moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Pilot Randomized Controlled Trial of PICTURE-Pediatric
Trial ID: NCT07304050
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-02-09
Completion date: 2027-01
Sponsor: University of Michigan
Conditions: Clinical Deterioration
Interventions: PICTURE-Pediatric scores and alerts; Control Arm - Scores and alerts hidden
Locations: University of Michigan
Countries: United States
Enrollment: 375
Age range: 30 Days - 25 Years
Sex: ALL

Summary:
The purpose of this study is to evaluate the effectiveness and user satisfaction of the study teams early warning system, called PICTURE, which utilizes artificial intelligence (AI) techniques and algorithms to identify patient deterioration on pediatric units within Mott Children's Hospital.

In this pilot study the patient care team will review the PICTURE information and alerts. Morning rounds will be partially informed by the PICTURE scores and the scores will be included in the hand off notes for the patients with a red score.

The primary purpose of this study is to test the hypothesis that the combination of the PICTURE-Pediatric model, the proposed workflow and the proposed interface results in at least 80% compliance.

No participants will be consented as the Institutional review board has approved a waiver of consent for THE clinicians and the patients information being reviewed.

The enrollment numbers will include only the clinicians.

Eligibility criteria:
Inclusion Criteria - Patients:

* Age ≥30 days and ≤25 years
* Be on the general care wards of Mott Children's Hospital or boarding in the Emergency Department (ED), but admitted to a pediatric floor medical team. These will be enforced by these criteria:

  1. Patient class: not emergency b. Patient level of care: not Intensive Care unit (ICU) c. Patient locations (per protocol)

     Exclusion Criteria - Patients:
* Patients that do not meet the inclusion criteria

Outcomes:
Compliance with the model workflow as measured by a pop-up questionnaire after each model alert aggregated as a proportion -clinicians
Staged Unimodal-to-Multimodal AI Analysis of Histopathology, CT/MRI, and Multiplex Tissue Imaging for Perioperative Risk Prediction in Colorectal Cancer (KIA-Korekt)
Trial ID: NCT07537491
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2011-01-01
Completion date: 2028-06-30
Sponsor: Rene Mantke
Collaborators: Technische Universität Dresden
Conditions: Colorectal Cancer Postoperative Complications
Locations: University Hospital Brandenburg an der Havel, Brandenburg an der Havel, Germany (Single-center)
Countries: Germany
Enrollment: 910
Age range: 18 Years - None
Sex: ALL

Summary:
Perioperative complications following surgery for colorectal cancer (CRC) represent a major cause of postoperative morbidity and mortality. Existing risk stratification tools lack the precision to capture the complex biological and morphological factors that determine individual patient vulnerability. Artificial intelligence (AI)-based analysis of medical imaging data offers a promising approach to improve preoperative risk prediction.

The KIA-Korekt study investigates whether perioperative complications in CRC patients can be predicted using multimodal AI-based image analysis. Three complementary imaging modalities are integrated: digital histopathology (haematoxylin-eosin whole-slide images, H\&E-WSIs), preoperative CT and MRI radiomics, and multiplex tissue imaging (mTI) including multiplex immunohistochemistry (mIHC) and imaging mass cytometry (IMC).

The study includes a retrospective cohort of approximately 750 CRC patients treated between 2011 and 2021, and a prospective validation cohort of approximately 210 patients recruited from 2026 to 2028. Deep learning and radiomic feature extraction pipelines are applied to all modalities individually and in multimodal combination. Predicted outcomes include anastomotic leakage, wound infection, sepsis, ICU admission, and in-hospital mortality within 30 days of surgery.

The study is conducted at the University Hospital Brandenburg, Brandenburg Medical School Theodor Fontane, in collaboration with the Department of Computational Pathology, TU Dresden.

Eligibility criteria:
Inclusion Criteria:

* Adult patients (≥18 years)
* Histologically confirmed colorectal adenocarcinoma
* Undergoing surgical resection (curative or palliative intent)
* Availability of H\&E-stained whole-slide images (WSIs) from the primary tumour

Exclusion Criteria:

* Patients not undergoing surgical treatment
* Missing H\&E-stained tissue slides of the primary tumour
* Histopathological material of insufficient quality for analysis

Outcomes:
Prediction accuracy of perioperative complications
Subjective and Objective Performance With the SONNET2EAS
Trial ID: NCT04721990
Status: WITHDRAWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2021-04
Completion date: 2023-04
Sponsor: University of North Carolina, Chapel Hill
Collaborators: Med-El Corporation
Conditions: Hearing Loss; Hearing Disability
Interventions: Automatic Sound Management 3.0
Locations: University of North Carolina at Chapel Hill
Countries: United States
Enrollment: 0
Age range: 18 Years - 99 Years
Sex: ALL

Summary:
Purpose: The purpose of this study is to demonstrate the non-inferiority and new features of an external speech processor for cochlear implant recipients.

Participants: This study seeks to enroll 15 cochlear implant recipients listening to previous technology.

Procedures (methods): Subjects will be programmed and tested with old and new technology.

Eligibility criteria:
Inclusion Criteria:

* Adult (≥18 years at date of enrollment/initial evaluation)
* MED-EL Cochlear Implant System recipient
* Unilateral cochlear implant recipient
* Unaided threshold of ≤65 decibels (dB) Hearing Level (HL) at 125 Hz in implanted ear
* Six months or greater of SONNETEAS listening experience
* Consistent device user, as deemed by research team
* Minimum of 10 enabled electrodes
* Consonant Nucleus Consonant (CNC) word score of ≥40% with SONNETEAS processor and contralateral ear plugged/masked
* Native English speaker (as all materials are written or spoken in English)

Exclusion Criteria:

* Unaided pure tone average (500, 1000, 2000 Hz) ≤60 dB HL in the contralateral (non-implanted) ear
* Hearing technology other than a conventional hearing aid in the contralateral ear
* Unwilling, unable, or geographic limitations to participate in study procedures
* Unwilling to complete datalogging with the processor

Outcomes:
Difference in Consonant-Nucleus-Consonant (CNC) Words Scores; Difference in Hearing-in-Noise-Test (HINT) Sentences in Diffuse Noise; Difference in reported device satisfaction on the Audio Processor Satisfaction Questionnaire (APSQ); Difference in reported subjective benefit on the Speech Domain of the Speech, Spatial and Qualities of Hearing (SSQ); Difference in reported subjective benefit on the Spatial Domain of the Speech, Spatial and Qualities of Hearing (SSQ); Difference in reported subjective benefit on the Qualities Domain of the Speech, Spatial and Qualities of Hearing (SSQ)
Development of Artificial Intelligence Golf Training Simulator: Integration of Inertial Sensing Device and Musculoskeletal Simulation Model for Movement Feedback System
Trial ID: NCT05949723
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2023-04-14
Completion date: 2024-01-31
Sponsor: National Taiwan University Hospital
Conditions: Musculoskeletal Model
Interventions: Golf simulator device
Locations: National Taiwan University
Countries: Taiwan
Enrollment: 12
Age range: 20 Years - 60 Years
Sex: ALL

Summary:
The golf simulator can accurately predict the flight trajectory and landing point of the ball using high-speed moving object measurement technology, giving the user the impression that they are playing on a real golf course.

Eligibility criteria:
Inclusion Criteria:

* Healthy golf players and more than 3 years of golf experience
* Age between 20\~60 years old

Exclusion Criteria:

* Musculoskeletal disease within 6 months

Outcomes:
Kinematics data_range of motion; Kinematics data_displacement of center of mass; Kinetic data_mean velocity; Kinetic data_mean distance; Kinetic data_mean frequency; Kinetic data_95% confidence ellipse area; Kinematics data by inertial measurement units_linear acceleration; Kinematics data by inertial measurement units_angular velocity
Diagnostic Performance of Artificial Intelligence Algorithms in Prediction of Acute Coronary Syndrome Based on White Blood Cell Properties (AI-ACS Trial)
Trial ID: NCT06384846
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2024-02-01
Completion date: 2026-12-31
Sponsor: RobotDreams GmbH
Conditions: Acute Coronary Syndrome; Angina Pectoris; NSTEMI - Non-ST Segment Elevation MI; STEMI - ST Elevation Myocardial Infarction
Locations: Landeskrankenhaus-Universitätsklinikum Graz
Countries: Austria
Enrollment: 2700
Age range: 18 Years - None
Sex: ALL

Summary:
The goal of this observational study is to find out if artificial intelligence (AI) can accurately predict acute coronary syndrome (ACS) using data on white blood cells in adults. The main question it aims to answer is:

\- Can AI algorithms based on white blood cell data predict ACS with accuracy comparable to that of high-sensitivity cardiac troponin (hs-cTn)? Researchers will look at how the AI model's predictions stack up against the standard hs-cTn blood tests to see which is more accurate in diagnosing ACS.

Participants in this study will have already had blood tests as part of their usual care. Their previously collected health information and blood test results will be used to help train and test the AI algorithms. Participants will not undergo any new procedures for the study itself.

Eligibility criteria:
Inclusion Criteria:

* Male or Female, aged 18 years or above
* Participant is willing and able to give informed consent for participation in the study
* Subjects presenting without chest pain or with stable angina pectoris but without indication for revascularization during coronary angiography; identical evaluation results by review board required
* Exclusion of elevated hs-cTn
* Criteria for timing of blood sampling for collection of WBC and hs-cTn data need to be fulfilled (see 5.14)

  o Subjects with no or stable angina pectoris must have provided WBC data and at least one hs-cTn value any time before start of coronary angiography.
* Between initial blood sampling to collect WBC data and coronary angiography, the subject must not develop suspicion of ACS.

Exclusion Criteria:

* Age \< 18 years old
* Subject refuses informed consent
* Collection of WBC and hs-cTn data is not possible
* Criteria for timing of blood sampling for collection of WBC and hs-cTn data cannot be fulfilled
* Suspicion of ACS occurred in subjects with no or stable angina pectoris any time between initial blood sampling and start of coronary angiography

Outcomes:
Training of AI models; Testing of AI models
Prospective Cardiac Ultrasound Imaging Study With Demonstrator
Trial ID: NCT05850741
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2021-06-04
Completion date: 2022-02-03
Sponsor: Fondazione IRCCS Policlinico San Matteo di Pavia
Conditions: Ventricular Tachycardia
Interventions: Cardiokit
Locations: IRCCS Policlinico San Matteo
Countries: Italy
Enrollment: 24
Age range: 18 Years - 100 Years
Sex: ALL

Summary:
The Prospective Cardiac Ultrasound Imaging Study is a single center, single arm, feasibility study. The study aims at collecting cardiac ultrasound data from patients diagnosed with ventricular tachycardia, during rest and short breath hold sequences. This study is part of the engineering studies linked to the CardioKit-Minimum Viable Product project. The CardioKit prototype used in the study can monitor cyclical heart-beat motion, cyclical respiratory motion and non-cyclical heart position changes while the patient is lying in supine position.

Eligibility criteria:
Inclusion criteria:

1. Patient with history of ventricular tachycardia
2. Over 18 years old
3. Ability and willingness to provide written informed consent

Exclusion criteria:

1\. Patient in arrhythmic storm

Outcomes:
Image quality in terms of allowing a correct identification of the phase of the cardiac cycle by the prototype software.; Image quality in terms of allowing a correct measurement of the heart displacement (mainly due to respiratory motion) by the prototype software.; Image quality in terms of the ability to distinguish typical cardiac structures, as assessed visually by the clinical operator.; Image quality in terms of the stability of the image throughout the respiration cycle, as assessed visually by the clinical operator.
Leveraging Artificial Intelligence for the Assessment of Severity of Depressive Symptoms
Trial ID: NCT04370002
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2020-01-28
Completion date: 2024-11-30
Sponsor: Massachusetts General Hospital
Collaborators: Massachusetts Institute of Technology
Conditions: Unipolar Depression
Locations: Depression Clinical Research Program; Massachusetts General Hospital
Countries: United States; United States
Enrollment: 100
Age range: 18 Years - 75 Years
Sex: ALL

Summary:
This is a longitudinal study where individual with Major Depressive Disorder (MDD) will be monitored for 12 weeks. The study aims to develop an objective, sensor-based, algorithm able to detect the presence of depression as well as predict treatment response. Measurement-based treatment is considered optimal and the development of a valid passive, objective, behavioral and biological assessment of depressive symptoms that does not rely on clinician interviews will improve monitoring and ultimately improve treatment significantly.

Eligibility criteria:
Inclusion Criteria:

1. Adults (ages 18-75),
2. Able to read, understand, and provide written informed consent in English,
3. Meet criteria for a primary psychiatric diagnosis of current major depressive disorder
4. Hamilton Depression Rating Scale (HDRS) total score ≥ 18,
5. Must have measurable skin conductance/electrodermal activity (as assessed at the screening visit),
6. Must own a working smartphone and use it regularly,
7. Must own a windows PC (or tablet) or a Mac computer (or laptop),
8. Must have access to Internet service every day.
9. Must have started antidepressant medications, changed medication dosage, or started therapy within 3 weeks

Exclusion Criteria:

1. Active drug or alcohol use disorder in the past 3 months,
2. History of psychotic disorder,
3. History of mania or hypomania,
4. Epilepsy or history of Seizure Disorder (including PNES), narcolepsy, Alzheimer Disease, Parkinson's Disease, ALS, Severe TBI, Dementia, MS, Cerebral Palsy, and Neuralgia.
5. Untreated hypothyroidism,
6. Unstable medical disease,
7. Cognitive impairment that would impede adherence to study procedures,
8. Acute suicide or homicide risk,
9. Current treatment with electroconvulsive therapy, vagal nerve stimulation therapy, deep brain stimulation, transcranial magnetic stimulation therapy, or phototherapy,
10. Cannot comprehend or communicate in English,
11. Lack of working smartphone or lack of daily access to Internet service,
12. Inability to measure skin conductance/electrodermal activity (as assessed at the screening visit), and
13. Inability or unwilling to, at minimum, wear the physiological sensor (E4) wristbands, download monitoring apps, and fill out the surveys.
14. Participants with more than two treatment failures (more than two adequate trials of meds on the basis of ATRQ) in the current mood episode.

Outcomes:
The Hamilton Depression Rating Scale (HDRS) 17-items
Prospective Multicenter Study to Evaluate the Performance of the Artificial Intelligence (AI)/ Machine Learning (ML) Technologies Utilized by the Origin Medical EXAM ASSISTANT
Trial ID: NCT06952439
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2025-03-19
Completion date: 2025-05-19
Sponsor: Origin Medical Systems, Inc.
Conditions: To Validate the Clinical Performance of AI/ML Technologies in First-trimester Pregnant Participants; First Trimester Pregnancy
Interventions: Images and cines captured through the capture card (ICC)
Locations: Harbinder S Brar MD Inc; Harbinder S Brar MD Inc; Harbinder S Brar MD Inc; Harbinder S Brar MD Inc; Harbinder S Brar MD Inc; Harbinder S Brar MD Inc; Sweet Pea 3D/4D Ultrasound Nola; Mobile Mama Ultrasound, LLC; Mid-Carolina OB/GYN; The Nest 4D Ultrasound LLC, DBA InFocus Ultrasound DBA Little Peanut 4D Ultrasound; The Nest 4D Ultrasound LLC, DBA InFocus Ultrasound DBA Little Peanut 4D Ultrasound; Tiny Blessings Ultrasound 4D Studio; Tiny Blessings Ultrasound 4D Studio; Total Womens Care PLLC; Reveal Ultrasound, LLC-S
Countries: United States; United States; United States; United States; United States; United States; United States; United States; United States; United States; United States; United States; United States; United States; United States
Enrollment: 551
Age range: 18 Years - None
Sex: FEMALE

Summary:
A multicenter study will be conducted to assess the role of the AI/ML technologies of Origin Medical EXAM ASSISTANT (OMEA) in interpreting first-trimester fetal ultrasound examinations (11 weeks 0 days - 13 weeks 6 days). The performance of the AI-based system will be compared against the ground truth provided by an independent reading panel of maternal-fetal medicine physicians.

Eligibility criteria:
Inclusion Criteria:

1. Maternal age ≥ 18 years
2. BMI \< 40 kg/m2
3. Live non-anomalous singleton pregnancies
4. Gestational age between 11 weeks + 0 days and 13 weeks + 6 days, as determined by:

   Last menstrual period (LMP) or, Ultrasound report if the the LMP date is uncertain Note: Gestational age determination follows standard American College of Obstetricians and Gynecologists (ACOG) guidelines.
5. Informed consent is obtained from the participant
6. Exams obtained as per the Image Acquisition Protocol

Exclusion Criteria:

1. Multiple Pregnancies
2. Cases with fetal demise or other fetal abnormalities observed/suspected after the ultrasound examination
3. Cases of planned diagnostic ultrasound follow-up exams within 2 weeks for known or suspected abnormality after the current ultrasound examination for the study

Outcomes:
To assess whether the AI/ML technologies used in OMEA can achieve an acceptable sensitivity for identifying the diagnostic view; To assess whether the AI/ML technologies used in OMEA can achieve an acceptable sensitivity and consistency for verifying the quality criteria of a given image; To evaluate the performance of the OMEA AI/ML technologies with respect to facilitating the determination of quantitative measure of crown-rump length (CRL) and nuchal translucency (NT), complementary evaluations of agreement will be performed.
Effect of Artificial Intelligence-Driven Mobile Application on Nurses' Knowledge and Attitude Toward Atraumatic Care for Hospitalized Children: A Randomized Controlled Trial
Trial ID: NCT07446920
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-12-29
Completion date: 2026-02-02
Sponsor: University of Baghdad
Conditions: Pediatric Nursing Education
Interventions: Atraumatic Care Training Program; No Intervention / Standard Care
Locations: Pediatric Hospital
Countries: Iraq
Enrollment: 97
Age range: 18 Years - 50 Years
Sex: ALL

Summary:
This randomized controlled trial aims to evaluate the effect of an artificial intelligence-driven mobile application on nurses' knowledge and attitudes toward atraumatic care for hospitalized children. Participants, who are pediatric nurses, will be randomly assigned to either the intervention group, using the mobile application, or the control group receiving standard education. The primary outcomes are improvements in nurses' knowledge and attitudes, measured after the intervention. The study seeks to determine whether the mobile application enhances nurses' competence in providing atraumatic care compared to usual training methods.

Eligibility criteria:
Inclusion Criteria:

* The study included nurses who:

Work in the participating units and provide direct nursing care to children or neonates.

Own a smartphone and are capable of using it (as the intervention is delivered via a mobile application).

Agree to participate and provide written informed consent

Exclusion Criteria:

* Nurses were excluded if they:

Perform only administrative tasks (e.g., statistics/records) without providing direct patient care.

Are expected to take long-term leave during the intervention/follow-up period

Outcomes:
Nurses' Knowledge about Atraumatic Care
Use of ChatGPT in Developing Health Materials on 'Immunization' for Nursing Students; A Randomized Controlled Trial
Trial ID: NCT06894888
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-11-25
Completion date: 2025-02-20
Sponsor: Near East University, Turkey
Conditions: Nursing Education
Interventions: artifial intelligence education
Locations: Near East University
Countries: Cyprus
Enrollment: 140
Age range: None - None
Sex: ALL

Summary:
Purpose: This study aims to determine the effect of using ChatGPT, an artificial intelligence program, on the development of health materials on immunization by senior nursing students. Materials and Methods: This study was conducted as a randomized controlled trial. The universe of the study consists of N=156 senior nursing students. However, 16 students who did not participate in the study were excluded from the study. The students filled out the 'Introductory Data Form' and 'Consent' form. Then, a faculty member other than the researchers completed the assignment of the students to the groups using the simple randomization technique (randomizer.org website) and forwarded the results to one of the researchers (DN) using the sealed envelope method. The study was completed with 63 students in the intervention group and n=77 students in the control group. Within the scope of a course taken by the students, the subject of 'immunization' was explained to the class as a theoretical lesson through traditional narration. The students were given the task of designing educational materials on the subject explained. One of the researchers (DN) made an appointment with the group of students assigned to the intervention group, arranged a meeting and provided 1-hour consultancy on the use of ChatGPT. Students were asked to conduct a search on ChatGPT using 3 keywords: 'nursing, health material and immunization'. On the specified date and time, students in the intervention and control groups were gathered in the classroom and introduced the health materials, students designed in the classroom. The health materials were evaluated by the researcher (KE) in the classroom using data collection forms ('Evaluation Form of Visual Design Principles and Elements' and 'Global Quality Scale'). The research was conducted in 3 moths. The data were evaluated using the SPSS 25.0 package program. In order to conduct the research, ethics committee permission, institutional permission, scale permission and written consents were obtained from the participants.

Eligibility criteria:
Inclusion Criteria:

Volunteering to participate in the study

* Being in the final year of the Turkish nursing department
* Attending the Evidence-Based Nursing Practices course

Exclusion Criteria:

* Having previously prepared health materials using an artificial intelligence program

Outcomes:
The Visual Design Principles and Elements Evaluation Form