moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Diagnosis of Hypertrophic Cardiomyopathy With Artificial Intelligence-enhanced Electrocardiogram
Trial ID: NCT06287892
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2024-01-01
Completion date: 2024-12-31
Sponsor: Shanghai Zhongshan Hospital
Conditions: Hypertrophic Cardiomyopathy
Locations: 180 Fenglin Road
Countries: China
Enrollment: 10000
Age range: 18 Years - None
Sex: ALL

Summary:
The study aims to establish a diagnostic model of hypertrophic cardiomyopathy with artificial intelligence-enhanced electrocardiogram.

Eligibility criteria:
Inclusion Criteria:

* Diagnosed with HCM

Exclusion Criteria:

* Patients with ventricular pacing

Outcomes:
Diagnosis of HCM with AI-ECG
Machine Learning Score Prediction of Cardiotoxicity in Cancer Patients Receiving Anthracycline Chemotherapy or HER-2-Targeted Therapies
Trial ID: NCT07191730
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-10-29
Completion date: 2028-11
Sponsor: University Hospital, Caen
Conditions: Cardiotoxicity; Heart Failure; Neoplasms; Breast Neoplasms
Locations: CHU de Caen
Countries: France
Enrollment: 600
Age range: 18 Years - None
Sex: ALL

Summary:
Cancer treatments have improved substantially over the past decades, but some effective therapies such as anthracyclines and HER2-targeted agents are associated with severe cardiovascular adverse effects, including heart failure. Existing cardiovascular risk prediction scores have limited evidence in this setting.

The ML-CardioTox study is a prospective, multicenter, observational cohort conducted in 15 centers in France. The primary objective is to develop a one-year prediction score for cancer therapy-related cardiotoxicity using machine learning methods. A dedicated software platform will be used to standardize data collection and support integration of artificial intelligence tools.

A total of 600 patients treated with anthracyclines or HER2-targeted therapies in cardio-oncology clinics will be enrolled over a one-year inclusion period starting in December 2024, with a 12-month follow-up. The primary endpoint is the occurrence of cardiotoxicity as defined by the 2022 European Society of Cardiology guidelines (hospitalization for heart failure, initiation or escalation of diuretic therapy, decline in cardiac function on imaging, or increase in cardiac biomarkers such as troponin or natriuretic peptides).

Secondary objectives include comparison of the predictive performance of the machine learning-derived score with the established HFA-ICOS risk score. Patients will be managed according to routine clinical practice.

This study aims to improve prognostic stratification tools for patients receiving anthracyclines or HER2-targeted therapies, with the goal of better identifying those at high risk of developing cardiotoxicity during follow-up.

Eligibility criteria:
Inclusion Criteria:

* Age ≥ 18 years
* Planned follow-up in cardio-oncology clinic as part of a baseline pre-treatment evaluation before a sequence of anthracycline and/or HER2-targeted therapy, according to the 2022 ESC recommendations
* Inclusion regardless of prior exposure to potentially cardiotoxic anticancer therapies or thoracic radiotherapy

Exclusion Criteria:

* Patients not covered by the French national health insurance system (Sécurité Sociale)
* Patients for whom 12-month follow-up is planned outside the center performing the baseline pre-treatment evaluation

Outcomes:
Incidence of cardiotoxicity at 12 months
Training Physicians to Differentiate the Paris Classification Using Artificial Colon Polyp Images
Trial ID: NCT06550908
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-04-15
Completion date: 2025-08-31
Sponsor: Wuerzburg University Hospital
Conditions: Colonic Polyp; Colon Adenoma
Interventions: Lutetia Training Plattform - real images; Lutetia Training Plattform - artifical images
Locations: University hospital Würzburg
Countries: Germany
Enrollment: 70
Age range: 18 Years - None
Sex: ALL

Summary:
Training in endoscopy is essential for the early detection of precursors of colorectal cancer. Up to now, this training has been carried out with image collections of findings and in practice when working on patients. The investigators want to use artificial intelligence (AI) to better train doctors to recognise these precursors. By using generative AI, the investigators were able to create realistic images that comply with data protection regulations and whose content can be predefined. Parts of the image can also be regenerated so that it is possible to create different precancerous stages in the same place in the image.

In this study the investigators want to train physicians using real images or artificial images in order to compare which version helps classify polyps better.

Eligibility criteria:
Inclusion Criteria:

* Physicians with or without experience in colonoscopy

Outcomes:
Accuracy for Paris classification
Derivation and Verification of Hemodynamic Clinical Subphenotypes in Patients Undergoing Cardiac Surgery Under Unsupervised Machine Learning
Trial ID: NCT07085208
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2016-04-01
Completion date: 2024-12-31
Sponsor: Nanjing First Hospital, Nanjing Medical University
Conditions: Phenotyping; Machine Learning; Cardiac Surgery; Hemodynamic Parameters
Interventions: Unsupervised Machine Learning for Clinical Phenotyping
Enrollment: 10847
Age range: 18 Years - None
Sex: ALL

Summary:
Background \& Objective:

Cardiac surgery patients differ significantly in their health conditions and how they react during operations. Standard risk assessments before surgery often miss the real-time changes happening inside a patient's body during the procedure, which can affect their recovery. Therefore, researchers conducted this study to find different groups (phenotypes) of patients who face varying risks for poor outcomes. They did this by using advanced computer learning techniques to analyze a lot of detailed health information collected both before and during surgery.

Methods:

This was a study that looked back at patient records from several hospitals. Researchers gathered a large amount of patient information from before surgery, including their basic health details and lab results. They also collected very detailed measurements of patients' vital signs taken during surgery, noting how these changed over time. Then, a computer program that can find patterns without being told what to look for (unsupervised hierarchical clustering) was used to sort patients into distinct groups based on this combined data.

Clinical Relevance:

This study expects to show that using data to identify patient groups can reveal differences that traditional methods miss. These new patient groups, which are based on how their blood flow and vital signs behave, offer a new way to understand risks in real-time. This could help doctors to predict problems more accurately and create personalized care plans for each patient around the time of surgery, which has great potential for practical use in hospitals.

Eligibility criteria:
Inclusion Criteria:

* Patients aged 18 years or older
* Patients who underwent cardiac surgery with cardiopulmonary bypass

Exclusion Criteria:

* Incomplete information on surgical procedures,
* With History of prior cardiac surgery or underwent second surgery during the same hospitalization
* Insufficient valid perioperative vital sign monitoring data

Outcomes:
Acute organ dysfunction
Multimodal Digital Image Fusion Technology Based on Deep Learning to Predict Significant Liver Fibrosis and Its Application in Multi-center Research
Trial ID: NCT06509230
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2024-07-20
Completion date: 2026-12-31
Sponsor: Huang Haijun
Collaborators: East China University of Science and Technology
Conditions: Liver Fibrosis
Interventions: [None]
Locations: Haijun Huang
Countries: China
Enrollment: 700
Age range: 18 Years - 60 Years
Sex: ALL

Summary:
The deep learning method based on convolutional neural network (CNN) was used to extract the relevant features of liver fibrosis classification from the multi-modal information of digital pathological sections, clinical parameters and biomarkers of a large number of existing cases of liver puncture, and the U-Net architecture of CNN was used to segment and extract the features of clinical medical images.

Eligibility criteria:
Inclusion Criteria:

1. Age of 18-60 years old
2. The diagnosis of chronic hepatitis B is in line with the diagnostic criteria of China's 2019 Chronic Hepatitis B Prevention and Treatment Guidelines, and the diagnosis of non-alcoholic fatty liver is in line with the Asian Pacific Hepatology Association guidelines
3. Imaging showed no liver cancer

Exclusion Criteria:

1. There are contraindications for liver biopsy
2. Liver pathology did not meet the criteria

Outcomes:
Model development
Evaluating the Feasibility of Remote Haemodynamic Monitoring in a Cohort of Multi-ethnic Chronic Stroke Patients Using a Novel User Interface and Artificial Intelligence Algorithms
Trial ID: NCT05649800
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2023-03-17
Completion date: 2026-08-26
Sponsor: University of Leicester
Conditions: Cognitive Dysfunction; Stroke; Dementia; Depression; Delirium
Locations: University Hospitals of Leicester NHS Trust; Leicestershire Partnership Trust
Countries: United Kingdom; United Kingdom
Enrollment: 140
Age range: 65 Years - None
Sex: ALL

Summary:
The goal of this observational study is to determine the feasibility of using integrated Transcranial Doppler Ultrasonography or Near Infrared Spectroscopy to detect changes in cerebral autoregulation and neurovascular coupling in healthy, stroke, dementia, depression and delirium populations. We also aim to:

* Determine the optimal stimulus for neurovascular coupling
* To derive sample size estimates for a future study
* To develop a multilevel, multivariate model that can be applied to future datasets

Eligibility criteria:
* Inclusion Criteria:
* Healthy adults aged over 65 years, free from medical comorbidities or medications that can adversely affect cognitive function or cerebral haemodynamics;
* Stable, well controlled comorbidities (e.g. hypertension);
* A diagnosis of dementia (major neurocognitive disorder), depression, or delirium, according to the Diagnostic and Statistical Manual of Mental Disorders, 5th edition (DSM-V) criteria;
* Participants on or off anti-dementia drug therapy (acetylcholinesterase inhibitors, NMDA receptor antagonists), and antidepressants;
* A diagnosis of ischaemic (IS) or haemorrhagic (ICH) stroke according to clinical and/or radiological findings, within 72 hours of symptom onset
* Exclusion Criteria:
* Poorly controlled medical comorbidities affecting cerebral haemodynamics or cognitive function (e.g., heart failure, hypertension, type two diabetes);
* Clinically unstable or too unwell to cooperate with the study protocol;
* Lacks capacity or personal consultee to consent to the study.

Outcomes:
Good quality TCD-NIRS data
Knowledge, Attitudes, And Perceptions of a Group of Egyptian Dental Students Towards Artificial Intelligence: A Cross-sectional Study
Trial ID: NCT06348758
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2024-02-11
Completion date: 2024-08-07
Sponsor: Cairo University
Conditions: Health Knowledge, Attitudes, Practice
Interventions: questionnaire
Locations: Cairo University- Faculty of Dentistry
Countries: Egypt
Enrollment: 384
Age range: 18 Years - 25 Years
Sex: ALL

Summary:
In the context of this ongoing AI revolution, it is essential to ensure that current and future clinical practitioners are up to date regarding this technology's current state and potential. It is valuable to assess the current attitude of clinical dentists and undergraduate students in dentistry regarding the future role of AI.

To the best of the author's knowledge, there is no study about Egyptian dental students' views and attitudes regarding the application of AI in dental practice. Therefore, this study aimed to evaluate the attitudes and perceptions of a group of dental students in Egypt towards AI.

Eligibility criteria:
Inclusion Criteria:

\- Dental students

Exclusion Criteria:

* Graduates

Outcomes:
knowledge, attitudes, and perceptions about artificial intelligence in Dentistry
International Multicentric Study ARON-1
Trial ID: NCT05287464
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2022-03-10
Completion date: 2027-09-30
Sponsor: Hospital of Macerata
Conditions: Metastatic Renal Cell Carcinoma (mRCC)
Interventions: pembrolizumab plus axitinib
Locations: Ospedale di Macerata, UOC Oncologia
Countries: Italy
Enrollment: 1220
Age range: 18 Years - None
Sex: ALL

Summary:
The ARON-1 Study is designed as an International Multicentric Retrospective Study to collect global experiences with the use of immuno-combinations in patients with metastatic RCC.

Two Supplementary Studies (ARON-1α and ARON-1β) have been designed. The ARON-1α Supplementary Study has been designed to investigate for the presence of genomic signatures from tumor samples of patients treated with first-line immuno-combinations for advanced RCC. The ARON-1β Supplementary Study has been designed to charaterize the immune cell populations and assess their relationship with the clinical outcome of mRCC patients treated with first-line immuno-combinations

Eligibility criteria:
Inclusion Criteria:

* Patients aged \>18y
* Cytological or Histologically confirmed diagnosis of clear cell or non-clear cell RCC
* Histologically or radiologically confirmed diagnosis of metastatic disease
* First-line treatment with nivolumab plus ipilimumab or nivolumab plus cabozantinib or pembrolizumab plus axitinib or pembrolizumab plus lenvatinib or avelumab plus axitinib or atezolizumab plus bevacizumab

Exclusion Criteria:

* Patients without histologically confirmed diagnosis of RCC
* Patients without histologically or radiologically confirmed metastatic disease
* Patients treated with immuno-combinations not included in the list reported in the Inclusion Criteria Section

Outcomes:
Progression-Free Survival (PFS); Overall Survival (OS); Overall Response Rate (ORR)
ARTIFICIAL INTELLIGENCE-BASED PREDICTION OF CLINICAL OUTCOMES IN PATIENTS SUSTAINING PROXIMAL HUMERAL FRACTURES
Trial ID: NCT06467006
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2024-09
Completion date: 2027-09
Sponsor: Consorci Sanitari de l'Anoia
Collaborators: Parc de Salut Mar; Parc Taulí Hospital Universitari
Conditions: Proximal Humeral Fracture
Interventions: Use of IA for proximal humeral fracture prognosis
Enrollment: 500
Age range: 18 Years - 90 Years
Sex: ALL

Summary:
Our smartphones can recognize the pictures of our family, loved ones and friends. Face recognition software leverages artificial intelligence (AI), image recognition and other advanced technology to map, analyze and confirm the identity of a face.

We humans do a poor job when classifying the injury related to a patient sustaining a proximal humeral fracture. In consequence, there is great heterogeneity in the treatment of proximal humerus fractures. Moreover, offering relevant information to patients regarding the risk of complications or fracture sequelae is challenging, given that the current series are based on obsolete classifications, and the published series bring together just over hundreds of patients analyzed. With these limitations, patients have few opportunities to participate in decision-making about their injury.

The present project aim is to integrate new technologies for the prediction of relevant clinical results for the patients presenting a proximal humeral fracture. In brief, AI can help identify similar fracture patterns without human inference, while humans can feed the algorithm with variables of interest such as the functional outcomes and complications related to this particular type of fracture.

Eligibility criteria:
Inclusion Criteria:

Patients sustaining a proximal humerus fracture treated nonoperatively under the criteria of the treating surgeon and patients' preference.

Subjects evaluated within the first 3 weeks after the injury. Patients between 18 and 90 years of age. Patients who have been studied with simple shoulder radiographs in anteroposterior and scapular outlet projections.

Participants who accept 1-year time follow-up.

Exclusion Criteria:

Patients with dementia or difficulty completing the evaluation after one year of follow-up.

Patients who have previously received surgical treatment on the affected limb. Patients who have suffered a previous fracture in the affected limb. Surgically treated patients.

Outcomes:
Constant-Murley Score
Artificial Intelligence System for Assessing Image Quality of Fundus Images and Its Effects on Diagnosis: A Clinical Trial
Trial ID: NCT04289064
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2020-02-01
Completion date: 2020-07-01
Sponsor: Sun Yat-sen University
Conditions: Retinal Diseases; Artificial Intelligence
Interventions: Taking a fundus image
Locations: Zhongshan Ophthalmic Center, Sun Yat-sen University
Countries: China
Enrollment: 300
Age range: None - None
Sex: ALL

Summary:
Fundus images are widely used in ophthalmology for the detection of diabetic retinopathy, glaucoma and other diseases. In real-world practice, the quality of fundus 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 fundus 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
Validation of the Utility of Rare Disease Intelligence Platform: A Multicenter Cluster Clinical Trial
Trial ID: NCT02748044
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2012-01
Completion date: 2016-04
Sponsor: Sun Yat-sen University
Collaborators: Ministry of Health, China; Xidian University
Conditions: Cataract; Artificial Intelligence
Interventions: CC-Cruiser
Locations: Zhongshan Ophthalmic Center, Sun Yat-sen University; Department of Ophthalmology, Guangdong General Hospital, Guangdong Academy of Medical Sciences; Department of Ophthalmology, First Affiliated Hospital, Guangzhou University of Traditional Chinese Medicine; Department of Ophthalmology, Qingyuan People's Hospital
Countries: China; China; China; China
Enrollment: 53
Age range: None - 80 Years
Sex: ALL

Summary:
The prevention and treatment of diseases via artificial intelligence represents an ultimate goal in computational medicine. The artificial intelligence for systematic clinical application has not yet been successfully validated. Currently, the main prevention strategy for rare diseases is to build specialized care centers. However, these centers are scattered, and their coverage is insufficient, resulting in inadequate health care among a large proportion of rare disease patients. Here, the investigators use "deep learning" to create CC-Cruiser, an intelligence agent involving three functional networks: "pick-up networks" for diagnostics, "evaluation networks" for risk stratification and "strategist networks" to provide assisted treatment decisions. The investigator also establish a cloud intelligence platform for multi-hospital collaboration and conduct clinical trial and website-based study to validate its versatility.

Eligibility criteria:
Inclusion Criteria:

* Patients who underwent ophthalmic examination of the eye and recorded their ocular information in the collaborating hospital.

Exclusion Criteria:

\-

Outcomes:
The proportion of accurate, mistaken and miss detection of CC-Cruiser.
Validation of Multi-contrast, High-resolution Cardiac Magnetic Resonance Imaging (CARDIO IRM)
Trial ID: NCT05877755
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-04-15
Completion date: 2030-03-31
Sponsor: University Hospital, Bordeaux
Collaborators: Grant Agreement ERC SMHEART
Conditions: Cardiac Disease
Interventions: Cardiac MRI acquisition
Locations: Chu de Bordeaux
Countries: France
Enrollment: 200
Age range: 18 Years - None
Sex: ALL

Summary:
Cardiovascular disease (CVD) causes at least 1.8 million European deaths annually, exceeding fatalities from cancer, chronic respiratory disease, and diabetes. Consequently, the fight against CVD has become the main priority of the World Health Organization. In the pursuit of understanding and treating CVD, cardiac magnetic resonance imaging (CMR) has remained the only modality capable of providing a comprehensive assessment of the heart's function and structure without harmful radiation. Unfortunately, current CMR systems remain too slow, too complex, require highly trained specialists and, as such, have presented a barrier to a wider adoption of CMR. The aim of CARDIO-IRM is to unleash the full potential of CMR to transform patient trajectories by introducing a fast, one-click, fully automated, and comprehensive imaging pipeline applicable to diagnosis, prognosis, and therapy selection in cardiology.

Eligibility criteria:
Inclusion Criteria:

* Adult patient (over 18 years of age) requiring an MRI scan as part of their care.
* Male or female.
* Affiliated or beneficiary of a social security scheme
* Having given his/her oral no objection after having read the information note

Exclusion Criteria:

* Patient unable to give oral consent (guardianship, non-French speaker, etc.)
* Patient deprived of liberty
* Patient who does not meet the specific eligibility criteria for an MRI examination: pregnant women, known pathology that may interfere with acquisition (e.g. Parkinson's disease), absolute or relative contraindication to an MRI examination
* Patient participating in a therapeutic interventional trial or in a period of relative exclusion in relation to another protocol

Outcomes:
Imaging integration success
Evaluation of a Chatbot to Maximize Hereditary Cancer Genetic Risk Assessment in an Underserved Gynecology Population
Trial ID: NCT05562778
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2023-01-15
Completion date: 2026-12
Sponsor: Weill Medical College of Cornell University
Conditions: Gynecologic Cancer; Hereditary Cancer Syndrome
Interventions: Chatbot
Locations: NYP Brooklyn Methodist Hospital; NYP Lower Manhattan Hospital; Weill Cornell Medicine; NYP Medical Group Queens
Countries: United States; United States; United States; United States
Enrollment: 150
Age range: 18 Years - None
Sex: ALL

Summary:
In this study, the investigators aim to compare a mobile health platform, known as a 'chatbot,' that leverages artificial intelligence and natural language processing to scale communication, to 'usual care' that patients would receive. This comparison will enable the investigators to determine if the chatbot system can improve rates of recommendation for genetic testing among patients at elevated risk of harboring a familial cancer syndrome in an all-Medicaid gynecology clinic. Furthermore, the investigators aim to evaluate facilitators of inequity in regard to patient access to and utilization of genetic testing services.

Eligibility criteria:
Inclusion Criteria:

* 8 years of age or older.
* Scheduled for a New Patient appointment in the gynecology clinic. Speaks and reads in English.
* Access to a telephone with texting capacity.
* Has not had prior genetic testing for hereditary cancer syndromes.

Exclusion Criteria:

* Under 18 years of age
* Has had previous genetic testing for hereditary cancer syndromes
* Does not read/speak in English
* Does not have access to a phone with texting capabilities

Outcomes:
Proportion recommended genetic testing