moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Stanford-PIPRA Study: The Accuracy of the Pre-Interventional Preventive Risk Assessment (PIPRA) Tool for the Prediction of ICU-Delirium in a Mixed Cardiothoracic Intensive Care Unit Population
Trial ID: NCT06519890
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2024-04-30
Completion date: 2024-07-27
Sponsor: Stanford University
Conditions: Delirium; Cognitive Impairment; Delirium, Postoperative
Locations: Stanford Medical Center
Countries: United States
Enrollment: 199
Age range: 18 Years - None
Sex: ALL

Summary:
The purpose of this study is to determine the accuracy of the Pre-Interventional Preventive Risk Assessment (PIPRA) tool in predicting clinical cases of Intensive Care Units (ICU)-delirium, in a population at high risk of developing this syndrome (i.e., admitted patients to Cardiothoracic Intensive Care Units). The population to be studied has already been enrolled in a parallel study intended to determine the accuracy of an electroencephalogram (EEG)-based diagnosis for delirium.

Eligibility criteria:
Inclusion Criteria:

1. Age is 18 years or older
2. Clinical suspicion of delirium or subject at risk for delirium 3. Admitted to the Intensive Care Unit (ICU)

4\. Expected ICU stay is greater than one (1) day 5. Subject must be fluent in English

Exclusion Criteria:

1. Age is younger than 18 years
2. Subjects with a primary neurological or neurosurgical condition such as stroke, epilepsy, brain tumor, or witnessed seizures, Parkinson's disease, traumatic brain injury, craniectomy, known severe dementia, or Central Nervous System (CNS) infection
3. Active CNS substance abuse, intoxication, or withdrawal
4. Unarousable or unresponsive subjects with Richmond Agitation Sedation Scale (RASS) \<-3
5. Subject is blind, deaf, or unable to speak or understand English

Outcomes:
Number of subjects with postoperative delirium accurately predicted by the PIPRA Tool; Development of post-operative Delirium; Development of ICU Delirium; Determination of Delirium Phenotype; Sensitivity and specificity of the PIPRA tool for detecting postoperative delirium.; Receiver operating characteristic (ROC) curve analysis of PIPRA tool
Clinical Study on the Diagnosis of Colorectal Lesions by Real-time Artificial Intelligence Assisted Endocytoscopy Combined With Narrow Band Imaging
Trial ID: NCT06982885
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-05-19
Completion date: 2025-12-31
Sponsor: The First Hospital of Jilin University
Conditions: Endocytoscopy
Interventions: artificial intelligence
Locations: First Hospital of Jilin University
Countries: China
Enrollment: 500
Age range: None - None
Sex: ALL

Summary:
Colorectal cancer (colorectal cancer, CRC) is the third most common malignant tumor globally and the second leading cause of cancer-related deaths. Colonoscopy is considered the preferred method for screening colorectal cancer; early detection and removal of colorectal neoplasms can significantly reduce the incidence and mortality of colorectal cancer. To improve the diagnostic accuracy of endoscopy in colorectal lesions, many endoscopic techniques have been applied clinically, such as image-enhanced endoscopy, including narrow band imaging (narrow-band imaging, NBI), magnifying endoscopy, chromoendoscopy, confocal laser endoscopy, and endocytoscopy (EC). However, with the increasing number of endoscopic resections, the costs associated with the pathological diagnosis of resected specimens have risen year by year. In clinical practice, some non-neoplastic colorectal lesions may not require resection, so it is important to differentiate the nature of lesions during colonoscopy.

Endocytoscopy is an ultra-high magnification endoscope that, when combined with chemical staining and narrowband imaging techniques, allows the endoscopist to observe the nuclear morphology of colorectal lesions, the shape of glands, and the morphology of microvessels through the naked eye. This approach avoids the need for pathological examination, achieving the goal of real-time biopsy in vivo. However, the accuracy of endocytoscopic image interpretation requires extensive experience to improve judgment, and there is a certain degree of subjectivity and error in the endoscopist's assessment process. Therefore, to address this issue, clinical applications have proposed using artificial intelligence (AI) for computer-aided diagnosis. The investigator's center has previously developed an AI-assisted diagnostic system based on endocytoscopy with NBI to assist in determining the nature of colorectal lesions. However, forward-looking clinical studies are still lacking to verify the effectiveness of this AI-assisted system. Thus, the investigator aim to conduct such clinical research to validate the clinical efficacy of this AI.

Eligibility criteria:
Inclusion Criteria:

* colorectal lesions

Exclusion Criteria:

* lesions lacking high-quality images;
* Inflammatory bowel disease, familial adenomatous polyposis and other special diseases;
* submucosal tumors;
* Pathological diagnosis of Peutz-Jeghers polyps, juvenile polyps, lymphoma and other pathological types.

Outcomes:
The accuracy of AI in diagnosing tumor lesions (including sensitivity, specificity, accuracy, positive predictive value (PPV), negative predictive value (NPV)) and high confidence rate were evaluated
An Exploratory Open-Label Potency and Precision Investigation of a Relational Agent Intervention
Trial ID: NCT06240091
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-03-13
Completion date: 2024-05-09
Sponsor: Woebot Health
Conditions: Depression, Anxiety
Interventions: DISC-MA
Locations: Woebot Investigational Site
Countries: United States
Enrollment: 203
Age range: 18 Years - 75 Years
Sex: ALL

Summary:
The overarching goal of this study is to establish initial proof of mechanism for precision interventions in an adult population.

Eligibility criteria:
Inclusion Criteria:

1. Must be 18-75 years of age
2. Must own or have regular access to a smartphone with a recent operating system installed (Android: OS 8.0 or higher, Apple: iOS 13.0 or higher) with reliable Wi-Fi access or sufficient data plan to engage with assigned treatment condition for the duration of the study
3. Must be available and committed to engage with the program and complete assessments for a 4-week duration
4. Must be able to read and write in English
5. Must have primary residence in the United States
6. Must have mild or greater symptoms of depression and/or anxiety at Screening/Baseline, indicated by a score \> 4 on the Patient Health Questionnaire (PHQ-8) and/or Generalized Anxiety Disorder (GAD-7)

Exclusion Criteria:

1. Current suicidal ideation with a plan and/or intent or a suicide attempt within the past 12 months
2. Previous Woebot use
3. Involuntary inpatient psychiatric hospitalization any time within the past 30 days
4. Lifetime diagnosis of bipolar disorder
5. Lifetime diagnosis of a psychotic disorder

Outcomes:
Working Alliance Inventory-Short Revised (WAI-SR), Total Scale
AI-assisted Endotracheal Intubation in Children and Neonates: A Prospective Randomized Controlled Simulation Trial
Trial ID: NCT07508826
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-04-01
Completion date: 2026-11-30
Sponsor: The Hospital for Sick Children
Conditions: Intubation Performance Using AI Versus Conventional Videolaryngoscope in a Mannikin
Interventions: AI-assisted videolaryngoscopy; Conventional Intubation
Enrollment: 40
Age range: None - None
Sex: ALL

Summary:
This study evaluates artificial intelligence (AI)-assisted videolaryngoscopy for endotracheal intubation in a simulated pediatric airway environment. Healthcare providers with varying levels of airway management experience will perform intubations on pediatric and neonatal mannequins using either AI-assisted videolaryngoscopy (larynGuide) or conventional videolaryngoscopy.

Participants will be randomized to perform intubation tasks using one of the two techniques. The primary outcome is the time required for successful intubation. Secondary outcomes include first-attempt success rate, number of attempts, airway visualization (POGO score), usability of the AI system measured by the System Usability Scale (SUS), and gaze tracking metrics evaluating user interaction with visual guidance.

This equivalence randomized controlled trial aims to determine whether AI-assisted videolaryngoscopy performs comparably to conventional videolaryngoscopy while potentially improving success rates and user experience.

Eligibility criteria:
Inclusion Criteria:

* Any healthcare worker or trainee

Exclusion Criteria:

* refusal to participate

Outcomes:
Time required for intubation
Establishing Automatic Method of Counting and Classify Bone Marrow and Peripheral Blood Cells
Trial ID: NCT04551235
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2020-08-28
Completion date: 2023-12-31
Sponsor: National Taiwan University Hospital
Collaborators: National Taiwan University Tai-Chen Cell Therapy Center, Biomdcare Corporation
Conditions: Hematologic Diseases; Artificial Intelligence; Acute Leukemia
Interventions: there are not any interventions in this study
Locations: National Taiwan University Tai-Chen Cell Therapy Center
Countries: Taiwan
Enrollment: 900
Age range: 20 Years - None
Sex: ALL

Summary:
Counting and classification of blood cells in a bone marrow smear and peripheral blood smear are essential to clinical hematology. To this date, this procedure has been carried out in a manual manner in the great majority of clinical settings. There is often inconsistency in the counting result between different operators largely due to its manual nature. There has not been an effective and standard method for blood smear preparation and automatic counting and classification. The recent advent of deep neural network for medical image processing introduced new opportunities for an effective solution of this long-standing problem. Numerous results have been published on the effectiveness of convolutional neural network in clinical image recognition task.

Eligibility criteria:
Inclusion Criteria:

* Patients who have suspected or confirmed hematological diseases and receive bone
* marrow or peripheral blood cell morphological examination in National Taiwan University Cancer Center
* Patients who are aged more than 20 y/o

Exclusion Criteria:

•Patients who are not willing to sign informed consents

Outcomes:
Evaluate the accuracy of cell counting and classifying between automatic method and manual method through digital microscopic photos of bone marrow smear and peripheral blood smear using deep convolutional neural networks
Population-based Breast Cancer Screening Study Using AI-Assisted Imaging Technology
Trial ID: NCT07411443
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-01-01
Completion date: 2027-12-31
Sponsor: Fudan University
Conditions: Breast Cancer Screening
Interventions: AI-assisted screening
Locations: Fudan University Shanghai Cancer Center
Countries: China
Enrollment: 16000
Age range: 35 Years - 69 Years
Sex: FEMALE

Summary:
Artificial Intelligence (AI)-assisted imaging technologies (including AI-assisted breast ultrasound and AI-assisted mammography) can effectively improve the accuracy and efficiency of breast imaging examinations, but their application in large-scale population-based breast cancer screening remains very limited.

This project aims to improve the effectiveness and feasibility of breast cancer screening by addressing the core issues and bottlenecks in population-based breast cancer screening. We will conduct a prospective cluster-controlled screening trial in the general population, with district-based cluster grouping. The intervention group will undergo combined screening using AI-assisted ultrasound plus AI-assisted mammography, while the control group will receive conventional screening: breast ultrasound for initial screening and mammography for secondary screening.

Based on population screening practices, we will evaluate the effectiveness of AI-assisted imaging diagnostic technology in various technical aspects of actual screening and perform cost-effectiveness analyses. This study will investigate the application of AI-assisted breast imaging technology in population-based breast cancer screening, providing scientific evidence for the large-scale implementation of AI-assisted imaging technologies. Furthermore, by combining population screening practices with model simulations, we will explore multi-dimensional breast cancer screening strategies to optimize screening approaches and technologies for the Chinese population.

Eligibility criteria:
Inclusion Criteria:

* women aged 35 to 69 years, who were attending the "Two Cancers (Breast and Cervical Cancer) Screening" project, and had no history of breast cancer, including in-situ cancer, or any other cancers in the previous five years.

Exclusion Criteria:

* have serious cardiopulmonary insufficiency, liver or kidney insufficiency, or other systemic diseases, and a life expectancy of less than five years

Outcomes:
The incidence of early-stage breast cancer over a one-year follow-up period, compared between women who underwent AI-assisted screening and those with routine screening; The detection rate of suspicious breast lesions (including masses and calcifications) over a one-year follow-up period, compared between women who underwent AI-assisted ultrasound combined with AI-assisted mammography and those who received routine scree
Real-World Evaluation of the Performance of Eko's Heart Murmur Detection Algorithm When Used by Front-line Healthcare Providers on Geriatric Patients
Trial ID: NCT04933890
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2021-06-01
Completion date: 2021-12-31
Sponsor: Eko Devices, Inc.
Conditions: Left Ventricular Dysfunction; Heart Failure
Interventions: Use of Eko DUO electronic stethoscope
Locations: Parker Jewish Institute of Health Care and Rehabilitation
Countries: United States
Enrollment: 68
Age range: 18 Years - None
Sex: ALL

Summary:
The purpose of this study is to evaluate how Eko AI performs in the real world, front-line setting where the availability of sophisticated, expensive diagnostic tools is limited, and where there is a premium on detecting VHD early in its course.

Eligibility criteria:
Inclusion Criteria:

* English-speaking adults who are 18 years and older
* Able and willing to provide informed consent
* Complete a clinical echocardiogram within 7 days before or after study procedures

Exclusion Criteria:

* Unwilling or unable to provide informed consent
* Patients who are hospitalized

Outcomes:
Single-lead ECG based algorithm development; Single-lead ECG based algorithm Performance
The Evaluation of the Effectiveness of General Artificial Intelligence Models in Extubation Decision-Making in the Intensive Care Unit
Trial ID: NCT06584890
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2024-09-15
Completion date: 2025-05-27
Sponsor: Kanuni Sultan Suleyman Training and Research Hospital
Conditions: Artificial Intelegence
Interventions: decision
Locations: Health Science University İstanbul Prof Dr Cemil Taşcıoğlu City Hospital
Countries: Turkey (Türkiye)
Enrollment: 398
Age range: 18 Years - None
Sex: ALL

Summary:
This clinical study aims to evaluate the effectiveness of General Artificial Intelligence (AI) models, specifically ChatGPT and Gemini, in assisting with the decision-making process for discharging patients from the Intensive Care Unit (ICU) to a general ward or home. The timing of ICU discharge is a critical decision that significantly impacts patient outcomes and the efficient use of ICU resources. This study seeks to determine whether AI models can accurately and efficiently predict the optimal time for patient discharge, supporting clinicians in making informed decisions.

The primary hypothesis is that AI models can improve the accuracy and speed of discharge decisions compared to traditional methods. The study will assess the agreement between the AI model predictions and the decisions made by ICU specialists. Additionally, the study will compare the performance of ChatGPT and Gemini AI models to identify which model offers the most reliable and timely discharge decisions.

By exploring the potential of AI in clinical decision-making, this research could contribute to the development of innovative tools for ICU management, ultimately enhancing patient care and optimizing ICU operations. The findings could lead to the integration of AI models into clinical decision support systems, facilitating more accurate and efficient patient management in the ICU.

Eligibility criteria:
Inclusion Criteria:

* Patients aged 18 years or older.
* Patients currently admitted to the Intensive Care Unit (ICU) during the study -period.
* Patients with sufficient clinical data available in the hospital\&#39;s information system, including demographic information, clinical indicators, and treatment history.
* Patients for whom a discharge decision (to a general ward or home) needs to be made during their ICU stay.

Exclusion Criteria:

* Patients younger than 18 years old. Patients with incomplete or insufficient clinical data in the hospital\&#39;s information system, making it difficult to assess their condition accurately.

Patients who are in the ICU for palliative care or end-of-life care, where discharge to a general ward or home is not anticipated.

Patients who have opted out of participating in the study or whose legal representatives have declined participation.

Outcomes:
accuracy
Deep Learning Image Reconstruction for Abdominal CT of Hepatocellular Carcinoma Compared With 3-TESLA MRI
Trial ID: NCT06037343
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2023-02-23
Completion date: 2023-12-30
Sponsor: Central Hospital, Nancy, France
Conditions: Hepatocellular Carcinoma
Locations: CHRU de Nancy
Countries: France
Enrollment: 50
Age range: 18 Years - None
Sex: ALL

Summary:
New algorithms for processing CT acquisitions, based on artificial intelligence, have been reported to improve acquisition quality. Thats' why it's possible to imagine that new scan post-processing algorithms enable better detection and characterization of hepatocellular carcinoma lesions than with standard reconstructions. DLIR reconstructions could even match with MRI detection.

The aim of the study is to compare the detection and characterization of hepatic lesions according to the LI-RADS classification in CT with DLIR artificial intelligence reconstruction, compared with ASIR-V reconstruction and the gold standard of MRI.

Eligibility criteria:
Inclusion Criteria:

* undergoing CT and MRI scans in the same week, with protocols dedicated to the detection of HCC lesions

Exclusion Criteria:

* imaging with radiological artefact

Outcomes:
Lesion size in mm; Hypervascular appearance of lesion; Hypervascular capsule
Prospective Multicentric Study for the Diagnostic Performance of CArdiac MAgnetic REsonance Imaging Used First for the Diagnosis of Coronary Artery Disease as the Etiology of Left Ventricular Dysfunction
Trial ID: NCT03231189
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2018-05-15
Completion date: 2024-08-31
Sponsor: Assistance Publique - Hôpitaux de Paris
Conditions: Ischemic Cardiomyopathy
Locations: Hopital Bichat Claude-Bernard
Countries: France
Enrollment: 415
Age range: 18 Years - None
Sex: ALL

Summary:
When a patient is newly diagnosed of systolic dysfunction without obvious etiology (such as rhythmic, ischemic, or valvular disease), most of the time a coronary angiography is performed. In this situation, the investigators aim to evaluate a strategy with CMR as the front line exam, and invasive coronary angiography performed only in case of ischemic scar on CMR Additionally, a secondary analysis of the collected CMR images will be performed by the MIRACL.AI core lab (AP-HP, Paris) using artificial intelligence (AI) algorithms. This analysis aims to enhance the diagnostic accuracy of coronary artery disease (CAD) detection in patients with reduced left ventricular ejection fraction (rLVEF).

Eligibility criteria:
Inclusion Criteria:

* Age ≥ 18 years
* Left Ventricular Ejection Fraction ≤ 45% on transthoracic echocardiography
* Informed signed consent
* Patient having had a preliminary clinical examination

Exclusion Criteria:

* Known significant coronary artery stenosis (history of myocardial infarction or coronary artery stenosis).
* Formal indication for coronary angiography other than LV dysfunction (typical angina, acute coronary syndrome, …).
* Obvious etiology for LV dysfunction (valvular, rhythmic…).
* Pregnancy or breastfeeding.
* Other contraindication for CMR (severe allergy to gadolinium known), or coronary artery angiography.
* First diagnosis of LVEF dysfunction \> 8 weeks.
* Non covered patient by the social security, the CMU
* Patients under guardianship, or unable to give consent

Outcomes:
Sensitivity of CMR for predicting the presence of significant coronary artery stenosis on coronary angiography in patients with reduced LVEF.
Intraoperative Detection of Breast Cancer by Electrosurgical Gas Analysis and Artificial Intelligence
Trial ID: NCT07131735
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2023-12-18
Completion date: 2025-09-18
Sponsor: Pontificia Universidad Catolica de Chile
Conditions: Breast Cancer
Locations: Hospital Clínico UC CHRISTUS; Facultad de medicina UC
Countries: Chile; Chile
Enrollment: 30
Age range: 18 Years - None
Sex: FEMALE

Summary:
The aim of this clinical trial is to assess the feasibility of training a device capable of distinguishing various gases emitted by tissues cauterized by an electrosurgical unit during a breast cancer resection surgery. The patients to be enrolled will be women over 18 years old diagnosed with breast cancer who are indicated for conservative breast cancer resection surgery as treatment.

The main questions to be answered are:

The specificity and sensitivity of the device in detecting margins compromised with tumor cells in resection surgeries.

Evaluate the applicability of the device in breast cancer surgeries for real-time detection of margins.

Evaluate the differences in the pattern of gases emitted in tumor cells vs normal cells.

By consenting, the study patients will allow the investigative team to access the clinical record, results of images, post-surgical biopsies, recording of the surgery while preserving the patient's anonymity, and the installation of the gas detection device. This device does not alter the flow of the surgery and does not add additional risk to it.

Eligibility criteria:
Inclusion Criteria:

* Histologically confirmed diagnosis of malignant breast cancer
* Scheduled for BCS at the Hospital UC
* Able and willing to provide informed consent

Exclusion Criteria:

* Pregnant or lactating women
* Patients with known hypersensitivity or allergy to any component of the BCGC device
* Participation in another interventional clinical trial within 30 days prior to enrolment

Outcomes:
Differentiation of cancerous tissue from normal tissue