proceed without feedback.

Eligibility criteria:
Inclusion Criteria:

* Native speakers of Chinese who are proficient in reading and writing Chinese.
* Signed a informed consent form and voluntarily agreed to participate.
* Owns a smartphone, tablet, or similar electronic device and is able to complete the required questionnaires and writing tasks.

Exclusion Criteria:

* Prior clinical diagnosis of schizophrenia or any other major psychiatric disorder.
* Presence of severe cognitive impairment, language-expression difficulties, or any other condition that would impede writing ability.
* Participation in another similar psychological intervention or psychotherapy within the past three months.
* Inability to tolerate a group-based intervention setting due to personal health conditions (e.g., serious illness).
* Unwillingness or inability to provide necessary cooperation or to sign the informed consent form.

Outcomes:
Revised Child Anxiety and Depression Scale (RCADS); Insomnia Severity Index (ISI)
An Integrated Two-Way Communication and Near-Real-Time Sensing System to Detect and Modify Daily Inactivity Among Adults Over Age 60
Trial ID: NCT04827212
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: EARLY_PHASE1
Start date: 2021-03-16
Completion date: 2022-12-13
Sponsor: Northeastern University
Collaborators: National Institute on Aging (NIA)
Conditions: Exercise; Physical Activity
Interventions: Companion
Locations: Northeastern Univeristy
Countries: United States
Enrollment: 51
Age range: 60 Years - 90 Years
Sex: ALL

Summary:
This pilot study is a small sample (N=46) 16-week clinical trial with a follow-up after 24-weeks among sedentary adults \>60 y with a BMI \>25 kg/m2 to tests the impact of a hybrid artificial intelligence behavior change system (Companion) on physical activity. Participants will be randomized to a control and intervention group. All participants will engage in a proven supervised exercise program from week 1 to 16. Only the intervention group will receive Companion from week 1 to 16.

Eligibility criteria:
Inclusion Criteria:

* Age 60 years and above- This intervention study focusses on this population demographic.
* Participants have a body mass index greater than 25 kg/m2- This threshold for BMI is used to classify an adult as overweight or obese
* No conditions preventing participation in physical activity lasting 10 to 30 min- Such conditions will limit prescription of physical activity/exercise components of the training program and that promoted using Companion.
* Have a smart phone- The absence of a smart phone will limit the ability of remote communication with the study participant using Companion.

Exclusion Criteria:

* Engage in structured physical activity for more than 2 days/week lasting 30 min/session- Physically active participants will not be representative of typical adults \>60 y. Being active prior to study participation may mask the effects of the intervention.
* Regularly use any assistive device for walking- Use of an assistive device would limit the ability to engage in prescribed physical activity. Future work can be tailored for sub-groups in the demographic being studied.
* Likely to alter medications pertaining to cardiovascular or metabolic health- Altering medications during the study will influence change in selected outcome measures.

Outcomes:
Physical Activity Hours Per Day Measured Using a Body-worn Accelerometer at Baseline; Physical Activity Hours Per Day Measured Using a Body-worn Accelerometer After 4 Months; Physical Activity Hours Per Day Measured Using a Body-worn Accelerometer After 6 Months; Sedentary Behavior Hours Per Day Measured Using a Body-worn Accelerometer at Baseline; Sedentary Behavior Hours Per Day Measured Using a Body-worn Accelerometer After 4 Months; Sedentary Behavior Hours Per Day Measured Using a Body-worn Accelerometer After 6 Months
Rehabilitation Exercise Intervention for Rheumatoid Arthritis Patients Using Artificial Intelligence-Based Exercise Prescription System
Trial ID: NCT07246811
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-11-30
Completion date: 2026-03-15
Sponsor: Peking University Third Hospital
Conditions: Rheumatoid Arthritis (RA)
Interventions: AI Exercise Prescription; education and basic joint mobility exercises; paper-based exercise prescription
Locations: Department of Rehabilitation Medicine, Peking University People's Hospital
Countries: China
Enrollment: 147
Age range: 18 Years - None
Sex: ALL

Summary:
This is a single-center, randomized, parallel-controlled trial evaluating the efficacy and safety of an artificial intelligence-based exercise prescription system for rehabilitation in rheumatoid arthritis (RA) patients. A total of 147 patients with low-to-moderate disease activity will be randomly assigned to three groups: AI exercise prescription group (personalized AI system guidance), paper exercise prescription group (traditional paper guidance), and routine care group. The intervention will last for 6 weeks with 6-month follow-up assessments evaluating joint function, pain, and quality of life indicators.

Eligibility criteria:
Inclusion Criteria:

* Age ≥18 years Meets 2010 ACR/EULAR classification criteria for RA Low-to-moderate disease activity (assessed by rheumatologist) Knee joint involvement Willing and able to provide informed consent

Exclusion Criteria:

* Existing joint ankylosis (fibrous or bony) Acute flare or extra-articular soft tissue lesions Comorbid with other rheumatic diseases (e.g., SLE, Sjögren's syndrome) Severe hepatic or renal impairment Knee joint VAS pain score \>4 (0-10 scale) Psychiatric disorders

Outcomes:
Knee flexion range of motion
A Vision-Based Characterization of Human Movement Scaling in Large-Amplitude Tasks
Trial ID: NCT07387887
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-01-30
Completion date: 2026-05-30
Sponsor: Biruni University
Collaborators: Gebze Technical University
Conditions: Digital Technology; Healthy
Interventions: Large-Amplitude Exercise Observation and Video Recording
Enrollment: 26
Age range: 18 Years - 65 Years
Sex: ALL

Summary:
This pilot study investigates the feasibility of objectively quantifying movement amplitude and amplitude decay during large-amplitude exercises using RGB camera-based video analysis in healthy volunteers. Six large-amplitude exercises are recorded across repeated trials, with the first repetition clinically confirmed as the individual's maximum reference. Movement amplitude is quantified using pose-based kinematic measures and complemented by an exploratory self-supervised visual transformer approach to characterize repetition-wise changes relative to the reference execution. The study aims to determine whether camera-based methods can detect amplitude decay patterns and identify moments requiring clinical verbal cues, thereby supporting future objective monitoring and feedback in large-amplitude exercise training.

Eligibility criteria:
Inclusion Criteria:

Ability to stand, walk, reach, step, and perform sit-to-stand movements independently without assistance.

No self-reported or clinically diagnosed neurological, musculoskeletal, or vestibular disorders.

Normal or corrected-to-normal vision sufficient to follow visual instructions.

Exclusion Criteria:

* History of neurological disorders (e.g., Parkinson's disease, stroke, multiple sclerosis, traumatic brain injury).

History of major musculoskeletal injury or surgery affecting upper or lower extremities that limits movement.

Presence of balance disorders or vestibular dysfunction.

Cardiovascular, respiratory, or metabolic conditions that may limit safe participation in physical exercise.

Current pain, dizziness, or excessive fatigue during movement or exercise.

Pregnancy or any condition for which moderate-intensity physical activity is contraindicated.

Use of assistive devices for walking or balance.

Refusal or inability to provide consent for RGB video recording.

Outcomes:
Change in Movement Amplitude Across Repetitions
Role of Immunoscore® in Stage II-III Colorectal Cancer
Trial ID: NCT05422547
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2022-04-01
Completion date: 2023-12-30
Sponsor: D'Or Institute for Research and Education
Collaborators: HalioDx; Diagnosticos da America SA
Conditions: Colon Adenocarcinoma
Interventions: Immunoscore Colon Test
Locations: Instituto D'Or de Pesquisa e Ensino - Brasília; Instituto D'Or de Pesquisa e Ensino - Rio de Janeiro
Countries: Brazil; Brazil
Enrollment: 100
Age range: None - None
Sex: ALL

Summary:
The proposed study is a bidirectional analytical study, composed of a retrospective and a prospective cohorts. Patients with stage II - III colorectal cancer undergoing oncological surgery will be analyzed. The study will include a convenience sample of 100 individuals. Fifty cases will be included retrospectively and fifty prospectively.

The study aims to evaluate the benefit of using the Immunoscore test as a predictor of recurrence in patients with operated stage II-III colon tumors. It will also be evaluated the impact on oncologic decision and costs.

Eligibility criteria:
Inclusion Criteria:

1. Anatomopathological diagnosis of colorectal adenocarcinoma
2. Stage II - III (AJCC 8th edition)
3. Patient undergoing surgery with curative intent

Exclusion Criteria:

1. Current or previous diagnosis (within the last 5 years) of another invasive primary malignant neoplasm, with the exception of non-melanoma skin neoplasm
2. Presence of distant metastatic disease evidenced in imaging tests

Outcomes:
Tumour recurrence
Effect of ChatGPT and DeepSeek-Guided Rehabilitation on Clinical Outcomes in Individuals With Subacromial Pain Syndrome
Trial ID: NCT07148687
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-09-25
Completion date: 2025-12-20
Sponsor: Kirsehir Ahi Evran Universitesi
Conditions: Subacromial Pain Syndrome
Interventions: Conventional Exercise Program; ChatGPT-Guided Rehabilitation; DeepSeek-Guided Rehabilitation
Locations: Kırşehir Ahi Evran University Training and Research Hospital, Physical Medicine and Rehabilitation Department
Countries: Turkey (Türkiye)
Enrollment: 75
Age range: 18 Years - 65 Years
Sex: ALL

Summary:
This study aims to evaluate the effects of rehabilitation programs supported by artificial intelligence-based large language models (LLMs), specifically ChatGPT and DeepSeek, in individuals with subacromial pain syndrome (SAPS). Participants will be randomly assigned to one of three groups: standard physiotherapy, standard physiotherapy plus ChatGPT-guided exercises, and standard physiotherapy plus DeepSeek-guided exercises. Outcomes including pain, disability, quality of life, muscle strength, and joint range of motion will be assessed before and after a 6-week intervention. The results will help determine whether AI-generated exercise recommendations can enhance clinical outcomes when combined with conventional physiotherapy.

Eligibility criteria:
Inclusion Criteria:

* Being between the ages of 18 and 65
* Having been diagnosed with Subacromial Pain Syndrome (SAPS) for at least four weeks, diagnosed through clinical and physical examination
* Actively maintaining shoulder range of motion
* Being cognitively and physically capable of participating in an exercise program
* Agreeing to participate voluntarily in the research process and providing written consent

Exclusion Criteria:

* Presence of other shoulder pathologies such as rotator cuff tear, frozen shoulder, glenohumeral instability, or labrum tear
* Presence of conditions included in the differential diagnosis such as cervical radiculopathy, neurological disorders, rheumatological diseases, or fibromyalgia
* History of shoulder surgery within the last 6 months
* Pregnancy
* History of systemic or neuromuscular disease
* Refusal to consent to voluntary participation

Outcomes:
Change in Disability (Shoulder Pain and Disability Index, SPADI)