moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Echocardiography Image Quality Management System Based on Deep Learning: A Single-center Prospective Study
Trial ID: NCT05633732
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2022-12-30
Completion date: 2025-12-31
Sponsor: The Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School
Collaborators: Southeast University, China
Conditions: Echocardiography
Locations: Affiliated Drum Tower Hospital of Nanjing University Medical School
Countries: China
Enrollment: 2000
Age range: 18 Years - None
Sex: ALL

Summary:
To develop an echocardiography image quality management system based on deep learning to achieve objective and accurate automatic echocardiography image quality control. A total of 2000 patients performing transthoracic echocardiography were prospectively enrolled in the Department of Ultrasound Medicine of the Affiliated Drum Tower Hospital with Medical School of Nanjing University. The data of 8 TTE view segmentations were collected, including the views of the parasternal long axis of the left ventricle (PLAX\_LV), parasternal short axis of the large vessel level (PSAX\_GV), parasternal short axis of the mitral valve level (PSAX\_MV), parasternal short axis of the papillary muscle level (PSAX\_PM), parasternal short axis of the apical level (PSAX\_AP), apical four cavity (A4C), apical three cavity (A3C), apical two cavity (A2C). The data of 1500 patients were used as the training set, and the rest were used as the validation set. These video data were classified into corresponding view segmentations and analyzed by the Video Swin Transformed Model. Then, the scoring module of different view segmentations combined key frame extraction, image segmentation, video target recognition and video classification model were established. At the same time, the scores achieved by the automatic echocardiography image assessment system were compared with the artificial score. By constantly correcting and learning and eventually building an primary automated grading system. At last, the automatic echocardiography image assessment system was constructed and performed on the rest 500 patients.

Eligibility criteria:
Inclusion Criteria:

1. aged ≥18years, gender unlimited;
2. Patients with standardized TTE views;
3. Subjects participated in the study voluntarily and signed informed consent;

Exclusion Criteria:

1. patients wirh incomplete standard TTE views;
2. patients with poor sound transmission conditions.

Outcomes:
the score of PSAX view; the score of apical view
Point of Care Lung Ultrasound Examination in Patients With Shortness of Breath
Trial ID: NCT07046234
Status: ENROLLING_BY_INVITATION
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-08-19
Completion date: 2025-12
Sponsor: GE Healthcare
Collaborators: Northwestern University; Rush University Medical Center; Yale University
Conditions: Lung Disease; Lung Ultrasound
Interventions: Lung ultrasound
Locations: Yale University School of Medicine; Rush University Medical Center; Northwestern University
Countries: United States; United States; United States
Enrollment: 150
Age range: 22 Years - None
Sex: ALL

Summary:
This is a prospective, non-randomized clinical validation research study. Subjects will consent and have two ultrasounds as part of the study.

Eligibility criteria:
Inclusion Criteria:

* 1\. Patients over the age of 22; AND
* 2\. Patients with known lung pathology or patients with no known lung pathology in the hospital or presenting to the hospital or outpatient setting with shortness of breath.

Exclusion Criteria:

* 1\. Patients in extremis and/or patients in whom a lung ultrasound would not normally be performed;
* 2\. Patients who are unable to consent;
* 3\. Patients who are prisoners; AND
* 4\. Patients who do not speak English fluently.

Outcomes:
Cohort 1: % of patient studies composed of images acquired by the THCPs with sufficient quality to make a clinical assessment; Cohort 2: % of patient studies composed of images acquired by the Qualified user to make a clinical assessment
Faculty of Medicine Siriraj Hospital
Trial ID: NCT02993393
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2015-08
Completion date: 2017-02
Sponsor: Siriraj Hospital
Conditions: Education
Interventions: Teacher; Student
Locations: Parichad Apidechakul
Countries: Thailand
Enrollment: 50
Age range: 25 Years - None
Sex: ALL

Summary:
Abstract Background and Goal of study Teaching and learning in airway management are essential in anesthetic field. Though simulation-based and problem-based learning are sophisticated learning tool, neither of them manifests the superior benefit. We would like to compare the teachers' and students' attitudes on these two learning methods.

Material and Methods After IRB approval No. 369/2558(EC3). A prospective, questionnaires-based study was performed amongst volunteered, consent-signed, 10 anesthesiologists and 40 nurse anesthetist students. After stratified randomization, ten students simultaneously attended either SBL or PBL course one at a time. Six weeks later, a crossover technique was applied for both groups. At the end of project, teachers and students had to response to Likert's scale questionnaires.

The teachers' questionnaire based on table of specification of the learning contents, consisted of 4 parts: airway evaluation, patient preparation, strategic planning and follow up care. The students' questionnaire comprised 3 parts: learning content, process and evaluation.

The validation of the questionnaire was determined by three board-certified anesthesiologists. The index of item objective congruence was 0.80 and 0.82 with Cronbach's Alpha of 0.97 and 0.92 respectively.

Eligibility criteria:
Inclusion Criteria:

* anesthesiologists who have involved in SBL and PBL with more than 3 years of experience in teaching.
* nurse anesthetist students in the academic years of 2015.

Exclusion Criteria:

* ones who did not fit all qualifications.

Outcomes:
Student's point of view
An Artificial Intelligence Model for Accurate Diagnosis of Renal Tumors Based on Multi-phase Contrast-enhanced CT and Laboratory Tests: A Model Development and Multi-center Evaluation Study
Trial ID: NCT06761742
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2024-01-01
Completion date: 2024-12-01
Sponsor: RenJi Hospital
Collaborators: Shanghai Jiao Tong University Affiliated Sixth People's Hospital; Fudan University Pudong Medical Center; Fudan University; The Affiliated Hospital Of Southwest Medical University
Conditions: Renal Tumors
Locations: Shanghai Jiaotong University School of Medicine, Renji Hospital
Countries: China
Enrollment: 1922
Age range: None - None
Sex: ALL

Summary:
This multi-center retrospective study aims to develop a multimodal artificial intelligence diagnostic model using preoperative contrast-enhanced CT images and routine laboratory parameters from patients with renal tumors. The model is designed to assist clinicians in accurately predicting the pathological subtypes of renal tumors preoperatively, enabling detailed diagnoses and advancing precision medicine.

Eligibility criteria:
Inclusion Criteria:

* Underwent renal tumor resection with a complete postoperative pathological report, and the pathological diagnosis is one of the following types: clear cell renal cell carcinoma, papillary renal cell carcinoma, chromophobe renal cell carcinoma, renal angiomyolipoma, or renal oncocytoma.
* Complete and available four-phase contrast-enhanced CT scans prior to surgery.
* Complete and available routine laboratory test results prior to surgery.

Exclusion Criteria:

* Incomplete CT data or poor image quality that affects diagnostic analysis.
* A time interval of more than three months between imaging or laboratory testing and pathological diagnosis.
* Patients diagnosed with fat-rich renal angiomyolipoma (AML).
* Pathological diagnosis indicating the coexistence of two or more pathological types of renal tumors.

Outcomes:
postoperative pathological report
Cardiovascular Kidney and Metaboolic (CKM) Health Assessment and Patient Empowerment in chROnic Disease Using a Health Coach INtervention ModEl: A Randomized Clinical Trial
Trial ID: NCT07403669
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-10-22
Completion date: 2027-04-22
Sponsor: Aventyn, Inc.
Collaborators: Dignity Health; Arizona Heart Foundation; Arizona State University; University of Arizona; University of Phoenix College of Nursing; Creighton School of Medicine, Phoenix Arizona; Karolinska University Hospital; Partnership for Economic Innovation Arizona; Kanasu Labs; Intel Corporation
Conditions: Acute MI; Heart Failure; Diabetes Mellitus; Kidney Disease
Interventions: CHAPERONE AI; CKMiq AI; Quality of Life
Locations: Fountains Family Care; Gilbert Cardiology; Arizona Heart Foundation; East Valley Diabetes; Karolinska University Hospital
Countries: United States; United States; United States; United States; Sweden
Enrollment: 300
Age range: 18 Years - None
Sex: ALL

Summary:
The primary objective of CHAPERONE solution is to evaluate the efficacy of engaging, assessing, and enabling long term treatment strategy with Health Artificial Intelligence (AI) Chatbot, Copilot, Large Language Model (LLM) and vital sign monitoring device in reducing CKM disease burden using an algorithm obtained from biomarkers and diagnostics utilizing remote sensor driven technology tools, care coordination and patient empowerment.

Eligibility criteria:
Inclusion Criteria:

* Men and women over 18 years of age are included
* Eligible subjects must have one of the risk factor/ disease component of CKM:
* Diabetes with A1C of 7.5 or higher;
* or Heart disease with stent placement;
* or Coronary Artery Bypass Graft (CABG) in the last 12 months;
* or Chronic Kidney Disease (CKD) stage 2- 4
* and, in those with Heart failure, a recent hospitalization within one week of enrollment is required

Exclusion Criteria:

* Those subjects who are not willing to sign an informed consent

Outcomes:
CHAPERONE AI Treatment
Comparison of a Large Language Model (LLM)-Facilitated Cognitive Debiasing Strategy Versus LLM-Generated Diagnostic Feedback Alone in Musculoskeletal Specialty Care: A Randomized Controlled Trial
Trial ID: NCT07022769
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-06-23
Completion date: 2025-12-31
Sponsor: University of Texas at Austin
Conditions: Any Chronic, Non-traumatic Orthopedic Condition
Interventions: LLM-facilitated cognitive debiasing aid
Locations: Dell Medical School, University of Texas at Austin
Countries: United States
Enrollment: 150
Age range: 18 Years - None
Sex: ALL

Summary:
The goal of this clinical trial is to find out whether using an artificial intelligence (AI) tool called a Large Language Model (LLM) can help patients think more clearly about their symptoms and improve their trust and experience during a visit to a musculoskeletal specialist.

The study will answer two main questions:

1. Does an LLM-guided checklist that encourages patients to reflect on their beliefs about their symptoms improve their trust in the clinician (measured using the TRECS-7 scale)?
2. Does the checklist improve how patients feel about their consultation overall?

Participants will be randomly assigned to one of two groups:

* One group will receive an LLM-guided checklist that helps them think more flexibly about their condition.
* The other group will receive an LLM-generated likely diagnosis and brief explanation of their symptoms.

In both groups, the information from the AI tool will be shared with both the patient and the clinician before the consultation.

Patients in the debiasing (intervention) group will:

* Complete a short set of questions with help from a researcher
* Receive a simple summary from the AI that reflects their beliefs and gently challenges any unhelpful thinking
* Attend their regular specialist appointment
* Complete a short survey afterwards capturing their thoughts, experience and basic demographics

Patients in the diagnosis-only (control) group will:

* Describe their symptoms to the AI LLM
* Receive a likely diagnosis and short explanation based on this description
* Attend their regular specialist appointment
* Complete a short survey afterwards capturing their thoughts, experience and basic demographics

Eligibility criteria:
Inclusion Criteria:

* Adults (18+)
* New or return patient seeking musculoskeletal specialty care at an Orthopedic outpatient clinic
* Total combined score on the 6 feelings and thoughts items of \> 10\* (Appendix 3 of study protocol)
* English-speaking
* Pre-visit diagnosis of chronic, non-traumatic musculoskeletal condition (including, but not limited to: osteoarthritis, carpal tunnel syndrome, trigger digit, Dupuytren's, De Quervain's, lateral epicondylitis)

Exclusion Criteria:

* Any impairment preventing completion of surveys on a tablet

Outcomes:
Trust and Experience with the Clinician Scale (TRECS-7)
Development of a Machine Learning Support for Reading Whole Body Diffusion Weighted Magnetic Resonance Imaging (WB-DW-MRI) in Myeloma for the Detection and Quantification of the Extent of Disease Before and After Treatment
Trial ID: NCT03574454
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2018-07-04
Completion date: 2022-12-31
Sponsor: Royal Marsden NHS Foundation Trust
Collaborators: Institute of Cancer Research, United Kingdom; Imperial College London
Conditions: Myeloma
Interventions: Machine Learning (ML)
Locations: Department of Radiology, The Royal Marsden NHS Foundation Trust; Institute of Cancer Research, London; Imperial College, London
Countries: United Kingdom; United Kingdom; United Kingdom
Enrollment: 50
Age range: 40 Years - 100 Years
Sex: ALL

Summary:
Diffusion-weighted Whole Body Magnetic Resonance Imaging (WB-MRI) is a new technique that builds on existing Magnetic Resonance Imaging (MRI) technology. It uses the movement of water molecules in human tissue to define with great accuracy cancerous cells from normal cells. Using this technique the investigators can much more accurately define the spread and rate of cancer growth. This information is vital in the selection of patients' treatment pathways. WB-MRI images are obtained for the entire body in a single scan. Unlike other imaging techniques such as computed Tomography (CT) or Positron Emission Tomography (PET) PET/CT there is no radiation exposure.

Despite the considerable advantages that this new technique brings, including "at a glance" assessment of the extent of disease status, WB-MRI requires a significant increase in the time required to interpret one scan. This is because one whole body scan typically comprises several thousand images. Machine learning (ML) is a computer technique in which computers can be 'trained' to rapidly pin-point sites of disease and thus aid the radiologist's expert interpretation. If, as the investigators believe, this technique will help the radiologist to interpret scans of patients with myeloma more accurately and quickly, it could be more widely adopted by the NHS and benefit patient care.

The investigators will conduct a three-phase research plan in which ML software will be developed and tested with the aim of achieving more rapid and accurate interpretation of WB-MRI scans in myeloma patients.

Eligibility criteria:
Inclusion Criteria (healthy volunteers):

* Able to provide written informed consent
* No contra-indication to MRI
* 40 years or above in age (age matched as far as possible to WB-MRI scan set)
* No known significant illness
* No known metallic implant

Exclusion Criteria:

* Not able to provide written informed consent
* A contra-indication to MRI
* \<40 years or above in age (age matched as far as possible to WB-MRI scan set)
* A known significant illness
* A known metallic implant

Outcomes:
Sensitivity of Machine Learning Algorithm to detect Myeloma
Structural Stability of Carotid Plaque and Symptomatology
Trial ID: NCT02476396
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2015-11-09
Completion date: 2024-07-14
Sponsor: University of Wisconsin, Madison
Collaborators: National Heart, Lung, and Blood Institute (NHLBI); National Institute of Neurological Disorders and Stroke (NINDS)
Conditions: Carotid Stenosis; Stroke; Carotid Artery Plaque; Transient Ischemic Attack; Cerebrovascular Accident; Carotid Artery Stenosis
Interventions: Carotid Endarterectomy or Stenting
Locations: University of Wisconsin-Madison
Countries: United States
Enrollment: 176
Age range: 18 Years - None
Sex: ALL

Summary:
The purpose of the research is to understand structural plaque abnormalities that make a carotid plaque unstable and brake off (embolize) which would help to predict and treat individuals who are likely to suffer not only classic episodic major strokes but also cognitive impairment.

Eligibility criteria:
Inclusion Criteria (Patients):

* Patients undergoing carotid endarterectomy or stenting (endovascular) for established clinical criteria
* Age \> 18 years
* Male or Female
* English speaking
* Patients must sign written informed consent form

Inclusion Criteria (Controls):

* A spouse or sibling of a Patient Subject
* Age \> 18 years
* Male or Female
* English speaking
* Control-Subject must sign a written informed consent form

Exclusion Criteria (Patients):

* Previous history of carotid artery surgery (endovascular or open) on the same side
* Previous cervical radiation
* Patients not felt be suitable for carotid endarterectomy or stenting (endovascular)
* Patients with impaired consent capacity
* Contraindication to MRI scans (impaired renal function, need for sedative medication during scans, inability to lie in scanner for 60 minutes)
* Prisoner status

Exclusion Criteria (Controls):

* Control-Subjects with impaired consent capacity
* Prisoner status
* Previous history of Stroke or TIAs
* Previous history of carotid artery surgery (endovascular or open)

Outcomes:
Change in Ultrasound Strain Measurements (Gray Scale Median Value) on Carotid Plaques; Change in In-vivo velocity measured by Transcranial Doppler (TCD); Change in Systolic to diastolic ratio measured by transcranial Doppler (TCD); Change in Pulsatility index measured by transcranial Doppler (TCD); Change in Resistive index measured by transcranial Doppler (TCD); Histopathologic classification of carotid atherosclerotic plaque after removal for plaque ulceration; Change in Impairment Index - General Cognitive Morbidity
Precision Imaging for Early Detection and Targeted Treatment Monitoring in Pancreatic Cancer
Trial ID: NCT06144762
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2023-12-19
Completion date: 2029-10
Sponsor: Institut du Cancer de Montpellier - Val d'Aurelle
Conditions: Pancreas Cancer
Interventions: Biological/Vaccine: Blood sample and tissue sample
Locations: NOUGARET Stephanie
Countries: France
Enrollment: 150
Age range: 18 Years - None
Sex: ALL

Summary:
Specifically, in this project, the objective will be developped a model to capture imaging-based tumor heterogeneity with multiscale radiomics approach by obtaining the mirror tumor image at in vivo MRI, ex vivo MRI at histology. This imaging model giving a perfect virtual histology tumor representation will be secondary implemented on routine in vivo clinical MRI for early cancer detection and treatment monitoring. Successful completion of this proposal will lead to a comprehensive non invasive characterisation of pancreatic cancer and will be a game changer in patient management.

Eligibility criteria:
Inclusion Criteria:

* Patient aged \>18 2.
* Pathologically proven pancreatic cancer which can beneficiate of upfront surgery or delayed surgery followed by neoadjuvant chemotherapy.
* Negative pregnancy test for women of childbearing potential
* Patients affiliated to a social protection system
* Written informed consent signed before project onset.

Exclusion Criteria:

* presence of metastases,
* Patient who will not have surgery
* Pregnant or breastfeeding women
* Mental or psychological state, physical or legal incapacity preventing participation in the project.

Outcomes:
the integration of in vivo and ex vivo MRI with histology and molecular caracteristic in order to increase the pancreatic cancer detection and therapeutic response monitoring
Electroencephalographic and Body Composition and Psychometric Mesures of a Training of Psychological Flexibility Skills Based on ACT on ACT Processes on University Students of Mexicali and Mexicali Valley.
Trial ID: NCT07492056
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-08-30
Completion date: 2027-03-15
Sponsor: Universidad Autonoma de Baja California
Conditions: General Population (no Specific Condition or Disease)
Interventions: A Psychological Flexibility Skills Training based on ACT.
Enrollment: 30
Age range: 18 Years - 30 Years
Sex: ALL

Summary:
Overweight and obesity are an increasing tendencies worldwide. Disordered Eating Behaviors (DEBs) are feeding dynamics commonly present on them, as in Feeding Disorders (FD). These eating and body conditions are highly correlated with specific psychopathological symtoms, such as anxiety and depression, weight-related shame and stigma, and academic stress. Additionally, electroencephalographic findings suggest that weight- and eating-related conditions may be mediated by neural pathways specific to executive functions, such as selective attention and inhibitory control. Therefore, this cuasi-experimental design, pre- and post- measurement, longitudinal cross-sectional study aims to evaluate the effect of a training on psychological flexibility skills, based on Acceptance and Commitment Therapy (ACT) processes, on psychological, electroencephalographic and body composition measures.

Eligibility criteria:
Inclusion Criteria:

* Between 18 and 30 years old.
* Currently enrrolled in university studies.
* Attend at least 80% of the intervention sessions.

Exclusion Criteria:

* Having a diagnosis of personality disorder.
* Failure to attend post-assessment.
* Currently attending psychological therapy or psychiatric care..

Outcomes:
Disordered Eating Behaviors.; Disordered Eating Behaviors.; Disordered Eating Behaviors.; Anxiety Symtoms.; Depression Symtoms.; Inhibitory Control; Cognitive Flexibility; Social Cognition; Perceived Stress; Body image satisfaction; Neuroelectrical activity.