moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Risk Evaluation by Coronary Imaging and Artificial Intelligence-Based Functional Analyzing Techniques: Integrated Plaque Analysis by Coronary CT Angiography and Intracoronary Imaging.
Trial ID: NCT06793774
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2024-07-01
Completion date: 2027-12-31
Sponsor: Ruijin Hospital
Conditions: Coronary Artery Disease; Coronary Atheroscleroses; Acute Coronary Syndromes (ACS)
Locations: Ruijin Hospital, Shanghai Jiaotong University School of Medicine
Countries: China
Enrollment: 300
Age range: 18 Years - None
Sex: ALL

Summary:
This is a single-center, prospective cohort study. This study is designed to accurately analyze coronary artery plaque characteristics and local hemodynamic parameters in patients diagnosed with chronic coronary syndrome (CCS) or non-ST-segment elevation acute coronary syndrome (NSTE-ACS), with marginal lesions or obstructive lesions in major coronary arteries by multimodality imaging including noninvasive coronary CT angiography (CCTA) and intracoronary imaging techniques, such as intravascular ultrasound (IVUS), optical coherence tomography (OCT), and near infrared spectroscopy (NIRS). The purpose of this study is to improve the accuracy and depth of plaque analysis by CCTA under the guidance of intracoronary imaging, therefore constructing a new CCTA-based high-risk plaque model.

Eligibility criteria:
Inclusion Criteria:

* Age ≥ 18 years
* Patients with CCS or NSTE-ACS
* Receive CCTA scan, with marginal lesions (DS between 40%-69%) or obstructive lesions (DS ≥70% or CT-FFR \<0.8) in major coronary arteries
* Receive invasive coronary angiography and intracoronary imaging examination, including IVUS, OCT, NIRS, within 1 month after CCTA

Exclusion Criteria:

* Unsuitable for CCTA (such as severe renal impairment, uncontrolled thyroid condition, allergic to iodine, etc.)
* Receive percutaneous coronary intervention (PCI) within 6 months
* Prior history of myocardial infarction or heart failure
* Prior history of coronary artery bypass graft (CABG)
* Abnormal liver function (serum alanine aminotransferase \[ALT\] level exceeding 3 times the upper limit of normal) or abnormal kidney function (eGFR ≤30%)
* Familial hypercholesterolemia
* Estimated survival ≤ 1 year
* Malignant tumor
* Pregnant or lactation, or have the intention to give birth within one year
* Poor compliance, unable to follow-up

Outcomes:
Prediction performance of high-risk plaques by CCTA
Implementation of Large Language Models in Anesthesia to Answer Patients' Questions During Pre-Anesthesia Visits: A Prospective, Observational Study
Trial ID: NCT06652854
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2024-12-09
Completion date: 2025-06-30
Sponsor: Ottawa Hospital Research Institute
Conditions: Preoperative Care; Patient Education; Anesthesia; Artificial Intelligence
Locations: Ottawa Hospital Research Institute
Countries: Canada
Enrollment: 190
Age range: 18 Years - None
Sex: ALL

Summary:
Patients are waiting for surgery longer than ever before. While hospitals are trying to get patients access to surgery as quickly as possible, a shortage of anesthesia doctors (called anesthesiologists) prevents this. Every surgery needs an anesthesiologist, but anesthesiologists also assess patients beforehand to ensure they can safely undergo surgery and help patients understand what to expect and the risks involved. Ensuring that patients move through surgery as safely and efficiently as possible requires that the limited number of anesthesiologists in Canada work as efficiently as possible. Technology like Artificial Intelligence (AI), in programs such as ChatGPT, might assist by answering common questions patients have, allowing anesthesiologists to focus their limited time on addressing complex and personalized issues for each patient. However, studies conducted so far on how well AI can answer questions about anesthesia and surgery have been poorly designed and have not included patients as members of the research team. This study will determine whether AI can answer patients' questions before surgery as effectively as both patients and expert anesthesiologists would expect, which could be beneficial to patients and the healthcare system.

Eligibility criteria:
Inclusion Criteria:

Patients

* 1\. Age \> 18 years 2. Elective non-cardiac surgery 3. In-person preoperative anesthesiology consultation 4. Ability to participate and provide informed consent independently.

Exclusion Criteria:

* 1\. Unable to communicate in English or French 2. Nurse consultation only

Physicians

Inclusion Criteria:

Anesthesiology residents or staff providing service at the PAU at TOH

Exclusion Criteria:

Anesthesiology residents or staff unwilling to participate in the research study.

Outcomes:
knowledge
CRYSTALSIGHT: ''Cohort 1.5" Clinical Investigation Study With OCCUTRACK and Tan Tock Seng Eye Clinic to Further Develop Remote Monitoring of Maculopathy at Home Using Artificial Intelligence for Eye Tracking
Trial ID: NCT07255885
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2025-05-08
Completion date: 2025-12-31
Sponsor: Tan Tock Seng Hospital
Collaborators: Occutrack Medical Solutions Pte Ltd; Agency for Science, Technology and Research; National Healthcare Group, Singapore
Conditions: Maculopathy; Maculopathy, Age Related; Macular Degeneration, Age-Related; Diabetic Retinopathy; Wet Age Related Macular Degeneration; Aged-related Macular Degeneration
Interventions: CRYSTALSIGHT Test
Locations: Tan Tock Seng Hospital
Countries: Singapore
Enrollment: 20
Age range: 21 Years - 100 Years
Sex: ALL

Summary:
The development of a next-generation 'CRYSTALSIGHT' solution using combinations of a novel and cost-effective eye-tracking system with artificial intelligence-based eye-tracking algorithms that detect macular abnormalities and enable clinicians to review and monitor the prognosis of patients via a web platform through the following deliverables.

1. Evaluate and improve a home-monitoring regimen involving the self-tests of the CRYSTALSIGHT gaze-tracking system
2. To demonstrate that CRYSTALSIGHT has the same or superior gaze-tracking capacities as Tobii.
3. Evaluate the CRYSTALSIGHT device for its functionality and ease of use as a qualitative measurement tool for patients.
4. Develop the Design History File (DHF) for regulatory filing requirements.

   2\. This study will improve on the existing gaze-based scoring methodology for disease activity monitoring over time (delta-change) by quantitatively measuring saccadic speed, pursuit and micro-saccades.

Eligibility criteria:
Inclusion Criteria:

1. Participants between ages 21 and 100
2. Both genders
3. Subjects with maculopathies
4. Ability to comply with the study protocol, in the investigator's judgment
5. Subjects must have the cognitive capacity to provide personal consent (i.e. no cognitively impaired persons will be recruited).

Exclusion Criteria:

1. Uncontrolled blood pressure, defined as systolic blood pressure \>180 millimeters of mercury (mmHg) and/or diastolic blood pressure \>100 mmHg while a patient is at rest.
2. Any concurrent intraocular condition in the study eye that, in the opinion of the investigator, could either reduce the potential for visual improvement or require medical or surgical intervention during the study.
3. History of idiopathic or autoimmune-associated uveitis in either eye.
4. Active ocular inflammation or suspected or active ocular or periocular infection in either eye.
5. Other protocol-specified exclusion criteria may apply.
6. Individuals with indications of cognitive impairment and unable to make decisions for themselves to provide informed consent will be excluded from the study.

Outcomes:
Agreement of CRYSTALSIGHT with Tobii Gaze Tracker for Macular Disease Monitoring
The Impact of Artificial Intelligence/Machine Learning (AI/ML) on Time to Palliative Care Review in an Inpatient Hospital Population
Trial ID: NCT03976297
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2019-08-19
Completion date: 2020-12-20
Sponsor: Mayo Clinic
Conditions: Palliative Care
Interventions: Control Tower
Locations: Mayo Clinic
Countries: United States
Enrollment: 2231
Age range: 18 Years - None
Sex: ALL

Summary:
Investigators are testing whether machine learning prediction models integrated into a health care model will accurately identify participants who may benefit from a comprehensive review by a palliative care specialist, and decrease time to receiving a palliative care consult in an inpatient setting.

Eligibility criteria:
Inclusion Criteria:

* Admitted to Mayo Clinic St. Mary's Hospital and Methodist Hospital during August 19, 2019 - August 19, 2020.
* Once a day Monday through Friday, the CT operator selects 12 patients from all of the nursing units that are participating in the trial (whether or not they are currently in the intervention group) with palliative scores of at least 7 (out of 100), i.e., those that are high risk and displayed as red in the CT GUI (unless they are already being seen by palliative care.)
* The CT operator chooses the selected patients by looking at the patients in sorted order starting with the highest score and proceeding down the list, evaluating each patient for exclusion criteria.
* Once the CT operator identifies 12 appropriate patients or once they reaches the end of the high-risk patients (score of 7 or higher) they stop.

Exclusion Criteria:

* We will exclude all patients who do not provide research authorization to review their medical records for general research studies in accordance with Minnesota Statute 144.335.
* We will exclude patients under the age of 18 years of age.
* We will exclude patients previously seen by Palliative care during the index hospital visit (i.e., green icon within CT user interface regardless of score)
* We will exclude patient who no longer have an active encounter (patients who have died or patients who have transferred to another facility are excluded) at the time of the review
* We will exclude patients currently enrolled with the Hospice service at Mayo
* We will exclude patients currently enrolled in the Palliative Homebound program (an alternative healthcare model at Mayo)
* We will exclude patients who are about to be discharged in the next 24 hours through indication of note

Outcomes:
Timely identification for need of palliative care
Laser Acupuncture Improves Fatigue and Quality of Life in Hemodialysis Patients: a Randomized Double-blind Clinical Trial Pilot Study
Trial ID: NCT07538310
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-04-08
Completion date: 2027-08-31
Sponsor: Taipei City Hospital
Collaborators: National Yang Ming Chiao Tung University; National Taiwan University of Science and Technology
Conditions: ESRD (End Stage Renal Disease); Dialysis; Fatigue
Interventions: Low-Level Laser Acupuncture (LA); Sham Laser Acupuncture
Locations: Taipei City Hospital, Renai Branch
Countries: Taiwan
Enrollment: 50
Age range: 18 Years - None
Sex: ALL

Summary:
The goal of this clinical trial is to learn if laser acupuncture (low-level laser therapy) works to treat fatigue and improve quality of life in adults with end-stage renal disease (ESRD) undergoing hemodialysis. It will also learn about how laser acupuncture affects blood circulation and autonomic nervous system balance.

The main questions it aims to answer are:

1. Does laser acupuncture reduce the severity of fatigue and its interference with daily life?
2. Can laser acupuncture improve related symptoms such as insomnia, poor appetite, and skin itching?
3. How does laser acupuncture affect objective markers like microcirculation and heart rate variability? Researchers will compare laser acupuncture to a sham treatment (a look-alike procedure using a device that does not emit a laser) to see if laser acupuncture works to treat fatigue.

Participants will:

1. Receive laser acupuncture or a sham treatment during their regular hemodialysis sessions 3 times a week for 4 weeks.
2. Have their fatigue and life quality assessed through questionnaires on the first day, at 4 weeks, and at 8 weeks.
3. Undergo non-invasive tests to measure microcirculation, heart rate variability, and pulse waves, and have photos of their tongue taken for Chinese medicine assessment.

Eligibility criteria:
Inclusion Criteria:

* Patients with End-Stage Renal Disease (ESRD) who require regular maintenance hemodialysis.
* Must undergo hemodialysis treatment at least 3 times per week.
* Aged 18 years or older.
* Willing and able to provide informed consent to participate in the study.

Exclusion Criteria:

* Presence of acute infectious symptoms.
* Unstable vital signs.
* Diagnosis of malignant tumors.
* Pregnancy.
* Presence of open wounds at the sites designated for laser intervention.
* Patients with implanted cardiac pacemakers or other cardiac devices.

Outcomes:
VAS for fatigue; Brief Fatigue Inventory (BFI)
Prediction of Duration of Mechanical Venylation in Patients Wit Acute Hypoxemic Respiratory Failure Usinf Machine Learning Approaches
Trial ID: NCT06815523
Status: ACTIVE_NOT_RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-02-02
Completion date: 2026-06-01
Sponsor: Jesus Villar
Collaborators: Hospital Universitario Dr. Negrín (Las Palmas de Gran Canaria); Instituto de Salud Carlos III
Conditions: Acute Hypoxemic Respiratory Failure
Interventions: Machine learning and logistic regression for the training/testing cohort and validation cohort
Locations: Hospital Dr. Negrin; Hospital Universitario La Paz
Countries: Spain; Spain
Enrollment: 1241
Age range: 18 Years - None
Sex: ALL

Summary:
Acute hypoxemic respiratory failure (AHRF) is a common cause of admission in intensive care units (ICUs) worldwide. We will assess machine learning (ML) techniques for prediction of prolonged duration (\> or = to 7 days) of mechanical ventilation (MV) in 1,241 patients enrolled in the PANDORA study in Spain. The study was registered with ClinalTrials.gov (NCT03145974). Our aim is to identify a model with the minimum number of variables that predict duration of prolonged ventilation in AHRF patients using data as early as from the first 48 hours with machine learning algorithms.

Eligibility criteria:
Inclusion Criteria:

* enotracheal intubation puls mechanical ventilation
* PaO2/FiO2 ratio \<or = 300 mmHg under MV with PEEP \>or =5 and FiO2 \>or = 0.3

Exclusion Criteria:

* Brain death patients

Outcomes:
MV duration
Multimodal, Multicentre Registry of Clinical and Imaging Data to Develop Predictive Models Based on Artificial Intelligence to Support the Diagnostic and Therapeutic Process for Patients with Atrial Fibrillation Undergoing Catheter Ablation and Cardioversion.
Trial ID: NCT06584266
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2024-01-01
Completion date: 2027-09
Sponsor: University in Zielona Góra
Collaborators: Military Institute od Medicine National Research Institute; National Institute of Cardiology, Warsaw, Poland; Medical University of Silesia, Katowice, Poland; St. Anne's University Hospital Brno, Czech Republic; John Paul II Hospital, Krakow; Nowa Sol Multidyscyplinary Hospital, Poland
Conditions: Atrial Fibrillation and Flutter; Heart Failure; Artificial Intelligence (AI); Echocardiography
Interventions: Transoesophageal echocardiography; Catheter ablation
Locations: St. Anne Hospital; Nowa Sol Multidyscyplinary Hospital; Medical University of Silesia; John Paul II Hospital; Military Institute of Medicine National Research Institute; National Institute of Cardiology; Collegium Medicum, University of Zielona Góra
Countries: Czechia; Poland; Poland; Poland; Poland; Poland; Poland
Enrollment: 3000
Age range: 18 Years - None
Sex: ALL

Summary:
The goal of this observational registry is to collect a curated dataset of multimodal imaging data that will serve for development of artificial-intelligence based solutions for prediction of risk and outcomes in patients with atrial fribrillation.

Type of study: observational study

Study Participants: Patients with atrial fibrillation or atrial flutter who undergo clinically indicated transesophageal echocardiography before catheter ablation or cardioversion.

We hypothesize, that automatic analysis of video images of transthoracic echocardiography with deep learning combined with clinical data can predict the presence of left atrial appendage thrombus (LAT). Therefore, our main aim is to create and validate an artificial intelligence model to predict the presence of LAT based on automatic analysis of transthoracic echocardiography with artificial intelligence.

Eligibility criteria:
Inclusion Criteria:

\- All patients with AF or AFl in whom TEE will be performed (to assess their eligibility for cardioversion or ablation), hospitalized in a participating center during study period (all consecutive patients).

Exclusion Criteria:

Age\&lt;18, lack of informed, written consent to the TEE

Outcomes:
Assessment of the presence of left atrial appendage thrombus in patients with atrial fibrillation or atrial flutter, in whom tranesophageal is performed before cardioversion or catheter ablation ablation.
COntinuous Signs Monitoring In Covid-19 Patients
Trial ID: NCT04581031
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2020-07-11
Completion date: 2022-04-22
Sponsor: The Christie NHS Foundation Trust
Collaborators: Manchester University NHS Foundation Trust; Aptus Clinical Ltd.; Zenzium Ltd.
Conditions: COVID-19
Interventions: Continuous vital sign monitoring - Isansys Patient Status Engine; Machine Learning/AI Algorithm
Locations: The Christie NHS Foundation Trust; Manchester University NHS Foundation Trust
Countries: United Kingdom; United Kingdom
Enrollment: 48
Age range: 16 Years - None
Sex: ALL

Summary:
This is a pilot study to assess whether artificial intelligence (AI) combined with continuous vital signs monitoring from wearable sensors can predict clinically relevant outcomes in patients with suspected or confirmed Covid-19 infection on general medical wards.

Eligibility criteria:
Inclusion Criteria:

Participants are eligible to be included in the study only if all of the following criteria apply:

1. Adult (aged 16 years or older), hospital inpatients
2. Suspected or confirmed COVID-19 infection (nasopharyngeal swab sent or planned):

   1. Positive nasopharyngeal swab during this admission OR
   2. Nasopharyngeal swab pending during this admission and the treating team suspect COVID-19 OR
   3. Negative nasopharyngeal swab during this admission but the treating team continue to suspect COVID-19 OR
   4. Positive nasopharyngeal swab in the last 7 days
3. Emergency admission to hospital within the last 72 hours and/or a positive nasopharyngeal test within the last 72 hours taken from a patient who was already an inpatient at the time the swab was taken.
4. Symptoms consistent with COVID-19 infection at the time of admission or when swab taken: cough, shortness of breath, alteration to sense of taste or smell, fevers or other symptoms in keeping with COVID-19 in the opinion of the study team.
5. For full active treatment (including escalation to critical care)
6. The patient is at risk of deterioration (as evidenced by a requirement for supplementary oxygen)

Exclusion Criteria:

Participants are excluded from the study if any of the following criteria apply:

1. Patients unable to give informed consent.
2. Patients with a life expectancy of \<24hours.
3. Known allergy or history of contact dermatitis to medical adhesives.
4. Patients with pacemakers, implantable defibrillators or neurostimulators.
5. Patients with an arterio-venous fistula in either arm.

Outcomes:
Development of an AI model to predict clinically relevant outcomes for ward-based patients with COVID-19 monitored for up to 20 days. Metrics to be employed depend on the algorithm used but include, Log-Loss, precision and/or recall and confusion matrix.
Evaluating the Effectiveness of Chatbot-Based Intervention on Enhancing Self-management and Decision Self-efficacy Among Men Having Lower Urinary Tract Symptoms With or Without Erectile Dysfunction in the Post COVID-19 Era
Trial ID: NCT05765331
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2023-01-02
Completion date: 2023-12-31
Sponsor: Cheng-Hsin General Hospital
Conditions: Benign Prostate Enlargement; Lower Urinary Tract Symptoms; Erectile Dysfunction
Interventions: Chatbot
Locations: Cheng Hsin General Hospital
Countries: Taiwan
Enrollment: 100
Age range: 45 Years - 80 Years
Sex: MALE

Summary:
The AI chatbot as an artificial intelligence technology provides disease information and health care through digital assistance. However, the effectiveness of chatbot in promoting men's health in the field of urology needs further research to evaluate its actual results. The purpose of this study is to explore the impact of AI chatbot-aid intervention on enhancing self-management, and decision self-efficacy among men with lower urinary tract symptoms (LUTS) due to an enlarged prostate, and with or without erectile dysfunction (ED) in the post COVID-19 era.

Eligibility criteria:
Inclusion Criteria:

* Age between 45 years to 80 years old
* Prostate enlargement with lower urinary tract symptoms
* Need a mobile phone and willing to download the line chatbot

Exclusion Criteria:

* Psychosis

Outcomes:
Men's Health Knowledge Score; Partners in Health Scale; Decision Self-Efficacy Scale
Artificial Intelligence for Histopathological Classification and Recurrence Prediction of Thymic Epithelial Tumors
Trial ID: NCT06301945
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2023-08-01
Completion date: 2027-08-01
Sponsor: Erasmus Medical Center
Collaborators: Maastro Clinic, The Netherlands; Hospices Civils de Lyon
Conditions: Thymic Epithelial Tumor; Thymic Carcinoma; Thymoma; Thymoma and Thymic Carcinoma
Interventions: Artificial Intelligence Diagnostics; Recurrence Prediction Tool
Locations: Erasmus MC
Countries: Netherlands
Enrollment: 1020
Age range: None - None
Sex: ALL

Summary:
Thymic epithelial tumors are rare neoplasms in the anterior mediastinum. The cornerstone of the treatment is surgical resection. Administration of postoperative radiotherapy is usually indicated in patients with more extensive local disease, incomplete resection and/or more aggressive subtypes, defined by the WHO histopathological classification.

In this classification thymoma types A, AB, B1, B2, B3, and thymic carcinoma are distinguished. Studies have shown large discordances between pathologists in subtyping these tumors. Moreover, the WHO classification alone does not accurately predict the risk of recurrence, as within subtypes patients have divergent prognoses.

The investigators will develop AI models using digital pathology and relevant clinical variables to improve the accuracy of histopathological classification of thymic epithelial tumors, and to better predict the risk of recurrence.

In this multicentric and international project three existing databases will be used from Rotterdam, Maastricht and Lyon. For all models one database will be used to build AI models, and the other two for external validation.

The ultimate goal of this project is to develop AI models that support the pathologist in correctly subtyping thymic epithelial tumors, in order to prevent patients from under- or overtreatment with adjuvant radiotherapy.

Eligibility criteria:
Inclusion Criteria:

Participants with specific diagnoses are eligible for inclusion in the study. The eligible diagnoses include various subtypes of thymoma and thymic carcinoma, specifically:

* Thymoma A
* Thymoma AB
* Thymoma B1
* Thymoma B2
* Thymoma B3
* Thymic Carcinoma

Inclusion is based on a consensus diagnosis with a level of agreement less than 70%. This criterion is applied during the training phase of the model.

Recurrence Criteria:

Participants with a documented recurrence outcome within a 5-year period are considered eligible for this aspect of the study. This criterion is primarily applied during the validation phase.

Outcomes:
WP1 - Databases/Data Pre-processing