moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
An Automated Virtual Reality Intervention to Enhance Firearm Safety Counseling in Pediatrics
Trial ID: NCT07265141
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-01-15
Completion date: 2026-08-31
Sponsor: Children's Hospital Medical Center, Cincinnati
Collaborators: Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD)
Conditions: Virtual Reality; Medical Education; Motivational Interviewing; Gun Violence Prevention; Artificial Intelligence (AI)
Interventions: An Automated Virtual Reality Intervention to Support Firearm Safety Counseling in Clinicians
Locations: Cincinnati Children's Hospital Medical Center
Countries: United States
Enrollment: 30
Age range: 18 Years - None
Sex: ALL

Summary:
Clinicians will complete a screen-based automated virtual reality training intervention to learn how to counsel caregivers of pediatric patients on secure firearm storage.

Eligibility criteria:
Inclusion Criteria:

\- Pediatric Clinicians must meet one of the following inclusion criteria at each study site: Physicians (attending, fellow or resident), Licensed Nurse Practitioner, Physician's Assistant

Exclusion Criteria:

* Unable to read and write in English

Outcomes:
Acceptability of Intervention Measure; Appropriateness of the Intervention Measure; Feasibility of the Intervention Measure
AI-human Collaborative Diagnosis of Liver Tumors Using CE-CT
Trial ID: NCT07153783
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-09-01
Completion date: 2025-11-07
Sponsor: Shengjing Hospital
Conditions: Hepatocellular Carcinoma (HCC); Intrahepatic Cholangiocarcinoma (Icc); Hepatic Metastasis; Hepatic Hemangioma; Cyst; Focal Nodular Hyperplasia
Interventions: AI-human collaboration for CE-CTs diagnosis
Locations: Shengjing Hospital of China Medical University
Countries: China
Enrollment: 10333
Age range: 18 Years - None
Sex: ALL

Summary:
Recent advances in artificial intelligence (AI), particularly deep learning technology, have transformed medical imaging analysis. AI systems have demonstrated diagnostic performance comparable to or exceeding that of expert radiologists in specific tasks. Liver-focused AI diagnostic systems have achieved promising results in multi-center validations; however, these retrospective studies have not yet addressed two critical gaps. First, large-scale prospective trials are required to establish real-world clinical effectiveness. Second, it remains unclear whether AI can be organically embedded into clinical diagnostic workflows to reshape diagnostic and therapeutic pathways, particularly by enhancing the detection and follow-up of hepatic malignancies and ultimately improving patient outcomes.

Eligibility criteria:
Inclusion Criteria:

1. Age range 18 years and above
2. Underwent dynamic contrast-enhanced abdominal CT examination with liver coverage
3. Imaging must include at least three required phases: non-contrast, arterial phase, and venous phase; an delayed phase is optional
4. Complete imaging data that meet AI system analysis requirements.

Exclusion Criteria:

1. History of recent upper-abdominal surgery (within 30 days) or major hepatobiliary-pancreatic surgery affecting liver evaluation (e.g., liver transplantation or Whipple procedure); patients with prior simple cholecystectomy or single-lesion interventional procedures are not excluded
2. History of recent hepatic trauma (within 30 days)
3. Poor image quality or severe noise artifacts (e.g., metal or motion artifacts)
4. Missing required imaging phases (required at least non-contrast, arterial, and venous phases) or inadequate scan range (e.g., lower-abdomen CT such as pelvic or rectal scans not covering the liver)

Outcomes:
Diagnostic accuracy of the AI System for malignancy diagnosis
Artificial Intelligence-based Prescription of Personalized Scalp Cosmetics: Efficacy Results
Trial ID: NCT06052241
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2021-07-27
Completion date: 2022-02-07
Sponsor: Seoul National University Bundang Hospital
Conditions: Dandruff; Folliculitis
Interventions: shampoos and scalp serums
Locations: Seoul National University Bundang Hospital
Countries: South Korea
Enrollment: 100
Age range: 19 Years - None
Sex: ALL

Summary:
The investigators aimed to evaluate whether they can be improved by an AI-based personalized prescription of scalp cosmetics.

Eligibility criteria:
Inclusion Criteria:

1. healthy adults aged \>19 years
2. presence of scalp-related discomfort (such as itching, a sensation of heat, dandruff, excessive sebum, dryness, pain, or pimple)

Exclusion Criteria:

1. presence of active infectious scalp diseases
2. history of treatment with topical or systemic steroids, oral immunosuppressants, topical or systemic antibiotics, or antifungal agents that could affect the scalp conditions, within 4 weeks prior to the baseline
3. difficulty understanding or following the protocol

Outcomes:
Total Scalp Photographic Index (SPI) score
ANEMEX UK Trial: Artificial Intelligence for Optimal Anemia Management in End-stage Renal Disease: The Anemia Control Model (ACM) Trial
Trial ID: NCT03214627
Status: TERMINATED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2018-12-10
Completion date: 2019-05-21
Sponsor: Vifor Fresenius Medical Care Renal Pharma
Collaborators: Worldwide Clinical Trials; Fresenius Medical Care Deutschland GmbH
Conditions: Anemia; End Stage Renal Disease
Interventions: Anemia Control Model (ACM); IV iron; Erythropoiesis-Stimulating Agent (ESA)
Locations: Kings College Hospital
Countries: United Kingdom
Enrollment: 88
Age range: 19 Years - 90 Years
Sex: ALL

Summary:
Fresenius Medical Care has developed a computer software programme called the Anaemia Control Management (ACM) software to assist in the anaemia management of patients with chronic kidney disease (CKD) undergoing hemodialysis. This trial is designed to assess the effectiveness of this ACM software on anaemia management in routine clinical practice. However, all ultimate decisions on therapeutic or diagnostic procedures, treatments, management of the disease, or resource utilisation will be at the discretion of the Investigator. The trial consists of a retrospective (historical) control period and a prospective (going forward) period. During the prospective period, the ACM will be used to assist the Investigators' decision making and will help the Investigators to administer a personalised intravenous (IV) iron and red blood cell stimulating agent (ESA) therapy, whereas treatment according to standard of care will be documented retrospectively for the same patients during the retrospective period of the trial. Thus, patients can serve as their own control.

Eligibility criteria:
Inclusion Criteria:

* Age 19 to 90 years
* On haemodialysis for the past 18 months prior to baseline
* Treatment with IV iron sucrose during the past 6 months according to the respective Summary of Product Characteristics (SmPC)
* Treatment with epoetin beta during the past 6 months according to the respective SmPC
* Regular Hb measurements and at least 5 (standard of care, approximately monthly) Hb measurements during the past 6 months
* Ferritin measurements during the past 6 months (at least 2 measurements)
* Signed informed consent

Exclusion Criteria:

* Life expectancy \<6 months
* One or more Hb measurements \<8 g/dl during the control period
* Living-donor transplant scheduled within the next 6 months
* Scheduled for switch to peritoneal dialysis or home haemodialysis
* Blood transfusion during the past 9 months
* Pregnancy or breast feeding
* Active infection
* Current malignancy or haematological disorder
* Previous severe hypersensitivity reaction to IV iron sucrose
* Serious allergic reactions to darbepoetin alfa or epoetin alfa/beta/zeta, respectively
* Current treatment with PEGylated erythropoietin
* Surgery in the past 6 months
* Surgery scheduled within the next 6 months
* Participation in a clinical trial in the past 7 months

Outcomes:
Change in the proportion of patients with haemoglobin within the target range as compared to the historical control period (non-inferiority testing)
Lung CT Scan Analysis of SARS-CoV2 Induced Lung Injury by Machine Learning: a Multicenter Retrospective Cohort Study.
Trial ID: NCT04395482
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2020-05-07
Completion date: 2022-03-31
Sponsor: University of Milano Bicocca
Conditions: covid19
Interventions: Lung CT scan analysis in COVID-19 patients
Locations: Ospedale Papa Giovanni XXIII; Policlinico San Marco-San Donato group; Azienda Ospedaliero-Universitaria di Ferrara; ASST di Lecco Ospedale Alessandro Manzoni; ASST Melegnano-Martesana, Ospedale Santa Maria delle Stelle; ASST Monza; AUSL Romagna-Ospedale Infermi di Rimini; Istituto per la Sicurezza Sociale-Ospedale della Repubblica di San Marino
Countries: Italy; Italy; Italy; Italy; Italy; Italy; Italy; San Marino
Enrollment: 44
Age range: 18 Years - None
Sex: ALL

Summary:
This is a multicenter observational retrospective cohort study that aims to study the morphological characteristics of the lung parenchyma of SARS-CoV2 positive patients identifiable in patterns through artificial intelligence techniques and their impact on patient outcome.

Eligibility criteria:
Inclusion Criteria (COVID-19 cohort):

* Patients 18 years old or above;
* Positive confirmation with nucleic acid amplification test or serology of SARS-CoV2 by naso-pharyngeal swab, bronchoaspirate sample or bronchoalveolar lavage;
* Lung CT scan performed within 7 days of hospital admission;

Inclusion criteria (ARDS cohort):

* Patients above 18 years old or above;
* Patients admitted to the hospital with a diagnosis of ARDS according to the Berlin criteria;
* Lung CT scan performed within 7 days of ARDS diagnosis;

Exclusion criteria (ARDS cohort):

● Positive confirmation with nucleic acid amplification test or serology of SARS-CoV2 by naso-pharyngeal swab, bronchoaspirate sample or bronchoalveolar lavage

Outcomes:
A qualitative analysis of parenchymal lung damage induced by COVID-19; A quantitative analysis of parenchymal lung damage induced by COVID-19
Detection of Ovarian Cancer Using an Artificial Intelligence Enabled Transvaginal Ultrasound Imaging Algorithm
Trial ID: NCT04214782
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2022-10-01
Completion date: 2024-10-01
Sponsor: Tongji Hospital
Collaborators: Hubei Cancer Hospital; Qilu Hospital of Shandong University; Henan Cancer Hospital; Xiangyang Central Hospital; The First People's Hospital of Jingzhou; First Affiliated Hospital, Sun Yat-Sen University
Conditions: Ovarian Cancer
Interventions: Artificial Intelligence Enabled Transvaginal Ultrasound Imaging algorithm
Enrollment: 10000
Age range: 18 Years - 80 Years
Sex: FEMALE

Summary:
Ovarian cancer is relatively rare but fatal with an annual incidence rate of 11.8 per 100 000 and a high mortality-to-incidence ratio of \>0.6. The modest diagnostic accuracy of TVU has risen some concerns about the over-treatment.Now, with the development of artificial intelligence (AI), we may have a better chance to interpret TVU imagines with high efficiency, reproducibility and accuracy.

Eligibility criteria:
Inclusion Criteria:

* Women scheduled for Transvaginal Ultrasound examination for adnexal lesions;
* Women aged over 18 years old;
* Women willing to participant in this study evidenced by signing the informed consent.

Exclusion Criteria:

* Women without adnexa for any reasons at the time of Transvaginal Ultrasound examination, including but not limited to receiving surgical removal for adnexa;
* Women with a pathologic diagnosis of ovarian cancer before the Transvaginal Ultrasound examination;
* Women with mental abnormal;
* Women did not cooperate or participate in other clinical trials;
* Pregnant or lactating women.

Outcomes:
diagnostic accuracy
The Impact of Multimodal Digital Fusion AI-Assisted Decision Support System-Based Comprehensive Management on Clinical Outcomes in County-Level Patients With Comorbid Cancer:A Prospective Non-randomized Controlled Interventional Study.
Trial ID: NCT07136727
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-08-15
Completion date: 2031-05-01
Sponsor: The First Affiliated Hospital of Xinxiang Medical College
Conditions: Oncological Comorbidities (e. g. Hypertension, Diabetes, Malnutrition)
Interventions: AI-assisted comprehensive management system
Locations: The First Affiliated Hospital of Xinxiang Medical University
Countries: China
Enrollment: 5000
Age range: 18 Years - None
Sex: ALL

Summary:
Combined with the digital whole process management data pool, a multi-modal data fusion framework is developed, and an AI model is established to realize risk stratification and personalized treatment Recommendation and dynamic prognosis prediction; validation of whole-process management based on multimodal digital fusion AI-aided decision support system through prospective non-randomized controlled interventional study The effect on survival, complication control and utilization of medical resources in patients with comorbid malignant tumors.

Eligibility criteria:
Inclusion Criteria:

* Patients with a definite diagnosis of malignancy by histopathology and/or cytology;
* Age ≥18 years;
* There is no gender limit
* Plan to receive antineoplastic therapy within 2 weeks or are receiving standard antineoplastic care (surgery, radiation, chemotherapy, or targeted therapy) ;
* Conscious and able to answer questions and use electronic devices autonomously;
* Patients were able to understand the study and voluntarily sign an informed consent form;

Exclusion Criteria:

* Having severe mental or cognitive impairments that prevent them from understanding the content of the study or implementing the programme;
* With severe heart disease, acute respiratory failure, liver kidney failure and other critical illness;
* Women during pregnancy or lactation；
* Have participated in other interventional studies in the past 1 month or are currently participating;
* Patients with ECOG ≥ 3 that do not respond to treatment;
* Patients with an expected survival of \< 3 months that do not respond to treatment;
* Cases deemed unsuitable for enrollment by the investigator.

Outcomes:
Progression-free survival (PFS); Overall survival (OS）
A Clinical Validation Study to Demonstrate the Effectiveness of an Artificial Intelligence Algorithm (DERM) to Identify Skin Cancer in Patients Undergoing a Skin Biopsy
Trial ID: NCT05126173
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2022-03-15
Completion date: 2022-09-15
Sponsor: Skin Analytics Limited
Conditions: Malignant Skin Melanoma T0; Basal Cell Carcinoma; Squamous Cell Carcinoma
Interventions: Deep Ensemble for the Recognition of Malignancy (DERM)
Locations: Stanford university; Vista Health Research, LLC; Dorisca Research Consulting, LLC; Northwestern University; ALLCUTIS Research, LLC; Allcutis Research; Universal Dermatology, PLLC; University of Pittsburgh Medical Center; Alpesh D. Desai, DO, PLLC, a Houston company institution; University of Utah; Universitaria Di Bologna; Dermatology University of Florence; Università degli Studi di Modena e Reggio Emilia; University of Campania; Università del Piemonte Orientale; UOSD Dermatology Oncology
Countries: United States; United States; United States; United States; United States; United States; United States; United States; United States; United States; Italy; Italy; Italy; Italy; Italy; Italy
Enrollment: 1111
Age range: 18 Years - None
Sex: ALL

Summary:
This study aims establish the effectiveness of Image Analysing Algorithm (DERM) to identify melanoma, Squamous Cell Carcinoma (SCC) and Basal Cell Carcinoma (BCC) when used to analyse dermoscopic images of skin lesions within the US and European population.

Eligibility criteria:
Inclusion Criteria:

* Willing and able to give informed consent for participation in the study,
* Male or Female, aged 18 years or above,
* Have at least one suitable skin lesion that will be biopsied due to a suspicion of skin cancer,

To be suitable for inclusion, a skin lesion must NOT have ANY of the following limitations:

located on an anatomical site of different skin structure: palms of hands or soles of feet (acral lesion), mucosal surfaces (lips and eyes) or under nail (ungal lesion), a diameter greater than the diameter of the dermoscopic lenses, located on an anatomical site unsuitable for photographing, including on surface of genitals and hair-bearing areas, has been previously biopsied, excised, treated or otherwise traumatised, located in an area of visible scarring or tattooing.

\- In the Investigator's opinion, able and willing to comply with all study requirements.

Exclusion Criteria:

* Any other significant disease or disorder which, in the opinion of the Investigator, may either put the participant at risk because of participation in the study, or may influence the result of the study, or the participant's ability to participate in the study.

Outcomes:
Sensitivity of DERM to detect "Malignant conditions"; Specificity of DERM to detect Malignant conditions.
Clinical Impact Through AI-assisted MS Care - A Prospective Multi-center Study
Trial ID: NCT07032246
Status: ENROLLING_BY_INVITATION
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-10-09
Completion date: 2028-11-30
Sponsor: icometrix
Collaborators: Ruhr University of Bochum; Technische Universität Dresden; Casa di Cura IGEA; General University Hospital, Prague; Charite University, Berlin, Germany
Conditions: Multiple Sclerosis; Radiologically Isolated Syndrome; Clinically Isolated Syndrome
Interventions: quantitative brain MRI (icobrain mr); Standard of Care (SOC)
Locations: General University Hospital Prague; Katholisches Klinikum Bochum - St. Joseph-Hospital; Technische Universität Dresden; Charité - Universitätsmedizin Berlin; ASST Papa Giovanni XXIII Bergamo; Casa di Cura Igea
Countries: Czechia; Germany; Germany; Germany; Italy; Italy
Enrollment: 750
Age range: 18 Years - None
Sex: ALL

Summary:
The goal of the PROCLAIM study is to assess the effectiveness of quantitative brain MRI, as measured by icobrain mr, on the detection of disease activity in multiple sclerosis, including the identification of smouldering disease, and the downstream effects on clinical decision making and patient outcomes in a real-world setting.

The study will compare an intervention arm implementing quantitative brain MRI (using the CE-marked medical device software icobrain mr) as part of Standard of Care, and a control arm implementing Standard of Care without quantitative brain MRI.

Eligibility criteria:
Inclusion Criteria:

* Able to independently understand, evaluate and sign the informed consent form.
* Able to comply with the study protocol, including having a mobile device capable of running the icompanion app.
* Have a definite diagnosis of MS, confirmed as per revised McDonald 2017 criteria (treated or untreated) and/or a diagnosis of RIS/CIS (following the revised McDonald criteria presented during ECTRIMS 2024)
* Patients early in the disease course of RRMS (CIS/RIS, RRMS with disease duration \< 7 years), patients with initial diagnosis of relapsing MS and disease duration 12-18 years (prone to convert to SPMS).
* Clinical and brain MRI data available at least one year prior to the study
* Age \>= 18 years

Exclusion Criteria:

* Unable or unwilling to understand, evaluate and sign the informed consent form.
* Any contraindications for undergoing an MRI.
* Currently involved in another interventional study.
* Currently pregnant or planning a pregnancy in the coming 2 years.
* Any concomitant disease that may require chronic treatment with systemic corticosteroids or immunosuppressants during the study.
* Retrospective MRI not meeting requirements: at least, 3D T1-weighted images and fluid-attenuated inversion recovery (FLAIR) images (3D, or 2D with maximal slice thickness of 3mm).
* Patients with confirmed diagnosis of PPMS.

Outcomes:
Impact of using icobrain mr on detecting (inflammatory or smouldering) disease activity; Impact of using icobrain mr on the clinical outcome
AI for Allograft Diseases Diagnosis and Prognosis After Kidney Transplantation
Trial ID: NCT05112770
Status: WITHDRAWN
Study type: OBSERVATIONAL
Start date: 2022-01-04
Completion date: 2027-08
Sponsor: Assistance Publique - Hôpitaux de Paris
Collaborators: URC-CIC Paris Descartes Necker Cochin
Conditions: Kidney Transplantation
Locations: Hôpital Necker-Enfants Malades
Countries: France
Enrollment: 0
Age range: 18 Years - None
Sex: ALL

Summary:
Kidney transplantation is the treatment of choice for patients with end stage renal disease. One of the major challenges is to better diagnose the attacks undergone by the kidney transplant in order to increase its longevity. Multiple attacks are caused by non-immune and immune mechanisms, first and foremost the acute rejection of the transplant.

Biopsy, an invasive method, remains the "Gold Standard" for diagnosing rejection and other pathologies affecting the kidney transplant.

The invasive nature of these biopsies limits their use and alternative biomarkers have been evaluated in order to diagnose kidney transplant pathologies in a non-invasive manner. It is in this context that the nephrology and renal transplantation department of the Necker hospital and Inserm U1151 have carried out several studies leading to the identification of the diagnostic and prognostic potential of acute rejection, by the determination of urinary concentrations of two chemokines, CXCL9 and CXCL10.

The most recent study conducted within these teams demonstrated that the diagnostic potential of urinary chemokines could be improved by taking into account standard clinicobiological parameters in multiparametric models.

The main objective of the study is to develop, train and validate artificial intelligence models including urinary chemokines, efficient, robust, explainable and interpretable for the diagnosis and non-invasive prognosis of acute renal transplant rejection, trained on a data set made up of clinical and biological parameters.

Eligibility criteria:
Inclusion Criteria:

* All renal transplant patients whose medical follow-up is provided by the nephrology and adult renal transplantation department of the Necker hospital between 2004 and 12/31/2020;
* Patient having signed a consent form for the storage, use and transfer of samples taken during treatment, for scientific research purposes;
* Patient not objecting to the processing of his personal data as part of the study.

Exclusion Criteria:

\- A deceased patient who, during his lifetime, objected in writing to the processing of his data for research purposes.

Outcomes:
Diagnostic model accuracy; Prognostic model accuracy
Computer-assisted Atrial Fibrillation Risk Detection In Oral-anticoagulant Use, Lowering Stroke Risk, and Optimizing Guidance With an Intelligent Screening Tool (CARDIOLOGIST): a Pragmatic Randomized Controlled Trial
Trial ID: NCT05127460
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2022-01-01
Completion date: 2023-01-31
Sponsor: National Defense Medical Center, Taiwan
Conditions: Atrial Fibrillation New Onset
Interventions: AI-enabled ECG-based Screening Tool
Locations: National Defense Medical Center
Countries: Taiwan
Enrollment: 25732
Age range: 18 Years - None
Sex: ALL

Summary:
This is a randomized controlled trial (RCT) to test a novel artificial intelligence (AI)-enabled electrocardiogram (ECG)-based screening tool for improving the diagnosis and management of Atrial Fibrillation.

Eligibility criteria:
Inclusion Criteria:

* Patients in emergency department or inpatient department
* Patients had at least 1 ECG
* Patients cared by non-cardiologist

Exclusion Criteria:

* Patients without history of atrial fibrillation diagnosis.
* Patients without history of long-term NOAC or warfarin usage.
* Patients without history of hemorrhagic stoke or ishemic stroke.
* Patients with low eGFR (\<30 ml/min)

Outcomes:
Proportion of taking AF treatment medications; Proportion of ischemic Stroke