moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Developing an Artificial Intelligence (AI)-Based mHealth Intervention to Improve HIV Testing in Malaysia
Trial ID: NCT05335096
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-11-26
Completion date: 2026-03-15
Sponsor: Yale University
Collaborators: University of Malaya; Fogarty International Center of the National Institute of Health
Conditions: HIV Infections
Interventions: An AI-chatbot-based mHealth intervention; An attention-chatbot-based mHealth intervention; Educational materials
Locations: University of Malaysia
Countries: Malaysia
Enrollment: 296
Age range: 18 Years - None
Sex: MALE

Summary:
The purpose of this study is to develop an artificial intelligence (AI)-chatbot-based mobile health (mHealth) intervention to promote HIV testing in Malaysia.

Eligibility criteria:
Inclusion Criteria:

* Cis-gender male
* condomless sex with men in the past 6 months
* HIV status unknown or previously tested negative
* speaks Bahasa Malaysia or English.

Exclusion Criteria:

* Does not have a smartphone
* HIV status previously tested positive
* cannot speak Bahasa Malaysia or English

Outcomes:
Number of participants that get tested for HIV within 180 days
A Phase 1, Single-Center, Double-Blind, Randomized, Placebo-Controlled Study to Explore the Effect of SP-624 on Brain Network Analytics in Cohorts of Healthy Adult Subjects and Subjects With Major Depressive Disorder
Trial ID: NCT06570369
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: PHASE1
Start date: 2024-08-22
Completion date: 2026-04
Sponsor: Sirtsei Pharmaceuticals, Inc.
Conditions: Major Depressive Disorder; Depression; Healthy
Interventions: SP-624; Placebo
Locations: CenExel CNS; Alivation Research
Countries: United States; United States
Enrollment: 36
Age range: 18 Years - 60 Years
Sex: ALL

Summary:
The main goal of this clinical trial is to learn if the active study drug (code name: SP-624) has any effect on the electrical activity of the brain in adult healthy volunteers and in adults with major depression. Another goal of the study is to learn if SP-624 improves memory and learning in adults with major depression. The study will also provide additional information on the safety of SP-624 and how well it is tolerated in adult healthy volunteers and adults with major depression.

Researchers will compare SP-624 to a placebo (a look-alike substance that contains no drug) to see if SP-624 has any effect on study tests.

Study participants will:

Take capsules of study drug (SP-624 or a placebo) once daily for 2 weeks; visit the clinic at Screening, Day 1 (first dose of study drug), and Day 15 (last dose of study drug) for checkups and tests; and have phone call check-ups on Day 7 and about 1 week after the last dose of study drug.

Eligibility criteria:
Key Inclusion Criteria:

* In generally good health
* Body mass index (BMI) between 18.0 and 40.0 kg/m2
* Willing to comply with the requirements of the study
* For participants with depression: must meet study criteria for moderate to severe major depressive disorder

Key Exclusion Criteria:

* Female who is pregnant or breastfeeding.
* Clinically significant health condition or clinically significant abnormal results on screening health tests
* For participants with depression: presence of exclusionary study criteria for co-morbid psychiatric conditions or medication history.

Outcomes:
Brain Network Analytics Profile
WATCH (Wearable Artificial inTelligence for Cardiac Function and Health Monitoring)
Trial ID: NCT07058064
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-07-18
Completion date: 2029-06-29
Sponsor: Mayo Clinic
Conditions: Lymphoma; Sarcoma
Interventions: Electrocardiogram; Echocardiogram; LVEF echocardiogram; Apple Watch; Biospecimen collection; Questionnaires; Cardio-pulmonary exercise test; 6 minute walk test; Grip strength test
Locations: Mayo Clinic in Rochester
Countries: United States
Enrollment: 250
Age range: 18 Years - None
Sex: ALL

Summary:
The purpose of this study is to find a cost effective cardiovascular (CV) surveillance of cancer survivors over long periods of time to prevent the risk of progression of heart failure (HF) and related morbidity and mortality. Mobile device-based artificial intelligence (AI)-electrocardiogram (ECG) can reliably detect cardiac dysfunction in lymphoma and sarcoma survivors and smart watch based surveillance for cardiotoxicity is feasible and reliable.

Eligibility criteria:
Inclusion Criteria:

* ability to understand study procedures and being willing to comply with them for the entire study length
* diagnosis of lymphoma or sarcoma with new start or 1 year after completion of anthracycline therapy

Exclusion Criteria:

* LVEF \<50% at baseline, or prior confirmed history of heart failure, persistent atrial fibrillation, left bundle branch block, or paced rhythm
* Unwilling or unable to give written informed consent
* Participants who have opted out of the Minnesota Research Authorization

Outcomes:
Detection of left left ventricular ejection fraction (LVEF) <50% by AI-ECG; Change in AI-ECG based probability of an LVEF <50% using mobile device ECGs; Change in peak VO2
Evaluation of Digital Cytology for the Triage of HPV-positive Women in a Same-day "Test-triage-treat" Cervical Cancer Screening Strategy in Cameroon
Trial ID: NCT07550010
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-09-08
Completion date: 2027-09-08
Sponsor: University Hospital, Geneva
Collaborators: Ecole Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech
Conditions: Human Papillomavirus Infection; Cervical Intraepithelial Neoplasia Grade 2 (CIN2); Cervical Cancer Screening Methods
Interventions: HPV testing (self-sampling); Digital telecytology; AI-assisted telecytology; Thermal ablation
Locations: Centre Hospitalier Universitaire de Yaoundé; Yaoundé University Teaching Hospital (CHU)
Countries: Cameroon; Cameroon
Enrollment: 1800
Age range: 25 Years - 50 Years
Sex: FEMALE

Summary:
This study evaluates the diagnostic accuracy and feasibility of a same-day cervical cancer screening strategy using HPV self-sampling followed by digital telecytology triage among HPV-positive women in Cameroon. Women aged 30-49 years (or 25-49 years if HIV-positive) will undergo primary HPV testing using the GeneXpert system. HPV-positive women will be managed according to an extended HPV genotyping-based algorithm, including immediate treatment, telecytology triage, or follow-up. Histological assessment serves as the reference standard for the detection of cervical intraepithelial neoplasia grade 2 or worse (CIN2+). Secondary objectives include evaluation of AI-assisted telecytology and visual inspection with acetic acid (VIA), as well as the acceptability of screening and treatment strategies among women and healthcare providers. The study aims to generate evidence to support scalable, WHO-recommended test-triage-treat approaches in low-resource settings

Eligibility criteria:
Inclusion Criteria:

* HIV-negative women aged 30-49 and HIV-positive women aged 25-49 years old
* Ability to understand study procedures and accepting voluntarily to participate by signing an informed consent form (ICF).

Exclusion Criteria:

* Pregnancy at the time of screening
* Previous hysterectomy
* Known cervical cancer
* Symptoms of cervical cancer (e.g. metrorrhagia, known pelvic mass)
* Conditions that can interfere with visualization of the cervix
* Severe pre-existing medical conditions (e.g. advanced cancer, terminal renal failure)
* Women who are not able to comply with the study protocol.

Outcomes:
Diagnostic accuracy of HPV-based screening with digital cytology triage for detection of CIN2+
Impact of an AI-Driven CT Angiography Model on Intracranial Aneurysm Detection and Clinical Outcomes in Regional Hospitals (IDEAL2): A Nationwide Stepped-Wedge Cluster-Randomized Trial
Trial ID: NCT07124624
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-10-09
Completion date: 2029-08-31
Sponsor: Jinling Hospital, China
Conditions: Intracranial Aneurysm; CT Angiography; AI (Artificial Intelligence); Cluster Randomized Trial
Interventions: AI-Assisted CTA Interpretation; Standard CTA Interpretation
Enrollment: 14400
Age range: 18 Years - None
Sex: ALL

Summary:
This study (IDEAL 2) is a nationwide stepped-wedge cluster-randomized trial designed to prospectively enroll over 14,400 patients undergoing outpatient head CT angiography (CTA). The trial will be conducted across more than 72 regional hospitals in China. Clusters were randomly assigned to nine randomization groups. In accordance with the stepped-wedge design, clusters will sequentially transition from the control condition (standard human diagnosis) to the intervention condition (AI-assisted diagnosis) at regular intervals over a 10-month period, until all clusters receive the intervention. The primary outcome is the detection rate of intracranial aneurysms. Secondary outcomes include patient prognosis and clinical outcomes.

Eligibility criteria:
Inclusion Criteria:

-Patients in the outpatient setting who are scheduled to undergo head CTA scanning

Exclusion Criteria:

* Age \< 18 years
* History of cerebrovascular surgery involving any metallic implants (e.g., aneurysm embolization, aneurysm clipping, or vascular stenting)
* Modified Rankin Scale (mRS) score \> 3
* Refuse to sign written informed consent
* Contraindications to CTA examination
* CTA scan failure, incomplete imaging data, or image quality insufficient for diagnostic evaluation

Outcomes:
Detection rate of intracranial aneurysms
Artificial Intelligence System for Assessing Image Quality of Slit-Lamp Images and Its Effects on Diagnosis: A Clinical Trial
Trial ID: NCT04314180
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2020-02-01
Completion date: 2020-07-01
Sponsor: Sun Yat-sen University
Conditions: Anterior Segment Disorders; Artificial Intelligence
Interventions: Taking slit-lamp images
Locations: Zhongshan Ophthalmic Center, Sun Yat-sen University
Countries: China
Enrollment: 300
Age range: None - None
Sex: ALL

Summary:
Slit-lamp images are widely used in ophthalmology for the detection of cataract, keratopathy and other anterior segment disorders. In real-world practice, the quality of slit-lamp images can be unacceptable, which can undermine diagnostic accuracy and efficiency. Here, the researchers established and validated an artificial intelligence system to achieve automatic quality assessment of slit-lamp images upon capture. This system can also provide guidance to photographers according to the reasons for low quality.

Eligibility criteria:
Inclusion Criteria:

* Patients should be aware of the contents and signed for the informed consent.

Exclusion Criteria:

* 1\. Patients who cannot cooperate with a photographer such as some paralytics, the patients with dementia and severe psychopaths.
* 2\. Patients who do not agree to sign informed consent.

Outcomes:
Performance of artificial intelligence system for distinguish between good image quality and poor image quality
Real-time Computer-aided Polyp/Adenoma Detection During Screening Colonoscopy: a Single-center Crossover Trial
Trial ID: NCT05734820
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2020-01-11
Completion date: 2024-09-01
Sponsor: Instituto Ecuatoriano de Enfermedades Digestivas
Conditions: Colorectal Polyp; Colorectal Cancer; Colorectal Adenoma
Interventions: HD- colonoscopy; HD-colonoscopy assisted by AI
Locations: Instituto Ecuatoriano de Enfermedades Digestivas (IECED)
Countries: Ecuador
Enrollment: 312
Age range: 45 Years - 89 Years
Sex: ALL

Summary:
Nowadays, colonoscopy is considered the gold standard for the detection of lesions in the colorectal mucosa. However, around 25% of polyps may be missed during the conventional colonoscopy. Based on this, new technological tools aimed to improve the quality of the procedures, diminishing the technical and operator-related factors associated with the missed lesions. These tools use artificial intelligence (AI), a computer system able to perform human tasks after a previous training process from a large dataset. The DiscoveryTM AI-assisted polyp detector (Pentax Medical, Hoya Group, Tokyo, Japan) is a newly developed detection system based on AI. It was designed to alert and direct the attention to potential mucosal lesions. According to its remarkable features, it may increase the polyp and adenoma detection rates (PDR and ADR, respectively) and decrease the adenoma miss rate (AMR).

Based on the above, the investigators aim to assess the real-world effectiveness of the DiscoveryTM AI-assisted polyp detector system in clinical practice and compare the results between expert (seniors) and non-expert (juniors) endoscopists.

Eligibility criteria:
Inclusion Criteria:

* Adults ≥45 years old
* Patients referred for screening colonoscopy
* Adequate bowel preparation, Boston Bowel Preparation Scale (BBPS) ≥8
* Patients who authorized for endoscopic approach.

Exclusion Criteria:

* Pregnancy
* Any clinical condition which makes endoscopy inviable.
* Patients with history of Colorectal Carcinoma.
* Patients with history of Inflammatory Bowel Disease (IBD)
* Inability to provide informed consent

Outcomes:
Adenoma detection rate (ADR); Polyp detection rate (PDR); Diagnostic performance of AI-assisted polyp detector
A Prospective Cohort Study Comparing Artificial Intelligence Multimodal Fusion Prediction Models With Conventional Imaging Assessment for the Diagnosis of Pelvic Lymph Node Metastasis in Cervical Cancer
Trial ID: NCT06541288
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-08
Completion date: 2027-12
Sponsor: Obstetrics & Gynecology Hospital of Fudan University
Conditions: Uterine Cervical Neoplasms
Interventions: AI Prediction Model; Conventional Imageing Assessment
Locations: The Obstetrics and Gynecology Hospital of Fudan University
Countries: China
Enrollment: 230
Age range: 18 Years - 80 Years
Sex: FEMALE

Summary:
The goal of this prospective cohort study is to learn whether artificial intelligence multimodal fusion prediction models are effective in diagnosing pelvic lymph node metastasis in cervical cancer. The main question it aims to answer is: can artificial intelligence multimodal fusion prediction models improve the accuracy of preoperative diagnosis of pelvic lymph node metastasis in cervical cancer? The researchers compared the AI multimodal fusion prediction model with traditional imaging physician assessments to see if the prediction model could yield more accurate lymph node metastasis determinations. Participants will undergo pelvic MRI after pathologically confirming a diagnosis of cervical cancer, and the results will be used to determine pelvic lymph node metastasis status by the predictive model and the imaging physician, respectively. Subsequent pathology results after surgical lymph node clearance will be used as the gold standard to determine the accuracy of the two preoperative lymph node diagnostic modalities.

Eligibility criteria:
Inclusion criteria:

1. patients with preoperative diagnosis of invasive cervical cancer stage I-III, with any type of pathology, and patients who underwent radical/modified radical cervical cancer surgery + pelvic lymph node dissection in our hospital or sub-center;
2. Age ≥18 years and ≤80 years;
3. patients who underwent preoperative pelvic MRI (plain/enhanced) imaging in our hospital or sub-centers.

Exclusion criteria:

1. patients during pregnancy or lactation, patients with abortion within 42 days;
2. patients who are undergoing or have undergone preoperative neoadjuvant chemotherapy or radiotherapy for this cervical cancer;
3. Patients with other malignant tumors within 5 years;
4. Combination of other underlying diseases that may lead to enlarged pelvic lymph nodes;
5. patients whose preoperative pelvic MRI date is more than 1 month from the day of surgery;
6. poor quality imaging images that are unrecognizable.

Outcomes:
Accuracy in determining pelvic lymph node metastasis
Intelligence System for Predicting Atezolizumab-Bevacizumab Response and Clinical Outcomes in Unresectable Hepatocellular Carcinoma
Trial ID: NCT07495735
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2026-03-22
Completion date: 2026-12-30
Sponsor: First Affiliated Hospital of Wenzhou Medical University
Conditions: HCC - Hepatocellular Carcinoma; Atezolizumab and Bevacizumab in Hepatocellular Carcinoma
Interventions: No intervention (observational study)
Enrollment: 400
Age range: 18 Years - None
Sex: ALL

Summary:
This study is a multicenter retrospective clinical research, led by the First Affiliated Hospital of Wenzhou Medical University, and jointly conducted by other sub-centers. The aim is to develop an non-invasive artificial intelligence system for predicting the response and clinical outcomes of patients with unresectable hepatocellular carcinoma (uHCC) to the treatment with atezolizumab combined with bevacizumab (T+A). In response to the clinical situation where approximately half of uHCC patients do not respond to the standard T+A therapy and traditional invasive biopsy is unable to fully reflect the heterogeneity of the tumor microenvironment, this study plans to retrospectively collect the data of 400 patients who met the inclusion and exclusion criteria from January 2020 to November 2025. The study will systematically summarize multi-dimensional data such as enhanced CT images within one month before treatment, baseline characteristics, serum markers, liver disease factors, and tumor stage. By integrating these clinical features with deep learning imageomics features extracted from images, the research team is dedicated to constructing and validating a safe, non-invasive, and reproducible prediction model, with the aim of achieving precise identification of the benefit population before implementing immunotherapy combined with anti-angiogenic treatment, and providing a powerful intelligent tool support for optimizing clinical treatment decisions and improving patient survival prognosis.

Eligibility criteria:
Inclusion Criteria:

* (i) Clinical or pathological diagnosis of HCC;
* (ii) Administration of atezolizumab-bevacizumab treatment;
* (iii) At least 18 years old.

Exclusion Criteria:

* (i) Lack of key clinical data (laboratory test results), or lack of enhanced CT images taken within one month before treatment;
* (ii) Comorbid with other malignant tumors;
* (iii) PS score \> 2 or Child-Pugh score \> 7;
* (iv) Unable to evaluate tumor response;
* (v) Loss to follow-up.

Outcomes:
Objective response rate
AI-powered Portable MRI Abnormality Detection
Trial ID: NCT06803420
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-02-01
Completion date: 2027-10
Sponsor: King's College Hospital NHS Trust
Collaborators: King's College London
Conditions: Head Injury
Interventions: Portable, ultra-low-field MRI scanner
Enrollment: 400
Age range: 18 Years - None
Sex: ALL

Summary:
This study aims to test a new AI-powered portable MRI scanner that can quickly identify whether a brain scan is normal or abnormal. Currently, standard MRI scans are expensive and have long waiting times. Our goal is to see if a smaller, cheaper, and more accessible MRI scanner-combined with artificial intelligence (AI)-can help doctors identify abnormalities faster and improve patient care.

We will invite patients from King's College Hospital (KCH) who are already having a standard MRI scan. They will be asked to have an extra scan using the portable MRI, which takes about 60 minutes. The AI tool will then analyse these scans and compare its results to those of expert radiologists.

By the end of the study, we hope to prove whether portable MRI with AI can be used in hospitals and GP clinics, making brain scans more accessible, reducing wait times, and helping doctors prioritise urgent cases.

This study is funded by the Medical Research Council (MRC) and has been approved by UK research ethics committees.

Eligibility criteria:
Inclusion Criteria:

Adults ≥18 years old. Undergoing standard brain MRI including T2-weighted sequences.

Exclusion Criteria:

Contraindications to MRI (e.g. pacemaker, pregnancy). Poor quality MRI scans without a neuroradiology report.

Outcomes:
Accuracy of AI toll for triaging scans as "normal or "abnormal"
Comparison of Dental Arch Surface Scanning With and Without AI Support: A Methodological Study
Trial ID: NCT05545696
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2021-09-01
Completion date: 2021-12-05
Sponsor: Kıvanç Akça
Collaborators: Hacettepe University
Conditions: Impression Technic, Dental
Interventions: AI on algorithm; AI off algorithm
Locations: Hacettepe University Faculty of Dentistry Department of Prosthodontics
Countries: Turkey (Türkiye)
Enrollment: 21
Age range: 18 Years - 44 Years
Sex: ALL

Summary:
Digitization (automation) of production stages with Computer-Aided Design and Computer-Aided Manufacturing (CAD/CAM) technologies has led to a digital revolution in dentistry, as in many other fields of the industry. In current dentistry, the digitalization process can be defined as direct and indirect workflow. The clinical reliability and success of the indirect digital workflow made possible by these technologies have been proven by scientific studies. With the development of intraoral scanners (IOS), a virtual model is obtained by direct digitalization. This method eliminated the conventıonale impression and dental cast model steps required for indirect digitalization. Intraoral scanners are used as an alternative to convantıonal impressions in single and short edentulous cases. It has already been emphasized that direct digitalization is a reliable alternative to the conventional impression method, as the clinical applicability, accuracy, and precision of the manufactured restorations have been scientifically verified. Therefore, the clinical use of direct digitalization is increasing (exponentially) day by day, and new upgraded versions of software, along with various modifications for the hardware equipment, are being developed. Integrating artificial intelligence (AI) supported software into the intraoral scanning process can be seen as the beginning of a new era for direct digitalization. Therefore the aim of this study is to evaluate the effect of artificial intelligence on the scanning time, the amount of data collected, and the image accuracy obtained.

Eligibility criteria:
Inclusion Criteria:

* Completed growth and development
* Good oral hygiene habits and healthy periodontal status.
* Absence of missing teeth in mandibula (or no teeth loss in mandibular)
* Signing of the informed consent form
* No temporomandibular joint (TMJ) disorder

Exclusion Criteria:

* Volunteers who are not fulfilling the inclusion criteria

Outcomes:
Scanning time and collected data