moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
The Effectiveness of Artificial Intelligence Family Involvement Generative Agent (AIFIGA) Program on Nursing Home Nursing Staff and Family and Residents'Health
Trial ID: NCT06574438
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-09-24
Completion date: 2027-07
Sponsor: Chang Gung Memorial Hospital
Conditions: Family; Nursing Home; Digital Health
Interventions: AIFIGA program
Locations: Catholic Mercy Hospital Attached Nursing Home
Countries: Taiwan
Enrollment: 210
Age range: 20 Years - None
Sex: ALL

Summary:
This study aims to develop and evaluate the longitudinal effectiveness of the Artificial Intelligence Family Involvement Generative Agent (AIFIGA) program in enhancing the health of nursing staff, family, and residents' health through the use of a sequential, mixed methods research design. The development of a real-time, interactive, and informative AIFIGA program that sparks innovation is necessary to achieve effective communication between families and nursing staff.

In Phase I, lasting 18 months, we will develop the AIFIGA program based on triangulate research design, observation and in-depth interview understanding the daily communication experiences and expectations from both families and nursing staff perception in Nursing Homes(NHs) after COVID-19, as well as our previous years' research results. The training data for AIFIGA will be derived from the collection of communication dialogues in these qualitative data. Furthermore, the intelligence of AIFIGA will be developed using publicly available large language models (LLMs).

In Phase II, spanning the next 18 months, we will evaluate the longitudinal effects of the AIFIGA program on the health of residents, families, and nursing staff, tracking changes over time (baseline, 1 month, 3 months, and 6 months). There will be two groups of participants: (a) an AIFIGA group that receives the AIFIGA program and uses it for 3 months, and (b) a control group that receives only routine care.

Eligibility criteria:
Inclusion Criteria:

All resident-caregiver pairs meeting the inclusion criteria will be invited to join the study.

1. residents aged ≥ 60 years, resident's family were aged equal or higher than 20 years.
2. both residents and family can communicate in Mandarin or Taiwanese
3. residents have a Mini-Mental State Examination (MMSE) score equal to or higher than 16 for residents with no formal education or higher than 24 for residents with at least a primary school education
4. both residents and family agree to participate
5. the family is a significant member of the resident, such as a caregiver.

For nurses and nursing aides:

1. working in the selected nursing home who agree to participate in our program.
2. aged equal or higher than 20 years.

Exclusion Criteria:

\-

Outcomes:
resident health-Geriatric Depression Scale (GDS); resident health-Activity of daily living (ADL); social support scale; brief Center for Epidemiological Studies Depression Scale(CESD-10); The General Health Questionnaire-12(GHQ-12); Family Meaning of Nursing-Home Visits scale
Refinement and Adaption of Reinforcement Learning to Personalize Behavioral Messaging for Healthy Habits
Trial ID: NCT05742685
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2023-08-23
Completion date: 2025-12-17
Sponsor: Brigham and Women's Hospital
Collaborators: Boston Medical Center; National Institute on Aging (NIA)
Conditions: Diabetes Mellitus, Type 2; Medication Adherence
Interventions: Reinforcement Learning
Locations: Boston Medical Center
Countries: United States
Enrollment: 28
Age range: 18 Years - 84 Years
Sex: ALL

Summary:
Reinforcement learning is an advanced analytic method that discovers each individual's pattern of responsiveness by observing their actions and then implements a personalized strategy to optimize individuals' behaviors using trial and error. The goal of the proposed research is to refine, adapt and perform efficacy testing of a novel reinforcement learning-based text messaging intervention to support medication adherence for patients with type 2 diabetes within a community health center setting. This study will be a parallel randomized pragmatic trial comparing medication adherence and clinical outcomes for adults in a community setting aged 18-84 with type 2 diabetes who are prescribed 1-3 daily oral medications for this disease. Participants will be randomized to one of two arms for the duration of the study period: (1) a reinforcement learning intervention arm with up to daily, tailored text messages based on time-varying treatment-response patterns; or (2) a control arm with up to daily, un-tailored text messages. Outcomes of interest will be medication adherence, as measured by electronic pill bottles, and HbA1c levels over 6 months.

Eligibility criteria:
Inclusion Criteria:

* Diagnosis of Type 2 Diabetes Mellitus (T2DM)
* Prescribed between 1-3 daily oral medications for diabetes
* Most recent HbA1c level of 7% or greater
* Suboptimal adherence, defined by proportion of days covered (PDC) \< 0.90 based on chart review
* Must have a smartphone for which they are the sole user
* Must have a basic working knowledge of English or Spanish

Exclusion Criteria:

* Currently using a pillbox and/or not willing to use electronic pill bottles for 6 months
* Receive help at home on a daily basis with taking medications

Outcomes:
Diabetes medication adherence
Intelligent Case Manage Platform to Improve Self-management and Health-related Outcome Among Liver Transplant Recipients: Connection With Artificial Intelligence and Deep Learning
Trial ID: NCT05953948
Status: ENROLLING_BY_INVITATION
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-02-01
Completion date: 2027-12-31
Sponsor: Chang Gung University
Conditions: Liver Transplant Disorder; Self Efficacy; Quality of Life
Interventions: Intelligent Case Manage Platform (ICMP) and self-management program
Locations: Chang Gung University
Countries: Taiwan
Enrollment: 333
Age range: 20 Years - None
Sex: ALL

Summary:
This study is a prospective, quasi-experimental design, with an experimental group and a control group, will be created. The aims of this study are as follows: 1. Describe the self-management and information needs of liver transplant recipients, 2. Create content or modules related to the self-management of liver transplant recipients, 3. Build an intelligent case management platform, 4. Evaluate the usability of the platform, and 5. Conduct deep learning and examine the effects of the intelligent case management platform on self-efficacy, self-management, health outcomes, and health-related quality of life. Data will be collected at discharge (baseline data) and 1, 3, 6, 9, and 12 months after discharge. An estimated 133 patients will be involved in this experiment: 44 in the experimental group and 89 in the control group. Statistical package software (SPSS 22.0) will be used to analyze the data. A generalized estimation equation model will examine the differences in self-efficacy, self-management, and health-related quality of life between the experimental and control groups. Survival analysis and the Kaplan-Meier method will be used to analyze health outcomes, including hospital readmission, emergency visits, episodes of infection and rejection of organs, and death.

Eligibility criteria:
Inclusion Criteria:

* Liver transplant recipients
* Age 20 years and above
* Ability to use smart-phone

Exclusion Criteria:

* liver encephalopathy

Outcomes:
Change of the score of self-management behavior; Change of the score of self-efficacy; Change of the score of health-related quality of life
Evaluating the Validation of MRI-based Artificial Intelligence Software (NNS-SOT) for Onset Time Estimation in Lesion With Acute Ischemic Stroke : a Single-center, Single-arm, Single-blind (Evaluator), Retrospective Pivotal Trial
Trial ID: NCT06160726
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2023-10-24
Completion date: 2023-11-07
Sponsor: Nunaps Inc
Conditions: Acute Ischemic Stroke
Locations: Asan Medical Center
Countries: South Korea
Enrollment: 474
Age range: 20 Years - None
Sex: ALL

Summary:
This study evaluates the efficacy of investigational device named Stroke Onset Time Artificial Intelligence(AI) (Model name:NNS-SOT). Using the investigational device, analyze the image and the onset time of occurrence of acute ischemic stroke lesion on the brain image (4.5 hours or more from the onset of symptoms) and evaluate the efficacy of correctly estimating the onset time.

Eligibility criteria:
Inclusion Criteria:

* Brain imaging in patients with acute ischemic stroke aged 20 years and older
* Brain imaging of ischemic stroke manifestation lesion confirmed by medical staff in brain MR image
* Brain imaging taken within 24 hours of ischemic stroke symptoms
* Brain imaging including Magnetic Resonance(MR) images (DWI and FLAIR)
* Patients with clearly recorded onset time of acute ischemic stroke symptoms

Exclusion Criteria:

* Brain imaging used in NNS-SOT development process
* Brain imaging with noise (Artifact)
* Brain images damaged in data

Outcomes:
Sensitivity and specificity
Memory, Auditory and Visual Attention in Healthy Children: Neurophysiological and Electrophysiological Aspects
Trial ID: NCT02858752
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2016-10-06
Completion date: 2020-10-06
Sponsor: Hospices Civils de Lyon
Conditions: Healthy Volunteers
Interventions: Experience 1 : Auditory and Visual Discrimination; Experience 2 : Passive and Active Auditory Perception; Experience 3 : Visual Attention Training; Experience 4 : Attention and Distractibility; Experience 5 : Sustained Visual Attention
Locations: Hopital Femme Mère Enfant
Countries: France
Enrollment: 400
Age range: 5 Years - 17 Years
Sex: ALL

Summary:
This project studies memory and attention in healthy children aged from 5 to 17 years. The processes investigated are short term memory (auditory and visual) and attention.

To characterize these processes involved in childhood, neuropsychological and neurophysiological assessments will be performed (using Electro-encephalography measures, behavioural responses and questionnaires).

Eligibility criteria:
Inclusion Criteria:

* Normal or corrected vision
* No neurological and psychiatric antecedent.
* No neuro-developmental disorder (dysphasia, dyslexia, dyspraxia, attention deficit disorder…)
* Motivation to participate to the study
* Signed parental informed consent authorizing their child to participate to the study

Exclusion Criteria:

* Children educated in a specialized institution.
* Subjects diagnosed with a neurological, psychiatric or neuro-developmental disorder
* Subjects with hearing loss
* Subjects with a visual impairment that cannot be corrected
* Subjects with an organic or chronic disease that can affect cognitive functions.
* Subjects who do not benefit from social security.

Outcomes:
Neuropsychological tests: Proportion of accurate answers; Neuropsychological tests: reaction time of answers (seconds); Neurophysiological tests: Event-related potentials in response to auditory and visual stimulations
Video-Based AI-Generated Peri-operative Instructions for Mitral Valve Surgery: A Randomized Controlled Trial on Health Outcomes and Patient Experience
Trial ID: NCT07036926
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-08-01
Completion date: 2026-12
Sponsor: Joanna Chikwe
Collaborators: Cedars Sinai Medical Center, Los Angeles, USA
Conditions: Mitral Valve Repair Surgery
Interventions: Artificial Intelligence Video Education Arm
Locations: Cedars Sinai Medical Center Smidt Heart Institute
Countries: United States
Enrollment: 100
Age range: 18 Years - None
Sex: ALL

Summary:
1. The purpose of this study is to evaluate the impact of a novel, AI-generated, interactive peri-operative video education program on total unplanned healthcare utilization
2. The main procedures are questionnaires and watching a video.
3. The subject population includes patients who are scheduled for isolated mitral valve repair surgery.
4. All study procedures will occur here at Cedars Sinai.

Eligibility criteria:
Inclusion Criteria:

1. Adult patients ≥ 18 years planned for elective, isolated MV repair.
2. Provide Informed consent

Exclusion Criteria:

1. Insufficient computer/smart device knowledge. Children or caregivers can assist, but patients should be able to access their own email or text messages and navigate to the internet to use the provided AI generated educational intervention.
2. Inconsistent access to a smart device with internet connection throughout the study period.
3. Inability to speak, read, and understand English or Spanish
4. Need for any concomitant procedure at the time of MV repair

Outcomes:
Number of Unplanned Healthcare Utilization events
International Study of Artificial Intelligence-based Diagnosis of Cardiomyopathy Using Cardiac MRI (AID-MRI)
Trial ID: NCT05793840
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2023-06-30
Completion date: 2025-07-30
Sponsor: University of Calgary
Collaborators: University of Alberta
Conditions: Cardiomyopathies
Locations: Foothills Medical Centre; South Health Campus
Countries: Canada; Canada
Enrollment: 1100
Age range: 18 Years - None
Sex: ALL

Summary:
The goal of this observational study is to test the accuracy of computer (machine learning-based) algorithms to diagnosis heart diseases and predict if and when heart complications will occur. The AID-MRI research team has developed algorithms aimed at modelling 3D heart structure and movement (deformation), showing these may be of value to achieve these tasks. The International AID-MRI study aims to test the performance of these algorithms across 11 international sites, using data obtained from a broad variety of patients using different MRI scanners. In addition to an established cohort of 10,000 patients, AID-MRI will recruit an additional 1100 patients from its international sites, these serving as an external validation cohort.

Eligibility criteria:
Inclusion Criteria:

* Must have provided informed consent in a manner approved by the Investigator's Institutional Review Board (IRB) prior to any study-related procedure being performed. If a participant is unable to provide informed consent due to his/her medical condition, the participant's legally authorized representative may consent on behalf of the study participant, as permitted by local law and institutional Standard Operating Procedures
* Age ≥18 years at the time of informed consent;
* In-patient or out-patient referral for CMR imaging;
* Referral for suspected acute or chronic cardiomyopathy state(s) of ischemic and/or non-ischemic etiology;
* Recently drawn (≤180 days) and available serum laboratory markers of hemoglobin, hematocrit, and creatinine;
* Willing and able to abide by all study requirements

Exclusion Criteria:

* Standard contraindication(s) to magnetic resonance imaging performance based upon local site policies;
* Able to breath hold (i.e. real-time cine imaging not supported);
* Current or recent (≤ 60 days) pregnancy;
* Current or recent (≤ 60 days) sepsis requiring intubation;
* Cardiac implantable electronic implanted device (CIED) of any type (excluded due to likelihood of reduced image quality and anticipated influence on algorithm performance), inclusive of permanent pacemaker, implantable cardioverter defibrillator or implantable loop recorder;
* Severe aortic valve stenosis (i.e., mean AVG \>40 mmHg);
* Prosthetic valve (mechanical or bioprosthetic) in mitral or aortic position
* Congenital heart disease, inclusive of any surgically-corrected disease, dextrocardia, Tetralogy of Fallot, uncorrected partial anomalous pulmonary venous return, or large atrial septal defect (Qp:Qs ≥1.5) \[Note: bicuspid aortic valve disease is not an exclusion criterion in isolation\];

Outcomes:
Prediction accuracy
The Auxiliary Effect of Artificial Intelligence in the Detection of Precancerous Lesions in Proximal Colon Cancer: A Multicenter Randomized Controlled Study
Trial ID: NCT07338643
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2026-02-26
Completion date: 2028-08-01
Sponsor: Limian Er
Conditions: Abdominal Pain; Black Stools; Constipation; Diarrhea
Interventions: AI group; Serial group; Standard colonoscopy group
Locations: the Fourth Hospital of Hebei Medical University
Countries: China
Enrollment: 1200
Age range: None - None
Sex: ALL

Summary:
The investigators conducted a multicenter randomized controlled trial to explore the adjuvant effect of artificial intelligence in the detection of precancerous lesions in the proximal colon.This is a prospective, multicenter, single-blind, parallel randomized controlled trial.During the colonoscopy retraction process, the investigators aimed to compare the detection rates of proximal colon adenomas with and without the assistance of an AI(Artificial Intelligence) diagnostic device.

Eligibility criteria:
Inclusion Criteria:

1.Patients with symptoms such as abdominal pain, black stools, constipation, or diarrhea undergo colonoscopy for diagnostic purposes.

Exclusion Criteria:

1. Those with inflammatory bowel disease, colorectal cancer and surgical history, as well as those with familial polyposis syndrome
2. Those with intestinal obstruction or malignant tumors
3. Patients with colorectal lesions that need to be treated in stages.
4. Other contraindications for colonoscopy examination.

Outcomes:
Adenoma detection rate; Average adenoma detection rate
Study of Imaging and Molecular Biomarkers in Uncomplicated Rhegmatogenous Retinal Detachment
Trial ID: NCT07386678
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2026-04-27
Completion date: 2027-01-27
Sponsor: University College, London
Conditions: Retinal Detachment Rhegmatogenous; Proliferative Vitreoretinopathy; Proliferative Vitreoretinopathy in Rhegmatogenous Retinal Detachment
Locations: Moorfields Eye Hospital
Countries: United Kingdom
Enrollment: 50
Age range: 18 Years - None
Sex: ALL

Summary:
Disease or general study area: Uncomplicated rhegmatogenous retinal detachment (RRD) and risk of proliferative vittroretinopathy (PVR)

Purpose and nature of the study:

1. Characterise the cytokine profile of vitreous fluid in uncomplicated RRD.
2. Develop a risk model to predict development of PVR after retinal detachment surgery using imaging and molecular biomarkers.
3. To develop deep learning/artificial intelligence (AI) models for PVR detection in retinal detachment.

Inclusion criteria:

50 adult ( ≥18 years) patients with uncomplicated rhegmatogenous retinal detachments without PVR.

What participating will involve:

Pre- and post-operative assessments and intervention will follow standard of care for patients with rhegmatogenous retinal detachments.

Additional intervention will include non-invasive imaging of anterior chamber flare, vitreous, wide-field retina, macula optical coherence tomography (OCT) and macula OCT-angiography (OCT-A) as well as, seeking participant's consent on collecting their vitreous fluid at time of their surgery for cytokine analysis.

Eligibility criteria:
Inclusion Criteria:

* Adults ≥18 years
* Uncomplicated primary rhegmatogenous retinal detachment
* PVD present
* No PVR-A/B/C
* Phakic or pseudophakic.

Exclusion Criteria:

* Patients \<18 years
* Patients lacking capacity
* Previous vitrectomy
* Previous cryopexy
* Aphakia
* No fundal view
* Diabetic retinopathy of any severity
* Retinal detachment secondary to infective causes e.g. acute retinal necrosis, toxoplasmosis scars
* Retinal detachment secondary to congenital defects e.g. optic disc pit/coloboma
* Exudative retinal detachment
* Tractional retinal detachment
* Ongoing involvement in another ocular trial.

Outcomes:
Characterise the cytokine milieu in an uncomplicated RRD eye.
Genomics and Personalized Medicine for All Though Artificial Intelligence in Haematological Diseases
Trial ID: NCT06019208
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2021-01-01
Completion date: 2024-12-31
Sponsor: Hospital Universitari Vall d'Hebron Research Institute
Conditions: Sickle Cell Disorders
Locations: APHP Henri Mondor; APHP Necker; Azienda Ospedale Università Padova; UMC Utrecht; Hospital Universitari Vall d'Hebron Research Institute
Countries: France; France; Italy; Netherlands; Spain
Enrollment: 1000
Age range: 1 Year - None
Sex: ALL

Summary:
GenoMed4All 'Genomics and Personalized Medicine for all though Artificial Intelligence in Haematological Diseases' aims to advance on individual SCD patients' disease characterisation and to improve the monitoring of patients' health status, optimise clinical therapy guidance and ultimately improved health outcomes by the identification of biomarkers and the development of individual (risk) models in SCD. Genomed4All supports the pooling of genomic, clinical data and other "-omics" health through a secure and privacy respectful data sharing platform based on the novel Federated Learning scheme, to advance research in personalised medicine in haematological diseases thanks to advanced Artificial Intelligence (AI) models and standardised interoperable sharing of cross-border data, without needing to directly share any sensitive clinical patients' data. The SCD Use case will gather multi-modal clinical and -OMICs data from 1,000 SCD patients in 4 EU-MS: France, Italy, Spain and The Netherlands.

In close collaboration with the European Reference Network on Rare Hematological Diseases (ERN-EuroBloodNet, GA101157011), GENOMED4ALL involves multiple clinical partners from the network, while leveraging on healthcare information and repositories that will be gathered incorporating interoperability standards as promoted by ERN-EuroBloodNet central registry, the European Rare Blood Disorders Platform.

Eligibility criteria:
Inclusion Criteria:

* Patients older than 1 year, diagnosed with SCD, all genotypes.

Exclusion Criteria:

* Patients treated with stem cell transplant or gene therapy.
* Patients younger than 1 year old.

Outcomes:
Improving SCD classification; Improve diagnosis of cerebrovascular complications.