moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Research of Intelligent Diagnosis and Automatic Lesion Tracking for Precise Treatment of Retinal Diseases Based on Deep-learning
Trial ID: NCT04718532
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2016-01-01
Completion date: 2023-12-31
Sponsor: Second Affiliated Hospital, School of Medicine, Zhejiang University
Conditions: Retina Disorder
Locations: The Second Affiliated Hospital of Zhejiang University
Countries: China
Enrollment: 2000
Age range: None - None
Sex: ALL

Summary:
With the advent of the era of precision medicine, based on FFA image deep learning to identify the area of fundus lesions, registration of fundus images, according to the severity of fundus diseases to design the optimal laser energy and path, the accurate treatment of fundus diseases has urgent clinical needs and very important significance

Eligibility criteria:
Inclusion Criteria:

* patients with retinal diseases

Exclusion Criteria:

* patients with other disease affect retinal exmination

Outcomes:
artificial intelligence
Application of Artificial Intelligence for Precision Medicine in TCM Tongue Diagnosis and Syndrome Differentiation in Myasthenia Gravis Compared to Healthy Controls
Trial ID: NCT06754020
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2023-01-17
Completion date: 2027-12-31
Sponsor: Yi Ting Yeh
Conditions: Myasthenia Gravis
Interventions: Heart Rate Variability,HRV - ANSWatch; Traditional Chinese Medicine Tongue Imaging
Locations: Yi Ting Yeh
Countries: Taiwan
Enrollment: 100
Age range: 20 Years - None
Sex: ALL

Summary:
Myasthenia gravis primarily results from neuromuscular junction dysfunction and damage, leading to chronic muscle weakness. While combined traditional and Western medicine treatments are now common, TCM tongue diagnosis remains a simple and practical clinical method. However, results can vary among different practitioners. Therefore, you are interested in using scientific methods to compare TCM tongue diagnosis images between patients with myasthenia gravis and healthy individuals to aid in diagnosis.

Eligibility criteria:
Inclusion Criteria:

1. Diagnosed with myasthenia gravis by a physician.
2. Able to understand and communicate in Mandarin and Taiwanese.
3. Cognitively alert and without a diagnosis of psychiatric disorders.
4. Willing to undergo tongue imaging and instrument measurements.

Exclusion Criteria:

1. Patients with severe organ diseases.
2. Hearing loss patients who are unable to complete the questionnaire.
3. Patients with aphasia who are unable to complete the questionnaire.
4. Have eaten food or drinks that can easily contaminate tongue coating within 2 hours.

Outcomes:
What are the differences in tongue diagnosis in traditional Chinese medicine between the disease group and the healthy population?
Democratization of Allergy Skin Patch Testing to Increase Efficiency and Scale of the Practice
Trial ID: NCT05339750
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2022-02-15
Completion date: 2022-09-23
Sponsor: Mayo Clinic
Conditions: Allergic Contact Dermatitis
Interventions: AI-based smartphone application; Allergen patch
Locations: Mayo Clinic Florida
Countries: United States
Enrollment: 233
Age range: 18 Years - None
Sex: ALL

Summary:
The purpose of this research is to assess human and artificial intelligence performance in grading contact dermatitis reactions in healthy volunteers.

Eligibility criteria:
Inclusion Criteria

* Adults over the age of 18
* Willing and able to provide informed consent

Exclusion Criteria

* Under 18 years of age
* Has used topical or oral steroids two weeks prior to patch testing
* Currently taking immunosuppression agents or is immunocompromised due to medical condition
* No sunburn or rash at site of testing
* Women who are breastfeeding or pregnant.
* Treatment with ultraviolet (UV) light (including tanning) during the two weeks prior to visit.
* Subjects unable to comply with patch test study requirements including multiple return visits and activity restrictions (e.g., protecting patch test area from excess moisture due to showering)

Outcomes:
AI algorithms predictions of reactions
Study to Explore How Core Temperature Reduction Influence Health
Trial ID: NCT06432491
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-01-01
Completion date: 2026-12-31
Sponsor: Shenzhen Institutes of Advanced Technology ,Chinese Academy of Sciences
Collaborators: Shenzhen University General Hospital; The Hong Kong Polytechnic University
Conditions: Body Temperature Changes; Body Composition; Aging
Interventions: Drinking cold water
Locations: Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences
Countries: China
Enrollment: 36
Age range: 50 Years - 65 Years
Sex: ALL

Summary:
The goal of this clinical trial is to learn if cold water drinking could promote body composition and further extend healthy lifespan in Chinese older adults. The main questions it aims to answer are:

1. Does cold water drinking lower the body fat percentage?
2. Will cold water drinking positively extend lifespan in a long-term Researchers will compare cold water intervention group to a control group (drinking 37℃ water instead) to see if cold water drinking works to promote health and slow down ageing process.

Participants will:

1. Drink 4℃ or 37℃ water 4 times (9a.m., 12p.m., 15p.m., 18p.m.) every day for 6 months.
2. Visit the institute and health checkup department for tests and checkup at baseline, the end of the 3rd month, and the end of 6th month.

Eligibility criteria:
Inclusion Criteria:

* Aged between 50 and 65 years old (including 50 and 65)
* No surgery in the past 6 months
* Healthy (No metabolic diseases, cardiovascular diseases, lung diseases (COPD, asthma, et al.), endocrine diseases and gastrointestinal diseases)
* Based in Shenzhen during the whole study period
* No significant change on body weight in the past 3 months

Exclusion Criteria:

* Those who need to take medication for a long term (including diet pills)
* On a diet
* Disabled
* Females who are trying to get pregnant, pregnant, or breastfeeding
* Presence of a metal implant in the body (e.g. pacemaker)
* Those with claustrophobia
* Those with blood phobia, pathological hypotension or hypertension
* Those who are in other intervention studies

Outcomes:
Value of core body temperature in centigrade assessed by e-Celsius; Value of weight in kilogram assessed by Seca body weight scale; Value of height in meters assessed by Seca 217 stadiometer; Value of body mass index (BMI) calculated by weight and height; Body composition- volume of fat mass in cm^3 assessed by uMR 790; Body composition-weight of fat free mass in gram assessed by Tanita MC-980; Body composition-wegiht of fat mass in gram assessed by Horizon Wi; Waist circumferences in centimeter assessed by TG2000-F; Hip circumferences in centimeter assessed by by TG2000-F; Brown adipose tissue-areas of temperature increase in cm^2 assessed by Fluke RSE600
Personalised Medicine in the Early Identification of Preclinical Cognitive Impairment. Development of a Predictive Risk Model.
Trial ID: NCT06114290
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2023-11-01
Completion date: 2025-07
Sponsor: Instituto de Salud Carlos III
Collaborators: Instituto de Investigación Biomédica de Salamanca; Carlos III Health Institute; Universidad Complutense de Madrid; Biomedical Research Networking Centre on Frailty and Healthy Ageing; Biomedical Research Networking Centre on Mental Health; Institute of Biomedical Research of Lleida; Institute of Health and Biomedical Research of Alicante; Carlos III University of Madrid; University of Vigo; Foundation for Biosanitary Research and Innovation in Primary Health Care; Biomedical Research Networking Centre on Epidemiology and Public Health; Research Network on Chronicity, Primary Care and Health Prevention and Promotion
Conditions: Cognitive Dysfunction
Locations: Sant Vicent I Health Center; Camps Blanc Health Center; Zone 8 Health Center; Gibraleón Health Center; Punta Umbría Health Center; Irala Health Center; Onze de Setembre Health Center; San Andres Health Centre
Countries: Spain; Spain; Spain; Spain; Spain; Spain; Spain; Spain
Enrollment: 1150
Age range: 55 Years - 70 Years
Sex: ALL

Summary:
The goal of this observational study is to use the combined power of the integration of clinical, molecular, proteomic, genomic, care, social, environmental and behavioural data in patients, using advanced artificial intelligence techniques for data processing and analysis, in order to generate predictive models for the preclinical detection of CI in the population aged 55-70 years.

Eligibility criteria:
Inclusion Criteria:

* Non-institutionalised subjects from the study locations.
* Aged between 55 and 70 years, attached to the PC centres of the territories included in the study
* Living history (at least one record in the last 12 months)
* Without an established diagnosis of CI.

Exclusion Criteria:

* Participants with significant difficulties in completing self-reported questionnaires
* Those in whom genetic or biological testing may be affected by an underlying genetic or health condition.
* Underlying genetic or health condition.
* Patients who are hospitalised or institutionalised during follow-up will be excluded.

Outcomes:
Cognitive level
Application of Artificial Intelligence Algorithm Based on CT Imaging for Muscle Parameter Measurement in the Diagnosis of Sarcopenia
Trial ID: NCT06845462
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2023-09-05
Completion date: 2024-12-31
Sponsor: RenJi Hospital
Conditions: Deep Learning; Sarcopenia; Computed Tomography; Body Composition
Locations: Shanghai Jiaotong University School of Medicine, Renji Hospital Ethics Committee
Countries: China
Enrollment: 1080
Age range: 18 Years - 90 Years
Sex: ALL

Summary:
To establish an artificial intelligence model for automated diagnosis of sarcopenia based on CT imaging

Eligibility criteria:
Inclusion criteria：

1. The population undergoing BIA and abdominal CT examinations;
2. Can cooperate to complete human body composition analysis, grip strength measurement, 6m walking time measurement, and questionnaire survey.

Exclusion criteria：

1. Age\<18 years old;
2. Existence of abdominal wall edema;
3. History of spinal surgery or vertebral fractures, or vertebral tumor lesions;
4. History of neuromuscular disorders.

Outcomes:
To automatedly and precisely quantify three-dimensional muscle volume and fat volume.; To establish an artificial intelligence model for diagnosis of sarcopenia.
Scalable, Clinician-Supervised Generative-AI Food-Chaining Assistant for Pediatric ARFID: A Pilot Randomized Controlled Trial
Trial ID: NCT07006961
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-06-15
Completion date: 2025-10-31
Sponsor: Boston Children's Hospital
Conditions: Avoidant Restrictive Food Intake Disorder
Interventions: Generative AI-based food chaining device
Enrollment: 125
Age range: 3 Years - 17 Years
Sex: ALL

Summary:
Children with Avoidant/Restrictive Food Intake Disorder (ARFID) often lack access to specialty dietitians, and scalable nutritional guidance/food chaining tools are currently not available. The investigators will evaluate a web-based, clinician-supervised, generative-AI assistant that produces individualized food-chaining plans.

Develop an AI assistant that generates ≥15 allergy-safe, evidence-based chaining steps per participant and meets ≥90 % expert agreement for safety/appropriateness.

Validate the assistant against gold-standard clinician recommendations (Cohen's κ ≥ 0.80).

Test clinical impact in a three-month pilot RCT (n = 96) by comparing change in Nine-Item ARFID Screen (NIAS) scores between intervention and usual-care groups.

Hypothesis: AI-generated plans will reduce NIAS scores by ≥3 points relative to controls.

Eligibility criteria:
Inclusion Criteria:

* Children must be aged 3-17 years
* Children must have caregiver- or participant-reported DSM-5 ARFID diagnosis and/or EDYQ-screen-positive Avoidant Restrictive Food Intake Disorder
* English proficiency.

Exclusion Criteria:

* Lack of English proficiency \[As there is no validated non-English version of the NIAS, we must exclude caregivers who do not have English proficiency\]
* Participants must not have been previously treated at Boston Children's for ARFID

Outcomes:
Nine Item ARFID Screen Score; Interventions Attempted
Integrating Electrocardiogram and Focused Cardiac Ultrasound for Enhanced Cardiac Disease Screening; Structural Cardiac Assessment With Networked AI-ECG & FOCUS (SCAN) Study
Trial ID: NCT06891222
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-04-14
Completion date: 2026-04
Sponsor: Mayo Clinic
Conditions: Cardiac Disease
Interventions: AI-ECG (artificial intelligence-enabled electrocardiogram); Focus Screening (focused cardiac ultrasonography)
Locations: Mayo Clinic
Countries: United States
Enrollment: 10500
Age range: 18 Years - None
Sex: ALL

Summary:
The purpose of this study is to evaluate and compare the effectiveness of active screening for SHD in asymptomatic outpatients referred for an ECG, using a combination of AI-ECG and FOCUS. Invites will be sent and participants enrolled electronically.

Eligibility criteria:
Inclusion Criteria:

* Adult patients (\>18 years of age)
* Echocardiogram is not clinically indicated

Exclusion Criteria:

* Prior history of congenital or acquired SHD, as defined by the following

  1. Aortic Stenosis:
  2. Heart Failure
  3. Left Ventricular Dysfunction
  4. Cardiac Amyloidosis
  5. Hypertrophic Cardiomyopathy (HCM)
  6. Rheumatic Heart Disease
  7. Congenital Heart Disease
* Recent (within last 12 months) echocardiogram
* Scheduled clinically indicated future echocardiogram
* Inability to provide informed consent to participate in the study

Outcomes:
Percentage of subjects to be diagnosed with SHD (structural heart disease); Positive predictive value (PPV)
Evaluation of the Impact of the MRI Restraint System on the Quality of the Synthetic Scan for Intracranial Radiotherapy Treatment: Feasibility Study
Trial ID: NCT06994702
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-05-19
Completion date: 2025-08-26
Sponsor: Institut Cancerologie de l'Ouest
Conditions: Device Ineffective
Interventions: MRI without mask; MRI with mask 1; MRI with mask 2
Locations: Institut de Cancerologie de L'Ouest
Countries: France
Enrollment: 3
Age range: 18 Years - None
Sex: MALE

Summary:
In radiotherapy, CT scans are the reference imaging tool for treatment planning, as they provide the electron density of the tissues required to calculate the dose. Magnetic Resonance Imaging (MRI) is used as a complement to CT to contour target volumes and organs at risk. While the properties of CT allow us to determine the electron density of tissues, the properties of MRI allow us to determine the proton density of tissues. MRI therefore provides better tissue contrast, but cannot be used directly to calculate the dose. In recent years, MRI-only protocols have been introduced to remove the need for a CT examination and avoid possible MRI-CT registration errors. Dose calculations are then performed on MRI images converted into synthetic CT images (sCT) using software based on artificial intelligence (for the most recent). Various commercial solutions are available for generating these sCTs, and the ICO already has experience of evaluating them.

While MRI scans for diagnostic purposes are performed in the free position, the generation of an sCT for treatment preparation requires the MRI scan to be performed in the radiotherapy position. For cranial lesions, a thermoformed mask fixed to the table is made for each patient. Depending on the type of treatment and its associated degree of precision, different types of mask exist at the ICO: '3-point' for normo-fractionated treatments and 'stereo' for treatments of 1 to 3 fractions. The use of these masks during the MRI examination can interfere with the correct positioning of the antennae and thus lead to a deterioration in the quality of the images obtained (MRI and, by repercussion, sCT).

The aim of this study was to evaluate the impact of the 2 thermoformed masks used at the ICO on the quality of a synthetic scan for 3 healthy volunteers.

Eligibility criteria:
Inclusion Criteria:

1. Male \> 18 years
2. Health professional practising at the ICO
3. Volunteer who has signed the consent form
4. Volunteer available to carry out the 3 MRI scans

Exclusion Criteria:

1. Claustrophobia
2. Foreign metal objects (metal splinters, filings, shotgun pellets, etc.)
3. Woman

Outcomes:
To evaluate quality of a synthetic scanner generated from an MRI performed in the position of cerebral radiotherapy treatment with 2 thermoformed masks
Neurodegenerative Diseases Progression Markers (MARKERS-NDD): a Real-world Data Longitudinal Prospective Study
Trial ID: NCT06596746
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2024-09-09
Completion date: 2034-09-09
Sponsor: Casa di Cura San Raffaele Cassino
Collaborators: IRCCS San Raffaele Roma; San Raffaele Telematic University; University of Urbino "Carlo Bo"; University of Cassino and Southern Lazio; University of Roma La Sapienza; Politecnico di Milano
Conditions: Neurodegenerative Diseases; Parkinson Disease; Synucleinopathies; Lewy Body Disease; Multiple System Atrophy; Tauopathies; Alzheimer Disease; Corticobasal Degeneration; Supranuclear Palsy, Progressive; Atypical Parkinsonism; TDP-43 Proteinopathies; Amyotrophic Lateral Sclerosis; Frontotemporal Lobar Degeneration; Movement Disorders; Cognitive Impairment; Dementia; Iron Overload; Neurodegeneration With Brain Iron Accumulation (NBIA)
Interventions: Observation
Locations: San Raffaele Cassino
Countries: Italy
Enrollment: 600
Age range: 10 Years - None
Sex: ALL

Summary:
MARKERS-NDD is a prospective, observational, longitudinal study, which aims to collect data from patients affected by neurodegenerative diseases (NDD) followed longitudinally for routine examinations performed as part of normal clinical practice. Data collected from clinical evaluations, movement analysis, brain imaging, neuropsychological and electroencephalographic assessments, blood chemistry tests will be analysed to carry out statistical investigations and predictive analyses, also using artificial intelligence systems, which allow the identification of new early markers of diagnosis and prognosis of neurodegenerative diseases.

Eligibility criteria:
Inclusion Criteria:

* Patients with diagnosis of Parkinson's Disease, Parkinsonism and Movement Disorders

  * Patients with diagnosis of Parkinson's Disease

    * Diagnosis of Parkinson's Disease according to the United Kingdom (UK) Parkinson's Disease Society Brain Bank
  * Diagnosis of Movement Disorder not related to Parkinson's Disease

    * Diagnosis of Multiple System Atrophy (MSA) in accordance with Second Consensus Statement on Diagnosis of Multiple System Atrophy;
    * Diagnosis of Progressive Supranuclear Palsy according to Movement Disorder Society for Diagnosis of Progressive Supranuclear Palsy;
    * Diagnosis of Essential Tremor
    * Willing to participate in the study, understand the procedures and sign the informed consent.
* Patients affected by cognitive impairment (CI) and dementia

  * Diagnosis of probable:

    * Lewy Body Dementia
    * Alzheimer's Disease
    * Mild Cognitive Decline
    * Subjective memory complaints
    * Willing to participate in the study, understand the procedures and sign the informed consent.

Exclusion Criteria:

* There are no restrictions for participation in the study based on age, severity of illness or presence of cognitive impairment, as long as the person is able to complete the research assessments.

Outcomes:
To monitor and predict the course of neurodegenerative diseases through the study of clinical markers and the creation of AI-models
A Proof-of-technology, Pilot, Prospective Clinical Study to Investigate the Feasibility and Performance of PROVIZ-a Radiomics-based Machine Learning Software, to Support Targeting of Prostate Biopsies on MRI Images in Biopsy-naive Patients
Trial ID: NCT06000046
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2023-12-20
Completion date: 2024-10-13
Sponsor: Norwegian University of Science and Technology
Collaborators: St. Olavs Hospital
Conditions: Prostate Cancer Diagnosis
Interventions: Automatic image analysis
Locations: St. Olav's University Hospital
Countries: Norway
Enrollment: 82
Age range: None - None
Sex: MALE

Summary:
To perform a traditional feasibility clinical investigation, as defined in ISO 14155:2020, to investigate preliminary feasibility, safety, and clinical performance information of a near-final design of the investigational software. This will be performed through a prospective clinical study on biopsy naïve men with suspected prostate cancer examined with MRI at St. Olavs Hospital, Trondheim, Norway, in order to adequately plan an appropriate pivotal clinical investigation.

Eligibility criteria:
Inclusion Criteria:

* Biopsy-naive men undergoing MRI examination for suspected prostate cancer via the Norwegian standardized care pathway.
* Patients who give consent to participate during the enrollment period.

Exclusion Criteria:

* Patients who have undergone a biopsy for prostate cancer in the past 3 years.
* Patients who currently enrolled in an active surveillance program for prostate cancer.
* Patients who have had hip replacements that may affect the quality of the image.
* Patients with claustrophobia.
* Patients who intolerance to glucagon or buscopan.
* Patients who unable to sign the informed consent themselves.

Outcomes:
Feasibility of the software in prospective study in a relevant clinical environment.; Safety of the software in prospective study in a relevant clinical environment.
Role of Artificial Intelligence in the Prediction of AKI in Critically Ill Patients
Trial ID: NCT06857188
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-05-14
Completion date: 2026-03-01
Sponsor: Assiut University
Conditions: Acute Kidney Failure; Artificial Intelligence (AI)
Enrollment: 1000
Age range: 18 Years - None
Sex: ALL

Summary:
The assessment of AI -based prediction models in detecting AKI early in critically ill patients. Specifically, the aim is to evaluate the model's ability to predict the onset of AKI before it clinically manifests allowing for early interventions

Eligibility criteria:
Inclusion Criteria:

* All adult (aged 18 years old and older) patients who were admitted to the ICU were included in this study.

Exclusion Criteria:

* • patients under 18 years old

  * End-stage renal disease
  * Acute Kidney Injury at ICU admission
  * Inability to obtain sufficient clinical data

Outcomes:
The assessment of AI -based prediction models in detecting AKI early in critically ill patients.