moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Intelligent Evaluation of Diabetic Retinopathy
Trial ID: NCT04624737
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2020-12-21
Completion date: 2025-12-18
Sponsor: Assistance Publique - Hôpitaux de Paris
Conditions: Diabetic Retinopathy
Interventions: Diagnostic
Locations: Chu Avicenne; Chu Bordeaux; Chu Brest; Chic Creteil; Chu Dijon; Chu Lyon Croix Rousse; Clinique Monticelli; Clinique ophtalmologique du CHU de Nantes; CHU de NICE HÔPITAL PASTEUR 2; Chu Lariboisiere; Centre Broca / Mutuelle Generale; Chu Pitie Salpetriere; Fondation Rothschild; Hôpital des 15-20
Countries: France; France; France; France; France; France; France; France; France; France; France; France; France; France
Enrollment: 3183
Age range: 18 Years - None
Sex: ALL

Summary:
Brief Summary: The main objective of EviRed project is to develop and validate a system assisting the ophthalmologist by improving prediction of evolution, and decision making during diabetic retinopathy (DR) follow-up for a patient. It will replace the current classification of diabetic retinopathy (DR) which provides an insufficient prediction precision. It will use modern available fundus imaging devices and artificial intelligence (AI) to properly integrate the amount of data they provide with other medical data of the patient. A cohort of 5000 diabetic patients will be recruited and followed for an average of 2 years in order to collect data to train and validate the new prediction system.

An economic study will also be carried out.

Eligibility criteria:
Inclusion Criteria:

* patient with type 1 or type 2 diabetes or other,
* aged 18 years or more,
* diabetes duration greater than 10 years for type 1 diabetes,
* patient affiliated to social security,
* women of childbearing potential who are unwilling or unable to use a method to avoid pregnancy; women who are pregnant or breastfeeding can be included in the study.

Exclusion Criteria:

* ungradable fundus photography or OCT/OCTA imaging in both eyes,
* previous treatment with vitrectomy in both eyes,
* participant is unable or unwilling to comply with study procedures (including foreign language speakers who are not assisted by a native language speaker),
* Gestational diabetes vulnerable participants (minors, legally detained),
* patients under legal protection (guardianship),
* prisoners or subjects who are involuntarily incarcerated,
* patients with untreated or treated proliferative DR and macular edema involving the center of the macula in both eyes or with panretinal photocoagulation and untreated or treated macular edema involving the center of the macula in both eyes.

Outcomes:
Progression to severe retinopathy
Application of Artificial Intelligence for Early Diagnosis of Esophageal Squamous Cell Carcinoma During Optical Enhancement Magnifying Endoscopy
Trial ID: NCT03759756
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2018-12-01
Completion date: 2020-04-01
Sponsor: Shandong University
Conditions: Artificial Intelligence; Optical Enhancement Endoscopy; Magnifying Endoscopy
Interventions: AI presentation; no AI presentation
Locations: Department of Gastroenterology, Qilu Hospital, Shandong University
Countries: China
Enrollment: 119
Age range: 18 Years - None
Sex: ALL

Summary:
Esophageal squamous cell carcinoma is one of the most common malignant tumor of upper digestive tract. However, the detection rate and diagnosis accuracy of early esophageal squamous cell cancer is low. The aim of this study is to develop a computer-assisted diagnosis tool combining with optical magnifying endoscopy for early detection and accurate diagnosis of it.

Eligibility criteria:
Inclusion Criteria:

* high risk patients for esophageal cancer aged 18 years or older;
* Histologically verified early esophageal squamous cell cancer.

Exclusion Criteria:

* patients whose images of esophagus not suitable for the training, validation and testing the computer-assist diagnosis tool.

Outcomes:
the diagnosis efficiency of the AI model
Registry Study on the Efficacy of a Self-test and Self-alert Applet in Detecting Susceptible Infection of COVID-19 --a Population Based Mobile Internet Survey
Trial ID: NCT04256395
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2020-02-01
Completion date: 2020-07-01
Sponsor: Beijing Tsinghua Chang Gung Hospital
Collaborators: Institute for precision medicine of Tsinghua University; Institute for artificial intelligent of Tsinghua University; Chinese Medical Doctor Association; Institute for network behavior of Tsinghua University; school of clinical medicine of Tsinghua University
Conditions: Susceptibility to Viral and Mycobacterial Infection
Interventions: mobile internet survey on self-test
Locations: Beijing Tsinghua Changgung Hospital
Countries: China
Enrollment: 102456
Age range: None - None
Sex: ALL

Summary:
The "COVID-19 infection self-test and alert system" (hereinafter referred to as "COVID-19 self-test applet") jointly developed by Beijing Tsinghua Changgung Hospital, Institute for precision medicine, artificial intelligence of Tsinghua University was launched on February 1，2020. Residents , according to their actual healthy situation, after answering questions online, the system will conduct intelligent analysis, make disease risk assessment and give healthcare and medical guidance. Based on the Internet population survey, and referring to the diagnosis and screening standards of the National Health Commission of the People's Republic of China, investigators carried out the mobile applet of Internet survey and registry study for the Internet accessible identifiable population, so as to screen the suspected population and guide the medical treatment.

Eligibility criteria:
Inclusion Criteria:

* people who lived in or out of China at present and threatened by the infection and spread of COVID-19

  * without gender and age restriction
  * people who have concerns of his health
  * voluntary completion of the self-test and evaluation.

Exclusion Criteria:

* people who are not internet accessible or can not use this Mobile Applet.
* people who can not recognize the questionnaire.

Outcomes:
positive number diagnosed by national guideline in the evaluated population
Clinical Study of Capeline Combined With Gemcitabine in the Treatment of Pancreatic Cancer
Trial ID: NCT05903703
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2023-10-01
Completion date: 2027-03-31
Sponsor: Zhang Xiaofeng,MD
Collaborators: College of Pharmaceutical Sciences at Zhejiang University; The Innovation Institute for Artificial Intelligence in Medicine, Zhejiang University
Conditions: Pancreatic Cancer
Interventions: Canagliflozin and Gemcitabine; Gemcitabine
Locations: Hangzhou first people's Hospital
Countries: China
Enrollment: 20
Age range: 18 Years - None
Sex: ALL

Summary:
Gemcitabine-based chemotherapy or combination with FOLFIRINOX is the leading treatment of pancreatic cancer. However, the overall response rate of pancreatic cancer to gemcitabine is less than 20%. Resistance to gemcitabine is the most important reason. There is an urgent need to develop new combination therapies to improve the efficiency of chemotherapy, avoid toxicity limitations, and improve the overall prognosis of pancreatic cancer. At present, it has been found that canagliflozin can reduce the expression level of PD-L1 in pancreatic cancer and restore the vitality of CD8+ T cells. Canagliflozin combined with gemcitabine may improve the efficiency of chemotherapy.

Eligibility criteria:
1. Inclusion criteria: # Age ≥18 years old; #Metastatic or unresectable pancreatic cancer is confirmed through histology or cytology; # Estimated survival time \> 3 months; # Without any chemotherapy treatment or more than one month from the end of the last chemotherapy course; #ECOG physical status score 0-2;
2. Exclusion criteria: # patients who had allergic reaction to therapeutic drugs; # patients with other types of cancer; # Patients with severe diseases of heart, liver, kidney, etc.; (4) gastrointestinal dysfunction or unable to oral medication.
3. Shedding/eliminating criteria: exiting in the midway; Lost to follow-up during the follow-up period; Treatment was not continued according to the treatment protocol.

Outcomes:
Evaluation the clinical complete response (CR) at 6 weeks intervals; Evaluation the clinical partial response (PR) at 6 weeks intervals; Evaluation the clinical stable disease (SD) at 6 weeks intervals; Evaluation the clinical disease progression (PD) at 6 weeks intervals
Application of Artificial Intelligence in Hypertension Pharmacotherapy Education: A Comparative Study of the GPT System and Traditional Teaching Methods
Trial ID: NCT06776354
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-10-20
Completion date: 2025-07-30
Sponsor: zhen wang
Conditions: Medical Education; Hypertension; GPT
Interventions: GPT-based training; traditional training
Locations: 678 Furong Road, Economic Development Zone, Hefei City, Anhui Province, China
Countries: China
Enrollment: 52
Age range: 18 Years - 30 Years
Sex: ALL

Summary:
Educational Trial: GPT-Based Training vs. Traditional Teaching for Hypertension Management

The goal of this educational trial is to determine whether a Generative Pre-trained Transformer (GPT)-based training system is more effective than traditional teaching methods in helping medical students master hypertension management plans. It will also evaluate the educational effectiveness and engagement of the GPT-based system. The main questions it aims to answer are:

Does the GPT-based training improve the ability of students to develop effective hypertension management plans compared to traditional methods? How do students perceive the use of the GPT system in their learning process? Researchers will compare the GPT-based training system to traditional teaching methods to see if the innovative AI approach enhances learning outcomes in medical education.

Participants will:

Engage with either the GPT-based system or traditional teaching materials. Visit the educational facility periodically for assessments and feedback sessions.

Keep a diary of their learning experiences, noting any difficulties or advantages they observe in the training method they are assigned.

Eligibility criteria:
Inclusion Criteria:

* Graduate or regulatory trainee, currently rotating in cardiology and willing to participate in research.
* Age between 18-30 years old, gender not limited.
* No specialized training experience in hypertension diagnosis and treatment, only possessing basic medical theoretical knowledge.
* Able to accept a 4-week teaching intervention (2 hours of study per week).
* Sign the informed consent form and be aware of the research purpose and process.

Exclusion Criteria:

* Graduate students or trained interns who have received specialized training in hypertension medication treatment.
* Students unable to complete the full course of study (e.g., rotations shorter than four weeks or potential mid-course withdrawal).
* Unable to consistently participate in educational interventions (e.g., conflicts with study schedule).
* Have severe physical or mental health issues that may affect the learning process.
* Have language comprehension barriers or cognitive impairments, making it difficult to complete learning tasks.

Outcomes:
structured theoretical exam scores; Clinical Case Test Scores on Hypertension
Adenoma Detection Rate in Artificial Intelligence-assisted Colonoscopy Performed by Endoscopists with Different Levels of Experience - a Quasi Randomized Controlled Multicenter Trial
Trial ID: NCT05740137
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2022-10-01
Completion date: 2023-03-03
Sponsor: Ismail Gögenur
Collaborators: Nykøbing Falster County Hospital; Naestved Hospital; Holbaek Sygehus; Slagelse Hospital
Conditions: Colorectal Cancer; Colorectal Adenoma; Colorectal Neoplasms
Interventions: AI-assisted colonoscopy
Locations: Holbæk Hospital; Zealand University Hospital; Nykøbing Falster County Hospital; Næstved Hospital
Countries: Denmark; Denmark; Denmark; Denmark
Enrollment: 795
Age range: 18 Years - None
Sex: ALL

Summary:
The goal of this quasi randomized multicenter controlled clinical trial (RCT) is to investigate whether a combined real time computer-aided polyp detection (CADe) and computer-aided polyp characterization (CADx) system (GI Genius, Medtronic) can increase the adenoma detection rate (ADR) and reduce the performance variability among endoscopists.

Participants will be randomized (1:1) to either receive an AI-assisted colonoscopy (AIC) or a conventional colonoscopy (CC).

If there is a comparison group: Researchers will compare the AIC-group and the CC-group to see if AIC can increase the ADR significantly.

Eligibility criteria:
Inclusion Criteria:

* Referred for screening colonoscopy due to a positive faecal immunochemical test (FIT) or for
* Diagnostic colonoscopy due to symptoms/signs or
* Post-polypectomy surveillance colonoscopy (only patients who had all detected polyps removed in the previous colonoscopy)

Exclusion Criteria:

* Referral for removal of previous detected polyps
* Emergency colonoscopy
* Control colonoscopy due to inflammatory bowel disease (IBD)

Outcomes:
Adenoma detection rate (ADR)
Evaluation of Artificial Intelligence Based Noise Reduction in Phonak Hearing Aid on Patients With Hearing Impairment and Cochlear Implants
Trial ID: NCT06743295
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-04-24
Completion date: 2027-01-01
Sponsor: Mayo Clinic
Conditions: Hearing Loss
Interventions: DEEPSONIC™ Deep Sound Optimized Neural Integrated Chip Artificial intelligence-based noise reduction
Locations: Mayo Clinic in Rochester
Countries: United States
Enrollment: 500
Age range: 18 Years - 110 Years
Sex: ALL

Summary:
The purpose of this study is to determine if artificial intelligence- based noise reduction can offer objective improvement in hearing and speech perception for hearing impaired patients.

Eligibility criteria:
Inclusion Criteria:

* Patients with hearing impairment not using hearing aids
* Individuals with hearing impairment currently using hearing aids
* Cochlear implant patients with a single-sided implant (using a hearing aid in the opposite ear)

Outcomes:
Single-word Testing; Sentence Testing
Validating a New Point-of-care Device for Estimation of Blood Count in Pregnant Women
Trial ID: NCT05012202
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2021-10-01
Completion date: 2022-12-10
Sponsor: The University of Texas Medical Branch, Galveston
Conditions: Blood Loss
Interventions: Hemi device
Locations: UTMB
Countries: United States
Enrollment: 41
Age range: 18 Years - 50 Years
Sex: FEMALE

Summary:
Postpartum hemorrhage is a well-known complication of delivery and the leading cause of maternal mortality worldwide. In developed nations, a complete blood count (CBC) is commonly collected as a means to assess or base treatment for blood loss. The Hemi device is a point-of-care device designed to provide hematocrit, followed by the stage of hemorrhagic shock using artificial intelligence algorithms. The ultimate goal of the product is to provide an accurate hematocrit from easily attainable samples such as vaginal blood during hemorrhage to remove yet another barrier to access for actively bleeding women. The purpose of this study is to compare the hematocrit of vaginal blood using the Hemi device with standard venipuncture.

Eligibility criteria:
Inclusion Criteria:

* Pregnant women between the ages of 18-50
* Admission for vaginal delivery
* Singleton pregnancy
* Term gestation (\>=37 weeks)

Exclusion Criteria:

* Pregnant women \< 18 years or \> 50 years
* Incarcerated patients
* Patient unwilling or unable to provide consent
* Enrolled in another trial that may affect outcome

Outcomes:
Hematocrit
Prospective Cohort Study on Thermal Ablation of Malignant Liver Tumors Within the IMAGIO Project (A-IMAGIO)
Trial ID: NCT06179602
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2024-01-01
Completion date: 2028-01-01
Sponsor: Leiden University Medical Center
Collaborators: Philips Healthcare; European Commission
Conditions: Liver Cancer; Liver Metastases; Liver Metastasis Colon Cancer; Hepatocellular Carcinoma
Locations: Leiden University Medical Centre
Countries: Netherlands
Enrollment: 1500
Age range: 18 Years - 110 Years
Sex: ALL

Summary:
The endpoint of this study is to develop and validate algorithms, using artificial intelligence and machine learning, to optimize patient selection, treatment planning, treatment evaluation and outcome prediction in patients undergoing thermal ablation of a malignant liver tumor. The long-term objective is to establish thermal ablation as the treatment of choice for the vast majority of patients with a primary or secondary liver tumor by development of an accessible workflow that can be widely implemented in different centers performing thermal ablation. Over a time span of at least four years, data will be collected prospectively, encompassing patient information, tumor characteristics, and treatment details. Additionally, pre-, intra-, and post-procedural imaging will be systematically gathered.

Eligibility criteria:
Inclusion Criteria:

* 18 years or above
* Candidate for percutaneous thermal liver ablation as discussed in a multidisciplinary tumorboard (MDT)
* Informed consent

Exclusion Criteria:

* Patients lacking capacity to give informed consent.

Outcomes:
Efficacy as assessed by local recurrence; Efficacy as assessed by time-to-recurrence; Efficacy as assessed by disease-free-survival; Efficacy as assessed by overall survival; Safety as assessed by complications according to CTCAE v6.0.
EndoStyle: Survey of Physicians on Endoscopic Image Style Transfer.
Trial ID: NCT06553326
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2024-08-15
Completion date: 2025-08-22
Sponsor: Wuerzburg University Hospital
Conditions: Colon Cancer; Colon Rectal Cancer
Interventions: EndoStyle
Locations: University Clinic of Würzburg
Countries: Germany
Enrollment: 40
Age range: 18 Years - None
Sex: ALL

Summary:
The study addresses the limitations of current AI systems in gastrointestinal endoscopy, which are tipically trained with data from a single type of endoscopy processor and have limited expert-annotated images. The investigators aim to develop and validate EndoStyle, an AI system that can generate images in the style of various processors from a single reference image. EndoStyle will be tested by showing endoscopists colonoscopy sequences with different image types to determine if they can distinguish AI-transformed images. Success would enhance AI training for diverse clinical setups.

Eligibility criteria:
Inclusion Criteria:

* Physicians with experience in colonoscopy.

Outcomes:
Perceptual Indistinguishability of AI-Transformed Endoscopic Images
Perception-based Interventions Affect Public Acceptance of Using Large Language Models in Medicine: Randomized Controlled Trial
Trial ID: NCT07304908
Status: ACTIVE_NOT_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-11-25
Completion date: 2026-12-31
Sponsor: Peking University
Collaborators: Peking University Third Hospital
Conditions: Large Language Models; Acceptability of Health Care; Perception, Self
Interventions: Perception-based interventions
Locations: Jue Liu
Countries: China
Enrollment: 3000
Age range: 18 Years - None
Sex: ALL

Summary:
Large language models (LLMs) show promise in medicine, but concerns about their accuracy, coherence, transparency, and ethics remain. To date, public perceptions on using LLMs in medicine and whether they play a role in the acceptability of health care applications of LLMs are not yet fully understood. This study aims to investigate public perceptions on using LLMs in medicine and if interventions for perceptions affect the acceptability of health care applications of LLMs.

Eligibility criteria:
Inclusion Criteria:

* ≥18 years
* Capable of completing an online survey
* Agree to sign an informed consent form

Exclusion Criteria:

* Unable to answer questions or communicate
* Not willing to participate in this study

Outcomes:
Number of participants who will change their attitudes towards medical applications of large language models
Real-Time Artificial Intelligence Assissted Colonoscopy to Identify and Classify Polyps
Trial ID: NCT05718193
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2022-06-01
Completion date: 2023-03-15
Sponsor: Xiangya Hospital of Central South University
Collaborators: Loudi Central Hospital
Conditions: Artificial Intelligence; Colonoscopy; Quality Control
Interventions: DeFrame; Classified DeFrame; conventional colonoscopy
Locations: Xiangya Hospital Central South University; Loudi Central Hospital
Countries: China; China
Enrollment: 2868
Age range: 18 Years - 85 Years
Sex: ALL

Summary:
To investigate the degree of the real-time detection and classification system for increasing the adenoma detection rate during colonoscopy.

Eligibility criteria:
Inclusion Criteria:

Aged 18 to 85 years old. Colonoscopies for primary CRC screening of the subjects are required.

Exclusion Criteria:

History of CRC,inflammatory bowel disease, previous colonic resection, antithrombotic therapy precluding polyp resection.

Outcomes:
adenoma detection rate; adenomas per colonoscopy
Construction and Validation of an Assessment Model of Pathological Complete Response After Neoadjuvant Treatment (NAT) on Breast Cancer Patients With Artificial Intelligence (AI) Technology
Trial ID: NCT05441098
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2022-04-01
Completion date: 2022-12-31
Sponsor: Chinese Academy of Medical Sciences
Conditions: Breast Cancer
Interventions: Neoadjuvant therapy
Locations: National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences
Countries: China
Enrollment: 1821
Age range: 18 Years - None
Sex: FEMALE

Summary:
Breast cancer is a major cause of survival for women worldwide. Neoadjuvant therapy as an important treatment for locally advanced breast cancer has had many positive effects for breast cancer patients. As drug therapy for breast cancer continues to evolve, the percentage of pathologic complete responses continues to increase. However, at present, pCR can only be judged by pathological testing of surgically resected specimens, and the question of whether pCR can be accurately judged preoperatively has become an urgent issue.Therefore, this project plans to establish and validate a model for determining pCR after NAT in breast cancer based on clinical information, imaging and pathological information of breast cancer patients in multiple centers using artificial intelligence technology in accordance with international guidelines and domestic expert consensus on breast cancer NAT, in order to solve the problem of surgical decision making for patients after NAT, by combining experts from breast medicine, surgery, pathology and imaging departments in several tertiary care hospitals across China. The model will be validated to solve the problem of surgical decision making for post-NAT patients.

Eligibility criteria:
Inclusion Criteria:

1. patients admitted to each study center between January 1, 2010 and December 31, 2021.
2. ≥18 years of age, female, with an ECOG score ≤2.
3. pathological biopsy confirmed invasive breast cancer.
4. were initially treated with neoadjuvant therapy.
5. have MRI imaging data prior to radical surgery after neoadjuvant treatment.
6. underwent surgery as planned after neoadjuvant therapy and obtained postoperative pathology information.

Exclusion Criteria:

1. Bilateral breast cancer, multiple lesions or occult breast cancer.
2. no data related to breast MRI.
3. no surgery after neoadjuvant therapy, no postoperative pathology results.

Outcomes:
pathological complete response