moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Development and Validation of Multi-scale Deep Neural Network-Based CT Intelligent Diagnosis System for Coronary Vulnerable Plaques: A Chinese Multicenter Study
Trial ID: NCT06025305
Status: ENROLLING_BY_INVITATION
Study type: OBSERVATIONAL
Start date: 2023-07-01
Completion date: 2027-12-31
Sponsor: Jinling Hospital, China
Conditions: Coronary Artery Disease; Plaque, Atherosclerotic
Interventions: Intravascular imaging test; Coronary plaque assessment
Locations: Research Institute Of Medical Imaging Jinling Hospital
Countries: China
Enrollment: 2000
Age range: 18 Years - None
Sex: ALL

Summary:
The goal of this observational study is to develop an automatic whole-process AI model to detect, quantify, and characterize plaques using coronary CT angiography in coronary artery disease patients. The main questions it aims to answer are:

1. Whether the AI model enables to detect and quantify coronary plaques compared with intravascular ultrasound or expert readers;
2. Whether the AI model enables to identify vulnerable plaques using intravascular ultrasound or optical coherence tomography as the reference standard.
3. Whether the AI model enables to predict future adverse cardiac events in a large cohort of 10,000 patients with non-obstructive CAD.
4. Whether the AI model enables to influnece downstream clincial decision-making.

Eligibility criteria:
Inclusion Criteria:

* Intravascular imaging (including intravascular ultrasound or optical coherence tomography) was performed within 3 months after CCTA;
* No change in medications or clinical symptoms during CCTA and intravascular imaging examinations;
* Coronary artery diameter stenosis of 30% to 90% on invasive coronary imaging.

Exclusion Criteria:

* Image quality of CCTA or intravascular US was inadequate to analyze;
* Intravascular imaging was performed after percutaneous coronary intervention (PCI) or pre-dilation of the target lesions;
* Lesions could not be co-registered between CCTA and intravascular US;
* Missing CCTA or intravascular US data

Outcomes:
Sensitivity and specificity of AI-assisted coronary CT angiography on identifying vulnerable plaques compared to intravascular imaging
Artificial Intelligence in Predicting Progression in Multiple Sclerosis Study
Trial ID: NCT05426980
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2021-12-13
Completion date: 2023-09-30
Sponsor: University of Ljubljana
Collaborators: Novartis; General and Teaching Hospital Celje; University Medical Centre Ljubljana; University Medical Centre Maribor; Izola General Hospital
Conditions: Multiple Sclerosis; Multiple Sclerosis Lesion; Multiple Sclerosis Brain Lesion
Locations: University medical center Ljubljana; General and teaching hospital Celje; General hospital Izola; University medical center Maribor
Countries: Slovenia; Slovenia; Slovenia; Slovenia
Enrollment: 654
Age range: 18 Years - 65 Years
Sex: ALL

Summary:
The study proposal focuses on multiple sclerosis (MS), a chronic incurable disease of the central nervous system (CNS). The MS disease is characterised by recurrent transient disability progression, quantified by increase in the extended disability status score (EDSS), and subsequent remission (disappearance of symptoms and reduced EDSS score) or, alternatively, a gradual EDSS disability progression and exacerbation of associated symptoms. At the same time, the MS is characterised by multifocal inflammatory lesions disseminated throughout the white and grey matter of the CNS, which can be observed and quantified in the magnetic resonance (MR) scans. The proposed study will address the critical unmet need of computer-assisted extraction and assessment of prognostic factors based from an individual patient's brain MR scan, such as lesion count, volume, whole-brain and regional brain atrophy, and atrophied lesion volume, in order to evaluate the capability for personalized future disability progression prediction.

Eligibility criteria:
Inclusion Criteria:

* persons diagnosed with MS (any phenotype; according to the 2010 McDonald criteria) and CIS patients
* availability of at least two MRI exams with both FLAIR and T1-weighted scans of the same participant over a period of at least 6 months at the most recent examination
* availability of demographic, clinical data and treatment information for the same participant over a period of at least 6 months at the most recent examination
* availability of EDSS score and at least one previous EDSS scores for the same participant over a period of at least 6 months at the most recent examination

Exclusion Criteria:

* other clinically relevant systemic diseases if the researcher considers them to be significant

Outcomes:
Atrophied lesion volume derived from MRI predicts confirmed EDSS disability progression
Automatic Detection in MRI of Prostate Cancer: DAICAP
Trial ID: NCT05513820
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2022-06-21
Completion date: 2024-12
Sponsor: Assistance Publique - Hôpitaux de Paris
Collaborators: University Hospital, Strasbourg, France; University Hospital, Bordeaux; University Hospital, Lille; The Civil Hospitals, Lyon; Institut National de Recherche en Informatique et en Automatique; INCEPTO
Conditions: Detection and Characterization of Prostate Cancer Based on Artificial Intelligence
Locations: La Pitié Salpétrière Hospital
Countries: France
Enrollment: 1250
Age range: 18 Years - None
Sex: MALE

Summary:
Prostate cancer is the most common cancer in France and the 3rd most common cancer death in humans. The introduction of pre-biopsy MRI has considerably improved the quality of prostate cancer (PCa) diagnosis by increasing the detection of clinically significant PCa , and by reducing the number of unnecessary biopsies.However the diagnostic performance of Prostate MRI is highly dependent on reader experience that limits the population based delivery of high quality multiparametricMRI (mpMRI) driven PCa diagnosis. The main objective of this study is the development and the test of diagnostic accuracy of an AI algorithm for the detection of cancerous prostatic lesions from mpMRI images.

The secondary objective is the development and the test of diagnostic accuracy of an AI algorithm to predict tumor aggressiveness from mpMRI images.

Eligibility criteria:
Retrospective substudy

Inclusion Criteria:

* Patients with clinical suspicion of prostate cancer (increased PSA and/or abnormality on digital rectal examination) who underwent a diagnostic workup including mpMRI and prostate biopsies according to national recommendations: in case of normal mpMRI (PI-RADS \< 3) 12 systematic samples; in case of pathological mpMRI (PI-RADS ≥3) 12 systematic samples associated with targeted samples (n= 2 to 4) by cognitive fusion, or image fusion software.

Exclusion Criteria:

* Patients with histologically proven prostate cancer and/or treatment for prostate cancer prior to the diagnostic workup

Prospective substudy

Inclusion Criteria:

* Patients with clinical suspicion of prostate cancer (increased PSA level and/or abnormality on digital rectal examination) who should receive a diagnostic workup including mpMRI and prostate biopsies according to national recommendations: in case of normal mpMRI (PI-RADS \< 3) 12 systematic samples; in case of pathological mpMRI (PI-RADS ≥3) 12 systematic samples associated with targeted samples (n= 2 to 4) by cognitive fusion, or image fusion software.

Exclusion Criteria:

* Patients with already histologically proven cancer, patients who have received treatment for prostate cancer, patients who cannot benefit from prostate biopsies, or patients with a contraindication to performing mpMRI.

Outcomes:
Performance (sensitivity and specificity) of the algorithm to predict the standardized radiological PI-RADS
Prospective Validation of the STOPSHOCK Score - Artificial Intelligence Based Predictive Scoring System to Identify the Risk of Developing Cardiogenic Shock (CS) in Patients Suffering From Acute Coronary Syndrome (ACS)
Trial ID: NCT07090382
Status: ENROLLING_BY_INVITATION
Study type: OBSERVATIONAL
Start date: 2025-06-01
Completion date: 2026-04-30
Sponsor: Premedix Academy
Conditions: Cardiogenic Shock; Cardiogenic Shock Acute; Cardiogenic Shock Post Myocardial Infarction; Acute Coronary Syndrome (ACS) Undergoing Percutaneous Coronary Intervention (PCI); PCI
Locations: Premedix Academy
Countries: Slovakia
Enrollment: 1046
Age range: 18 Years - None
Sex: ALL

Summary:
Cardiogenic shock (CS) is a severe complication of acute coronary syndrome (ACS) with mortality approaching 50% despite the use of percutaneous mechanical circulatory support devices (pMCS). Identifying high-risk patients prior to the development of CS could allow pre-emptive use of pMCS possibly preventing CS. For this purpose, we derived and externally validated a machine learning score to predict in-hospital CS in patients with ACS with c-statistics: 0.844 (95% confidence interval, 0.841-0.847). STOPSCHOCK score is available as a web or smartphone application.

The aim of this study is to prospectively validate the STOPSHOCK score on a large cohort of ACS patients in a real- world clinical environment.

Eligibility criteria:
Inclusion Criteria:

* Patients aged \>18 years.
* Admitted for acute coronary syndrome in CCU

Exclusion Criteria:

* Patients aged \< 18 years.
* Patients in CSWG-SCAI C, D or E CS the before the admission to CCU.

Outcomes:
Discriminatory Power of the STOPSHOCK Score for Predicting Cardiogenic Shock
Artificial Intelligence for Enhancing the Diagnosis and Treatment of Mental Illness：a Prospective, Randomized Controlled Multicenter Clinical Trail
Trial ID: NCT04515173
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2021-04-01
Completion date: 2023-12-01
Sponsor: Shanghai Mental Health Center
Conditions: Artificial Intelligence; Mental Illness
Interventions: Artificial intelligence diagnosis and treatment assistance system
Enrollment: 4000
Age range: 18 Years - 60 Years
Sex: ALL

Summary:
To improve the quality of mental health services, we will develop a robot that includes disease screening, diagnosis, treatment, and follow-up. The effectiveness of robots will be verified in a prospective, randomized, multi-center clinical trial. We assume that the robot will reduce the differences in the experience of doctors of different years and will improve mental health care across the country, and improve the uneven distribution of mental health resources through remote resource sharing.

Eligibility criteria:
Inclusion Criteria:

* Community high-risk groups and institutional groups.
* According to the ICD-11 diagnostic guidelines of anxiety-related disorders, depression-related disorders, addiction-related disorders, compulsive-related disorders diagnosis,
* age 18-60 years old,
* skilled in the operation of computers or smartphones,
* language, reading ability is normal,
* willing to participate in the project and sign informed consent study.

Exclusion Criteria:

* The symptoms of mental illness are severe and require hospitalization, or the patient is unable to complete the required assessment examination and treatment.
* Those who have a higher risk of self-injury or suicide.
* People with severe physical illness, central system disease, or substance abuse.
* Receive physical therapy at the same time.

Outcomes:
System sensitivity; System specificity; Cure rate; Recurrence rate
Checklist for AI in Medical Imaging (CLAIM) Consensus Panel
Trial ID: NCT05984082
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2023-08-01
Completion date: 2024-05-01
Sponsor: University of Pennsylvania
Conditions: Artificial Intelligence; Machine Learning; Diagnostic Imaging
Locations: Hospital of the University of Pennsylvania
Countries: United States
Enrollment: 72
Age range: 18 Years - 89 Years
Sex: ALL

Summary:
The investigators will revise the Checklist for AI in Medical Imaging (CLAIM) guideline using Delphi consensus methods. An international panel of physicians, researchers, and journal editors with expertise in AI in medical imaging -- including radiology, pathology, dermatology, GI endoscopy, and ophthalmology -- will complete up to 3 web-based surveys. Participants who complete all survey rounds will be credited as contributors on resulting publications.

Eligibility criteria:
Inclusion Criteria:

* More than 70 invited panelists -- physicians, AI researchers, and journal editors -- have agreed to participate in the surveys. Those who participate in all rounds of the survey will be named as contributors in any resulting publications

Exclusion Criteria:

* Non physicians, AI researchers and journal editors

Outcomes:
Revise the Checklist for AI in Medicine Imaging (CLAIM) guideline
Assistance for Optical Diagnosis of Neoplasia Using Artificial Intelligence (FAIR Study)
Trial ID: NCT07158203
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-09
Completion date: 2025-12
Sponsor: Fundacin Biomedica Galicia Sur
Conditions: Polyps Colorectal; Colonoscopy; Optical Biopsy; Colorectal Cancer Control and Prevention; Colorectal Cancer Screening; Behavior Change; Psychological Factors; Psychological Intervention
Interventions: CADx simultaneously; CADx delayed
Locations: Clinical Effectiveness Research Group; Research Group in Gastrointestinal Oncology Ourense
Countries: Norway; Spain
Enrollment: 70
Age range: 18 Years - None
Sex: ALL

Summary:
Computer-aided diagnosis (CADx) for colonoscopy aims to enhance optical diagnosis but often underperforms when used alongside humans due to under-reliance on AI. Psychological interventions like cognitive forcing, such as delaying CADx suggestions, may improve human-AI interaction by fostering critical assessment. However, their impact on patient-important outcomes remains unexplored.

The investigators will conduct an ex-vivo randomized study with 70 endoscopists assessing 100 polyp videos (≤5 mm) using a CADx tool (GI Genius, Medtronic). Participants will be randomized to either:

* Intervention group: CADx suggestions will be shown in the last 3 seconds of the 15 second polyp video.
* Control group: CADx suggestions will be shown in real-time throughout the playback of the 15 second polyp video.

The primary endpoint is sensitivity for high-confidence neoplasia detection, with secondary endpoints assessing endoscopists' reliance on AI.

CADx systems on the market function in various ways, such as real-time, delayed, or on-demand diagnosis. Our study aims to inform users and manufacturers whether cognitive forcing through delayed CADx suggestions enhances human-AI interaction, leading to improved clinical outcomes.

Eligibility criteria:
Endoscopists who meet the following inclusion/exclusion criteria Inclusion criteria

* Endoscopists experienced with more than 100 colonoscopies. Exclusion criteria
* Endoscopists who are involved in the development of the protocol of the present study.

Inclusion and exclusion criteria for the study videos. Inclusion criteria.

* Videos with a duration of 15 seconds including both WL and NBI.
* Videos with diminutive polyps with confirmed pathology. Exclusion criteria.
* Videos with no clear image of the polyps.
* Videos with more than one polyp on-screen.
* Inflammatory bowel disease
* Polyposis
* Hereditary colorectal disease
* Videos which CADx cannot provide sufficient number of outputs.

Outcomes:
Sensitivity of the optical diagnosis of neoplastic lesions.
Beijing Anzhen Hospital
Trial ID: NCT06796127
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-02-01
Completion date: 2026-08-01
Sponsor: Beijing Anzhen Hospital
Conditions: Congenital Heart Disease (CHD); Artificial Intelligence (AI)
Locations: Beijing Anzhen Hospital affiliated to Capital Medical University
Countries: China
Enrollment: 10000
Age range: None - None
Sex: FEMALE

Summary:
This study addresses congenital heart disease in fetuses, a specific birth defect, by collaborating with multicenter medical institutions across China. It prospectively collects echocardiographic screening, diagnostic images, and structured parameter data from both normal and abnormal fetuses. Cases are followed up, and artificial intelligence (AI) models are developed to extract fetal heart echocardiographic image features and analyze the data. This process allows for the analysis of the AI models' accuracy, sensitivity, and specificity, and verifies the application value of AI models in fetal heart echocardiographic screening and diagnosis.

Eligibility criteria:
Inclusion Criteria:

* Single or multiple pregnancy;
* Gestational age 16-40 weeks;
* Fetal echocardiographic images are clear and complete;
* Clinical data are complete and detailed;
* Sign informed consent and be able to cooperate with follow-up

Exclusion Criteria:

* Fetal echocardiographic images are incomplete or of poor quality that cannot be used for research;
* Refusal or loss of follow-up

Outcomes:
Diagnostic efficiency of AI model for fetal CHD
Employing Machine Learning and Behavioral Science Solutions to Increase Women's Wellness Scheduling and Attendance: A Randomized Controlled Trial
Trial ID: NCT05509049
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2022-09-07
Completion date: 2024-01-09
Sponsor: Lirio
Collaborators: Rochester Regional Health
Conditions: Women's Health; Annual Visit; Well Visit; Health Behavior; Prevention
Interventions: Precision nudging; Standard of care
Locations: Rochester Regional Health
Countries: United States
Enrollment: 30068
Age range: 21 Years - 65 Years
Sex: FEMALE

Summary:
The purpose of the present randomized controlled trial is to explore the effectiveness of a 12-month well woman digital health intervention leveraging Precision Nudging - the application of behavioral science and reinforcement learning to create individualized, tailored health messaging at scale that matches the right message to the right person at the right time - in promoting behavior change. Specifically, it is hypothesized that scaling behavioral science through reinforcement learning will be more effective at motivating participants to engage with well woman messages and to schedule and to attend a well woman visit compared to a standard of care message.

Eligibility criteria:
Inclusion Criteria:

* Patient of health care system.
* Visit with OBGYN office in the last 36 months.
* OBGYN office provider is a Women's Wellness group approved provider.

Exclusion Criteria:

* Completed a women's wellness within the last 366 days.
* Upcoming women's wellness appointment.
* Had a childbirth episode (e.g., C-section vaginal delivery, or other event, including stillbirth) within the last 3 months or has a pregnancy due date in the future.
* Unsubscribed.

Outcomes:
Proportion of participants who attended a well woman visit; Proportion of participants who attended a well woman visit; Proportion of participants who attended a well woman visit; Proportion of participants who attended a well woman visit; Proportion of participants who scheduled a well woman visit; Proportion of participants who scheduled a well woman visit; Proportion of participants who scheduled a well woman visit; Proportion of participants who scheduled a well woman visit; Proportion of participants who scheduled a well woman visit and did not attend (and did not Cancel and/or Reschedule); Proportion of participants who scheduled a well woman visit and did not attend (and did not Cancel and/or Reschedule); Proportion of participants who scheduled a well woman visit and did not attend (and did not Cancel and/or Reschedule); Proportion of participants who scheduled a well woman visit and did not attend (and did not Cancel and/or Reschedule)
Artificial Intelligent Image Processing and Diagnosis of Pulmonary Vessels in CT
Trial ID: NCT06589843
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2024-09-10
Completion date: 2029-09-01
Sponsor: Xin Lou
Conditions: Radiology; Vascular Diseases
Interventions: Deep learning imaging enhancement
Enrollment: 15000
Age range: 18 Years - 100 Years
Sex: ALL

Summary:
In this study, patients with chest pain, lung cancer, pulmonary embolism, and routine inpatient physical examination were selected as the research objects, and the experimental design of retrospective cohort study was adopted to carry out artificial intelligence analysis related to pulmonary vascular diseases in patients with multi-dimensional big data. The multi-modal CT acquisition process included plain scan CT(NCCT) and CT pulmonary angiography (CTPA). Ctpa-like image effects can be simulated or reconstructed by non-enhanced plain scan CT images, so that CTPA-like image quality can be obtained without injecting contrast agent. The synthetic CTPA images were further analyzed by artificial intelligence to assist doctors in the intelligent diagnosis of pulmonary vascular diseases.

Eligibility criteria:
Inclusion Criteria:

* Age ≥18≤100 years old Scan the pulmonary artery and its major branches Patients with suspected pulmonary embolism who received CTPA had a set of CTPA and CT scans The image quality meets the requirements of diagnosis and post-processing Patients who completed the examination in accordance with the data collection criteria Clinical data and follow-up were complete

Exclusion Criteria:

* Age \&lt;18 years or age \&gt;100 years The image is incomplete or incorrect Pulmonary artery absent or underenhanced Severe motion artifacts or image noise affect evaluation of pulmonary embolism History of aortic reconstruction, replacement, or stent implantation Congenital variations in the whole or important branches of the aorta in adults (e.g. bovine aortic arch, abnormal right subclavian artery) Severe hypovolemia and hemodynamic instability Severe heart failure with low ejection fraction Dialysis patient

Outcomes:
The performance of deep enhanced imaging in lesion detection and diagnosis
Rosie the Chatbot: Leveraging Automated and Personalized Health Information Communication to Reduce Disparities in Maternal and Child Health
Trial ID: NCT06053515
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2023-10-13
Completion date: 2026-01-23
Sponsor: University of Maryland, College Park
Collaborators: National Institutes of Health (NIH); National Institute on Minority Health and Health Disparities (NIMHD)
Conditions: Pregnancy; Postpartum Depression; Infant Development; Infant Conditions
Interventions: Rosie the Chatbot
Locations: University of Maryland
Countries: United States
Enrollment: 400
Age range: 14 Years - 99 Years
Sex: FEMALE

Summary:
Rosie the Chatbot is an educational chatbot that moms can have on their computers or cellphones and will work by moms typing in their questions about pregnancy, health, infant milestones, and other variety of health related topics and receiving back a response immediately. Rosie only provides information from verified sources such as children's hospitals, health organizations and government agencies. Rosie does not ask moms to provide any personal information on her or her child, her chat is completely confidential, it works in English and Spanish and will be free.

Eligibility criteria:
Inclusion Criteria:

* Age 14 years and older
* Racial minority women
* Pregnant or has an infant \< 6 months
* Must be able to read English or Spanish

Exclusion Criteria:

* Mother younger than 14 years of age at time of child's birth.
* Non-hispanic white

Outcomes:
Maternal postpartum depression; Number of emergency room visits; Attendance of well-baby visits (preventive healthcare visits)