moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Clinical Utility of Artificial Intelligence-Augmented Endobronchial Ultrasound-Elastography in Lymph Node Staging for Lung Cancer
Trial ID: NCT04816981
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2021-09-01
Completion date: 2022-05-01
Sponsor: St. Joseph's Healthcare Hamilton
Conditions: Artificial Intelligence; Endobronchial Ultrasound; Elastography; NSCLC; Lung Cancer
Interventions: EBUS-Elastography
Locations: St. Joseph's Healthcare Hamilton
Countries: Canada
Enrollment: 100
Age range: 18 Years - None
Sex: ALL

Summary:
Before any treatment decisions are made for patients with lung cancer, it is crucial to determine whether the cancer has spread to the lymph nodes in the chest. Traditionally, this is determined by taking biopsy samples from these lymph nodes, using the Endobronchial Ultrasound Transbronchial Needle Aspiration (EBUS-TBNA) procedure. Unfortunately, in 40% of the time, the results of EBUS-TBNA are not informative and wrong treatment decisions are made. There is, therefore, a recognized need for a better way to determine whether the cancer has spread to the lymph nodes in the chest. The investigators believe that elastography, a recently discovered imaging technology, can fulfill this need. In this study, the investigators are proposing to determine whether elastography can diagnose cancer in the lymph nodes. Elastography determines the tissue stiffness in the different parts of the lymph node and generates a colour map, where the stiffest part of the lymph node appears blue, and the softest part appears red. It has been proposed that if a lymph node is predominantly blue, then it contains cancer, and if it is predominantly red, then it is benign. To study this, the investigators have designed an experiment where the lymph nodes are imaged by EBUS-Elastography, and the images are subsequently analyzed by a computer algorithm using Artificial Intelligence. The algorithm will be trained to read the images first, and then predict whether these images show cancer in the lymph node. To evaluate the success of the algorithm, the investigators will compare its predictions to the pathology results from the lymph node biopsies or surgical specimens.

Eligibility criteria:
Inclusion Criteria:

* Patients that are diagnosed with suspected or confirmed NSCLC that have been referred to mediastinal staging through EBUS-TBNA at St. Joseph's Healthcare Hamilton will be eligible for this study.

Exclusion Criteria:

* No exclusion criteria will apply.

Outcomes:
Stiffness Area Ratio
Evaluating the Clinical Effectiveness of "Endotracheal Tube Position Anomaly Alerting System"
Trial ID: NCT06857019
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-02-24
Completion date: 2025-12-31
Sponsor: Kaohsiung Medical University Chung-Ho Memorial Hospital
Conditions: Critical Care Medicine; Mechanical Ventilation; Radiographic Image Interpretation, Computer-Assisted; Alarms
Interventions: "Endotracheal Tube Position Anomaly Alerting System"
Locations: Kaohsiung Medical University Chung-Ho Memorial Hospital
Countries: Taiwan
Enrollment: 200
Age range: None - None
Sex: ALL

Summary:
The goal of this clinical trial is to determine whether the Endotracheal Tube Position Anomaly Alerting System (ETPAAS) effectively reduces the duration of endotracheal tube malposition in patients admitted to the intensive care unit (ICU). Additionally, the study will assess the clinical impact, usability, and user satisfaction of ETPAAS.

The main questions it aims to answer are:

* Does ETPAAS reduce the duration of endotracheal tube malposition?
* What are healthcare workers' perspectives on the usability and satisfaction of ETPAAS?

Participants will receive standard care, with or without ETPAAS, in the intensive care unit.

Researchers will evaluate the duration of endotracheal tube malposition. Healthcare workers will complete a questionnaire assessing the usability and satisfaction of ETPAAS.

Eligibility criteria:
Inclusion Criteria:

* Patients admitted to the intensive care unit
* At least 18 years old

Exclusion Criteria:

* Patients disagree to participate in the study

Outcomes:
Duration of endotracheal tube malposition
Institute of Sciences in Emergency Medicine - International Big Data Centre in Emergency Medicine
Trial ID: NCT05616416
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2011-01-01
Completion date: 2027-12
Sponsor: Guangdong Provincial People's Hospital
Conditions: Emergency Medicine
Locations: Guangdong Provincial People's Hospital
Countries: China
Enrollment: 500000
Age range: None - None
Sex: ALL

Summary:
This observational study aims to use electronic health records to build an International Big Data Centre in Emergency Medicine, within the Institute of Sciences in Emergency Medicine (ISEM) at the Guangdong Provincial People's Hospital. The main questions it seeks to answer are not limited to the following:

* Identify the relationship between Emergency Department Length of Stay (EDLOS), Mortality, and Adverse Events (AE)
* Identify the risk factors associated with high mortality and AE rate among patients who experience prolonged EDLOS
* Other research questions related to emergency medicine, such as building prediction and cluster models for acute diseases

Eligibility criteria:
Inclusion Criteria:

* Every patient who ever visited Emergency Department from 2011-2022

Exclusion Criteria:

* Patients with missing demographic (age, gender, etc.) and triage data

Outcomes:
In-hospital Mortality
Comparing the Virtual Operative Assistant to Expert-based Instruction in Surgical Education: A Randomized Controlled Trial
Trial ID: NCT04700384
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2021-01-15
Completion date: 2021-05-15
Sponsor: McGill University
Conditions: Surgical Education
Interventions: Virtual Operative Assistant Training; Remote-Based Expert Instructor Training
Locations: Neurosurgical Simulation and Artificial Intelligence Learning Centre
Countries: Canada
Enrollment: 70
Age range: None - None
Sex: ALL

Summary:
Brief Summary:

Background:

Although surgical experience and technical skill are associated with better patient outcomes, quantitating surgical ability in the operating room is challenging. In surgical education, large datasets generated by high-fidelity virtual reality simulators can be employed by machine learning algorithms to objectively measure trainee performance and competence on expert benchmarks. This allows repetitive practice of surgical skills in safe and risk-free environments with immediate feedback.

Our group developed and has a patent pending for an intelligent tutoring system called the Virtual Operative Assistant (VOA). Utilizing an Artificial Intelligence (AI) support vector machine algorithm, the VOA assesses data derived from the NeuroVR (CAE Healthcare) simulator platform and provides individualized audiovisual feedback to improve learner performance during simulated brain tumor resections. The effectiveness of intelligent tutoring systems such as the VOA to the human surgical apprenticeship pedagogy remains to be elucidated.

The aim of this study is to compare the effectiveness and educational impact of personalized VOA feedback to expert instruction on medical student's technical skills learning of a virtual reality tumor resection procedure.

Specific Aims: 1) To assess if medical students receiving personalized VOA feedback statistically improve their surgical performance when compared to those having (a) no expert instructor feedback or (b) expert instructor-mediated feedback. 2) To outline if different emotions are elicited by the VOA intelligent tutoring system in medical students while performing this achievement task as compared to human instruction

Eligibility criteria:
Inclusion Criteria:• First- and second-year medical students from any Canadian institution who do not meet the exclusion criteria.

\-

Exclusion Criteria: • Participation in any of our group's previous trials involving the NeuroVR (CAE Healthcare) simulator.

\-

Outcomes:
Change in procedural performance .; Change in learning
Developing a Psychosexual Education App With Artificial Intelligence for National Health Service Patients on a Psychosexual Services Waiting List: A Pilot Pre Test Post Test Study
Trial ID: NCT07152847
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-09
Completion date: 2027-03
Sponsor: London Metropolitan University
Collaborators: Homerton University Hospital NHS Foundation Trust; Barts & The London NHS Trust
Conditions: Sexual Dysfunction
Interventions: Psychosexual education app with artificial intelligence
Enrollment: 150
Age range: 18 Years - 100 Years
Sex: ALL

Summary:
The goal of this clinical trial is to establish the effectiveness of a resource based educational psychosexual app for patients attending a waiting list for psychosexual therapy. The app will provide information on sexual dysfunction, kegel exercises, cognitive and mindfulness exercises, along with suitable resources providing information on sexual functioning for men and women. The app will integrate artificial intelligence to improve user experience and efficiency.

The current waiting lists for the National Health Service Psychosexual services are approximately 6 months. Based on this, a pre test post test study measuring participants' sexual functioning, well being, cognitive mindfulness and quality of life will be taken at points 0, 8 and 16 weeks.

Outcomes will assess whether the app was sufficient for these domains. This might further reduce the strain on the National Health Service's resources.

It is hypothesised that app use from weeks 0, 8, and 16 follow up will improve sexual functioning, well being, quality of life, and cognitive mindfulness.

Eligibility criteria:
Inclusion Criteria:

* Must be aged 18 years and above
* Must have a laptop, computer, or mobile phone. The App can only be accessed on smartphones and laptops.
* Must have been referred to the National Health Service with a psychosexual presentation
* Must be currently awaiting psychosexual therapy
* Must have been on a National Health Service psychosexual waiting list for less than one month
* Must be fluent in reading and writing English, as this is a clinical trial, the investigators wanted to ensure that participants fully understand what was expected.
* The Patient Health Questionnaire 2 screening for depression must have a score range between 0 to 2 negative
* The Generalised Anxiety Disorder questionnaire 2 screening for anxiety must have a score range between 0 to 2 negative

Exclusion Criteria:

* Aged below 18 years
* Do not possess a mobile phone, computer or laptop
* Are not part of a National Health Service referral system
* Do not have a psychosexual presentation
* Are currently or starting psychosexual therapy
* Have difficulties understanding or speaking English
* The Patient Health Questionnaire 2 screening outcomes 3 and above
* The Generalised Anxiety Disorder questionnaire 2 screening outcomes 3 and above

Outcomes:
Female Sexual Function Index; The International Index of Erectile Function; Premature Ejaculation Diagnostic Tool
A Randomized Trial Comparing the Effectiveness of Pre-test Genetic Counseling Using an Artificial Intelligence Program and Traditional In-person Genetic Counseling in Women Newly Diagnosed With Breast Cancer Who do Not Currently Meet National Comprehensive Cancer Network (NCCN) Criteria for Genetic Testing.
Trial ID: NCT04354675
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2021-06-29
Completion date: 2023-03-30
Sponsor: Case Comprehensive Cancer Center
Conditions: Breast Cancer
Interventions: Automated program (ChatBot); Genetics counselor; BCGCKQ Survey; Satisfaction Survey; Genetic testing
Locations: Cleveland Clinic Taussig Cancer institute, Case Comprehensive Cancer Center
Countries: United States
Enrollment: 35
Age range: 18 Years - None
Sex: FEMALE

Summary:
The purpose of this study is to help better understand the uptake and impact of genetic testing for women diagnosed with breast cancer who do not meet National Comprehensive Cancer Network (NCCN) criteria for genetic testing. By doing so, the research team will gain a better understanding of the clinical implications for offering genetic testing for all patients recently diagnosed with breast cancer versus only offering genetic testing to those meeting NCCN criteria.

By offering genetic counseling and genetic testing to all women recently diagnosed with breast cancer, there will be a shortage of genetic counselors. This study will also assess the feasibility of using artificial intelligence to assist in the genetic counseling process.

Eligibility criteria:
Inclusion Criteria:

* Subjects diagnosed with Stage 0-3 breast cancer.
* Patients who do not satisfy current NCCN criteria for referral to a genetics counselor and genetics testing.
* Must have the ability to understand and the willingness to sign a written informed consent document as well as complete the study questionnaires.

Exclusion Criteria:

\- N/A

Outcomes:
Percent of participants who took up genetic testing after not meeting NCCN guidelines for genetic testing; Mean overall patient satisfaction with decision about genetic testing: survey; Overall comprehension as assessed by BCGCKQ
Functionality Assessment of RadiSpine, an Artificial Intelligence Software as Medical Device for Lumbar Spine Quantification System
Trial ID: NCT06950411
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2024-09-12
Completion date: 2025-03-31
Sponsor: RadiRad Co., Ltd.
Collaborators: Taipei Veterans General Hospital, Taiwan
Conditions: Spinal Cord Compression
Locations: Taipei Veterans General Hospital
Countries: Taiwan
Enrollment: 150
Age range: 22 Years - 75 Years
Sex: ALL

Summary:
Spinal degeneration and its associated clinical diseases are common ailments in aging societies. With the advent of a super-aging society, the importance of assistive technologies for spinal image interpretation is increasingly significant to enhance care efficiency and reduce medical personnel expenditure. Recently, due to the rapid development of artificial intelligence (AI) algorithm, AI-based computer-assisted detection (CADe) devices gradiually become a convenient method for spinal anatomy measurement. However, the accuracy of these devices has not been fully established. This study aims to validate the performance of RadiSpine (an application program) in spinal anatomy segmentation and measurement.

Eligibility criteria:
Inclusion Criteria:

The subjects should be aged 20 or older and younger than 75, with an equal gender distribution of 50% male and 50% female. From this group, 150 subjects with reasonable datavalues will be selected, with a requirement that at least 30% of them are male and at least 30% are female.

Exclusion Criteria:

1. With history of spinal surgery
2. Spinal trauma
3. Spinal osteoporosis
4. Spinal metastasis or infection

Outcomes:
Segmentation accuracy (Mean)
GeoHealth: Geographic Information System for Health Management and Clinical, Epidemiological and Translational Research
Trial ID: NCT05637411
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2020-12-09
Completion date: 2023-12-31
Sponsor: Hospital Universitario Virgen Macarena
Conditions: Asthma; Allergy; Heart Diseases; Obesity; Rheumatic Diseases
Interventions: Predictive model
Locations: Alberto Moreno Conde
Countries: Spain
Enrollment: 214
Age range: 6 Years - None
Sex: ALL

Summary:
The objective of the project is to develop a potentially marketable system for clinical, epidemiological and translational research capable of associating contextual variables and geospatial data with clinical patient information. The GeoHealth system will include a section to perform exploratory analysis that will help identify risk factors to optimize clinical decision making. The system will be used in a study of the influence of environmental factors in more than 800,000 asthmatic patients in the region of Andalucía, Spain. The results of this study will help to develop predictive models of risk of exacerbation in asthma patients. Currently, disease management guidelines state that asthma control has two components: current disease control and future risk of exacerbations. These two components are key when assessing disease management, following a stepwise model that seeks to improve current control and minimize future risk. However, although the current control of the disease is defined and has established methodologies for its measurement (Asthma Control Test -ACT- and Asthma Control Questionnaire -ACQ-), the risk of suffering exacerbations is not yet a measurable component.

Therefore, these predictive models could help in professional decision making for asthmatic patients.

The predictive models will be validated prospectively and implemented in a decision support system for the management of asthmatic patients.

Eligibility criteria:
Inclusion Criteria:

* Patients diagnosed with bornchial asthma.

Exclusion Criteria:

* Women who are pregnant, breastfeeding or want to become pregnant during the duration of the study.
* Subjects with malignant disease, uncontrolled risk factors or any pathology that compromises the safety of the study.
* Subjects with problems that limit their ability to cooperate.

Outcomes:
Exacerbations
Smartphone Multimodal Health Intervention for Neurological Enhancement (SHINE) in High Stroke-risk Population: a Randomized Trial
Trial ID: NCT07092371
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-08-01
Completion date: 2029-07-01
Sponsor: Second Affiliated Hospital, School of Medicine, Zhejiang University
Conditions: Cognitive Impairment; Dementia; Artificial Intelligence (AI); Stroke; Cognitive Decline
Interventions: Digital Multimodal Intervention
Locations: Second Affilated Hospital of Zhejiang University, School of Medicine
Countries: China
Enrollment: 1036
Age range: 40 Years - None
Sex: ALL

Summary:
This randomized controlled trial will enroll individuals aged ≥ 40 years without dementia who are at high risk for stroke, collecting multi-modal MRI imaging, serum, and fecal samples to investigate the effects of a smartphone-based multimodal health intervention on cognitive function measured using digital-evaluated cognitive scores.

Eligibility criteria:
Inclusion Criteria:

* Patients aged ≥ 40 years
* High risk of stroke (with ≥ 3 of 8 stroke risk factors, including hypertension, dyslipidemia, diabetes, atrial fibrillation or valvular heart disease, smoking history, obvious overweight or obesity, lack of exercise, family history of stroke, or with transient ischemic attack)
* Written informed consent available
* Primary school education or above
* Proficient in using smartphones
* Willingness to complete all assessments and participate in follow-up

Exclusion Criteria:

* previously diagnosed dementia
* Suspected dementia after clinical assessment by study physician at screening visit
* Previous history of major head trauma and any intracranial surgery
* Intracranial abnormalities, such as intracerebral hemorrhage, subarachnoid hemorrhage and other space occupying lesions
* Extrapyramidal symptoms or mental illness which may affect neuropsychological measurement
* Severe loss of vision, hearing, or communicative ability
* Patients presenting a malignant disease with life expectancy \< 3 years
* Participation in an ongoing investigational drug study

Exit Criteria:

* Not meet the inclusion criteria
* For any poor adherence, not comply with the requirements of the follow-up, or safety reasons determined by investigator
* Any adverse or serious adverse events during the study period judged by Investigator

Outcomes:
Global cognitive function change assessed with Z-score of a digital cognitive assessment protocol
Accelerated Body Diffusion-Weighted MRI Using Artificial Intelligence
Trial ID: NCT05380609
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2022-05-06
Completion date: 2025-04
Sponsor: Royal Marsden NHS Foundation Trust
Conditions: Cancer
Locations: Department of Radiology, The Royal Marsden NHS Foundation Trust
Countries: United Kingdom
Enrollment: 450
Age range: 18 Years - None
Sex: ALL

Summary:
Whole-body diffusion-weighted MRI (WBDWI) is a non-invasive tool used for staging and response evaluation in oncologic practice and is at the core of emerging response criteria in advanced prostate and breast cancers.

WBDWI is a sensitive tool that radiologists can use to review the extent of disease and is achieved using a series of sequential imaging stations from the head to the mid-thigh. WBDWI accounts for more than 50% of the acquisition time of conventional whole-body MRI studies with a 1-hour duration. Despite national and international guidance for using whole-body MRI, a recent UK survey indicated that only 27% of UK radiology departments were offering a whole-body MRI service with a lack of scanner availability cited by 50% of respondents as the main challenge to service delivery. In the context of the ever-increasing capacity pressures on MRI departments, reducing acquisition times would facilitate the wider adoption of clinical WBDWI, reduce costs, and improve the patient experience.

DWI is also embedded into consensus MRI protocols across almost all tumour types including primary prostate and breast cancers, metastatic liver disease, gynaecological cancers \& GI cancers, where acquisition time savings could also be beneficial. The investigators have previously published accelerated DWI with deep learning based denoising filters (quickDWI), which can provide up to 50% reduction in whole-body MRI acquisition times. The goal of the deep-learning algorithm is to remove the noise in these subsampled images, producing an image with acceptable clinical quality.

The aim of this investigation is to extend this work by testing quickDWI within a larger retrospective data cohort, incorporating other cancers such as disease of the abdomen and pelvis, primary prostate cancer, liver metastases, and pancreatic cancer.

Eligibility criteria:
1. Patients who have received one of the following MRI examinations (scan type shown in parentheses):

   * Multiple Myeloma (whole-body)
   * Metastatic Prostate Cancer (whole-body)
   * Metastatic Breast Cancer (whole-body)
   * Liver Metastases (stacked abdomen/pelvis)
   * Pancreatic Cancer (stacked abdomen/pelvis)
   * Gynaecological Cancers (stacked abdomen/pelvis)
   * Gastrointestinal Cancers (stacked abdomen/pelvis)
   * Primary Prostate Cancers (multiparametric Prostate exam)
2. Patients who have received one of the above-mentioned scan types using a modified clinical multi-direction diffusion-weighted (MDDW) MRI protocol as part of standard of care.

Exclusion Criteria:

* No exclusion criteria as this is a retrospective data study only.

Outcomes:
Qualitative image comparison