moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
A Study to Collect Data to Build Artificial Intelligence Derived Algorithms for Estimating Risk of Iron Deficiency and Anaemia in African Children
Trial ID: NCT07186205
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2025-10-13
Completion date: 2026-01-09
Sponsor: Danone Asia Pacific Holdings Pte, Ltd.
Collaborators: Institut National de Sante Publique; Hospital Center Régional D'abobo Félix Houphouët Boigny; Hôpital Mère Enfant Bingerville
Conditions: Iron Deficiencies
Interventions: Standard blood test; Imaging and videography of anatomical sites
Locations: Hospital Center Régional D'abobo Félix Houphouët Boigny; Institut National de Sante Publique; Hôpital Mère Enfant Bingerville
Countries: Côte d’Ivoire; Côte d’Ivoire; Côte d’Ivoire
Enrollment: 126
Age range: 6 Months - 6 Years
Sex: ALL

Summary:
An exploratory study to collect data to build artificial intelligence derived algorithms for estimating iron status in African children

Eligibility criteria:
Inclusion Criteria:

* Children over 6 months of age and under 6 years of age.
* Written consent from the legally acceptable parent(s) or representative(s), who are themselves ≥ 18 years of age, to consent to and comply with the terms and conditions of the study.
* The parent(s) must be able to understand the content of the study and complete the study questionnaires.
* Parents should be comfortable with their child having blood drawn.
* The participant must have a haemoglobin level corresponding to a haemoglobin group with open slots for participation.

Exclusion Criteria:

* Children with a medical condition that, in the opinion of the investigator, would render them unsuitable for enrolment
* Children with nail bed obstructions, nail polish, nail discoloration (e.g., leukonychia, melanonychia, bruising, etc.), or scarring of the lips, or lip pigmentation due to thyroid dysfunction (e.g., Peutz-Jeghers syndrome)
* Children who are unable to cooperate with study procedures
* Employees and/or children/family members or parents of employees of the Danone Global Research \& Innovation Center or Danone affiliates participating sites
* Children whose health condition requires emergency care

Outcomes:
Accuracy of the Iron digital tool in estimating hemoglobin level as compared to standard blood test.
The Impact of Artificial Intelligence-Supported Preoperative Education on Fear and Anxiety Levels in Total Mastectomy Patients: A Randomized Controlled Trial
Trial ID: NCT06796166
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-08-01
Completion date: 2025-08-01
Sponsor: Bezmialem Vakif University
Collaborators: Acibadem University
Conditions: Breast Cancer; Anxiety; Mastectomy; Patient Education as Topic; Artificial Intelligence (AI)
Interventions: AI-Supported Preoperative Education; Standard Preoperative Education
Locations: Acibadem International Hospital
Countries: Turkey (Türkiye)
Enrollment: 40
Age range: 18 Years - None
Sex: FEMALE

Summary:
This study aims to evaluate the impact of artificial intelligence (AI)-supported preoperative education on fear and anxiety levels in patients undergoing total mastectomy. It is a randomized controlled trial involving two groups: one receiving AI-supported education and the other receiving standard preoperative education. The primary outcomes are to assess changes in surgical fear and anxiety levels. The study targets adult female patients diagnosed with breast cancer who are scheduled for total mastectomy.

Eligibility criteria:
Inclusion criteria:

* Literate female patients
* Patients who will undergo total mastectomy surgery
* Patients diagnosed with breast cancer
* Patients who are over 18 years of age
* Patients who agree to participate in the study and from whom written consent has been obtained will be included in the study.

Exclusion criteria:

* Patients who cannot understand or communicate in Turkish
* Patients with active psychiatric disorders (e.g., major depression, bipolar disorder, schizophrenia)
* Patients with advanced hearing or vision problems
* Patients with mental disabilities
* Patients who have previously participated in a similar training program will be excluded from the study.

Outcomes:
Surgical Fear Questionnaire / SFQ; State-Trait Anxiety Inventory /STAI
Rrol of Deep Learning Algorithm in Assessment and Management of Scoliosis
Trial ID: NCT06589583
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2024-07-01
Completion date: 2024-09-01
Sponsor: Delta University for Science and Technology
Conditions: Scoliosis Patients
Locations: Delta university
Countries: Egypt
Enrollment: 5000
Age range: 15 Years - 37 Years
Sex: ALL

Summary:
The aim of the study to use artificial intelligence technology in assessment of scoliosis degree of severity and to personalize for treatment plan for each pa tient .

Eligibility criteria:
Inclusion Criteria:

* ). Patient with scoliosis were

  * diagnosed with scoliosis for different etiology.
  * Clear X- Ray for spine for all spinal curvatures including; cervical, thoracic and lumber(PA)view.
  * C shaped and double C shape scoliosis such as (Thoracolumbar-cervicothoracic scoliosis).
  * Mild, moderate and severe scoliotic degrees.
  * Adolescents with mean age 17 years old
  * Male and female gender

Exclusion Criteria:

* children with scoliosis
* age less than 12 years old .
* lateral view spine x- ray.

Outcomes:
test the validity and reliability of software; determine degree of curve severity via measurement cobb's angle by degree; suggested treatment program according to each patient need .in form points
Electrocardiographic Signal Assessment for the Development of the Electrocardiogram Automatic Analysis in Children Suffering From Heart Rhythm Disturbances
Trial ID: NCT05272722
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2021-04-01
Completion date: 2023-04-01
Sponsor: Medical University of Warsaw
Collaborators: National Center for Research and Development, Poland; Cardiomatics
Conditions: Arrhythmia in Children
Interventions: ECG,
Locations: Medical University of Warsaw
Countries: Poland
Enrollment: 675
Age range: 1 Day - 18 Years
Sex: ALL

Summary:
The project is a direct response to the identified lack of ECG diagnostic solutions dedicated to children. There are several tools for automatic ECG signal analysis in adults, but these cannot be used in the diagnosis of heart disorders among children. A digital ECG analysis technology developer, Cardiomatics, and the Medical University of Warsaw team have taken the challenge of developing an internationally innovative tool for automatic assessment, analysis, and interpretation of electrocardiographic signals in pediatric patients. The developed tool will allow cardiac arrhythmias in children to be assessed more effectively and minimize the time needed for cardiologists to evaluate data received from the Holter monitor due to the use of algorithms, which are based on artificial intelligence.

Eligibility criteria:
Inclusion Criteria:

* age 0-18
* signed informed consent form by parents/guardians and children (if at the age of 16 or more).
* diagnosed arrhythmia

Exclusion Criteria:

* age above 18 years old
* lack of consent
* coexisting conditions and/or drugs which can cause changes in the ECG recording

Outcomes:
Development and validation of ECG automatic analysis tool
Multimodal Data Collection for the Development and Validation of the STRATUM Tool to Assist Surgery in Patients Affected by Brain Tumours: Observational Study
Trial ID: NCT07036783
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-07-01
Completion date: 2027-04-30
Sponsor: Fundación Canaria de Investigación Sanitaria
Collaborators: Karolinska University Hospital; Hospital Universitario de Gran Canaria Doctor Negrín; Fundación para la Investigación Biomédica del Hospital 12 de Octubre
Conditions: Brain (Nervous System) Cancers; Brain Tumor, Primary
Enrollment: 320
Age range: 18 Years - None
Sex: ALL

Summary:
This observational study (STRATUM-OS) aims to collect the necessary data from a cohort of patients with planned surgery for suspected intra-axial malignant brain tumours (both primary and secondary) following the standard surgical procedure established in current clinical protocols. These data will serve two primary purposes:

i) To gather multimodal data (pre, intra and postoperative) essential for the development and technical validation of a 3D decision support tool for brain surgery guidance and diagnostics integrating augmented reality and multimodal data processing powered by artificial intelligence algorithms (called STRATUM tool);

ii) To collect outcome measures that will facilitate a subsequent comparative study (a non-randomized controlled clinical trial, called STRATUM-NRCCT) assessing the standard procedure alone versus the standard procedure augmented with the STRATUM tool. Patients from STRATUM-OS will act as a historical control group in the subsequent historically controlled clinical trial (STRATUM-NRCCT), which will be performed once STRATUM-OS has been completed.

In STRATUM-OS patients will receive standard care as per established clinical protocols, with no modification to their treatment. However, patients will be asked to grant access to their clinical information, complete questionnaires, and provide relevant pre, intra and postoperative information related to the surgical intervention. Data will be gathered from multiple sources, such as the Electronic Health Records (EHR), patient completed questionnaires, interviews, and reports from healthcare professionals involved in the surgical procedure. Additionally, intraoperative data will be collected from the different devices in the operating room.

Eligibility criteria:
Inclusion Criteria:

* Adult patients (≥18 y/o).
* Patients with planned surgery for suspected intraaxial malignant brain tumours (both primary and secondary) at any of the participating clinical institutions.

Exclusion Criteria:

* Inability to deliver informed consent (unless provided by the tutor).
* Participants in this study who have already undergone brain surgery during STRATUM-OS and need a revision surgery.

Outcomes:
STRATUM Tool intraoperative tumour identification against anatomopathological analysis
Comparative Evaluation of Artificial Intelligence and Endoscopists´ Accuracy in Endoscopic Ultrasound for Identifying Normal Anatomical Structures: A Multi-institutional, Cross-sectional Study
Trial ID: NCT06279546
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2023-05-01
Completion date: 2024-01-26
Sponsor: Instituto Ecuatoriano de Enfermedades Digestivas
Collaborators: The Methodist Hospital Research Institute; Baylor Saint Luke's Medical Center; Beth Israel Deaconess Medical Center; Barra Life Medical Center, Brazil; Hospital Clinico Universitario de Santiago; Universitair Ziekenhuis Brussel; Hospital Civil de Morelia, Michoacan; ELIAS Emergency University Hospital; Larkin Community Hospital; Carol Davila University of Medicine and Pharmacy; mdconsgroup, Guayaquil, Ecuador
Conditions: Gastrointestinal Diseases
Interventions: Detection of structures
Locations: IECED
Countries: Ecuador
Enrollment: 30
Age range: 18 Years - 99 Years
Sex: ALL

Summary:
Endoscopic ultrasound (EUS) visual impression is operator-dependant and can hinder diagnostic accuracy, especially in less experienced endoscopists. The implementation of artificial intelligence can potentially mitigate operator dependency and interpretation variability, helping or improving the overall accuracy.

The investigators therefore aim to compare diagnostic accuracy between artificial intelligence (AI)-based model and the endoscopists when identifying normal anatomical structures in EUS-procedures.

Eligibility criteria:
Inclusion Criteria:

* Expert gastrointestinal EUS-endoscopists.
* Non-expert gastrointestinal endoscopists training for EUS.
* Patients with chronic dyspepsia without other findings.
* Patients with previous CT images or upper digestive endoscopy reporting no other findings.
* Patients requiring EUS for surveillance due to family history of pancreatic cancer without findings on MRI.

Exclusion Criteria:

* Internet connection less than 100 MBs per second.
* Patients with abnormal structures or with visible lesions.

Outcomes:
Diagnostic accuracy
A Study on the Effectiveness of Artificial Intelligence-assisted Colonoscopy in Improving the Effect of Colonoscopy Training for Trainees
Trial ID: NCT04912037
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2021-06-01
Completion date: 2022-02-01
Sponsor: Renmin Hospital of Wuhan University
Conditions: Colonoscopy; Artificial Intelligence; Gastrointestinal Disease
Interventions: artificial intelligence assistance system
Locations: Renmin hospital of Wuhan University
Countries: China
Enrollment: 385
Age range: 50 Years - None
Sex: ALL

Summary:
In this study,the AI-assisted system（EndoAngel）has the functions of reminding the ileocecal junction, withdrawal time, withdrawal speed, sliding lens, polyps in the field of vision, etc. These functions can improve the colonoscopy performance of novice physicians and assist the colonoscopy training。

Eligibility criteria:
Inclusion Criteria:

1. Male or female ≥50 years old;
2. Able to read, understand and sign informed consent
3. The investigator believes that the subjects can understand the process of the clinical study, are willing and able to complete all study procedures and follow-up visits, and cooperate with the study procedures
4. Patients requiring colonoscopy

Exclusion Criteria:

1. Have drug or alcohol abuse or mental disorder in the last 5 years
2. Pregnant or lactating women
3. Patients with known multiple polyp syndrome;
4. patients with known inflammatory bowel disease;
5. known intestinal stenosis or space-occupying tumor;
6. known colon obstruction or perforation;
7. patients with a history of colorectal surgery;
8. Patients with previous history of allergy to pre-used spasmolysis;
9. Unable to perform biopsy and polyp removal due to coagulation disorders or oral anticoagulants;
10. High risk diseases or other special conditions that the investigator considers the subject unsuitable for participation in the clinical trial.

Outcomes:
CUSUM learning curve for colonoscopy (ACE scoring scale); Average test score difference before and after training
Artificial Intelligence-enabled Screening and Diagnosis of Cardiomyopathies Using Coronary Computer Tomography Angiography
Trial ID: NCT06748261
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2024-12-30
Completion date: 2025-12-30
Sponsor: Shanghai Zhongshan Hospital
Conditions: Cardiovascular Diseases; Hypertrophic Cardiomyopathy (HCM); Dilated Cardiomyopathy (DCM); Restrictive Cardiomyopathy; Amyloid Cardiomyopathy; Ischemic Cardiomyopathy; Arrhythmogenic Right Ventricular Cardiomyopathy; Myocarditis; Cardiomyopathies
Interventions: CCTAI model
Enrollment: 5000
Age range: None - None
Sex: ALL

Summary:
The goal of this observational and diagnostic study is to develop and validate an artificial intelligence assisted approach for coronary computer tomography angiography-(CCTA)-based screening and diagnosis of cardiomyopathies in patients with suspected coronary artery diseases. This study aims to develop a computerized CCTA interpretation using artificial intelligence for multi-label classification task to assist cardiomyopathy diagnosis in the clinical workflow.

Eligibility criteria:
Cardiomyopathy cohort:

* Inclusion Criteria:

  1. A clinical diagnosis of cardiomyopathies, including hypertrophic cardiomyopathy, dilated cardiomyopathy, restrictive cardiomyopathy, cardiac amyloidosis, myocarditis, arrhythmogenic right ventricular cardiomyopathy, and coronary artery disease/ischemic heart disease.
  2. At least one CCTA before surgery or implantable device treatment.
* Exclusion Criteria:

  1. No recorded diagnosis of cardiomyopathy or undetermined type of cardiomyopathy.
  2. A clinical diagnosis of secondary cardiac abnormalities due to other organic or systemic diseases.
  3. Surgery or implantable device treatment before CCTA examination.

Control cohort:

* Inclusion Criteria: participants with at least one CCTA examination.
* Exclusion Criteria: clinical diagnosis of cardiovascular diseases (including cardiomyopathy, history of myocardial infarction, history of cardiac surgery, stent implantation, ICD implantation and so on) or secondary cardiac abnormalities due to systemic diseases.

Outcomes:
Diagnostic performance
Personalised Continuous Dose Titration of Combination Therapy for Hypertension Using CURATE: AI Personalised Continuous Dose Titration Platform
Trial ID: NCT05376683
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2023-04-01
Completion date: 2024-04-20
Sponsor: Alexandra Hospital
Collaborators: National University Hospital, Singapore
Conditions: Hypertension; Cardiovascular Diseases
Interventions: Curate.AI; Telemonitoring
Locations: Alexandra Hospital
Countries: Singapore
Enrollment: 45
Age range: 30 Years - 80 Years
Sex: ALL

Summary:
Hypertension - a chronic condition of elevated blood pressure (BP) - is a highly prevalent condition. However, effective prevention and management of hypertension remain challenging under the current standard of care (SOC). There has been a growing recognition that one-off, irregular office BP measurements are not sufficient and that regular home BP monitoring will likely be an adjunct to conventional office BP measurements. By using artificial intelligence (AI), via the CURATE.AI platform, the goal is to use patients' BP data to rapidly generate personalized anti-hypertensive dose titrations. The main aim of this study is to assess the feasibility of CURATE.AI-assisted dose titration.

Eligibility criteria:
Inclusion Criteria:

* At least \>=30 years of age and \<=80 years
* History of uncontrolled primary hypertension, BPs more \>=140/90mm Hg, treatment-naive or on single antihypertensive medication
* Not known to have complications of hypertension
* Sufficiently fluent in English language
* Able to give informed consent
* eGFR \> 60 ml/min
* Eligible to undergo CCB + ARB/ACE-i therapy for =30 days

Exclusion Criteria:

* Suspected or known secondary hypertension
* Drug allergies to anti-hypertensive agents or agents of the same drug class used in the protocol
* Known postural hypotension or standing systolic blood pressure \< 110 mmHg
* Malignant hypertension (BPs = 180/110 mmHg) that requires emergency treatment
* Arm circumference that does not fit BP cuff size which can affect the accuracy of BP measurement
* Pregnant women, trying to become pregnant or of child-bearing potential and not using birth control
* History of cancer
* Chronic kidney disease (eGFR \<50ml/min) or end stage renal failure
* Liver cirrhosis or hepatic failure
* Chronic heart failure
* Chronic lung disease
* Diabetes Mellitus on insulin and/or with microvascular/macrovascular complications.
* Established cardiovascular disease such as stroke/transient ischemic attack and ischemic heart disease
* Potential psychosocial factors or serious medical conditions limiting the ability to self-monitor their blood pressure or limit adherence to interventions (E.g. dementia, active substance abuse, nursing home resident)
* Participants without informed consent

Outcomes:
Logistical feasibility of a protocol using telemonitoring and CURATE.AI-assisted dose titration.; Scientific Feasibility
A Multi-Reader Multi-Case (MRMC) Study Assessing the Impact of Legit.Health Plus on the Diagnostic Accuracy and Referral Decision-Making of Primary Care Physicians for Skin Lesions.
Trial ID: NCT07428941
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2024-06-04
Completion date: 2024-09-13
Sponsor: AI Labs Group S.L
Collaborators: Puerta de Hierro University Hospital
Conditions: Skin Lesions; Melanocytic Nevi; Melanoma (Skin Cancer); Basal Cell Carcinoma of Skin; Urticaria; Pustular Psoriasis; Actinic Keratoses; Plaque Psoriasis; Hidradenitis Suppurativa (HS)
Interventions: AI-based medical device for aided diagnosis in dermatological conditions
Locations: AI Labs Group S.L.
Countries: Spain
Enrollment: 9
Age range: 18 Years - None
Sex: ALL

Summary:
This study aims to determine if an artificial intelligence (AI) medical device can help primary care doctors more accurately identify and manage various skin conditions. Skin issues are a frequent reason for doctor visits, but differences in expertise between general practitioners and specialists can sometimes lead to misdiagnoses or unnecessary referrals.

The researchers hypothesized that the information provided by the AI device would increase the true diagnostic accuracy of primary care practitioners for multiple dermatological conditions. To test this, the study followed a prospective, self-controlled design where each participating doctor served as their own comparison.

During the study, 9 primary care physicians evaluated 30 clinical images representing a variety of skin pathologies. For each image, the doctors followed a two-step process:

* First, they provided a diagnosis based only on the image and the patient's medical history.
* Second, they were shown the AI's analysis-including the top 5 suggested diagnoses and confidence levels-and asked to provide a final diagnosis.

The study also investigated if the AI could help doctors decide whether a patient truly needs a referral to a specialist or if the condition could be handled remotely via teledermatology. The primary question was whether using this AI support would significantly increase the number of correct diagnoses made by primary care doctors and lead to more efficient patient care.

Eligibility criteria:
Inclusion Criteria:

* Board-certified primary care physicians regardless of their professional experience.
* High-quality images of patients with different skin conditions.

Exclusion Criteria:

* Low-quality images of patients which can not be properly analyzed.

Outcomes:
Diagnostic Accuracy for Multiple Dermatological Conditions with and without Artificial Intelligence Support.