moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
EKO SENSORA: Detecting Clinically Significant Murmurs
Trial ID: NCT06155643
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2023-10-26
Completion date: 2026-07-26
Sponsor: Corewell Health South
Conditions: Heart Murmurs
Interventions: Eko Sensora
Locations: Corewell Health Lakeland
Countries: United States
Enrollment: 300
Age range: 18 Years - None
Sex: ALL

Summary:
The Eko Artificial Intelligence (AI) has primarily been evaluated in the primary care setting. The digital stethoscope records a phonocardiogram of heart sounds of the patient and uses machine learning artificial intelligence to identify if there are abnormalities present (Eko Health, 2023).

The Eko SENSORA will be tested in the emergency department. Chest pain, fatigue, shortness of breath and syncope are all symptoms that could indicate a cardiac dysfunction.

The hypothesis is that this device will allow us increased ability to detect valvular heart disease that is clinically significant.

Eligibility criteria:
Inclusion Criteria:

* Chief complaint of chest pain, shortness of breath, difficulty breathing, or syncope/near-syncope.
* Patients with chief complaint of Weakness would be eligible if over the age of 50 and have a history of at least two of hypertension, BMI\>/= 30, diabetes, hyperlipidemia, atrial fibrillation, heart attack, stroke/Transient ischemic attack, prior cardiac surgery or angiography

Exclusion Criteria:

* Contact isolation patients
* Pediatric patients under the age of 18

Outcomes:
Number of new clinically significant heart murmurs detected
AI4HOPE - Artificial Intelligence Based Health, Optimism, Purpose, and Endurance in Palliative Care Ofr Dementia: Pilot Study 2 Digital Dementia Care Planning
Trial ID: NCT06292143
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-02
Completion date: 2027-12
Sponsor: University of Bonn
Collaborators: University College Cork; University of Leeds; Fundacion Intras; Universidade do Porto; University Medical Centre Maribor
Conditions: Dementia
Interventions: Advance Care Planning Toolkit
Enrollment: 150
Age range: None - None
Sex: ALL

Summary:
This pilot study will test a digital care planning decision support system for patients with dementia.

Eligibility criteria:
Inclusion Criteria:

* Mild or moderate dementia of any type
* Montreal Cognitive Assessment (MoCA) score of 16-25
* Living at home, or in residential or nursing home care
* Receiving adequate social support

Exclusion Criteria:

* Moderate/severe cognitive impairment (MoCA \< 16)
* No cognitive impairment (MoCA \>25)
* Inpatient or acute hospital treatment
* Infectious diseases or lokal skin conditions preventing the use of wearable body sensors

Outcomes:
Feasibility and acceptability of digital ACP-platform, the Dementia Journey Companion (DJC)
A Prospective Validation Study of Radiomics in the Differential Diagnosis of Uterine Leiomyoma and Uterine Sarcoma
Trial ID: NCT07269535
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-11-30
Completion date: 2050-01-01
Sponsor: Tongji Hospital
Conditions: Uterine Fibroid; Uterine Sarcoma; AI (Artificial Intelligence); Radiomic; Prospective Observational Study; MRI
Interventions: No intervention (observational study)
Enrollment: 500
Age range: None - None
Sex: FEMALE

Summary:
In our previous study, based on the multi-center clinical big data collected from January 2012 to January 2025, we have completed the construction of a multimodal early warning model for the malignant transformation of uterine fibroids. The model was mainly based on T2WI and DWI sequences, and was trained and optimized by support vector machine (SVM) algorithm. In the retrospective study and internal validation, the model shows high sensitivity and specificity, which preliminarily proves that it has good application potential in identifying high-risk groups and predicting the risk of malignant transformation of uterine fibroids.

However, there are still some limitations in retrospective studies and internal validation results, and its application value, universality and stability in real clinical environment have not been fully verified. Therefore, we plan to conduct a prospective validation study in consecutive patients enrolled after January 2025 to evaluate the clinical performance and generalization of the model in predicting the malignant tendency or risk of malignant transformation of uterine fibroids through practical application in the real population, and further analyze the operability in the actual diagnosis and treatment process and the potential value for patient management. This study will provide reliable evidence for early screening, follow-up management and individualized treatment of high-risk population, and has important clinical and public health significance for improving the early diagnosis rate, reducing the risk of malignant transformation and improving the prognosis of patients with uterine fibroids.

Eligibility criteria:
1. Inclusion Criteria:

   1.1 Patients clinically evaluated and radiologically examined (including MRI, particularly T2WI and DWI sequences) who are diagnosed with uterine leiomyoma or considered highly suspected of uterine sarcoma, in combination with preliminary pathological findings.

   1.2 Patients scheduled for surgical treatment or those eligible for long-term standardized follow-up.

   1.3 Patients who are able to understand the study procedures and voluntarily sign the written informed consent form.
2. Exclusion Criteria:

2.1 Patients with severe organic diseases or a previous confirmed diagnosis of other malignant uterine tumors.

2.2 Patients unable to complete baseline examinations, unable to comply with long-term follow-up, or unwilling to provide written informed consent.

Outcomes:
AUC; Sensitivity; Specificity
Detection of Proximal Caries in Bitewing Radiography Using Artificial Intelligence - A Diagnostic Clinical Study
Trial ID: NCT07404007
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2023-01-15
Completion date: 2025-12-01
Sponsor: Cairo University
Collaborators: Ain Shams University
Conditions: Proximal Caries; Tooth Caries
Interventions: Artificial Intelligence (AI): Deep learning that is applied in Diagnosis of the proximal Caries; Manual annotation of Digital Bitewing Radiograph by human experts
Locations: Ain Shams University
Countries: Egypt
Enrollment: 2000
Age range: 18 Years - 70 Years
Sex: ALL

Summary:
Using a sequence of bitewing radiographs, Artificial intelligence assists in identifying interproximal caries. For the identification of dental caries in bitewing, periapical, and panoramic radiographs, a trained deep learning network will be created This study aimed to investigate the reliability of a novel Artificial Intelligence model based on deep learning in the detection of Proximal Caries using Digital Bitewing Radiographs. (BW).

Eligibility criteria:
Inclusion Criteria:

* Patients having all Permanent premolars and molars (maximum one tooth missing on each side)

Exclusion Criteria:

* 1-Dental Anomalies →Amelogenesis Imperfecta, Dentinogenesis Imperfecta, taurodontism 2- Severe crowding which prevent visualization of teeth Contacts 3-Orthodontic wires bonded to Enamel of the tooth

Outcomes:
Reliability of the artificial intelligence model in detecting proximal caries on digital bitewing radiographs
Evaluation of a Training Recommendation Strategy for Trail Runners.
Trial ID: NCT07374016
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-03
Completion date: 2027-12
Sponsor: Centre Hospitalier Universitaire de Saint Etienne
Conditions: Endurance Training
Interventions: Training recommendations GEN; Training recommendations PERSO; Training recommendations PERSO-ADAPT
Locations: Unités de Myologie et de Médecine du Sport
Countries: France
Enrollment: 1209
Age range: 18 Years - 65 Years
Sex: ALL

Summary:
Finding the optimal training, i.e. the training that enables the athlete to achieve the best performance while preserving their physical and psychological integrity, is a multi-factorial issue.

Each individual's background and training capacity are very different, hence the need for personalised training. The availability of data combined with advances in Artificial Intelligence (data modelling and analysis sciences, big data, machine learning, deep learning, etc.) offer the opportunity to refine our understanding of training and adapt recommendations according to the runner's profile for a given objective (achieving a time over a distance, completing an event, etc.). The strategy that the investigators wish to evaluate as part of this trial could make it possible to recommend an optimal training load, as well as the distribution of intensities (as a percentage of the aerobic threshold, anaerobic threshold, VVO2max, etc). The two features are (i) the personalisation will be based on training history, the level of the runner, age and gender, and the runner can focus on training, and (ii) the strategy will be adaptive, i.e. the algorithm will update the training load in real time based on contextual data about the athlete (level of fatigue via heart rate variability, feedback on recent training sessions, feelings of stress).

Eligibility criteria:
Inclusion Criteria:

* Have no contraindications to trail running (Fédération Française d'Athlétisme test procedures)
* Have been trail running competitively for at least 1 year
* In possession of an ITRA performance index
* Intends to run at least one ITRA-indexed race in the second half of the follow-up period
* Own a training watch, and intend to record all training sessions of more than 10 minutes in their shared database
* Have freely given their written consent.

Exclusion Criteria:

* Any person with chronic joint pathologies (e.g. recurrent sprains, patellar or ligament problems) or cardiac pathologies.
* Any person with chronic or central neurological pathologies.
* Any person deprived of their liberty or under legal protection (guardianship, curatorship, safeguard of justice, family habilitation).

Outcomes:
Comparison of the ITRA performance index
Accuracy of Dental Plaque Detection From Intraoral Images Using Different Artificial Intelligence Models Versus Clinical Assessment Among a Group of Children: A Diagnostic Accuracy Study.
Trial ID: NCT06760104
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-01-01
Completion date: 2025-12-30
Sponsor: Naema Ahmed
Conditions: Dental Plaque
Interventions: Dental Plaque Detection Using AI Models
Locations: Cairo University
Countries: Egypt
Enrollment: 323
Age range: 7 Years - 12 Years
Sex: ALL

Summary:
This diagnostic accuracy study aims to evaluate the effectiveness of various artificial intelligence models in detecting dental plaque from intraoral images compared to clinical assessments performed by dentists among children. The study seeks to determine the accuracy, sensitivity, specificity, and overall performance of AI technologies in identifying dental plaque. study study Design: Observational study

Eligibility criteria:
Inclusion Criteria:

.Study participants: Children within age range (7-12) years old. .Teeth without metal crowns or amalgam restoration.

Exclusion Criteria:

* Children with developmental enamel defects
* Children who are unwilling to cooperate or who has mental retardation and are prohibited from having their images taken. .Children who's their legal guardians will not approve to participate in the study.

Outcomes:
accuracy of dental plaque detection
Menu to Meal (M2M): Evaluating the Effectiveness of a Digital Nutritional Labeling Intervention in Hispanic Restaurant Settings
Trial ID: NCT07329452
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-11-17
Completion date: 2026-12
Sponsor: Texas A&M University
Conditions: Behavior, Eating
Interventions: Paper menu; Digital menu condition
Locations: Local Restaurant Site
Countries: United States
Enrollment: 240
Age range: 18 Years - None
Sex: ALL

Summary:
This study evaluates the impact of culturally tailored menu labeling interventions on psychosocial factors and ordering behavior among customers in two independently owned Hispanic restaurants in East-Central Texas. Participants will be exposed to one of three sequential menu conditions at each restaurant: (1) a standard menu without nutrition information, (2) a paper menu with bilingual nutrition labels, or (3) a digital menu app enhanced with Augmented Reality (AR) and Artificial Intelligence (AI) providing interactive nutrition guidance. The study will assess whether menu formats influence nutrition literacy, decision-making confidence, self-efficacy, attitudes, behavioral intentions, and actual ordering behavior. Data will be collected through surveys, purchase receipts, and app interaction logs.

Eligibility criteria:
Inclusion Criteria:

* Adults aged 18 years or older
* Patrons dining at participating Hispanic restaurants during designated study hours
* Able to read and speak English or Spanish
* Willing and able to provide informed consent
* Pregnant women are eligible to participate, as the study procedures pose minimal risk

Exclusion Criteria:

* Minors under 18 years of age
* Individuals with visual or cognitive impairments that prevent interaction with the menu formats or AR/AI technology
* Individuals who have participated in a prior phase of the study
* Restaurant staff involved in study implementation

Outcomes:
Attitudes Toward Healthy Menu Choices; Intention to Make Healthier Food Choices; Perceived Behavioral Control Over Healthy Menu Choices; Self-Efficacy for Using Nutrition Information When Ordering; Awareness of Nutrition Information on the Menu; Use of Nutrition Information to Inform Menu Selection
Detection of Valvular Heart Disease Using Artificial Intelligence-based Stethoscope
Trial ID: NCT06386016
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2024-05-01
Completion date: 2024-09-01
Sponsor: Xiao-dong Zhuang
Conditions: Valvular Heart Disease
Interventions: artificial intelligence-based stethoscope
Locations: First Affiliated Hospital, Sun Yat-Sen University
Countries: China
Enrollment: 2000
Age range: 18 Years - 100 Years
Sex: ALL

Summary:
The aim of this study is to develop a deep learning-based application of heart sounds in the diagnosis of valvular heart disease, which can be used to screen patients with valvular heart disease and promote earlier clinical monitoring and intervention.

Eligibility criteria:
Inclusion Criteria:

1. age\>18 years
2. In the past 1 month, echocardiography was performed to diagnose valvular heart disease (positive group) and echocardiography to exclude valvular heart disease (negative group).
3. Consent to study and be able to sign informed consent

Exclusion Criteria:

1. After prosthetic valve replacement
2. Congenital heart disease (except bilobal aortic valve)
3. Hypertrophic Cardiomyopathy（HCM）
4. Pregnant, lactating women

Outcomes:
valvular heart disease
A Study to Collect Data to Build Artificial INtelligence Derived Algorithms For Estimating Height and Weight in childRen (INFER)
Trial ID: NCT06578338
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2024-09-09
Completion date: 2025-03-04
Sponsor: Danone Asia Pacific Holdings Pte, Ltd.
Collaborators: KK Women's and Children's Hospital
Conditions: Growth; Stunting, Nutritional
Interventions: Physical height measurement
Locations: KK Women's and Children's Hospital
Countries: Singapore
Enrollment: 250
Age range: 24 Months - 6 Years
Sex: ALL

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

Eligibility criteria:
Inclusion Criteria:

* Children aged above 24 months old and below 6 years old.
* Parent(s) should have access to the internet and a smartphone or table to complete study questionnaires, take images and upload images.
* Parent(s) should be able to comprehend the content of the study and complete the study questionnaires in English.
* Written consent from parents and/or legally acceptable representative

Exclusion Criteria:

* Children who are unable to stand upright against a wall
* Children who are unable to cooperate with standing height measurement

Outcomes:
Accuracy of the height AI (cm)
An Artificial Intelligence-Assisted Magnetic Resonance Imaging Diagnostic Strategy in a Tertiary Stroke Center-a Diagnostic Accuracy Study
Trial ID: NCT07390708
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2026-02-16
Completion date: 2027-07-01
Sponsor: Aarhus University Hospital
Conditions: Stroke; Stroke Assessment
Interventions: Index test (AI-ResRad); Reference test (SpecNeuroRad); Comparative Index tests: ResRad and AI-SpecNeuroRad; Neurologist Diagnostic Accuracy (Sub-study outcome)
Locations: Aarhus University Hospital; Gødstrup Regional Hospital
Countries: Denmark; Denmark
Enrollment: 500
Age range: 18 Years - None
Sex: ALL

Summary:
Quality improvement study with prospective observational design. The study monitors the diagnostic accuracy of an AI-assisted resident radiologist-termed the AI-ResRad diagnostic strategy-compared to an on-call specialist neuroradiologist-termed the SpecNeuroRad strategy-in interpreting stroke MRIs in patients with known onset.

The study includes a pre-planned sub-study evaluating the diagnostic accuracy of neurologists and AI-assisted neurologists.

Eligibility criteria:
Inclusion Criteria:

* Age ≥ 18 years
* Stroke team activation
* Thrombolysis candidate
* Known symptom onset
* MRI candidate

Exclusion Criteria:

* Prior inclusion
* Previous MRI in the same course of hospitalization
* No resident radiologist on call

Participation in the neurologists' sub-study is limited to participants enrolled at one of the two participating sites at Gødstrup Regional Hospital.

Outcomes:
Composite agreement
Continuous EKG Monitoring Using S-Patch Ex : Prospective Observational Study (S-patch Registry)
Trial ID: NCT05119725
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2021-03-15
Completion date: 2023-02
Sponsor: Yonsei University
Conditions: Patient With Atrial Fibrillation or High Stroke Risk
Interventions: continous 3 day EKG monitoring with S-Patch Cardio
Locations: Severance Hospital, Yonsei University Health System
Countries: South Korea
Enrollment: 2450
Age range: 19 Years - None
Sex: ALL

Summary:
Atrial fibrillation (AF) is associated with increased mortality and morbidity, and is a dominant, yet preventable, cause of cardioembolic stroke, which has more severe outcomes than other ischaemic stroke causes if left untreated. Approximately 10% of ischemic strokes are associated with AF (AF) first diagnosed at the time of stroke. Detecting asymptomatic AF would provide an opportunity to prevent these strokes by instituting appropriate anticoagulation.

Early diagnosis of AF might enable oral anticoagulant therapy and prevent unwanted consequences of undetected disease, leading to the suggestion that screening for AF might be beneficial in populations at risk. However, there is still debate about whether screen-detected AF bears a similar stroke and mortality risk profile to clinically detected AF, particularly when AF screening is done at a higher intensity than single-time point.

The absence of studies reporting on hard clinical endpoints in AF screening has led to differences in recommendations globally. Most notably, systematic screening for AF is to be considered according to 2020 European guidelines, whereas the US Preventive Services Task Force concluded that current evidence is insufficient to assess the balance of benefits and harms of screening for AF.

The incidence of screen-detected AF strongly depends on the population screened and duration/intensity of screening. Single-time point screening of a general population ≥65 years of age detects undiagnosed AF in 1.4%, and the AF detected is largely persistent. In a large population-based study of individuals 75 to 76 years of age, a more intense 2-week screening program using twice-daily intermittent handheld ECG recordings identified AF in 3.0% (0.5% on the initial ECG4). The identical protocol restricted to those with ≥1 additional stroke risk factor identified 7.4% with AF.

This study was designed in to two arms. The purpose of Arm 1 is to upgrade the artificial intelligence by collecting the continued ECG monitoring data in patients with previous diagnosed AF. The purpose of Arm 2 is to investigate the detection rate of AF using systematic, intensive AF screening with continuous ECG monitoring and the rate of clinical outcome in individuals at high risk during one year follow-up.

Eligibility criteria:
Inclusion Criteria:

1. Patients who are over the age of 19
2. Those with previous diagnosis of atrial fibrillation
3. Those without atrial fibrillation, but high stroke risk

Exclusion Criteria:

1. Those who refuse to participate in the trial.
2. Those without internet connection

Outcomes:
Number of partipants with atrial fibrillation
The Efficacy and Safety of Auricular Acupressure for Reducing the Rebound Phenomenon in Myopic Children After Discontinuing 0.01% Atropine Eye Drops: A Three-Arm, Blinded Randomized Controlled Trial Study Protocol
Trial ID: NCT07129889
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-12-02
Completion date: 2027-06-30
Sponsor: Ningbo Eye Hospital
Conditions: Myopia
Interventions: gradual withdrawal of medication; Auricular acupressure; Sham auricular acupressure
Locations: Ningbo Eye Hospital
Countries: China
Enrollment: 180
Age range: 6 Years - 12 Years
Sex: ALL

Summary:
0.01% atropine is an effective measure for controlling myopia in children and is widely used in Asia for this purpose. However, there is a phenomenon of rebound and worsening of myopia after discontinuation of the medication. Auricular acupressure (AA) is gaining attention as a complementary therapy for myopia control. However, there is a lack of rigorous studies evaluating the effectiveness of AA in reducing rebound after discontinuing atropine eye drops in myopic children. Our study aims to assess the efficacy and safety of AA in reducing rebound after discontinuing atropine in myopic children.

Eligibility criteria:
Inclusion Criteria:

1. Meets the diagnostic criteria for myopia;
2. Age 6-12 years, no gender restrictions;
3. Single or bilateral spherical refractive error between -1.00 and -4.00 D (astigmatism ≤ 1.50 D, anisometropia ≤ 1.50 D);
4. Best-corrected visual acuity in both eyes of 0.20 logMAR or higher;
5. Intraocular pressure (IOP) less than 21 mmHg;
6. Informed consent form signed by a guardian, voluntarily participating.

Exclusion Criteria:

Patients who meet any of the following criteria will not be eligible to participate in the study:

1. Eye conditions other than refractive errors (e.g., strabismus, amblyopia, keratitis, glaucoma, cataracts, retinal detachment, etc.);
2. Patients with systemic conditions that may affect refractive development (e.g., Down syndrome, Marfan syndrome);
3. Patients with uncontrolled systemic diseases or debilitating conditions, immune deficiencies, or severe primary diseases of the cardiovascular, hepatic, renal, or hematopoietic systems, immune system disorders, or psychiatric conditions;
4. Patients with a history of allergies or hypersensitivity to multiple medications;
5. Patients who have undergone ocular surgery within the past 4 weeks prior to screening;
6. Patients planning to undergo ocular surgery within one year of enrollment;
7. Patients who have participated in other drug clinical trials within the past 3 months prior to screening; Patients who are unable to cooperate with treatment, observation, and assessment.

Outcomes:
Myopia Rebound Rate