moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Fall Detection and Prevention for Memory Care Through Real-time Artificial Intelligence Applied to Video: A Randomized Control Trial
Trial ID: NCT03685240
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2023-10-31
Completion date: 2023-12-31
Sponsor: SafelyYou
Collaborators: National Institute on Aging (NIA)
Conditions: Alzheimer's Disease and Related Dementia; Fall Injury
Interventions: SafelyYou Fall Prevention System
Locations: SafelyYou
Countries: United States
Enrollment: 460
Age range: 18 Years - None
Sex: ALL

Summary:
The purpose of the research is to study a new safety monitoring system developed by SafelyYou to help care for a loved one with dementia. The goal is to provide better support for unwitnessed falls.

The SafelyYou system is based on AI-enabled cameras which detect fall related events and upload video only when these events are detected. The addition of a Human in the Loop (HIL) will alert the facility staff when an event is detected by the system.

Eligibility criteria:
The study population includes residents of care facilities that are a high fall risk with a particular focus on care facilities with high populations of individuals with Alzheimer's disease and related dementias. There are no gender, race, ethnicity, language or literacy requirements for participation and all residents are eligible.

Inclusion criteria - Living at a participating skilled nursing facility or equivalent, CCRC,

Exclusion criteria

\- 18 years old or younger

Outcomes:
Enrollment rate; Fall rate due to sit to stand transition detection; Fall rate due to gait change detection
Artificial Intelligence in Echocardiography: a Pilot Study of Bay Labs Technology in Image Acquisition, Education, and Analysis
Trial ID: NCT03936413
Status: WITHDRAWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2020-01-13
Completion date: 2020-01-13
Sponsor: New York Presbyterian Hospital
Collaborators: Caption Health, Inc.
Conditions: Cardiovascular Diseases
Interventions: Bay Labs EchoGPS Echcoardiogram; Native Terason Echocardiogram
Enrollment: 0
Age range: 18 Years - None
Sex: ALL

Summary:
The goal of this study is to determine whether the Bay Labs artificial intelligence (AI) system can be used by minimally trained operators to obtain diagnostic quality echocardiographic images.

Eligibility criteria:
Inclusion Criteria:

* Patients must be admitted to the resident cardiology ward service under a cardiology attending (general, heart failure, or private)
* The patient must have either have undergone or be planned to undergo a formal echocardiogram within 1 day of the study echocardiogram
* The patient must consent to the study
* The patient's inpatient attending physician must give permission for the patient to be approached for consent

Exclusion Criteria:

* Patient refusal
* No recent or planned echocardiogram within 1 day of the study echocardiogram
* Clinical need for an emergent echocardiogram that will immediately impact clinical decision making that should instead trigger obtaining a formal echocardiogram (for example, concern for cardiac tamponade or acute myocardial infarction).

Outcomes:
Echocardiogram image acquisition quality; Educational outcome
Artificial Intelligence Guided Personalised Medicine in Patients With Hypertension and Diabetes
Trial ID: NCT04769141
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2021-08
Completion date: 2023-01
Sponsor: Alexandra Hospital
Conditions: Diabetes Mellitus; Hypertension
Interventions: CURATE.AI
Enrollment: 40
Age range: 21 Years - None
Sex: ALL

Summary:
Hypertension and diabetes are chronic diseases that require long-term management and disease control is often sub-optimal, leading to complications which place additional burden on patients and the healthcare system.

This study aims to address factors such as gender, ethnicity and specific genetics that are not routinely considered during drug dosing but account for considerable variations in drug responses by tailoring the treatment to individual patient using CURATE.AI. CURATE.AI is an artificial intelligence guided dosing decision tool and can be applicable across many different conditions, as it is disease agnostic. This platform had demonstrated initial success in cancer and transplant populations but has yet to be applied to chronic disease patients.

The aim of this study is demonstrate that artificial intelligence guided treatment in chronic disease of hypertension and type 2 diabetes mellitus will yield actionable medication dosing optimization.

Eligibility criteria:
Inclusion Criteria:

* Adult patients (≥ 21years) with T2DM or hypertension
* Expected to be followed up in Alexandra Hospital for the next 4-months
* Signed informed consent form

Exclusion Criteria:

* Patients with cognitive impairment
* Patients with active cancer undergoing chemotherapy
* Patients on hemodialysis or peritoneal dialysis (which can cause rapid fluctuation of BP and blood sugar, respectively)
* Pregnant patients
* Patients whose medications for T2DM and hypertension are changed simultaneously during the first clinic visit
* Serious concomitant disorders that would compromise the safety of the patient or compromise the patient's ability to complete the study, at the discretion of the investigator.

Outcomes:
Medication dosing optimization
Deep Learning With MRI-based Multimodal-data Fusion Enhanced Postoperative Risk Stratification of Breast Cancer
Trial ID: NCT06546072
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2011-03-23
Completion date: 2021-12-06
Sponsor: Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
Conditions: Breast Cancer
Interventions: MRI
Enrollment: 1199
Age range: 18 Years - None
Sex: FEMALE

Summary:
Breast cancer poses a significant global health challenge, especially among women, with high rates of recurrence and distant spread despite early interventions. The timely identification of metastasis risk and accurate prediction of treatment strategies are critical for improving prognosis. However, the complex heterogeneity of breast tumors presents challenges in precise prognosis prediction. Therefore, the development of innovative methods for tumor segmentation and prognosis assessment is essential.

The research conducted is a multicenter study that enrolled 1,199 non-metastatic breast cancer patients from four independent centers. Our study leverages the advancements in artificial intelligence (AI) to address this challenge. This study is the first successful application of MRI-based multimodal prediction system to precisely identify the risk of postoperative recurrence in breast cancer patients.

Eligibility criteria:
Inclusion Criteria:

* Histologically confirmed stage I-III invasive BC
* Age ≥ 18 years
* The patient having undergone surgery
* The existence of MRI scans

Exclusion Criteria:

* Lacked pathological results
* Had other, simultaneous malignancies
* Had MR imaging issues were excluded

Outcomes:
DFS
Étude Exploratoire Des Performances de Diagnostic Électrocardiographique de l'Apple Watch augmentée d'un Algorithme d'Intelligence Artificielle
Trial ID: NCT04792905
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2021-03-30
Completion date: 2021-07-19
Sponsor: Cardiologs Technologies
Conditions: Atrial Fibrillation
Interventions: Cardiologs
Locations: Institut Cardiologique Paris Sud Hôpital Privé Jacques Cartier
Countries: France
Enrollment: 200
Age range: 22 Years - None
Sex: ALL

Summary:
This observational exploratory prospective study will evaluate the performance on an Artificial Intelligence (AI) solution interpreting ECG (electrocardiogram) collected from an Apple Watch (AI-AW) in the detection of Atrial Fibrillation (AF)

Eligibility criteria:
Inclusion Criteria:

* Patient aged 22 or older, able and willing to participate in the study
* Patient admitted to hospital for ablation, cardioversion or cardiac electrophysiological exploration or who come for regular rhythmology consultations
* Patient who has read the information note and has given his or her consent before any procedure related to the study
* Patient affiliated to social security

Exclusion Criteria:

* Pregnant or breastfeeding women
* Patient with a pacemaker, implantable defibrillator or cardiac resynchronisation therapy device.
* Subject related to the investigator or any other staff member directly involved in the conduct of the study
* Patient incapable of giving consent, minor or adult patient protected by law

Outcomes:
Performance of AF detection
AIDA - A Digital Health Intervention Based on Artificial Intelligence to Support the Personalized Recovery of Long COVID Patients: Feasibility Study.
Trial ID: NCT05483829
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2022-08-01
Completion date: 2022-08-28
Sponsor: Adhera Health, Inc.
Collaborators: Fundacio d'Investigacio en Atencio Primaria Jordi Gol i Gurina
Conditions: Post Acute COVID-19 Syndrome
Interventions: Adhera® Fatigue for Long COVID program
Locations: Gerencia Territorial de la Catalunya Central - Institut Catala de la Salut
Countries: Spain
Enrollment: 15
Age range: 18 Years - None
Sex: ALL

Summary:
The AIDA feasibility study builds upon mental health and technology acceptance theoretical frameworks. It examines the feasibility of a mobile-based digital program to support patients with long covid affected by fatigue. The program Adhera ® Fatigue Self-management is adapted for long covid patients.

Eligibility criteria:
Inclusion Criteria:

* Legal adults (older than 18)
* Long COVID diagnosis within the last year
* In possession of a compatible smartphone (Android OS v4.4 or higher)
* Reporting any of these symptoms:

  * Fatigue
  * Dyspnea
  * Anxiety
  * Stress
  * Depression
  * Sleep disorder

Exclusion Criteria:

* Hospital admission during follow-up due to pathology not related to COVID-19
* Patients without digital literacy or who cannot use the mobile application.
* Known severe psychiatric illness or cognitive impairment
* Being pregnant (or suspected to be pregnant)

Outcomes:
Behavioral outcome: Usability
Evalution of the Effectiveness of a Shoulder Dystocia Training Program Designed With Inverted Learning Supported by Generative Artificial Intelligence
Trial ID: NCT07514702
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2026-04
Completion date: 2027-06
Sponsor: Fenerbahce University
Conditions: Shoulder Dystocia Training Using AI-supported Flipped Learning in Midwifery Education
Locations: Fenerbahce University
Countries: Turkey (Türkiye)
Enrollment: 114
Age range: None - None
Sex: FEMALE

Summary:
With the ongoing digital transformation in health education, the importance of learner-centered and technology-integrated approaches has been increasing. Particularly in teaching complex and unpredictable clinical situations in healthcare, the limited effectiveness of traditional methods has led to a growing need for innovative approaches such as flipped learning and self-directed learning. Generative artificial intelligence (GAI)-supported educational applications have the potential to enhance academic achievement by making the learning process more interactive. However, research evaluating the effectiveness of these technologies in education remains limited. In this context, GAI-supported flipped learning enables students to gain individualized learning experiences through simulated classroom or clinical environments and to be better prepared for clinical encounters and specific clinical skill training. One of the critical clinical topics that should be addressed within this framework is shoulder dystocia. Shoulder dystocia is an obstetric emergency, most cases of which are unpredictable and unavoidable. Accordingly, this study aims to evaluate the effects of a GAI-supported flipped learning approach applied to shoulder dystocia training on midwifery students' knowledge levels and self-directed learning skills through effective use of technology.

Eligibility criteria:
Inclusion Criteria:

* -Being a 3rd year student in the Midwifery Department of Fenerbahçe University and Istanbul-Cerrahpaşa Faculty of Health Sciences.
* Having sufficient or unlimited internet access.

Exclusion Criteria:

* Having previously taken a course on shoulder dystocia
* Student's absence from class/sick leave

Outcomes:
Knowledge level on shoulder dystocia management
Blinded Randomized Controlled Trial of Artificial Intelligence Guided Assessment of Cardiac Function
Trial ID: NCT05140642
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2022-04-01
Completion date: 2022-06-29
Sponsor: Cedars-Sinai Medical Center
Conditions: Heart Failure, Systolic; Heart Failure, Diastolic
Interventions: Automated annotation of the left ventricle through deep learning; Sonographer Measurement of LVEF
Locations: Cedars-Sinai Medical Center
Countries: United States
Enrollment: 3495
Age range: 18 Years - 110 Years
Sex: ALL

Summary:
To determine whether an integrated AI decision support can save time and improve accuracy of assessment of echocardiograms, the investigators are conducting a blinded, randomized controlled study of AI guided measurements of left ventricular ejection fraction compared to sonographer measurements in preliminary readings of echocardiograms.

Eligibility criteria:
Inclusion Criteria:

* The study imaging studies will include patients who underwent imaging (limited or comprehensive transthoracic echocardiogram studies) and a LVEF was adjudicated in the echocardiography/non-invasive cardiac imaging laboratory.
* The study participants are cardiologists reading in the echocardiography/non-invasive cardiac imaging laboratory.

Exclusion Criteria:

* The study imaging studies will exclude transesophageal echocardiogram imaging.
* The study will exclude cardiologists who decline to participate

Outcomes:
Frequency of >5% change in LVEF between preliminary and final report; Average change in LVEF between preliminary and final report
AI-Powered Neonatal Risk Assessment for Improved Perinatal Outcomes
Trial ID: NCT07064356
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-07
Completion date: 2026-06
Sponsor: FetalFirst Limited
Conditions: Congenital Anomalies; Neonatal Complications; Perinatal Outcomes; Perinatal Outcomes of the Mother and Fetus
Enrollment: 50000
Age range: 1 Year - 1 Year
Sex: ALL

Summary:
This study aims to develop advanced artificial intelligence (AI) models that predict neonatal risks and complications based on historical multimodal health data, including ultrasound and MRI scans. The objective is to empower clinicians and provide clear, compassionate support for families navigating complex prenatal diagnoses.

Eligibility criteria:
Inclusion Criteria:

* Historical, de-identified neonatal records including ultrasound images, MRI scans, and clinical documentation available for analysis.

Exclusion Criteria:

* Cases with incomplete or missing critical data elements required for AI model analysis.

Outcomes:
Accuracy of AI Model Predictions for Neonatal Risk
Optimizing Implementation of Mental Health Intervention Within Orthopedic Care
Trial ID: NCT05194722
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2022-01-06
Completion date: 2022-06-18
Sponsor: Washington University School of Medicine
Collaborators: National Institute of Mental Health (NIMH)
Conditions: Anxiety; Depression; Back Pain; Neck Pain
Interventions: Wysa mobile app
Locations: Washington University School of Medicine
Countries: United States
Enrollment: 30
Age range: 18 Years - None
Sex: ALL

Summary:
This study will utilize qualitative cross-sectional and quantitative longitudinal methods. In semi-structured interviews, orthopedic providers and patients with spine conditions will discuss their overall perspectives, specific needs, and preferred solutions to addressing mental health impairment in the context of orthopedic care and research. In these sessions, patient stakeholders will also user test two specific mental health interventions: a customized version of a commercial digital mental health intervention (Wysa) and a prototype of a print-based mental health resource guide. The patient stakeholders will be provided one month of Wysa access, and they will complete baseline and follow-up measures related to usability, clinical effectiveness, and intermediate behavioral mechanisms through which Wysa is hypothesized to act. Wysa usage data will be obtained from the app company. Delivery of Wysa and the printed resource guide will be iteratively refined based on user feedback.

Eligibility criteria:
Inclusion criteria:

* Orthopedic patient presenting for outpatient clinic evaluation to a spine specialist for neck and/or back pain
* Musculoskeletal spine pain for 3+ months

Exclusion criteria:

* Routine post-operative visit with no plans for further surgery
* Endorses mental health crisis (active suicidal or homicidal ideation, psychosis)
* Cognitive impairment which would interfere with meaningful engagement with research interview, questionnaires, and/or intervention

Outcomes:
Adult PROMIS CAT Depression v1.0; Adult PROMIS CAT Anxiety v1.0
Neoadjuvant Camrelizumab, Nab-paclitaxel and Carboplatin in Patients With Stage IB-IIIA Non-small Cell Lung Cancer (NANE-LC): A Prospective, Single-arm, Multicenter, Phase II Study
Trial ID: NCT04541251
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: PHASE2
Start date: 2020-08-01
Completion date: 2023-09-30
Sponsor: Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
Collaborators: Guangdong Provincial People's Hospital; Third Affiliated Hospital, Sun Yat-Sen University
Conditions: Lung Cancer, Non-small Cell; Artificial Intelligence
Interventions: Camrelizumab + Nab-paclitaxel + Carboplatin
Locations: Sun Yat-Sen Memorial Hospital of Sun Yat-sen University
Countries: China
Enrollment: 40
Age range: 18 Years - None
Sex: ALL

Summary:
This prospective, single-arm, multicenter, phase II trial enrolled 40 patients who underwent surgery after three cycles of neoadjuvant therapy with camrelizumab, nab-paclitaxel, and carboplatin. The MPR is the primary endpoint, and the pCR, the complete resection rate, the objective response rate, the disease-free survival, adverse events, and quality of life are the secondary endpoints. The exploratory endpoints will be used to establish a multiomics artificial intelligence system for neoadjuvant therapy effect prediction and decision-making assistance based on radiomics, metabolism, genetic, and clinic-pathological characteristics and to explore drug resistance mechanisms.

Eligibility criteria:
Inclusion criteria:

* The patients have stage IB-IIIA NSCLC.
* Patient ages are ≥ 18 years old, regardless of gender.
* The patients have an ECOG ps of 0-1, with a condition suitable for surgery.
* The patients have not received any anti-tumor treatment.
* The patients have adequate blood function.
* The patients have adequate organ function.
* The patients had been using an appropriate method of contraception, and there exists a negative pregnancy test (serum or urine) for women.
* The patients will give their signed informed consent.

Exclusion criteria:

* Patients who had a prior allogeneic tissue or organ transplantation.
* Patients who have multiple cancers.
* Patients who have any severe or uncontrolled systemic diseases.
* Patients with a positive test for HIV, HBV, or HCV.
* Patients with severe infection or with an infection that required antibiotic therapy.
* Patients with a history of interstitial lung disease, active tuberculosis, or autoimmune disease.
* Patients who have participated in any other clinical trials.
* Patients who are considered ineligible by the investigator.

Outcomes:
Major pathologic response (MPR) Rate
Optimizing Noninvasive assessMent Of DysmEtabolic Compensated Advanced Liver Disease by Integration of Artificial Intelligence Model and omicS Data
Trial ID: NCT06888310
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-12-06
Completion date: 2027-03-01
Sponsor: Fondazione Policlinico Universitario Agostino Gemelli IRCCS
Conditions: NASH; NAFLD
Interventions: extra blood sampling
Locations: Fondazione Policlinico Universitario Agostino Gemelli IRCCS, UOC Medicina Interna e Trapianto di Fegato
Countries: Italy
Enrollment: 408
Age range: 18 Years - None
Sex: ALL

Summary:
Non-alcoholic fatty liver disease (NAFLD) is responsible for a significant proportion of liver-related deaths and healthcare costs in the United States, accounting for approximately 36% of liver-related deaths and over one billion dollars in annual healthcare expenses. \[PMID: 34863359\] A recent analysis of healthcare costs in Italy showed that out of the 9,729 NAFLD/NASH patients who were hospitalized and analyzed, the vast majority (97%) did not have advanced liver disease, while 1.3% had compensated advanced liver disease (cACLD), 3.1% had decompensated cirrhosis, 0.8% had hepatocellular carcinoma, and 0.1% underwent liver transplantation.

The burden of comorbidities was high across all patient cohorts, and patients with cACLD required a greater number of inpatient services, outpatient visits, and the pharmacy fills compared to those without advanced liver disease. As disease severity increased, mean total annual costs also increased primarily due to higher inpatient services costs. In Italy, as in other EU countries, most of the healthcare costs for patients were attributed to NAFLD/NASH-related liver complications. Thus, the optimization of the non-invasive diagnosis of cACLD represents an urgent need in dysmetabolic liver disease. These advancements will play a crucial role in early detection, risk stratification, and effective management of highly prevalent liver diseases such as NAFLD/NASH and their progression.

Eligibility criteria:
Inclusion Criteria:

* age\>=18; sex (M,F);
* dysmetabolic liver disease according new nomenclature definition;
* suspicion of cACLD by LSM\>=10 with VCTE;
* routine esogastroduodenoscopy report within 12 months of VCTE for identification of high-risk varices (HRV).

Exclusion Criteria:

* portal vein thrombosis,
* infiltrative liver neoplasms, and conditions are known for their potential influence on the LSM results (congestive liver disease, extrahepatic biliary obstruction, ALT \> 5x upper normal limit).

Outcomes:
number of patients identified with single diagnostic method
ATTRACT - ArTificial inTelligence-based RAdiogenomics in Colon Tumors
Trial ID: NCT06108310
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2021-01-02
Completion date: 2025-12-31
Sponsor: University of Roma La Sapienza
Collaborators: Associazione Italiana per la Ricerca sul Cancro
Conditions: Colon Cancer Stage II; Colon Cancer Stage III
Locations: AOU Sant'Andrea
Countries: Italy
Enrollment: 500
Age range: 18 Years - None
Sex: ALL

Summary:
The goal of this clinical trial is to develop an artificial intelligence-based model to assess radiogenomics signature of colon tumor in patients with stage II-III colon cancer. The main question it aims to answer is:

• Can artificial intelligence-based algorithm of radiomics features combined with clinical factors, biochemical biomarkers, and genomic data recognise tumor behaviour, aggressiveness, and prognosis, identifying a radiogenomics signature of the tumor?

Participants will

* undergo a preoperative contrast-enhanced CT examination;
* undergo surgical excision of colon cancer
* undergo adjuvant therapy if deemed necessary based on current guidelines

Eligibility criteria:
Inclusion Criteria:

* patients with pathologically proven stage II and stage III colon cancer;
* availability of a CT scan with portal-venous phase at the time of diagnosis;
* availability of immunohistochemical panel

Exclusion Criteria:

* patients with no CT images prior to surgical resection;
* patients with CT scans characterized by motion artifacts preventing radiomics analysis

Outcomes:
Identification of radiogenomics signature (ATTRACT AI-model) of stage II-III colon tumors