moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Prostate Cancer Malignancy Grading Using Prostate Specific Membrane Antigen (PSMA) PET and Machine Learning
Trial ID: NCT07266129
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2024-06-03
Completion date: 2027-12
Sponsor: University Hospital of North Norway
Conditions: Prostate Cancer (Diagnosis)
Locations: Universitetssykehuset Nord Norge, Tromsø
Countries: Norway
Enrollment: 220
Age range: None - None
Sex: MALE

Summary:
Prostate cancer is the most common type of cancer in Norwegian men, but many tumors are slow-growing and do not require treatment. Today, MRI is good at detecting suspicious lesions, yet it cannot reliably distinguish aggressive tumors from low-grade ones. As a result, many men undergo repeated invasive biopsies. New PET tracers targeting PSMA improve tumor localization and may correlate with cancer aggressiveness, offering potential for better assessment.

This project aims to develop a method to predict Gleason Score non-invasively by applying machine learning to PET and MRI data. The work involves early static and dynamic PSMA PET imaging, tracer kinetic modelling, deep learning, and validation of PET-based measurements of PSMA internalization using ex-vivo cellular methods.

If successful, the project could reduce the number of biopsies, improve diagnostic accuracy, offer full 3D assessment of the prostate, shorten clinical workflows, and help identify patients who would benefit most from PSMA-based radioligand therapy.

Eligibility criteria:
Inclusion Criteria:

* For patients who have undergone biopsies prior to PET:
* Patients referred to clinical PET examination
* For patients who have not had biopsies prior to PET:
* Patients referred to urologist for suspected prostate cancer based on clinical symptoms or elevated PSA-levels

Exclusion Criteria:

* Prostatektomy
* Body weight under 100 kg
* MRI incompatible implants or other incompatibilities

Outcomes:
PET imaging
Genesis: Clinical Trial of Better Speech's Artificial Intelligence Helper for Speech Therapy
Trial ID: NCT07061730
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-05-01
Completion date: 2026-02
Sponsor: Syracuse University
Collaborators: Better Speech
Conditions: Speech Sound Disorder
Interventions: Traditional Therapy; Hybrid therapy
Locations: Syracuse University
Countries: United States
Enrollment: 120
Age range: 5 Years - 17 Years
Sex: ALL

Summary:
The purpose of this research study is to evaluate the effectiveness of a hybrid Artificial Intelligence-assisted speech therapy model, which combines the use of an AI speech therapist (Jessica) with traditional speech therapy sessions led by a Speech-Language Pathologist (SLP). This study aims to determine whether the hybrid model improves articulation in children more effectively than traditional SLP-only therapy. Over the course of 8 weeks, participants will receive weekly therapy sessions, and those in the hybrid therapy group will also practice with Jessica between sessions. The study will also gather feedback from parents and speech therapists to assess their experiences and satisfaction with treatment.

Eligibility criteria:
Inclusion Criteria:

\- Response to be filled out by the Parent after sign up.

* Does the child speak American English as one of their primary languages: Yes
* At what age did the child first begin to learn English? Age 4 or younger
* Child's current age: \> 5 years or \< 17 years 8 months
* Does the child have any known history of any of the following: autism spectrum disorder, Down Syndrome, cerebral palsy, intellectual disability, permanent hearing loss, epilepsy/antiepileptic medication, or brain injury/neurosurgery/stroke: NO
* Does the child have a diagnosis of ADD/ADHD, Tourette's Syndrome, or OCD? NO
* Does the child currently have orthodontic appliances that block the roof of the mouth (e.g., palate expanders)? NO
* Does the child currently have a cleft palate, fluency disorder (e.g., stuttering), or voice disorder? NO
* Is one of your goals for your child to work on saying the "r" or "s" sounds? YES

Exclusion Criteria:

* EXCLUSIONARY Response to be filled out by the Parent after sign up.
* Does the child speak American English as one of their primary languages: No
* At what age did the child first begin to learn English? Age 4 or older
* Child's current age: \<5 years or \> 17 years 8 months
* Does the child have any known history of any of the following: autism spectrum disorder, Down Syndrome, cerebral palsy, intellectual disability, permanent hearing loss, epilepsy/antiepileptic medication, or brain injury/neurosurgery/stroke: YES
* Does the child have a diagnosis of ADD/ADHD, Tourette's Syndrome, or OCD? YES
* Does the child currently have orthodontic appliances that block the roof of the mouth (e.g., palate expanders)? YES
* Does the child currently have a cleft palate, fluency disorder (e.g., stuttering), or voice disorder? YES
* Is one of your goals for your child to work on saying the "r" or "s" sounds? NO

Outcomes:
Motor Learning - Percent Improvement on Outcome Words/Phrases
A Prospective, Open-Label, Randomized Controlled Trial of an Artificial Intelligence Enabled Electrocardiography System for Preoperative Detection of Pulmonary Hypertension and Related Diseases(GUARD-PH)
Trial ID: NCT07344207
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-01-05
Completion date: 2027-01-05
Sponsor: Taipei Veterans General Hospital, Taiwan
Conditions: Pulmonary Hypertension (Diagnosis); Pre-operative Assessment; Postoperative Complications (Cardiopulmonary); Treatment Outcomes
Interventions: Artificial Intelligence-Enabled Electrocardiography System
Locations: Taipei Veterans General Hospital
Countries: Taiwan
Enrollment: 1380
Age range: 18 Years - None
Sex: ALL

Summary:
This study is a prospective, open-label, randomized controlled trial designed to evaluate a new artificial intelligence (AI) tool for heart monitoring. Researchers will use an AI-enabled electrocardiography (ECG) system to screen patients before they undergo surgery. The main goal is to determine if this AI system can accurately detect pulmonary hypertension and related heart diseases in the preoperative setting. The study is being conducted at Taipei Veterans General Hospital.

Eligibility criteria:
Inclusion Criteria:

1. Age between 18 and 80 years.
2. Patients scheduled for non-cardiac surgery under general anesthesia.
3. Patients who have a scheduled standard preoperative electrocardiogram (ECG).
4. Patients capable of understanding the study and willing to provide medical records for research purposes, and who have signed the informed consent form.

Exclusion Criteria:

1. Patients scheduled for emergency surgery.
2. Patients who explicitly refuse to participate or withdraw consent.
3. Patients with specific comorbidities that interfere with ECG interpretation or data collection (e.g., implanted pacemakers).

Outcomes:
The incidence of newly diagnosed pulmonary hypertension or pulmonary hypertension-related cardiopulmonary diseases
Data Construction Project for Artificial Intelligence Learning: Chest Auscultation Sound Data
Trial ID: NCT05320900
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2022-05-01
Completion date: 2022-12-31
Sponsor: Yonsei University
Conditions: Auscultation for Clinical Evaluation
Interventions: Chest auscultation
Locations: Yongin Severance Hospital; Soonchunhyang University Bucheon Hospital; Severance Hospital
Countries: South Korea; South Korea; South Korea
Enrollment: 6000
Age range: 20 Years - 90 Years
Sex: ALL

Summary:
The purpose is to establish chest auscultation data and related clinical data for diagnosing heart and lung diseases.

Eligibility criteria:
Inclusion Criteria:

* Adults who are 20 years and older

Exclusion Criteria:

* Patient refusal
* Uncertain radiographs
* Uncertain tests results

Outcomes:
Incidence of valvular heart disease
Artificial Intelligence Delivered Cardiac Magnetic Resonance - Prospective Validation
Trial ID: NCT06061822
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-05-01
Completion date: 2027-12-01
Sponsor: Imperial College London
Collaborators: British Heart Foundation; Medical Research Council; Rosetrees Trust
Conditions: Cardiovascular Diseases; Healthy Volunteers
Interventions: AI-assisted cardiac magnetic resonance imaging
Locations: Imperial College Healthcare NHS Trust
Countries: United Kingdom
Enrollment: 150
Age range: 18 Years - None
Sex: ALL

Summary:
Cardiac MRI (CMR) scanning allows doctors to create detailed images of the heart. However, the need for experienced cardiac radiographers to perform each scan can make CMR's delivery difficult, and some patients in the UK wait more than half a year for a scan. These radiographers must take pictures of different part of the heart, termed "views", each of which must be precisely positioned.

The investigators believe they can revolutionise CMR, by using artificial intelligence to automatically position the views so radiographers can focus on more difficult tasks.

The investigators have used a retrospective database of pseudonymised (anonymised and linked) CMR scans at our hospital to create these artificial intelligence (AI) algorithms, and they have validated them retrospectively on previous studies. The investigators now wish to test the algorithms prospectively.

In this study, the investigators will recruit patients undergoing clinical CMR scans. In addition to the routine images acquired by expert radiographers, the investigators will require a duplicate set of images, positioned and planned by the AI algorithms.

The investigators will then compare, within each patient, the AI-planned and expert-radiographer-planned scanning in terms of both speed and image quality.

Eligibility criteria:
Inclusion Criteria:

* Adult (aged at least 18 years)

Exclusion Criteria:

* Children (patients below age 18).
* Pregnant patients.

Outcomes:
Time taken to acquire images; Image quality
Effect of Artificial Intelligence-Based Education Before Planned Cesarean Section on Surgical Fear, Anxiety, and Postpartum Comfort Levels of Primigravida Women: A Randomized Controlled Trial
Trial ID: NCT07419529
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-08-15
Completion date: 2025-12-31
Sponsor: Nigde Omer Halisdemir University
Conditions: Surgical Anxiety Scale for Adult Patients; Surgical Fear Scale; Postpartum Comfort Scale
Interventions: individual education about cesarean surgery; about cesarean surgery using artificial intelligence; routine maintenance of the institution
Locations: Niğde Ömer Halisdemir Univercity
Countries: Turkey (Türkiye)
Enrollment: 75
Age range: 18 Months - 35 Months
Sex: FEMALE

Summary:
This study was planned to determine the effect of artificial intelligence use before planned cesarean section on surgical fear, anxiety and postpartum comfort levels of primigravida women. The research will be conducted in an experimental design. The research will be initiated after obtaining written permission from Niğde Ömer Halisdemir University Ethics Committee, Niğde Provincial Health Directorate and Ömer Halisdemir Training and Research Hospital. Pregnant women between the ages of 18-35, who are primigravida, who applied for planned cesarean section, who have live singleton pregnancies and who are planned for spinal anesthesia will be included in the research. Before starting the research, the study will be initiated after obtaining the institutional permissions and written permissions from the pregnant women. Preliminary Assessment Form, Personal Information Form, Surgical Fear Scale, Surgical Anxiety Scale for Adult Patients, Postpartum Comfort Scale and Perianesthesia Comfort Scale will be used in data collection. Data will be evaluated in a computer environment using IBM SPSS Statistics 24 (Statistical Package for the Social Sciences for Windows) package program. Appropriate statistical analyzes will be used in data evaluation.

Eligibility criteria:
Inclusion Criteria:

* Are between 18 and 35 years of age
* Are primigravida
* Are applying for a planned cesarean section
* Have a live singleton pregnancy
* Are scheduled for spinal anesthesia

Exclusion Criteria:

* Women with known mental health problems,
* Those who have performed relaxation exercises during pregnancy,
* Women who have developed maternal or neonatal complications after birth,
* Those who have received general anesthesia.

Outcomes:
Surgical Anxiety Scale for Adult Patients; Surgical Fear Scale
The Studies of Early Intelligent Diagnosis of Limb Deformity in Children by AI and Clinic Application
Trial ID: NCT04527029
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-03
Completion date: 2027-12-01
Sponsor: Children's Hospital of Fudan University
Conditions: Limb Deformity
Interventions: No interventions
Enrollment: 9000
Age range: None - 18 Years
Sex: ALL

Summary:
The limb deformity in children include congenital limb malformations or acquired from the damage of epiphyseal plate which caused by tumor, inflammation and trauma. Due to the complexity of the disease itself, rapid dynamic development and the characteristics of children's growth and development, the deformities are constantly changing. In addition, the serious lack of clinical diagnosis and treatment resources in the Department of Pediatric Orthopedics has led to the misdiagnosis and improper treatment of children's limb deformities. Thus, its necessary to find an intelligent way to help doctor to early diagnosis of limb deformity and provide a proper treatment in children.

Eligibility criteria:
Inclusion Criteria:

Children with limb deformity

Exclusion Criteria:

Children without limb deformity

Outcomes:
Deformity detection
Feasibility of an Augmented Reality Novel Approach for the Rehabilitation of Chronic Low Back Pain Patients With Kinesiophobia
Trial ID: NCT07527845
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2022-12-01
Completion date: 2024-11-12
Sponsor: Pontificia Universidad Catolica de Chile
Conditions: Low Back Pain; Kinesiophobia (Fear of Movement); Healty Volunteers
Interventions: Augmented Reality (AR) Rehabilitation System
Locations: Pontificia Universidad Católica de Chile
Countries: Chile
Enrollment: 17
Age range: 18 Years - None
Sex: ALL

Summary:
The goal of this clinical trial is to evaluate the feasibility, safety, and initial effectiveness of a new augmented reality (AR) system in adults over 18 years old suffering from chronic low back pain and kinesiophobia (fear of movement).

The main questions it aims to answer are:

Is the AR system safe to use and well-tolerated by participants without causing significant motion sickness or discomfort?

Can the visual "illusion" provided by the AR technology help patients increase their range of motion and reduce their fear of bending forward?

Participants will:

* Perform 10 to 12 repetitions of spinal bending exercises while wearing an AR headset that shows a video of them moving further than they actually are.
* Complete questionnaires regarding their pain levels, physical disability, and fear of movement.
* Report any symptoms of "cybersickness" (like dizziness or nausea) experienced during the use of the technology.
* Participate in a brief interview to discuss their experience, how they felt using the device, and provide suggestions for improvement.

Eligibility criteria:
Inclusion Criteria:

* Participants must be over 18 years old.
* For the pilot phase: Healthy volunteers with no chronic pathologies or musculoskeletal conditions
* For the clinical phase: Diagnosis of non-specific chronic low back pain (CLBP) lasting longer than three months.
* For the clinical phase: Presence of less than 40 degrees of spinal forward bending movement.

Exclusion Criteria:

* History of spine surgery.
* Diagnosis of vestibular disorders.
* Diagnosis of Meniere's syndrome

Outcomes:
Simulator Sickness Questionnaire (SSQ)
Using Artificial Intelligence to Improve Outcome From Traumatic Cardiac Arrest in Denmark
Trial ID: NCT05294575
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2022-02-22
Completion date: 2023-01-01
Sponsor: Prehospital Center, Region Zealand
Conditions: Out-Of-Hospital Cardiac Arrest; Trauma
Locations: Prehospital Center
Countries: Denmark
Enrollment: 31200
Age range: None - None
Sex: ALL

Summary:
This study evaluates data from patients in The Danish Medical Service electronical registry over a 6-year period from 2016 to 2021 with traumatic cardiac arrest. The objective of this study is to use artificial intelligence to evaluate reversible causes and relevant circumstances regarding traumatic OHCA in order to improve treatment and survival.

Eligibility criteria:
Inclusion Criteria:

* OHCA related to trauma

Exclusion Criteria:

* All other causes of OHCA

Outcomes:
Survival to hospital admission; Longterm Survival
Prospective Evaluation of a Model-Agnostic Meta-Verification Framework (SCOUT) for Scalable Clinical Oversight of Large Language Model Outputs in Coronary Heart Disease Diagnosis: A Multi-Reader, Randomized, Crossover Trial
Trial ID: NCT07414966
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-02-19
Completion date: 2026-02-28
Sponsor: China National Center for Cardiovascular Diseases
Conditions: Coronary Heart Disease (CHD)
Interventions: SCOUT-Assisted Review Workflow; Standard Manual Review Workflow
Enrollment: 7
Age range: 18 Years - None
Sex: ALL

Summary:
This prospective, multi-reader, randomized crossover trial evaluates SCOUT (Scalable Clinical Oversight via Uncertainty Triangulation), a model-agnostic meta-verification framework that selectively defers unreliable large language model (LLM) predictions to clinicians by triangulating three orthogonal uncertainty signals: model heterogeneity, stochastic inconsistency, and reasoning critique. The trial assesses whether SCOUT-assisted review can reduce physician review time compared with standard manual review of AI-generated diagnoses while maintaining non-inferior diagnostic accuracy in coronary heart disease (CHD) subtyping.

Eligibility criteria:
Inclusion Criteria:

* Board-certified or in-training cardiologists at Fuwai Hospital
* Spanning three experience strata: junior residents, senior residents, attending physicians

Exclusion Criteria:

* Clinicians involved in the development or optimization of the SCOUT framework
* Clinicians involved in the gold-standard adjudication process

Outcomes:
Mean physician review time per case (minutes)
The Weight of Cardiovascular Disease: A Prospective Pilot Study of Obese Adults With Cardiovascular Disease Evaluated in a Novel Cardiometabolic Clinic
Trial ID: NCT06053645
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2024-04-22
Completion date: 2026-07
Sponsor: Mayo Clinic
Conditions: Cardiovascular Diseases; Obesity
Locations: Mayo Clinic in Rochester
Countries: United States
Enrollment: 60
Age range: 18 Years - None
Sex: ALL

Summary:
The purpose of this study is to evaluate the impact of a cardiometabolic clinic on percent body weight loss, body mass index (BMI), and visceral adiposity distribution in obese adults with cardiovascular disease (CVD) at 12 months.

Eligibility criteria:
Inclusion Criteria:

* BMI ≥30 kg/m2
* At least one of the following CVD: atrial fibrillation (AF), coronary artery disease (CAD), HFpEF (heart failure with preserved ejection fraction), HFrEF (heart failure with reduced ejection fraction), ACHD (adult congenital heart disease), or valvular heart disease
* Patients evaluated in the Cardiometabolic clinic
* Willingness to complete questionnaires/surveys
* Ability to complete monthly self-assessments at home

Exclusion Criteria:

* BMI \<30 kg/m2
* Pregnancy or lactating women
* Hemodynamically unstable CVD
* Active malignancy
* Autoimmune or systemic inflammatory diseases
* Severe renal or hepatic failure
* Being considered unsafe to participate as determined by the study physician
* Taking medications affecting weight or energy intake/energy expenditure in the last 6 months, including weight loss medications, antipsychotic medications, or other medications as determined by the study NP or physician
* Patients with active psychosis, mania, or substance use disorders

Outcomes:
Percent body weight loss; Change in Body Mass Index (BMI); Change in visceral adiposity distribution
The Effect of Artificial Intelligence on the Development of Projective Methods Used in the Determination of Dental Anxiety in Children
Trial ID: NCT07387055
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2024-08-01
Completion date: 2024-11-30
Sponsor: Sümeyra Akkoç
Conditions: Dental Anxiety; Artificial Intelligence (AI); Test Anxiety Scale
Locations: Kütahya Health Sciences University, Faculty of Dentistry, Department of Pediatric Dentistry
Countries: Turkey (Türkiye)
Enrollment: 171
Age range: 3 Years - 6 Years
Sex: ALL

Summary:
The goal of this observational study was to evaluate an artificial intelligence-assisted projective method for assessing dental anxiety in young children and to understand how the first child-dentist interaction affected dental anxiety. The main questions it aimed to answer were:

Did the artificial intelligence-assisted projective method provide a valid and reliable assessment of dental anxiety in children aged 3 to 6 years? Did dental anxiety change after the child's first interaction with the dentist during the first dental visit? Children aged 3 to 6 years who attended their first dental visit as part of routine dental care took part. During the same visit, dental anxiety was assessed before and after the initial child-dentist interaction using picture-based dental anxiety scales and the newly developed projective method. All assessments were completed on the same day.

Eligibility criteria:
Inclusion Criteria:

* The child and the parent/guardian agree to participate in the study.
* The child's native language is Turkish.
* The child is attending their first dental visit.
* The child has no systemic disease.
* The child is able to cooperate sufficiently to complete the initial dental examination.

Exclusion Criteria:

* Current use of medication, a known history of allergy, or the presence of any mental or physical disability

Outcomes:
AI-Assisted Projective Dental Anxiety Assessment Tool Score; Facial Image Scale (FIS) Score; Venham Picture Test (VPT) Score; Raghavendra, Madhuri, and Sujata Pictorial Scale (RMS-PS) Score
Assessing the Use of a Growth Artificial Intelligence Algorithm for Estimating the leNgth of Children in Real-world Setting
Trial ID: NCT05079776
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2021-11-08
Completion date: 2022-06-20
Sponsor: Danone Asia Pacific Holdings Pte, Ltd.
Collaborators: KK Women's and Children's Hospital
Conditions: Growth; Stunting, Nutritional
Interventions: Physical length measurement
Locations: KK Women's and Children's Hospital
Countries: Singapore
Enrollment: 200
Age range: 0 Months - 18 Months
Sex: ALL

Summary:
An exploratory study to explore the possibility of using computer vision algorithms to estimate a child's length using images taken by a healthcare professional or parents.

Eligibility criteria:
Inclusion Criteria:

1. Children aged 0 to 18 months old.
2. Parent(s) should have access to the internet and a smartphone or tablet to complete study questionnaires, take images and upload images.
3. Parent(s) should be able to comprehend the content of the study and to complete the study questionnaires in English.
4. Written consent from parent.

Exclusion Criteria:

1. Parent(s) incapable of completing the study questionnaires and uploading of the images using smart phone or tablet with internet.
2. Children unable to undergo length measurement (e.g. children with structural abnormalities of the lower limbs or orthopedic conditions such as club foot, hip dysplasia, etc).

Outcomes:
Accuracy of the length AI