moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
RET-US Cohort: Prospective Evaluation of an AI-Ultrasound Model for Detecting RET Gene Alterations and Predicting Lateral Cervical Lymph-Node Metastasis in Papillary Thyroid Carcinoma
Trial ID: NCT07042984
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-07-01
Completion date: 2029-12-31
Sponsor: Fujian Medical University
Conditions: Papillary Thyroid Carcinoma; Thyroid Neoplasms; RET Proto-Oncogene Mutation; Lymphatic Metastasis
Interventions: AI-Ultrasound RET Prediction
Locations: Fujian Medical University Union Hospital
Countries: China
Enrollment: 800
Age range: 18 Years - 75 Years
Sex: ALL

Summary:
Why is this study being done? RET gene alterations occur in only 5-10 % of papillary thyroid cancers, but they can change how surgeons treat the disease. Gene testing is costly and not always performed, so many RET-positive tumours are missed. Researchers have built a computer program (artificial-intelligence or "AI" model) that reads routine thyroid ultrasound images and predicts whether the tumour carries a RET alteration and whether the cancer has already spread to lymph-nodes in the side of the neck.

What will happen in this study?

About 800 adults who are scheduled for thyroid-cancer surgery will take part. Each participant will:

* have a standard pre-operative ultrasound exam (no extra scanning time),
* give a routine fine-needle sample for a 14-gene panel test (results in 24 h), and
* allow the AI model to analyse the ultrasound images in the background. Doctors making treatment decisions will not see the AI result. After surgery, the research team will compare the AI predictions with the gene-panel result and the final pathology report.

Main goal: To find out how accurately the AI model detects RET alterations. Secondary goals: To measure the model's ability to predict lymph-node spread, and to compare costs between ultrasound-only prediction and full gene testing.

Benefits and risks: Participants will receive the current standard of care; there is no added risk beyond the usual ultrasound and needle biopsy. The study could lead to faster, less expensive ways to identify high-risk thyroid cancers in the future.

Eligibility criteria:
Inclusion Criteria:

* Age 18-75 years, able to provide written informed consent.
* Pre-operative ultrasound findings highly suggestive of papillary thyroid carcinoma.
* Planned thyroidectomy (any extent) at a participating institution.
* Willing to undergo rapid 11-gene next-generation sequencing (NGS) panel and allow use of ultrasound DICOM images for AI analysis.

Exclusion Criteria:

* Prior thyroid or major neck surgery.
* Known medullary thyroid carcinoma, anaplastic carcinoma, or metastatic disease outside the neck.
* Multiple endocrine neoplasia (MEN) syndromes or clinical suspicion of multi-gland disease.
* Pregnant or breastfeeding.
* Severe renal impairment (eGFR \< 30 mL/min/1.73 m²) or other condition that precludes surgery or gene testing.

Outcomes:
Area Under the ROC Curve (AUC) for AI-Ultrasound Detection of RET Alterations
The Use of Artificial Intelligence for the Prioritization of Causative Genetic Background in a Brugada Syndrome Cohort: an Observational Retrospective Study
Trial ID: NCT06376552
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2018-12-19
Completion date: 2022-06-06
Sponsor: IRCCS San Raffaele
Conditions: Brugada Syndrome
Locations: IRCCS San Raffaele; Milano-Bicocca University
Countries: Italy; Italy
Enrollment: 200
Age range: 18 Years - None
Sex: ALL

Summary:
Brugada Syndrome (BS) is an inherited heart condition that can cause sudden cardiac arrest in young individuals. It's diagnosed through specific changes seen on an electrocardiogram (ECG). Currently, the only treatment option is a cardioverter defibrillator (ICD). Despite advances, much about BS remains unclear, including its genetic basis. This study aims to use advanced genetic sequencing and artificial intelligence to uncover new genetic factors contributing to BS. By understanding these factors better, we hope to improve risk assessment and treatment for affected individuals.

Eligibility criteria:
Inclusion Criteria:

* The 200 BS patients have been selected and clinically evaluated based on the presence of a type I electrocardiogram (ECG), either spontaneous or induced by flecainide or ajmaline.

Exclusion Criteria:

* No exclusion criteria are adopted for this study. The entire previously sequenced cohort of 200 BS patients will be investigated and considered, exploiting AI approach for the prioritization of the sequencing available data.

Outcomes:
New candidate genes, likely associated with Brugada Syndrome using an AI based approach.
Study of Conjunctival and Retinal Blood Vessels for the Evaluation of the Load of Small Vessel Disease in Patients With Transient Ischemic Attacks (TIAs) and Minor Strokes
Trial ID: NCT04447599
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2020-10-20
Completion date: 2022-10-20
Sponsor: University Hospital, Toulouse
Conditions: Cerebral Stroke
Interventions: photography of conjunctival vessels
Locations: NASR Nathalie
Countries: France
Enrollment: 850
Age range: 55 Years - 80 Years
Sex: ALL

Summary:
The purpose of the study is to use a simple photography of conjunctival vessels to search for an association between conjunctival vessels abnormalities and the load of small vessel disease as quantified by MRI in patients with TIA s and minor strokes.

The artificial intelligence (AI) tools will permit to classify abnormalities of conjunctival vessels that predict the load of small vessel disease in TIAs and strokes.

Eligibility criteria:
Inclusion Criteria:

* Patients treated for a minor stroke or transient ischemic attack justifying an MRI.

Exclusion Criteria:

* Severe eye dryness with conjunctiva hyperhemia,
* Taking eye treatments for glaucoma
* Inflammatory or infectious eye damage resulting in conjunctiva hyperhemia: conjunctivitis, herpetic keratitis, ulcer, abscess, uveitis, peripheral ulcerative keratitis, interstitial keratitis, scleritis, episcleritis, pingueculitis, scarring eye pemphigoide, acute seizure by closing of the angle,
* History of eye surgery. Among eye surgeries, those that can modify the conjunctiva are strabismus surgeries, glaucoma, conjunctiva resection surgery (pterygial or tumor) or wound surgery or finally recent cataract surgery (2 years).
* Persons under the legal protection of adults (safeguarding justice, guardianship, curatorship, institutionalized, or under mandate of future protection)
* Administrative problems: inability to give about informed information, no coverage by a social security plan, refusal to sign a consent.

Outcomes:
Numbers of conjunctival vascular abnormalities
Prospective Cohort Study on Predicting the Progression of Diabetic Microangiopathy Using Multimodal Eye Imaging
Trial ID: NCT06727955
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-01-01
Completion date: 2028-12-31
Sponsor: Peking University Third Hospital
Conditions: Diabetic Retinopathy
Locations: Peking University Third Hospital
Countries: China
Enrollment: 250
Age range: 40 Years - 80 Years
Sex: ALL

Summary:
To develop an artificial intelligence (AI) model to predict DR progression based on a prospective cohort database.With use of an innovative AI model and a validated machine learning algorithm, based on multimodal vascular and neuro imaging of the eye, the progression of diabetic microangiopathy especially the DR could successfully be predictse and fundamentally facilitate diabetes management.

Eligibility criteria:
Inclusion Criteria:

* (1) age between 40 and 80 years, regardless of gender, (2) meet the diagnostic criteria of T2DM based on the Standards of Medical Care in Diabetes (2021)11, and complete evaluation at the Endocrinology Department of Peking University Third Hospital, (3) meet the Diabetic Retinopathy Severity Scale (DRSS) score level of mild to moderate-severe NPDR (i.e. 35 ≤ DRSS level ≤ 60, as shown in eTables 1 \& 2 in the Supplement) based on the montages ultra-widefield (UWF) fundus photographs,12 without clinically significant macular edema (CSME, defined as one or more of the followings: retinal thickening at or within 500 μm of the center of the macula; hard exudates at or within 500 μm of the center of the macula, if associated with adjacent retinal thickening; or a zone or zones of retinal thickening one disc area in size, at least part of which is within one disc diameter of the center of the macula),13 (4) willing to sign the informed consent form.

Exclusion Criteria:

* (1) conjunctivitis, keratitis, blepharitis, episcleritis, scleritis, uveitis, severe dry eye disease, using anti-glaucoma eyedrops, or any condition causing conjunctival congestion or ciliary congestion, (2) pterygium, wearing contact lens in the past week, ocular surface burn, conjunctival tumors, or any condition causing significant conjunctival vascular deformities, (3) previously undergone intraocular surgeries involving manipulation of the conjunctiva, or corneal refractive surgery, (4) history of hematological disorder, dysthyroidism, rheumatic disorder, malignant tumor, (5) refractive media opacity resulting in poor visualization of fundus photographs, (6) difficulty in maintaining fixation or comprehension, making it challenging to cooperate with the examination, (7) difficulty in completing follow-up visits in 3 years.

Outcomes:
DRSS Score and Classification
External Validation of an Individualized Patient Centered Machine Learning Model for the Prediction of Ischemic and Bleeding Risk in Patients on Dual Antiplatelet Therapy After Percutaneous Coronary Intervention
Trial ID: NCT06736626
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2012-01-01
Completion date: 2024-12-01
Sponsor: Icahn School of Medicine at Mount Sinai
Conditions: Coronary Artery Disease; Percutaneous Coronary Intervention
Interventions: Percutaneous coronary intervention
Locations: Icahn School of Medicine at Mount Sinai
Countries: United States
Enrollment: 30000
Age range: 18 Years - None
Sex: ALL

Summary:
Dual antiplatelet therapy (DAPT) is routinely recommended after percutaneous coronary intervention (PCI) with drug-eluting stent (DES) implantation to prevent thrombotic complications. However, DAPT is also associated with an increased risk of bleeding, which may have a similar or even greater impact on prognosis compared to recurrent ischemic events.

To balance these risks, individualized risk stratification at the time of PCI is crucial for determining the optimal DAPT composition and duration, aiming to reduce thrombotic risk while minimizing bleeding complications. For this purpose, an artificial intelligence-based risk stratification tool (xDAPT, Abbott) was introduced and demonstrated strong clinical performance in its development study (ClinicalTrials.gov identifier: NCT06089304).

This analysis aims to evaluate the performance of xDAPT in a real-world cohort of patients who underwent PCI over the past decade at a large urban center (Mount Sinai Hospital, New York).

Eligibility criteria:
Inclusion criteria:

* Consecutive patients undergoing PCI with any DES implantation at Mount Sinai Hospital (New York, US) between January, 2012 and December, 2022.
* Age ≥18 years
* Ability to provide informed consent for the procedure and subsequent data collection

Exclusion criteria:

* PCI with bare-metal stents (BMS) or balloon angioplasty only
* Cardiogenic shock or cardiac arrest as indication to PCI
* In-hospital mortality
* No available clinical follow-up
* Missing data from any of the variables included in the risk models

Outcomes:
Number of Major Adverse Cardiovascular Events
Risk Stratification of Orbital Tumors Based on MRl and Artificial Intelligence
Trial ID: NCT06336499
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2012-01-01
Completion date: 2023-12-31
Sponsor: Beijing Tongren Hospital
Conditions: Orbital Neoplasms
Interventions: Multi-parametric MRI and image analysis by deep learning or machine learning algorithms
Enrollment: 600
Age range: 18 Years - None
Sex: ALL

Summary:
Orbital tumors can be categorized into benign and malignant tumors, and there are significant variations in their biological behavior, treatment, and prognosis. This study aims to enhance the accurate diagnosis and risk stratification of orbital tumors using artificial intelligence (AI) technology and multiparameter magnetic resonance imaging (MRI) data. It further explores the intrinsic relationship between MRI and the differential diagnosis of benign and malignant orbital tumors, as well as the pathological subtypes of malignant tumors and Ki-67 expression levels. This research aims to aid in guiding personalized diagnosis and treatment decision-making for patients with orbital tumors while promoting the practical application and incorporation of AI technology.

Eligibility criteria:
Inclusion Criteria:

* The patients with orbital tumors who underwent pre-operative multiparametricMRl (mp-MRl) at Beijing Tongren Hospital from 2015 to 2022.

Exclusion Criteria:

* The patients without pre-operative multiparametric MRl (mp-MRl) or clear pathological diagnosis.

Outcomes:
The area under the curve of Receiver Operating Characteristic of the diagnostic models for the differential diagnosis of malignant and benign orbital tumors, high and low grades of histological types, and levels of Ki-67 expression in malignant ones.
Personalised Prediction of Disease Course in Ulcerative Colitis Using Multimodal Machine Learning - Part of the Presager Project
Trial ID: NCT05479617
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2022-06-20
Completion date: 2026-07
Sponsor: Copenhagen University Hospital, Hvidovre
Conditions: Ulcerative Colitis
Interventions: Deep learning model
Locations: Gastrounit, medical section, Copenhagen University Hospital Hvidovre
Countries: Denmark
Enrollment: 400
Age range: 18 Years - None
Sex: ALL

Summary:
In patients achieving clinical remission following a flare, artificial intelligence can reliably predict a new flare within the next 12 months utilizing clinical and objective information at day 0 and week 8.

Secondary endpoints:

* An artificial intelligence model's precision in predicting a new flare within 2 and 3 years
* An artificial intelligence model's precision to rule out patients who will not experience a new flare within 1, 2 and 3 year

Eligibility criteria:
Inclusion criteria:

* Signed informed consent
* Diagnosis of UC for at least 1 year
* Relapse due to UC.
* First endoscopic and histological evaluation of the flare Exclusion criteria
* Well-founded doubt that the flare is due to other than the patients UC

Outcomes:
Flare I
Development and Validation of Artificial Intelligence Models for Non-invasive Glycaemic Event Detection Using ECG in Type 1 Diabetics
Trial ID: NCT05461144
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2022-09-30
Completion date: 2027-05-01
Sponsor: University Hospitals Coventry and Warwickshire NHS Trust
Collaborators: University of Warwick
Conditions: Metabolic Disease
Enrollment: 30
Age range: 18 Years - None
Sex: ALL

Summary:
This observational study aims to recruit up to thirty T1DM patients from a diabetic outpatient clinic at the University Hospital Coventry and Warwickshire for a two-phase study. The first phase involves attending an inpatient protocol for up to thirty-six hours in a calorimetry room at the Human Metabolism Research Unit under controlled conditions, followed by a phase of free-living, for up to three days, in which participants will go about their normal daily activities without restriction. Throughout the study, the participants will wear commercially available wearable sensors to measure and record physiological signals (e.g., electrocardiogram and continuous glucose monitor). Data collected will be used to develop and validate an AI model using state-of-the-art deep-learning methods for the purpose of non-invasive glycaemic event detection.

Eligibility criteria:
Inclusion Criteria:

The study will be open to all individuals living independently, over 18 years without acute illness or ongoing clinical investigation, or volunteers with a stable medical condition may be included. Volunteers with an ongoing medical condition will only be included after detailed consultation with our clinical and dietetics members of the team; however, it is imperative that volunteers are able to provide written informed consent.

Exclusion Criteria:

Whilst the study employs a deliberately open inclusion criterion, the following exclusion measures will be employed:

* Children (under 18 yrs)
* Any adult who lacks decisional capacity
* Claustrophobia, isolophobia, recent abnormal exercise, radiation exposure within the preceding 24 hours of entering the whole-body calorimeter and feeling unwell in any way.
* Needle phobia
* Any medical/endocrine problem that could affect energy expenditure (e.g. thyroid problems, Cushing's syndrome)
* Chronic inflammatory disorders like rheumatoid arthritis, or long term use of steroids or other immunomodulators like cyclosporine, azathioprine.
* Beta blockers
* Currently actively losing weight
* Depression or any psychiatric illness

Outcomes:
Interstitial Glucose
Testing the Validity of an Artificial Intelligence-based Program to Identify Foods and Estimate Food Portion Size Among Adults, a Pilot Study
Trial ID: NCT05343585
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2022-04-27
Completion date: 2022-06-03
Sponsor: Pennington Biomedical Research Center
Collaborators: National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)
Conditions: Nutrition Assessment
Interventions: PortionSize AI
Locations: Pennington Biomedical Research Center
Countries: United States
Enrollment: 24
Age range: 18 Years - 62 Years
Sex: ALL

Summary:
The purpose of this study is to test the accuracy of the Nutrition Artificial Intelligence in the Openfit app during meals in a controlled laboratory setting

Eligibility criteria:
Inclusion Criteria:

* Male or female
* Aged 18-62 years
* Self-reported body mass index (BMI) 18.5-50 kg/m2

Exclusion Criteria:

* Any condition or circumstance that could impede study completion
* Unfamiliar with or not able to use an iPhone

Outcomes:
Identification of Food Plated Using the Openfit Mobile App; Portion Size Estimation (kcal) of Food Plated Using the Openfit Mobile App; User Satisfaction of the Openfit Mobile App for Recording Food Plated; Usability of the Openfit Mobile App for Recording Food Plated
A Comparative Evaluation of the Effectiveness of an Artificial Intelligence-Generated Reformer Pilates Protocol and a Studio-Designed Reformer Pilates Protocol in Individuals With Forward Head Posture
Trial ID: NCT07205835
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-11-01
Completion date: 2025-01-10
Sponsor: Uskudar University
Conditions: Posture; Neck Disorder
Interventions: ChatGPT Exercise Group; Clinical Pilates Group
Locations: Üsküdar Üniversitesi NP Sağlık Yerleşkesi
Countries: Turkey (Türkiye)
Enrollment: 40
Age range: 18 Years - 50 Years
Sex: ALL

Summary:
Posture is the alignment of body segments. While ideal posture places minimal stress on the joints, poor posture increases joint stress. Forward head posture (FHP) is a common postural abnormality characterized by the anterior displacement of the head in the sagittal plane. This condition may lead to muscle imbalances, pain, reduced cervical range of motion (ROM), decreased quality of life, and spinal degeneration.

The most common method for evaluating FHP is the craniovertebral angle (CVA). Causes of FHP include excessive smartphone and computer use, mouth breathing, carrying heavy bags, and rounded shoulders. Cervical stabilization exercises and holistic approaches are recommended for prevention and treatment.

Pilates, developed by Joseph Pilates, especially in its clinical form, has been shown to improve posture, strengthen core muscles, and enhance quality of life. Literature indicates that stretching- and strengthening-based exercise programs targeting FHP positively affect CVA, neck pain, and ROM.

In recent years, artificial intelligence (AI) has gained importance in physiotherapy as a diagnostic and therapeutic support tool. Its potential in generating exercise prescriptions is being explored, though whether AI can replace physiotherapists remains debated.

The aim of this study is to compare the effectiveness of an AI-generated reformer Pilates protocol and a studio-based reformer Pilates protocol in individuals with FHP. Hypotheses:

H0: The studio-based reformer Pilates protocol has no effect on CVA, ROM, pain, muscle endurance, or quality of life in individuals with FHP.

H1: The studio-based reformer Pilates protocol has a positive effect on these parameters.

Eligibility criteria:
Inclusion Criteria:

* Inclusion Criteria:

Volunteers aged between 18-50 years

Craniovertebral angle less than 54°

Ability to participate in exercise sessions twice a week for 6 weeks

Willingness to provide informed consent

Exclusion Criteria:

History of musculoskeletal, neurological, or cardiovascular disorders

Previous surgery affecting posture or spine

Current participation in another structured exercise program

Pregnancy

\-

Outcomes:
Change in Craniovertebral Angle
Smart Steps to Growth: Unleashing AI for Assessing Motor Skills in School Children
Trial ID: NCT07083726
Status: ACTIVE_NOT_RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-08-01
Completion date: 2028-07-31
Sponsor: Chang Gung Memorial Hospital
Conditions: AIMAS; Developmental Coordination Disorder
Locations: Chang Gung Memorial Hospital
Countries: Taiwan
Enrollment: 250
Age range: 6 Years - 12 Years
Sex: ALL

Summary:
The purpose of this study is to develop an AI-based automated motor function assessment system (AIMAS) to improve early identification of developmental coordination disorder (DCD) in school-age children. The main hypothesis for this study is: Integrating AI into motor skill assessments will enhance the reliability, validity, efficiency, and accuracy of evaluating motor performance in children aged 6 to 12.

Eligibility criteria:
Inclusion Criteria:

* Children aged 6 to 12 years.
* For DCD group: formal diagnosis of Developmental Coordination Disorder (DCD).
* For typically developing group: no disabilities or developmental delays.

Exclusion Criteria:

* Acute illnesses (e.g., pneumonia, upper gastrointestinal hemorrhage).
* Significant developmental delays or disabilities.
* Genetic diseases or disorders.
* Neurological disorders or injuries.

Outcomes:
Motor Function Assessment Score; DCD Risk Classification