moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Establishing AI Assessment and Prediction Models for Geriatric Trauma and Effects of AI Incorporated Into an Innovative Health Service Program in Geriatric Trauma Patients
Trial ID: NCT05176054
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2021-08-01
Completion date: 2024-10-31
Sponsor: Kaohsiung Medical University Chung-Ho Memorial Hospital
Conditions: Trauma; Aging
Interventions: Health service program
Locations: Kaohsiung Medical University
Countries: Taiwan
Enrollment: 418
Age range: 65 Years - 74 Years
Sex: ALL

Summary:
To fill up the research gaps and build up a healthcare service model for geriatric trauma patients, this two-stage study aims to (a) develop geriatric trauma Artificial Intelligence (AI) prediction models by comparing the outcomes from clinical assessment and AI joint images; and (b) examine the long-term effects of an innovative health service program in geriatric trauma patients.

Eligibility criteria:
Inclusion Criteria:

* admitted to the hospital due to injury first time
* able to communicate in Mandarin
* agree to participate in this study from hospital discharge to one month after hospital discharge
* patients and their primary caregivers have smart mobile phones and internet access available
* can move independently before trauma

Exclusion Criteria:

* not frequent user for smart phones
* severe cognitive impairment
* victims of hangings and poisonings

Outcomes:
The Numeric Pain Rating Scale; The Numeric Pain Rating Scale; The Geriatric Depression Scale Short Form; The World Health Organization Quality of Life Questionnaire; The Berg Balance Scale; The key body points that match the items in the Berg Balance Scale
Point of Care Ultrasound Lung Artificial Intelligence (AI) Validation Data Collection Study
Trial ID: NCT04891705
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2021-09-11
Completion date: 2023-12-14
Sponsor: Philips Clinical & Medical Affairs Global
Conditions: Pleural Effusion; Lung Consolidation
Interventions: Lung Ultrasound Scan
Locations: Arizona Emergency Medical Research Center; University of Colorado Hospital; Yale School of Medicine; Beth Israel Deaconess Medical Center; Prisma Health Greenville Memorial Hospital
Countries: United States; United States; United States; United States; United States
Enrollment: 554
Age range: None - None
Sex: ALL

Summary:
This study is being conducted to collect image data and relevant clinical data from medical records of patients with suspicion of lung consolidation or pleural effusion. The information will be used to test the performance of Artificial Intelligence (AI) in identification of features associated with the above lung conditions.

Eligibility criteria:
Inclusion Criteria:

1. Subjects presenting to the Emergency Department, admitted to the hospital, or seen in the outpatient setting with suspicion of pleural effusion or lung consolidation who received chest imaging to include chest X-ray or CT, or for whom chest X-ray or CT are planned.

   Suspicion of pleural effusion or lung consolidation may be based upon imaging findings or presentation of symptoms including (but not limited to) dyspnea, hypoxia, tachypnea, rales, or dullness.
2. All attempts should be made for subjects to undergo scanning of 14 lung zones (adults) and 12 lung zones (pediatrics). Attempts should also be made to capture all required lung zones in adults and pediatrics. Instances where all zones cannot be captured need to be documented and reason for inability to scan them provided in EDC. See Section 8.1.3 Lung Ultrasound Exams.
3. Subject is willing to provide informed consent (or assent where age appropriate) to participate in the study. Subjects under 18 years of age require informed consent of a parent or guardian and assent as required by institutional IRB.
4. Weight of subject is greater than or equal to 10 kg.

Exclusion Criteria:

1. Subject has pathology precluding safe and pain-free ultrasound probe placement such as burn, significant soft tissue injury or surgical incisions.
2. Current imprisonment.
3. Pregnancy (self-reported)
4. Subjects with breast implants
5. Prior pleurodesis procedure.
6. History of severe lung disease and home oxygen use
7. Presence of subcutaneous air that precludes imaging
8. Body Mass Index (BMI) \> 40
9. Significant scoliosis (Cobb angle ≥ 20 degrees).
10. Pediatric subjects with chronic lung diseases other than asthma.
11. Pediatric subjects on chronic respiratory support (i.e., nasal canula, CPAP, BiPAP, or tracheostomy).

Outcomes:
The study data will be used to support future performance assessments for Artificial Intelligence based algorithms as described by FDA guidance for Computer Assisted Detection and Diagnostic Devices
Artificial Intelligence-supported Reading Versus Standard Double Reading for the Interpretation of Magnetic Resonance Imaging in the Detection of Local Recurrence for Nasopharyngeal Carcinoma: a Randomised Controlled Multicenter Study
Trial ID: NCT06356441
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2024-04
Completion date: 2026-04
Sponsor: Sun Yat-sen University
Conditions: Artificial Intelligence Supported Image Reviewing
Interventions: AI
Locations: Sun Yat-Sen University Cancer Center
Countries: China
Enrollment: 10400
Age range: None - None
Sex: ALL

Summary:
The aim of this randomized controlled study is to investigate whether the previously developed artificial intelligence model can triage post-radiotherapy magnetic resonance images of patients with nasopharyngeal carcinoma and assist radiologists in their interpretation.

Eligibility criteria:
Inclusion Criteria:

* Patients with treatment naive nasopharyngeal carcinoma who had finished radiotherapy for 6 months or more
* The previous magnetic resonance imaging examination had showed complete remission in the primary site
* Images are acquired using a 3T magnetic resonance imaging device, including unenhanced T1-weighted and T2-weighted sequences and contrast-enhanced T1-weighted sequences

Exclusion Criteria:

* Patients are enrolled in this study for a specific magnetic resonance imaging scan and not for subsequent follow-up magnetic resonance imaging scans.

Outcomes:
sensitivity
A Superiority Randomized Controlled Trial of the Effect of a Novel Intelligent Language Model on the Self-learning Ability of Medical Researchers
Trial ID: NCT06015178
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2023-08-30
Completion date: 2024-04-30
Sponsor: Zhongshan Ophthalmic Center, Sun Yat-sen University
Conditions: Medical Artificial Intelligence; Interdisciplinary Research; Self-Directed Learning
Interventions: Intelligent Language Model; control
Locations: Zhongshan Ophthalmic Center, Sun Yat-sen University
Countries: China
Enrollment: 60
Age range: 20 Years - 28 Years
Sex: ALL

Summary:
Solving medical scientific problems is a crucial driving force behind the advancement of medical disciplines. As the complexity of scientific questions increases, an increasing number of problems require interdisciplinary collaboration to be resolved. However, most medical researchers lack interdisciplinary background knowledge and require substantial time to systematically learn relevant knowledge and skills. Furthermore, the continuous emergence of new knowledge and skills emphasizes the importance of researchers' ability for autonomous learning in the medical field. Therefore, to promote the development of medical disciplines, there is an urgent need for an effective method to enhance researchers' self-directed learning abilities for conducting interdisciplinary research.

The next-generation artificial intelligence language models, exemplified by ChatGPT, hold great potential in assisting researchers to access knowledge and information from various domains. Whether researchers can leverage such AI tools to enhance their self-directed learning abilities for conducting interdisciplinary research remains to be further explored. Additionally, concerns have been raised regarding the potential degradation of cognitive abilities through their use, although valid evidence is currently lacking.

To investigate whether AI tools, represented by ChatGPT, can effectively assist medical researchers in conducting interdisciplinary research and whether their usage may negatively impact researchers' cognitive abilities, a randomized controlled trial is warranted. This trial aims to ascertain the potential benefits and risks associated with utilizing AI tools in the medical research domain.

Eligibility criteria:
Inclusion Criteria:

* Junior ophthalmologist with 1-3 years of clinical experience
* 20-28 years old, regardless of gender
* No prior experience in interdisciplinary research involving digital medicine
* Self-reported a minimum of 20 hours of participation in this study during the trial period
* Agree to participate in this study and sign informed consent

Exclusion Criteria:

* Individuals with reading difficulties or reading disabilities
* Reluctance to participate in this study

Outcomes:
completion rate
Development and Validation of an AI-Based Tool for Identifying Responders and Stratifying Risks in Ulinastatin Treatment in Cardiac Surgery: A Multicenter Real-world Study
Trial ID: NCT07583290
Status: ACTIVE_NOT_RECRUITING
Study type: OBSERVATIONAL
Start date: 2018-01-01
Completion date: 2027-03-31
Sponsor: Beijing Anzhen Hospital
Conditions: Cardiac Surgical Procedures; Cardiopulmonary Bypass; Systemic Inflammatory Response Syndrome; Multiple Organ Dysfunction Syndrome; Postoperative Complications
Interventions: ulinastatin
Locations: Center for Cardiac Surgery and Critical Care
Countries: China
Enrollment: 10000
Age range: 18 Years - None
Sex: ALL

Summary:
This is a multicenter, retrospective, real-world observational study aimed at developing and validating an artificial intelligence-based tool for identifying ulinastatin treatment responders and risk stratification in cardiac surgery patients undergoing cardiopulmonary bypass (CPB).

Ulinastatin, a glycoprotein extracted from human urine, has shown potential benefits in reducing postoperative complications and inflammatory responses in cardiac surgery. However, evidence supporting its efficacy and optimal application in specific patient populations remains insufficient.

This study will collect clinical data from approximately 4 tertiary cardiac centers in China, including patients who underwent cardiac surgery with CPB. Using machine learning algorithms (such as weighted K-modes clustering and XGBoost), the study aims to: (1) construct a multicenter real-world database for cardiac surgery; (2) identify clinical characteristics associated with ulinastatin treatment response; (3) develop and validate an AI-based risk stratification tool to assist clinical decision-making. This study may provide evidence-based guidance for personalized perioperative anti-inflammatory treatment in cardiac surgery.

Eligibility criteria:
Inclusion Criteria:

* Patients underwent extracorporeal circulation heart surgery, including coronary artery bypass grafting, valve repair or replacement surgery, congenital heart defect repair surgery, and major vascular and aortic disease surgeries;
* Patients received standard treatment (such as anticoagulation, circulatory support), with or without ulinastatin.

Exclusion Criteria:

* Patients who had undergone cardiopulmonary bypass surgery multiple times;
* Patients with incomplete clinical records, lacking key information such as patient ID, age, gender and disease diagnosis.

Outcomes:
All-cause mortality
Mobile Application (App)-Based Mindfulness Intervention for Older Korean Immigrants
Trial ID: NCT07550166
Status: ENROLLING_BY_INVITATION
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-08-01
Completion date: 2026-09-30
Sponsor: University of South Florida
Conditions: Stress; Anxiety; Depression; Psychological Distress; Mindfulness
Interventions: AI-Enabled Mobile App-Based Mindfulness Intervention
Locations: The Korean Association of West Florida; University of South Florida School of Social Work
Countries: United States; United States
Enrollment: 30
Age range: 60 Years - None
Sex: ALL

Summary:
The goal of this clinical trial (single-arm pilot trial) is to learn whether a mobile application-based mindfulness intervention can improve mental health outcomes in older Korean immigrants aged 60 years and older. The main questions it aims to answer are whether the 8-week mobile app-based mindfulness intervention is feasible and acceptable, as indicated by recruitment, retention, and adherence rates, and whether participation in the intervention leads to improvements in positive psychological well-being (e.g., positive affect, optimism, life engagement, and mindfulness) and reductions in adverse mental health outcomes (e.g., anxiety, depressive symptoms, and perceived stress). Participants will complete baseline and post-intervention assessments, receive training on how to use the mobile mindfulness application, engage with the app for approximately 8 weeks (recommended 10-15 minutes per day), and participate in weekly check-ins to support engagement and address any challenges encountered during the intervention.

Eligibility criteria:
Inclusion Criteria:

Aged 60 years or older Self-identified as a Korean immigrant Able to read and understand Korean or English Owns or has access to a smartphone compatible with the mobile application Willing and able to provide informed consent Willing to participate in an 8-week mobile mindfulness intervention and complete study assessments

Exclusion Criteria:

Significant cognitive impairment that would interfere with participation (e.g., based on screening) Severe psychiatric conditions (e.g., active psychosis or severe depression requiring immediate clinical intervention) Current participation in another behavioral or psychological intervention study Physical or sensory limitations that would prevent use of a mobile application Any condition that, in the opinion of the investigators, would make participation unsafe or not feasible

Outcomes:
Psychological Well-Being and Mental Health Outcomes
Clinical Study of GREE(a Name of Chinese Company)-AI(Artificial Intelligence) Medical Software in the Detection of Ureteral Stent Encrustation
Trial ID: NCT05495659
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2022-08-01
Completion date: 2024-11-30
Sponsor: Fifth Affiliated Hospital, Sun Yat-Sen University
Conditions: Ureteral Stent-Related Symptom
Enrollment: 156
Age range: 12 Years - 80 Years
Sex: ALL

Summary:
Ureteral stents are the most commonly used postoperative implantable materials in urology. The indwelling ureteral stents often lead to the formation of encrustation, with an incidence of 10%, which will lead to complications such as obstruction, lower urinary tract symptom (LUTS）, and difficulty in extubation. At present, there is no accurate and sensitive diagnostic criteria for ureteral stents encrustation. Previously, we developed the artificial intelligence-based ureteral stents encrustation identification software - GREE-AI Medical Software (GAIMS) with the collaboration of GREE ELECTRIC APPLIANCES,INCORPORATED.OF ZHUHAI(a city of Guangdong, China). In order to further evaluate the diagnostic sensitivity and accuracy of the software in practical clinical applications, and to verify its predictive ability for ureteral stents encrustation, the following single-center, prospective, double-blind, diagnostic cohort study is planned.

This study will use the gold standard as a reference to evaluate the sensitivity and specificity of GAIMS for detecting ureteral stents encrustation. At the same time, it will be compared with non-enhanced contrast Computed Tomography examinations interpreted by clinical radiologists to evaluate the pros and cons of GAIMS in detecting ureteral stents encrustation.

Eligibility criteria:
Inclusion Criteria:

1. Patients who plan to undergo ureteral stent placement due to urinary calculi or urinary system obstruction;
2. Aged over 12 years old;
3. The estimated time of ureteral stent placement is more than 2 weeks but less than 1 year;
4. Voluntarily participate in this clinical study, abide by the requirements of this clinical study and sign the informed consent.

Exclusion Criteria:

1. Transplanted kidney, severe urinary system deformity, hydronephrosis caused by the compression of tumors of the urinary system and surrounding organs (such as cervical cancer);
2. There are serious diseases in heart, lung, liver and other major organs;
3. Pregnancy;
4. The subject cannot complete the study or comply with the requirements of the study (due to administrative reasons or other reasons).

Outcomes:
Number of participants with ureteral stent encrustation longer than 4mm measured by a ruler
Resa Diagnostica Aggiuntiva Dell'Intelligenza Artificiale Nella Colonscopia (GENIAL COLONOSCOPY), Per lo Screening Del Carcinoma Colorettale
Trial ID: NCT04441580
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2020-05-04
Completion date: 2023-12-31
Sponsor: Fondazione Poliambulanza Istituto Ospedaliero
Conditions: Colonic Neoplasms; Colonic Adenocarcinoma; Polyps of Colon; Rectal Neoplasms; Rectal Adenocarcinoma
Interventions: CADe colonoscopy using GI Genius device
Locations: Fondazione Poliambulanza Istituto Ospedaliero
Countries: Italy
Enrollment: 900
Age range: 50 Years - 69 Years
Sex: ALL

Summary:
Even if colonoscopy is considered the reference standard for the detection of colonic neoplasia, polyps are still missed. The risk of early post-colonoscopy cancer appeared to be independently predicted by a relatively low polyp/adenoma detection rate. When considering the very high prevalence of advanced neoplasia in the FIT-positive enriched population, the risk of post-colonoscopy interval cancer due to a suboptimal quality of colonoscopy may be substantial. Available evidence justifies therefore the implementation of efforts aimed at improving adenoma detection rate, based on retraining interventions and on the adoption of innovative technologies, designed to enhance the accuracy of the endoscopic examination. Artificial intelligence seems to improve the quality of medical diagnosis and treatment. In the field of gastrointestinal endoscopy, two potential roles of AI in colonoscopy have been examined so far: automated polyp detection (CADe) and automated polyp histology characterization (CADx). CADe can minimize the probability of missing a polyp during colonoscopy, thereby improving the adenoma detection rate (ADR) and potentially decreasing the incidence of interval cancer. GI Genius is the AI software that will be used in the present trial and is intended to be used as an adjunct to colonic endoscopy procedures to help endoscopists to detect in real time mucosal lesions (such as polyps and adenomas, including those with flat (non-polypoid) morphology) during standard screening and surveillance endoscopic mucosal evaluations. It is not intended to replace histopathological sampling as a means of diagnosis.

The objective of this study was to compare the diagnostic yield obtained by using CADe colonoscopy to the yield obtained by the standard colonoscopy (SC).

Eligibility criteria:
Inclusion Criteria:

* Patients aged 50 to 69 undergoing colonoscopy examination following a positive fecal immunochemical test (FIT) performed in the context of a regional mass-screening program.

Exclusion Criteria:

* Patients unwilling or unable to give informed consent.
* Patients reporting use of anti-platelet agents or anticoagulants precluding removal of polyps.

Outcomes:
Rate of advanced adenomas; Rate of patients detected with 3 or more adenomas.
Development of an Artificial Intelligence Model of Image Recognition Through Images of Intravascular Catheters From Inpatients and Outpatients to Identify the Presence of Local Signs Associated With Infection
Trial ID: NCT05440396
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2022-09-01
Completion date: 2023-12-31
Sponsor: Outcome Rea
Collaborators: Assistance Publique - Hôpitaux de Paris; National Network Surveillance and Prevention of Infections Associated with Invasive Devices SPIADI; University Hospital, Clermont-Ferrand; University Hospital, Grenoble; UNICANCER; University Grenoble Alps
Conditions: Catheter Infection
Interventions: Photographs collection phase
Locations: CHU Grenoble Alpes; CHU Clermont-Ferrand; Réseau RéPIAS SPIADI; Hôpital Bichat - Claude-Bernard
Countries: France; France; France; France
Enrollment: 1000
Age range: 18 Years - None
Sex: ALL

Summary:
Deepcath is the first step to the introduction of artificial intelligence in catheter care. A better use of visualisation of catheter exit site should be used not only by the HCWs but also by the patients and their family.

A deep learning system able to detect visual abnormalities of the catheter exit site will be an helpful tools to develop a continuous follow-up of intravascular catheters.

Eligibility criteria:
Inclusion Criteria:

Patients over 18 years of age Patients with one or more implanted central venous, midline, piCCline, arterial, or peripheral catheters.

Patient and/or trusted person and/or family who have verbally stated their non-objection to the study Patient affiliated or beneficiary of a social security plan

Exclusion Criteria:

Patients presenting a peripheral identification sign close to the catheter insertion point cannot be masked when the photograph is taken. Thus, jewelry, clothing, tattoos, scars, and birthmarks are identifying features.

Patients whose catheter insertion point is not visible.

Outcomes:
The number of correct predictions for redness divided by the total number of predictions
A Feasibility Study of a Hybrid-Classic/Deep-Learning Enabled Clinical Decision Aid for Personalized and Individualized Pharmacological Depression Treatment Selection
Trial ID: NCT04061642
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2019-12-16
Completion date: 2020-12-31
Sponsor: Aifred Health Inc.
Conditions: Depression
Interventions: Clinical Decision Aid
Locations: Douglas Mental Health University Institute
Countries: Canada
Enrollment: 17
Age range: 18 Years - None
Sex: ALL

Summary:
The Clinical Decision Aid (CDA) is a predictive model that takes as input individual patient characteristics, called 'features', which are inputted by the physician or by patient self-report, and outputs a list of possible treatments, with each treatment associated with a predicted efficacy (likelihood to achieve response and likelihood to achieve remission, each expressed as a percentage). The treatments, which may include any approved treatment for depression, will be presented to the physician who will then make a treatment choice.

Eligibility criteria:
Inclusion Criteria:

* All patients of the physicians in the study are diagnosed with major depressive disorder by a physician using DSM-V criteria.
* All participants must be able to provide informed consent.
* Contraception will be used as per established clinical guidelines and usual clinical practice for medications known to cause birth defects. The medications prescribed and the use of and type of contraception will be determined by the physicians in the study in consultation with their patients as would usually occur in clinical practice.

Exclusion Criteria:

* Bipolar disorder type 1 or 2, as the data we have used to train the model does not allow for generalization to bipolar disorder (either pre-existing or as diagnosed according to DSM-5 criteria).
* Inability or unwillingness of individual to give informed consent.

Outcomes:
Subjective length of outpatient visits; Objective length of outpatient visits; Physician retention rates; Patient retention rates; Patient self-rated experience using the study software