moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Using ChatGPT to Determine the Stage and Degree of Periodontontitis
Trial ID: NCT05926999
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2023-06-25
Completion date: 2023-07-30
Sponsor: Necmettin Erbakan University
Conditions: Periodontitis
Locations: Zeynep Taştan Eroğlu
Countries: Turkey (Türkiye)
Enrollment: 62
Age range: 18 Years - 65 Years
Sex: ALL

Summary:
The goal of this observational study is to test periodontitis classification ability of ChatCPT. The main questions it aims to answer are:

question 1: Could ChatCPT classify periodontitis? question 2: Is there a better result if ChatCPT is trained for perodontitis classification?

Eligibility criteria:
Inclusion Criteria:

* The cases whose clinical, photographic, and radiological records are excellent quality and have good diagnostic sensitivity.

Exclusion Criteria:

* Acute periodontal lesions
* gingival diseases
* the presence of dental implants
* periodontitis as a manifestation of systemic diseases were considered as exclusion criteria.

Outcomes:
Classficiation ability of ChatCPT
Deep Learning-enhanced Personalized Monitoring of Aortic Stenosis - The DETECT-AS Prognostic Study
Trial ID: NCT06749132
Status: ENROLLING_BY_INVITATION
Study type: OBSERVATIONAL
Start date: 2026-05-06
Completion date: 2028-08-31
Sponsor: Yale University
Collaborators: National Institute on Aging (NIA); Icahn School of Medicine at Mount Sinai; The Methodist Hospital Research Institute
Conditions: Aortic Stenosis
Interventions: Transthoracic echocardiogram
Locations: Yale New Haven Health System; Icahn School of Medicine at Mount Sinai; The Methodist Hospital Research Institute
Countries: United States; United States; United States
Enrollment: 210
Age range: 65 Years - None
Sex: ALL

Summary:
This study will evaluate the validity of a digital biomarker score for precision risk stratification among older adults with aortic sclerosis or mild aortic stenosis (AS) at three US health systems.

Eligibility criteria:
Inclusion Criteria:

1. Age 65 years or older
2. Prior transthoracic echocardiogram within the past 2-3 years showing aortic sclerosis without stenosis or mild AS

Exclusion Criteria:

1. Opted out of research studies
2. Non-English speaking
3. Any echocardiogram within 24 months of medical record review.
4. Prior history of aortic valve replacement or repair, including transcatheter and surgical AVR with either a bioprosthetic or mechanical valve
5. Presence of implantable cardiac devices, including permanent cardiac pacer, implantable cardioverter-defibrillator, or left ventricular assist device
6. Prior heart transplant
7. History of dementia
8. Unable to attend study visit at echocardiogram lab within four years of most recent echocardiogram.
9. Documented life expectancy of \<1 year or current participation in hospice services

Outcomes:
Annualized change in AV Vmax in m/sec/years
Effectiveness of Exercises Supervised Via an Artificial Intelligence-Supported Mobile Application in Patients With Knee Osteoarthritis
Trial ID: NCT07066514
Status: WITHDRAWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-08
Completion date: 2025-10
Sponsor: Uludag University
Conditions: Knee Osteoarthritis
Interventions: AI-Supervised Mobile Exercise Program; Booklet-Guided Home Exercise Program
Locations: Bursa Uludag University
Countries: Turkey (Türkiye)
Enrollment: 0
Age range: 40 Years - 80 Years
Sex: ALL

Summary:
This study aims to evaluate the effectiveness of an artificial intelligence (AI)-supported mobile application that supervises home-based exercise programs in patients with knee osteoarthritis. A total of 80 participants aged 40 to 80 will be randomly assigned to one of two groups: a mobile application exercise group or a home exercise booklet group. Both groups will receive the same standardized stretching, strengthening, and range of motion exercises designed for knee osteoarthritis. The mobile app provides real-time feedback and supervision using the device's camera and artificial intelligence algorithms to track and guide exercise performance.

Participants in the app group will perform exercises with supervision via the app interface, while the control group will follow the same exercises using printed instructions. Both groups will exercise 3 to 4 times per week for 4 weeks. The study will compare pain levels, physical function, and balance before and after the intervention using validated outcome measures such as the WOMAC Index and the Visual Analog Scale.

This study may help determine whether AI-supported digital tools can improve exercise adherence and outcomes in patients with knee osteoarthritis.

Eligibility criteria:
Inclusion Criteria:

* Aged between 40 and 80 years
* Diagnosed with knee osteoarthritis according to ACR criteria
* Referred to physical therapy by a physician for knee osteoarthritis
* Able to use a smartphone or tablet
* Having regular access to the internet
* Voluntarily agrees to participate in the study

Exclusion Criteria:

* History of knee surgery or currently undergoing surgical treatment
* Received physical therapy or rehabilitation for knee OA within the past 6 months
* Lack of access to a mobile device or internet
* Any neurological, cardiovascular, or musculoskeletal condition that prevents safe participation in exercise
* Cognitive impairment or psychiatric disorder interfering with exercise adherence
* Inability to follow instructions or use the mobile app interface
* Any condition that would contraindicate participation in a home-based exercise program

Outcomes:
Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC); Visual Analog Scale (VAS); Timed Up and Go (TUG) Test; Functional Reach Test (FRT); Single-Leg Stance Test; Exercise Execution Accuracy Assessed by Mobile Application
The Use of Artificial Intelligence for Clinical Assessment of Assisted Reproductive Techniques and IVF Outcomes
Trial ID: NCT04255615
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2020-02-12
Completion date: 2029-09-30
Sponsor: Weill Medical College of Cornell University
Conditions: Infertility; in Vitro Fertilization (IVF); ART
Interventions: AI to analyze 3 D ultrasound
Locations: Weill Cornell Medicine
Countries: United States
Enrollment: 4000
Age range: 18 Years - 89 Years
Sex: ALL

Summary:
The use of machine learning techniques using an artificial intelligence tool is proposed to analyze clinical data to predict best possible IVF/ART outcomes. This tool has been utilized to accurately predict embryo quality here at Cornell. Utilizing this tool to assess objective clinical findings and predict outcomes of assisted reproductive techniques is sought, with the ultimate goal of an automated tool to reduce implicit physician bias. Within this goal, using this tool to objectively and accurately assess baseline ovarian reserve at the start of an ART cycle is proposed, using 3D sonography to image the ovary and artificial intelligence tool to objectively identify baseline antral follicle counts.

Eligibility criteria:
Inclusion Criteria:

* All patients undergoing ovarian stimulation (including OI and IVF cycles)
* Treatment for fresh embryo transfer and cryopreservation of oocytes or embryos upfront
* Healthy male partners of the female subjects who agree to be part of the study.

Exclusion Criteria:

* None

Outcomes:
Number of baseline antral follicle count; Number of retrieved oocytes
CHinese pulmOnary Embolism Multimodality Imaging-artifiCial intelligencE Study
Trial ID: NCT06526468
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2010-09-01
Completion date: 2028-09-01
Sponsor: China-Japan Friendship Hospital
Conditions: Pulmonary Embolism; Chronic Thromboembolic Pulmonary Hypertension; Chronic Thromboembolic Pulmonary Disease
Interventions: Artificial Intelligence
Locations: China-Japan Frendship hospital
Countries: China
Enrollment: 1500
Age range: 14 Years - None
Sex: ALL

Summary:
The CHinese pulmOnary Embolism Multimodality Imaging-artifiCial intelligencE Study (CHOICE) is a prospective observational multi-center study that will collect imaging text data and raw data of patients with pulmonary embolism (PE) in China. By combining artificial intelligence technology, it aims to identify imaging markers to assist in early diagnosis, differential diagnosis, risk stratification, and prognosis assessment of PE.

Eligibility criteria:
Inclusion Criteria:

* 14 Years and older
* Patients suspected of PE

Exclusion Criteria:

* Pregnant women
* Refuse to follow up
* Incomplete or discontinued imaging scans
* Insufficient quality of image data to allow for analysis

Outcomes:
Diagnostic rate of PE; APE risk stratification rates (low, intermediate low, intermediate high and high risk); Disease severity of chronic thromboembolic pulmonary disease (CTEPD)/chronic thromboembolic pulmonary hypertension (CTEPH); 30 day mortality
Usefulness of Artificial Intelligence (AI) for Gastric Intestinal Metaplasia Diagnosis
Trial ID: NCT04358198
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2020-05-01
Completion date: 2024-02-28
Sponsor: King Chulalongkorn Memorial Hospital
Conditions: GIM Diagnosis; Artificial Intellegence
Interventions: Artificial intelligence
Locations: Rapat Pittayanon
Countries: Thailand
Enrollment: 120
Age range: 18 Years - None
Sex: ALL

Summary:
This study will use artificial intelligence (AI) for diagnosing gastric intestinal metaplasia.

Eligibility criteria:
Inclusion Criteria:

* More than 18 years of age
* Able to sign a consent form

Exclusion Criteria:

* History of gastric surgery
* Coagulopathy
* Pregnancy/Breast feeding

Outcomes:
Accuracy of AI for GIM diagnosis
Use of Sentiment Analysis and Social Media to Understand HIV Prevention Needs Among Young Women in Kenya
Trial ID: NCT06566417
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2024-02-01
Completion date: 2025-07-20
Sponsor: Massachusetts General Hospital
Collaborators: Kenya Medical Research Institute; KEMRI-Wellcome Trust Collaborative Research Program; North Carolina State University; National Institute of Mental Health (NIMH)
Conditions: HIV Prevention; HIV Preexposure Prophylaxis
Locations: KEMRI
Countries: Kenya
Enrollment: 400
Age range: 18 Years - 24 Years
Sex: FEMALE

Summary:
The impact of effective HIV prevention tools is limited because many people do not know that they are at risk for HIV acquisition, despite the availability of various risk assessment scores and criteria. This proposal aims to use a novel data science approach to assessing HIV prevention needs among 400 young women in Kisumu, Kenya- namely, topic modeling and network analysis of text and/or social media messages (e.g., WhatsApp, Instagram, Twitter). The study will involve in-depth assessment of relevant ethical and logistical factors to ensure appropriate and optimized use of a sentiment analysis tool for implementation in routine clinical care.

Eligibility criteria:
Inclusion Criteria:

* Identifying as a young woman (age 18-24 years)
* Attending clinic for any health services, including PrEP and HIV testing
* Smart phone ownership
* Ability to understand Kiswahili, DhoLuo, and/or English
* Use of SMS, WhatsApp, and/or other types of social media

Exclusion Criteria:

• Inability to provide informed consent (e.g., intoxication, developmental delay)

Outcomes:
Association of artificial intelligence measure datasets with the VOICE risk score; Association of artificial intelligence measure datasets with the Wand risk score
Validation of Artificial Intelligence-Based Facial Paralysis Assessment in Patients With Bell's Palsy
Trial ID: NCT07573358
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2026-06-01
Completion date: 2026-12-01
Sponsor: Cairo University
Conditions: Bell's Palsy; Facial Nerve Paralysis
Interventions: Sunnybrook Facial Grading System (FGS); AI-Based Facial Assessment
Locations: Faculty of Physical Therapy, Cairo University
Countries: Egypt
Enrollment: 63
Age range: 25 Years - 40 Years
Sex: ALL

Summary:
This observational study aims to assess the concurrent validity of an artificial intelligence (AI)-based facial paralysis assessment system in patients with unilateral Bell's palsy. Currently, clinical assessment relies on subjective scales like the Sunnybrook Facial Grading System, which can vary between different observers. This study will compare AI-generated composite asymmetry scores-derived from real-time computer vision analysis of facial landmarks-with scores from the Sunnybrook system. The goal is to determine if AI can provide a valid, objective method for monitoring facial nerve recovery.

Eligibility criteria:
Inclusion Criteria:

* Patients with unilateral Bell's palsy.
* Patients must be within one month of onset of Bell's palsy symptoms at the time of enrollment.
* Body mass index (BMI) less than 30 $kg/m\^2$.
* Patients must be cooperative and able to follow simple verbal instructions during facial movement tasks.

Exclusion Criteria:

* Bilateral facial paralysis or recurrent Bell's palsy.
* Facial nerve palsy due to known secondary causes (e.g., trauma, neoplasm, infection, stroke, Ramsay Hunt syndrome, or otitis media).
* Facial deformities, scars, or burns that interfere with facial motion detection.
* Uncooperative or cognitively impaired individuals unable to follow instructions or maintain required facial postures.

Outcomes:
Spearman's Rank Correlation Coefficient between AI-derived scores and Sunnybrook Facial Grading System scores.
Validation of a Risk Assessment Model for Postoperative Delirium Based on Artificial Intelligence
Trial ID: NCT05639348
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2022-11-21
Completion date: 2024-06-15
Sponsor: University Hospital, Basel, Switzerland
Collaborators: Innosuisse - Swiss Innovation Agency
Conditions: Postoperative Delirium (POD)
Interventions: Data collection on POD for calculation of the PIPRA score
Locations: University Hospital Basel, Anaesthesiology; Hôpitaux Universitaires de Genève, Anesthesiology; Centre Hospitalier Universitaire Vaudois, Anesthesiology
Countries: Switzerland; Switzerland; Switzerland
Enrollment: 993
Age range: 60 Years - None
Sex: ALL

Summary:
Postoperative delirium (POD) is a frequent postoperative complication in the elderly, characterised by fluctuating disturbances in attention, awareness, and cognition. Identifying the patients at highest risk of developing POD was the aim of the artificial intelligence (AI)-based algorithm PIPRA. This prospective cohort study is to externally validate the AI-based PIPRA algorithm. The primary endpoint is the performance (AUC) of the PIPRA algorithm in predicting POD. The secondary endpoint is the performance (AUC) of the clinicians in predicting POD (and how it compares with the performance of the PIPRA algorithm).

Eligibility criteria:
Inclusion Criteria:

* Surgical patients ≥60 years old
* Planned postoperative hospital stay ≥ 2 days
* Consent from patient

Exclusion Criteria:

* Preoperative delirium
* Insufficient knowledge in German or French
* Intracranial surgery
* Cardiac surgery
* Surgery within the two previous weeks
* Patient unable to consent

Outcomes:
Change in 4 'A's Test (4AT) to evaluate presence of POD; Change in Intensive Care Delirium Screening Checklist (ICDSC) to evaluate presence of POD
Evaluating the Impact AI Avatar-led Mental Health Interventions for Keeping Employees in Work
Trial ID: NCT07436117
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-10-07
Completion date: 2026-06-30
Sponsor: University of Roehampton
Conditions: Depression
Interventions: Remi, AI avatar-led cognitive behavioural therapy intervention
Locations: University of Roehampton
Countries: United Kingdom
Enrollment: 50
Age range: 18 Years - None
Sex: ALL

Summary:
This study will examine whether an artificial intelligence (AI) avatar-led mental health programme can help employees experiencing symptoms of depression. The programme delivers structured, self-guided psychological support over several short sessions that participants complete online over a period of several weeks.

Approximately 50-80 adults will take part. Participants will be randomly assigned either to start the AI programme immediately or to continue with their usual support and access the programme later. All participants will complete questionnaires before and after the study period to assess changes in mood, anxiety, and work functioning. Some participants will also be invited to take part in brief interviews to share their experiences of using the programme.

The aim of the study is to evaluate whether this AI-based intervention can improve mental health and workplace functioning, and to assess how acceptable and safe it is for use in a working population.

Eligibility criteria:
Inclusion Criteria:

* Participants will be eligible if they currently experience symptoms of depression (score above 5 on the PHQ-9)
* Participants should be aged 18 or above.
* Participants need to be resident in the UK.

Exclusion Criteria:

* Participants with frequent thoughts of suicide/self-harm will be excluded (scoring above 2 on Question 9 of the PHQ-9 "Thoughts that I would be dead or hurting myself in some way")
* Participants who are currently undergoing other forms of therapy will be excluded.

Outcomes:
Depression (PHQ-9)
Generation of Synthetic [18F]FDG PET From Early-Phase Amyloid PET in Alzheimer's Disease
Trial ID: NCT07431255
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2026-03-01
Completion date: 2026-12-31
Sponsor: IRCCS San Raffaele
Conditions: Alzheimer Disease (AD)
Locations: IRCCS Ospedale San Raffaele
Countries: Italy
Enrollment: 35
Age range: 50 Years - None
Sex: ALL

Summary:
This study aims to test a new artificial intelligence (AI) method to create brain scan images without needing an extra scan. Currently, patients with memory problems often undergo two types of PET scans (Amyloid PET and FDG PET) to assess Alzheimer's disease. This study will use existing scan data from patients who already had both scans as part of their routine care.

The AI model will try to generate the FDG PET image using only the Amyloid PET scan and an MRI. If successful, this method could reduce radiation exposure, costs, and time for future patients by eliminating the need for a separate FDG injection and scan.

No new scans, injections, or procedures will be performed for this study. All data will be fully anonymized (personal information removed) before analysis. The study involves approximately 35 adult patients (age 50+) whose data were collected between January 2025 and December 2025 at IRCCS Ospedale San Raffaele in Milan, Italy.

Eligibility criteria:
Inclusion Criteria:

* Age ≥ 50 years at the time of imaging.
* Clinically indicated amyloid PET scan performed with Florbetaben or Flutemetamol between January 1, 2025 and December 31, 2025.
* Availability of paired structural MRI (3D T1-weighted) and real \[18F\]FDG PET scan acquired within ±6 months of the amyloid PET.
* All three imaging modalities (Amyloid PET, FDG PET, MRI) are of sufficient technical quality for co-registration and quantitative analysis.

Exclusion Criteria:

* Presence of other major neurological disorders that may confound FDG metabolism (e.g., Parkinson's disease, frontotemporal dementia, brain tumor, or recent stroke).
* Severe motion artifacts or technical failures in any of the three imaging modalities that prevent reliable co-registration or SUVR calculation.
* Incomplete or irreversibly corrupted DICOM data preventing anonymization or conversion to analysis-ready format.

Outcomes:
Quantitative Accuracy of Synthetic FDG PET Images (SUVR Correlation and MAE)
Brugada Syndrome and Artificial Intelligence Applications to Diagnosis
Trial ID: NCT04641585
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2021-01-15
Completion date: 2023-09-15
Sponsor: Istituto di Fisiologia Clinica CNR
Collaborators: Fondazione Toscana Gabriele Monasterio; Azienda USL Toscana Sud Est; Azienda USL Toscana Nord Ovest; Azienda Ospedaliero-Universitaria Careggi; Azienda Ospedaliero, Universitaria Pisana
Conditions: Brugada Syndrome 1
Interventions: Patients affected by Brugada Syndrome 1
Locations: Azienda USL Toscana Sud Est - U.O.C Cardiologia; Azienda Ospedaliera Universitaria Careggi - SOD Aritmologia; Azienda Ospedaliero Universitaria Pisana - Cardiologia 2; Fondazione Toscana Gabriele Monasterio; Istituto di Fisiologia Clinica IFC-CNR; Azienda Usl Toscana Nord Ovest - U.O.C. Cardiologia
Countries: Italy; Italy; Italy; Italy; Italy; Italy
Enrollment: 144
Age range: 14 Years - 65 Years
Sex: ALL

Summary:
Aim of the project is the development of an integrated platform, based on machine learning and omic techniques, able to support physicians in as much as possible accurate diagnosis of Type 1 Brugada Syndrome (BrS).

Eligibility criteria:
Inclusion Criteria:

* Brugada patients: patients with Brugada Syndrome 1 spontaneous or induced by the ajmaline test; patients with non-diagnostic electrocardiographic pattern for Brugada Syndrome 1 or negative in the presence of high clinical suspicion (family history for Brugada Syndrome, patients who survived cardiac arrest without organic heart disease)
* Control patients: patients with frequent premature ventricular complex and normal left and right ventricular function; patients with suspected Brugada Syndrome 1 not confirmed by ajmaline test

Exclusion Criteria:

* organic heart disease or diseases interfering with protocol completion
* lack of signed informed consent
* pregnancy
* acute coronary artery disease, heart failure in the previous 3 months
* severe renal or liver failure

Outcomes:
Machine Learning recognition of Brugada Syndrome 1; Machine Learning recognition of Brugada Syndrome 1; Machine Learning recognition of Brugada Syndrome 1; Machine Learning recognition of Brugada Syndrome 1
The Aspirometer: A Noninvasive Tool for Detecting Aspiration
Trial ID: NCT06637774
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2022-09-30
Completion date: 2023-05-31
Sponsor: University of Pittsburgh
Collaborators: University of Toronto; Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD)
Conditions: Deglutition Disorders
Interventions: Aspirometer; Videofluoroscopic X-ray
Locations: University of Pittsburgh Medical Center Oakland campus
Countries: United States
Enrollment: 50
Age range: 18 Years - 100 Years
Sex: ALL

Summary:
This investigation evaluates the effectiveness of a device called the Aspirometer, which uses high resolution cervical auscultation (HRCA), in detecting when food or liquids enter the airway (aspiration) of the person swallowing, whether the person swallowing shows signs of aspiration (coughing) or not.

Eligibility criteria:
Inclusion Criteria:

\- referred by an attending physician as an inpatient, for a modified barium swallow examination due to suspected dysphagia and aspiration

Exclusion Criteria:

* tracheostomy
* pregnant
* unable to follow verbal commands
* anatomic disruption of head neck
* radiation therapy to head or neck

Outcomes:
Numbers of Participants for Which the Aspirometer/HRCA Predicted the Passed Versus Failed Swallow Test Result; Percentage of PAS Scores That Were Accurately Predicted by the Aspirometer/HRCA, in Comparison to Modified Barium Swallow Test.