moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Conventional Home Exercise Programs Versus Electronic Home Exercise Versus Artificial Intelligence "Virtual Therapy" for Anterior Knee Pain: a Prospective Randomized Comparative Trial.
Trial ID: NCT01696162
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2012-09
Completion date: 2013-10
Sponsor: Simpletherapy
Conditions: Patellofemoral Pain Syndrome
Interventions: Algorithm based Exercise Videos; PDF Exercise Sheets; Limited Exercise Videos
Locations: Simpletherapy
Countries: United States
Enrollment: 85
Age range: 18 Years - 64 Years
Sex: ALL

Summary:
This is a clinical research trial to evaluate the efficacy of delivering exercise therapy for knee pain via the Internet utilizing an algorithm designed to adjust a home exercise program based on user input. The investigators hypothesize that exercise therapy can be delivered safely and effectively with increased participant satisfaction compared to the current standard.

Eligibility criteria:
Inclusion Criteria:

* Age 18-64 with anterior knee pain
* Have computer access
* Cleared for exercise by a medical professional, if not possible or a medical professional on the study team over the phone and the PAR-Q (Physical Activity Readiness Questionnaire)
* Have consented to participate in the trial

Exclusion Criteria:

* Age \< 18 or \> 64
* Screened as unsafe for exercise by a medical professional or the, study team and/ or Screened out by the PAR-Q
* Does not consent to the trial

Outcomes:
The 3 and 6 week survey data evaluating efficacy of the algorithm
the Study on AI-Integrated Fully Quantitative Treatment Decision and Surgical Planning System for Liver Cancer
Trial ID: NCT07042841
Status: ENROLLING_BY_INVITATION
Study type: OBSERVATIONAL
Start date: 2015-06-30
Completion date: 2027-10-30
Sponsor: Beijing Tsinghua Chang Gung Hospital
Conditions: Liver Cancer, Adult
Locations: Beijing Tsinghua Chang Gung Hospital
Countries: China
Enrollment: 300
Age range: 18 Years - None
Sex: ALL

Summary:
This study aims to develop and validate an integrated AI-powered system for liver cancer that includes intelligent tumor boundary detection, micro-metastasis prediction, staging, treatment decision-making, and surgical planning. The system builds upon prior 3D reconstructions of liver, vessels, and bile ducts. In a retrospective multi-center, self-controlled, fully crossed multi-reader multi-case clinical trial, the researchers will compare diagnostic accuracy, staging, and planning performance between AI-assisted reads and conventional reads using CT images and pathological gold standards.

Eligibility criteria:
Inclusion Criteria:

* 1\. Dynamic contrast-enhanced CT scan of the liver is available. 2. Age ≥ 18 years. 3. CT image slice thickness ≤ 5 mm. 4. An official CT report is available. 5. Meets at least one of the following subcategories:HCC-positive with liver lesion: CT report suggests suspected hepatocellular carcinoma (HCC), and complete hospitalization records are available to assess CNLC staging (including performance status, Child-Pugh classification, and extrahepatic metastasis). HCC-negative with liver lesion: CT report indicates at least one liver-occupying lesion, but not suspected to be HCC. No liver lesion: CT report indicates no liver-occupying lesion.

Exclusion Criteria:

* 1\. Incomplete liver coverage in CT images. 2. Severe noise artifacts in CT images. 3. History of liver resection or significant anatomical/volume alterations of the liver. 4. Deemed unsuitable for inclusion by the investigator.

Outcomes:
Diagnostic accuracy of the intelligent liver cancer diagnosis and surgical planning system
Prediction and Management of Acute Kidney Injury With Explainable Artificial Intelligence: The PRIME Randomized Controlled Trial
Trial ID: NCT05937451
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2023-09-01
Completion date: 2025-12-31
Sponsor: Seoul National University Hospital
Conditions: Acute Kidney Injury
Interventions: PRIME solution
Locations: Bundang Seoul National University Hospital
Countries: South Korea
Enrollment: 1438
Age range: 19 Years - None
Sex: ALL

Summary:
The goal of this clinical trial is to compare the occurrence of acute kidney injury (AKI) in inpatients when information from the 'PRIME solution' (AKI prediction program utilizing artificial intelligence) is provided. The main questions it aims to answer are: •\[When Artificial intelligence (AI) provides information regarding AKI occurrence prediction within 48 hours, what would change in the physician's behavior?\] •\[If provided with AI information, what would be the incidence of AKI, severe AKI (stage 2 or 3), kidney replacement therapy, and changes in mortality during hospitalization?\] In the case of the intervention group that receives AI information, autonomous treatment is conducted by referring to AI prediction information. Researchers will compare it with a usual-care group that does not receive AI prediction results.

Eligibility criteria:
Inclusion Criteria:

* Inpatients aged \> 18 years
* Admitted to the Department of Nephrology, Geriatrics, Urology, Surgery, and Orthopedics
* written consent

Exclusion Criteria:

* Patients with a scheduled hospital stay of 2 days or less
* Patients who have been hospitalized for more than 8 days since the date of hospitalization
* Patients scheduled to be discharged the next day
* Patients with last measured serum creatinine greater than 4.0 mg/dL or less than Estimated Glomerular Filtration Fate Chronic Kidney Disease Epidemiology Collaboration (eGFR-CKD-EPI) 15 ml/min/1.73 m2
* Patients undergoing dialysis (Hemodialysis, peritoneal dialysis) due to end-stage renal disease
* Declined to participate

Outcomes:
physician's behavior change
Development of Artificial Intelligence-assissted Diagnostic Program of Glaucoma
Trial ID: NCT03268031
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2017-08-15
Completion date: 2020-02-01
Sponsor: Sun Yat-sen University
Collaborators: Chinese Academy of Sciences
Conditions: Artificial Intelligence; Glaucoma
Interventions: Visual field and OCT tests
Locations: Zhongshan Ophthalmic Center
Countries: China
Enrollment: 10800
Age range: None - None
Sex: ALL

Summary:
Glaucoma is currently the second leading cause of irreversible blindness in the world. Our study intends to combine clinical data of glaucoma patients in Zhongshan Ophthalmic Center with Artificial Intelligence techniques to create programs that can screen and diagnose glaucoma.

Eligibility criteria:
Inclusion Criteria:

1. BCVA\>0.1
2. able to complete reliable visual field test
3. no history of intraocular surgery or fundus laser

Exclusion Criteria:

1\. unable to complete visual field test

Outcomes:
Accuracy of diagnosis by artificial intelligence algorithm
Rapid Diagnosis of Molecular Biomarkers and Visualization of IDH Molecular Boundaries in Glioma Using Preoperative Magnetic Resonance Images, Intraoperative Ultrasound Signals and Intraoperative Ultrasound Images
Trial ID: NCT05656053
Status: ACTIVE_NOT_RECRUITING
Study type: OBSERVATIONAL
Start date: 2021-11-15
Completion date: 2026-09
Sponsor: Mingge LLC
Collaborators: Huashan Hospital; Fudan University
Conditions: Glioma, Malignant; Computer-Assisted Surgery
Locations: Fudan University
Countries: China
Enrollment: 30
Age range: 18 Years - 80 Years
Sex: ALL

Summary:
The aim of this observational study is to enable rapid diagnosis of molecular biomarkers in patients during surgery by medical imaging and artificial intelligence models, to help clinicians with strategies to maximize safe resection of gliomas. The main questions it aims to answer are:

1. To solve the current clinical shortcomings of intraoperative molecular diagnosis, which is time-consuming and complex, and enables rapid and automated molecular diagnosis of glioma, thus providing the possibility of personalized tumor resection plans.
2. To implement a neuro-navigation platform that combines preoperative magnetic resonance images, intraoperative ultrasound signals and intraoperative ultrasound images to address real-time molecular boundary visualisation and molecular diagnosis for glioma, providing an approach to improve glioma treatment.

Participants will read an informed consent agreement before surgery and voluntarily decide whether or not to join the experimental group. they will undergo preoperative magnetic resonance imaging, intraoperative ultrasound, and postoperative genotype identification. Their imaging data, genotype data, clinical history data, and pathology data will be used for the experimental study. The data collection process will not interrupt the normal surgical process.

Eligibility criteria:
Inclusion Criteria:

* Age over 18 years old
* Tumor in non-functional areas of the cerebral hemisphere.
* Preoperative diagnosis of glioma.
* Undergo glioma removal surgery.

Exclusion Criteria:

* Postoperative confirmation of non-glioma.
* Magnetic resonance or ultrasound data not available.

Outcomes:
Accuracy rate, describing the number of correct people or correct regions predicted by the computer-aided algorithm as a proportion of the total sample.
Comparison of Flat Colorectal Lesion Detection by Artificial Intelligence-assisted Colonoscopy Versus Endoscopists: AIChallenge - Medtronic
Trial ID: NCT05942677
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2022-01-01
Completion date: 2023-12-30
Sponsor: Hospices Civils de Lyon
Conditions: Flat Colorectal Lesion
Interventions: proportion of colorectal lesions
Locations: Hôpital Edouard Herriot
Countries: France
Enrollment: 160
Age range: 18 Years - None
Sex: ALL

Summary:
The development of artificial intelligence (AI) systems in the field of colorectal endoscopy is currently booming, colorectal cancer being, by its frequency and severity, a real public health problem.

In terms of image analysis, AI is indeed able to perform many tasks simultaneously (lesion detection, classification, and segmentation) and to combine them.

Lesion detection is thus the starting point of the whole chain to choose at the end the most appropriate treatment for the patient. Large-scale studies have demonstrated the superiority of artificial intelligence-assisted detection over the usual detection by gastroenterologists, mainly for the detection of sub-centimeter polyps.

However, the investigators have shown that a recent computer-aided detection system (CADe) such as the ENDO-AID software in combination with the EVIS X1 video column (Olympus, Tokyo, Japan) may present difficulties in the detection of flat lesions such as sessile serrated lesions (SSLs) and non-granular laterally spreading tumors (LST-NGs).

This represents a major challenge because in addition to their shape being difficult to identify for the human eye in practice and where AI assistance would be of great value, these rare lesions are associated with advanced histology.

In addition, the investigators recently described the case of a worrisome false negative of AI-assisted colonoscopy, which failed to detect a flat adenocarcinoma in the transverse colon.

Therefore, it is important to measure the false negative rate of AI detection based on the macroscopic shape of the lesion. Comparing this rate to the human endoscopist's false negatives would improve the performance of AI for this specific lesion subtype in the future.

Eligibility criteria:
Inclusion Criteria:

* both gender patients even or older than 18 years old
* patient with French Health Insurance coverage
* obtaining of oral non opposition to research after loyal, clear and complete delivery of information
* patients addressed to our center for colorectal lesion resection
* patients presenting a colorectal lesion discovered during a diagnostic colonoscopy

Exclusion Criteria:

* patients with health disorders needing short procedure times
* patients with no colorectal lesion
* difficulty continuing colonoscopy due to poor sedation
* difficulty continuing colonoscopy due to a serious adverse event
* inappropriate participation after colonoscopy is completed
* unwillingness to participate after colonoscopy is completed

Outcomes:
Evaluation of the proportion of colorectal lesions
Survival prEdiction in bLadder Cancer Patients Treated by nEoadjuvant Chemotherapy Before cysTectomy
Trial ID: NCT06294054
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2023-04-01
Completion date: 2026-03-31
Sponsor: University Hospital, Rouen
Collaborators: Institut Curie
Conditions: Bladder Cancer; Genomic Instability
Interventions: neoadjuvant chemotherapy with cisplatine
Locations: Centre de lutte contre le cancer François Baclesse; Centre de recherche des Cordeliers; Hôpital Saint-Louis AP-HP; Institut Curie Centre de Recherche; Institut Curie; Institut Gustave Roussy; Mines ParisTech
Countries: France; France; France; France; France; France; France
Enrollment: 707
Age range: 18 Years - 80 Years
Sex: ALL

Summary:
This study will allow the investigators to better assess the efficiency of neoadjuvant chemotherapy before cystectomy by training a predictive model on different patient cohorts with bladder cancer.

Eligibility criteria:
Inclusion Criteria:

\- Inclusion Criteria of Patients enrolled in VESPER study:

Blood and tumors from patient having muscle invasive bladder cancer who benefit from neoadjuvant chemotherapy with cisplatine included in VESPER cohort and :

having signed an informed consent form for the participation to the collection or dead/lost to follow-up without prior opposition expressed against research program, genetic analysis will only be carried out for patients who have signed the genetic consent form.

\- Inclusion Criteria of Patients from St Louis cohort not enrolled in VESPER study:

Blood and tumors from patient having muscle invasive bladder cancer who benefit from neoadjuvant chemotherapy with cisplatine included in Saint-Louis cohort and :

being informed for the participation to the collection and not having expressed opposition against research program or dead/lost to follow-up without prior opposition expressed against research program,

\- Inclusion Criteria of Patients from St Louis cohort not enrolled in COBLaNCE study:

Blood and tumors from patient having muscle invasive bladder cancer who benefit from neoadjuvant chemotherapy with cisplatine included in COBLaNCE cohort and :

being informed for the participation to the collection and not having expressed opposition against research program or dead/lost to follow-up without prior opposition expressed against research program,

Exclusion Criteria:

* Patients not fulfilling eligibility criteria

Outcomes:
Progression-free survival
Residual CardioVascular Risk (RCVR) After Percutaneous Coronary Intervention: A Prospective Study
Trial ID: NCT06619353
Status: ACTIVE_NOT_RECRUITING
Study type: OBSERVATIONAL
Start date: 2024-06-17
Completion date: 2029-02
Sponsor: CHA University
Conditions: Coronary Artery Disease
Interventions: Blood Sample; DICOM Dataset
Locations: CHA Bundang Medical Center
Countries: South Korea
Enrollment: 500
Age range: 20 Years - 90 Years
Sex: ALL

Summary:
The goal of this observational study is to evaluate whether residual cardiovascular risk assessed at 1 month after percutaneous coronary intervention (PCI) is associated with long-term clinical outcomes in adult patients with coronary artery disease who are free from major adverse clinical events at 1 month after PCI. The main questions it aims to answer are:

* Does residual cardiovascular risk at 1 month after PCI predict long-term net adverse clinical events?
* Which components of residual cardiovascular risk are associated with subsequent adverse clinical outcomes?

Participants will:

* Undergo comprehensive laboratory testing at 1 month after PCI and annually thereafter.
* Undergo artificial intelligence-based quantitative coronary angiographic analysis using DICOM datasets after PCI.
* Be followed annually for up to 3 years after the 1-month assessment to evaluate clinical outcomes.

Eligibility criteria:
Inclusion Criteria:

* Patients who have undergone percutaneous coronary intervention.
* Patients who are free from death, myocardial infarction, stroke, unplanned revascularization, or major bleeding within 1 month after the index PCI.
* Patients who are able to undergo artificial intelligence-based quantitative coronary angiography using DICOM datasets.
* Patients who have provided written informed consent at the 1-month post-PCI visit.

Exclusion Criteria:

* Under 19 years of age.
* Pregnant, breastfeeding, or women of childbearing age.
* Currently has a malignancy or has a history of malignancy within the past 5 years.
* Life expectancy of less than 5 years.
* Occurrence of death, myocardial infarction, stroke, unplanned revascularization, or major bleeding within 1 month after the index PCI.
* Inability or unwillingness to comply with scheduled follow-up assessments.

Outcomes:
Net adverse clinical event
Artificial Intelligence-Driven 3D Kidney Model for Real-Time Augmented Reality and Surgical Navigation in Minimally Invasive (Robotic/Laparoscopic) Partial Nephrectomy: A Prospective Validation Study
Trial ID: NCT07020169
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-05-01
Completion date: 2027-03-01
Sponsor: Shao Pengfei
Collaborators: Southeast University, China
Conditions: Renal Cell Cancer
Interventions: an AI-based real-time image-guided kidney model system
Locations: The First Affiliated Hospital of Nanjing Medical University (Jiangsu Provincial People's Hospital); The First Affiliated Hospital of Nanjing Medical University (Jiangsu Provincial People's Hospital)
Countries: China; China
Enrollment: 232
Age range: 18 Years - 80 Years
Sex: ALL

Summary:
The goal of this study is to develop a real-time artificial intelligence-driven 3D kidney model to assist robotic or laparoscopic partial nephrectomy:

• Can this AI-powered model optimize the workflow of partial nephrectomy and enhance surgical benefits?

Eligibility criteria:
Inclusion Criteria:

* Ages 18-80 years, regardless of gender
* Written informed consent obtained from the patient or legally authorized representative after full protocol disclosure
* Preoperative imaging (CT/MRI) confirming clinical stage T1a or select T1b renal tumors suitable for partial nephrectomy (R.E.N.A.L. nephrometry score ≤10)
* Localized renal tumors without lymph node/distant metastasis per NCCN Guidelines® (v2023)
* Elective minimally invasive partial nephrectomy (laparoscopic/robotic) after comprehensive surgical counseling

Exclusion Criteria:

* Multifocal renal tumors (bilateral or unilateral)
* Prior systemic anticancer therapy (targeted agents/immunotherapy/chemotherapy) within 6 months
* Absolute surgical contraindications (e.g., ASA class ≥IV, uncontrolled coagulopathy)
* Intraoperative conversion to radical nephrectomy or open approach
* Postoperative adjuvant therapy during protocol-defined follow-up (12 months)
* Major comorbidities (e.g., NYHA class III/IV heart failure, eGFR \<30 mL/min/1.73m²) affecting outcome assessment
* Concurrent enrollment in interventional clinical trials
* Investigator-determined ineligibility based on risk-benefit analysis

Outcomes:
Operative Time
Testing a Speech-to-text Tool for Heart Failure Patient-Reported Data
Trial ID: NCT07217366
Status: ENROLLING_BY_INVITATION
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-10-13
Completion date: 2026-06
Sponsor: Stanford University
Conditions: Heart Failure
Interventions: Artificial intelligence speech-to-text tool
Locations: Stanford University
Countries: United States
Enrollment: 100
Age range: 18 Years - None
Sex: ALL

Summary:
The goal of this study is to see whether this type of Artificial Intelligence (AI) Voice Assistant can reliably capture patient-reported health information to support communication between patients and their healthcare team.

Eligibility criteria:
Inclusion Criteria:

* Adults with heart failure receiving cardiology care at Stanford University

Exclusion Criteria:

* Diminished decision-making capacity
* Inability to understand and communicate in English sufficiently to complete study procedures
* Inability to complete study procedures to the best of the participant's and investigator's knowledge
* Any disorder or condition that, in the opinion of the investigator would pose a risk to participant safety or interfere with the study evaluation, procedures or completion

Outcomes:
Accuracy of AI Voice Assistant Compared With Expert Extraction
A Smartphone Intervention for Pregnancy Smoking Cessation With Peer Support
Trial ID: NCT05337722
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2023-01-11
Completion date: 2026-08-31
Sponsor: Michigan State University
Collaborators: Babes-Bolyai University; Wake Forest University Health Sciences; University of Michigan
Conditions: Smoking Cessation; Tobacco Smoking
Interventions: SFT 2.0; Active control
Locations: Babes-Bolyai University
Countries: Romania
Enrollment: 790
Age range: 18 Years - None
Sex: ALL

Summary:
The objectives of this project are to develop, implement, and test the feasibility and effectiveness of an artificial intelligence adaptive mobile pregnancy tobacco cessation app-based intervention using deep tailoring and a self-nominated support person, and to build mHealth research capacity in Romania. The central hypothesis is that the intervention will show evidence of feasibility and effectiveness in increasing positive support, pregnancy cessation, and postnatal abstinence. The intervention is grounded in Self-Determination Theory (SDT) and Motivational Interviewing (MI), a counselling style that is effective in assisting people to quit smoking. The app will be novel in its use of the unique functionality of smartphones, use of reinforcement learning (RL) and deep tailoring to continuously adapt the intervention, the emphasis on increasing positive support, and the use of the app by both smoker and support person. The long-term goal of the research program is to use mHealth for smoking cessation leveraging the unique functionality of smartphones and to continue building mHealth research capacity and developing research networks in Central and Eastern Europe (CEE) and other LMICs.

Aim 1 (R21 phase). Develop and test the feasibility and acceptability of the SFT2.0 app-based mobile smoking cessation intervention with a support person during pregnancy and postpartum in Romania. Through a user-centered and iterative design the investigators will enhance the SFT1.0 app, deepen the tailoring, incorporate RL, expand the app for use by any support person, and test the intervention including the app and MI video counseling in a series of usability studies and a 12-week open trial (n=20).

Aim 2 (R33 phase). Test the SFT2.0 app-based smoking cessation intervention in a hybrid effectiveness and implementation randomized controlled trial. The investigators will randomize 375 pregnant smokers and their support persons to i) a fixed arm, including the SFT2.0 app for both, and fixed pre- and postnatal MI counseling; ii) an RL-adaptive arm, with the app continuously optimizing as-needed MI counseling; or iii) control group.

Aim 3 (R21 and R33 phases). Develop mHealth research capacity by enhancing individual and institutional research capabilities in Romania and expanding the existing international research network.

Eligibility criteria:
Inclusion Criteria:

* age\>=18
* pregnant
* user of regular cigarettes
* availability of Android phone with broadband data

Exclusion Criteria:

\-

Outcomes:
Change in smoking cessation; Change in postpartum abstinence; Reach; Adoption; Implementation; User engagement