moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
A Multicenter, RAndomlzed, coNtrolled, umBrella Trial fOr Minimally Invasive Neurosurgery With Al-assisted Robotic Guidance for Moderate Basal Ganglia Hemorrhage (RAINBOW-MBH)
Trial ID: NCT06465719
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-09-30
Completion date: 2027-12-31
Sponsor: Second Affiliated Hospital, School of Medicine, Zhejiang University
Collaborators: Huashan Hospital; The Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School; China-Japan Friendship Hospital
Conditions: Basal Ganglia Hemorrhage
Interventions: Minimally invasive basal ganglion hematoma evacuation with Tract-based AI Robot Guiding System; Patients will be managed with guideline-based medications
Locations: the Second Affiliated Hospital of Zhejiang University School of Medicine
Countries: China
Enrollment: 330
Age range: 18 Years - None
Sex: ALL

Summary:
A nationwide, prospective, multicenter randomized controlled clinical trial to evaluate the therapeutic effects of the fiber tract-based artificial intelligence (AI) Robot Guiding System on the perioperative and long-term recovery of patients with moderate-volume basal ganglion hemorrhage.

Eligibility criteria:
Inclusion Criteria:

1. Age ≥18 years at randomization
2. Hypertensive basal ganglia hemorrhage confirmed by CT/CTA
3. Hematoma volume 15-30 mL (excluding 30 mL)
4. Functional impairment (aphasia, hemiparesis with strength ≤3, or NIHSS ≥15)
5. CT shows stable hematoma (≥6h interval, change \<5 mL)
6. GCS ≥9
7. Surgery feasible within 72h after onset
8. Pre-ICH mRS ≤1
9. Informed consent obtained according to laws and ethics

Exclusion Criteria:

1. Hematoma involving the thalamus, midbrain, or other areas
2. Radiologically diagnosed cerebrovascular abnormalities, as well as ischemic infarction converting to intracerebral hemorrhage, or recent (within 1 year) recurrence of intracerebral hemorrhage
3. Signs of impending brain herniation, such as midline shift \>1 cm or changes in the ipsilateral pupil;
4. Any irreversible coagulopathy disorders or known coagulation system disease; platelet count \<100,000; INR \>1.4; use of anticoagulants within 7 days prior to this hemorrhage;
5. Pregnant or possibly pregnant
6. Patients with severe concomitant diseases that may interfere with outcome assessment
7. Poor compliance or follow-up difficulties

Outcomes:
Utility-weighted modified Rankin Scale (UW-mRS)
Research on the Application of AI Image Recognition-Based Smartphone Apps in Personalized Bowel Preparation
Trial ID: NCT06610630
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-10-01
Completion date: 2025-03-31
Sponsor: Jianning Yao
Conditions: Colorectal Polyps
Interventions: Assessment of Bowel Preparation Using AI-Driven Smartphone Applications
Locations: The First Affiliated Hospital of Zhengzhou University
Countries: China
Enrollment: 513
Age range: 18 Years - 80 Years
Sex: ALL

Summary:
Colorectal cancer, as one of the most common malignant tumors of the digestive system, has a high incidence and mortality rate worldwide. High-quality colonoscopy is essential for the early detection and prevention of colorectal cancer and is key to improving the survival rates of patients. However, traditional colonoscopy faces numerous challenges in bowel preparation, such as inadequate preparation and a lack of personalized cleansing approaches. Therefore, it is particularly important to develop timely and efficient individualized bowel preparation methods. Artificial intelligence, especially deep learning technologies, has shown great potential in the medical field. This study aims to leverage the advantages of artificial intelligence to optimize the bowel preparation process before colonoscopy, creating a personalized bowel preparation plan that effectively improves the efficacy of colorectal cancer screening and diagnosis.

Eligibility criteria:
Inclusion Criteria:

* Patients who underwent painless colonoscopy at the Digestive Endoscopy Center of the First Affiliated Hospital of Zhengzhou University and signed the informed consent for the clinical trial.

Individuals who use smartphones.

Exclusion Criteria:

* Patients with contraindications for colonoscopy. Individuals in whom successful cecal intubation could not be achieved.

Outcomes:
Boston Bowel Preparation Scale; Dosage of Bowel Cleansing Agents
Advanced AI-powered Workstation for Radiologists Based on Generative Artificial Intelligence
Trial ID: NCT07057830
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-07-01
Completion date: 2026-02-01
Sponsor: Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department
Conditions: Study Aims to Develop and Validate a Generative AI-assistant Designed to Optimize Radiologists' Workflows and to Evaluate Its Performance
Locations: MIREA - Russian Technological University; Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department
Countries: Russia; Russia
Enrollment: 27
Age range: 18 Years - None
Sex: ALL

Summary:
This study aims to develop a generative AI assistant for radiologists to automate the processing of electronic medical records (EMRs) and provide relevant clinical information, optimizing diagnostic interpretation workflows.

Eligibility criteria:
Inclusion Criteria:

* Board-certified practicing radiologist;
* ≥3 years of experience in diagnostic imaging;
* Proficiency in using UMIAS systems;
* Signed informed consent form.

Exclusion Criteria:

* Participation in other studies;
* Unwillingness to adopt new technologies in daily practice;
* Conflict of interest.

Outcomes:
Radiologist satisfaction levels
Evaluation of Artificial Intelligence's Ability to Analyze and Recognize Cranial Trauma CT Imaging
Trial ID: NCT06230419
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2023-09-01
Completion date: 2024-03-01
Sponsor: RenJi Hospital
Conditions: Cerebral Hemorrhage
Locations: Brain Injury Center, Renji Hospital, School of Medicine, Shanghai Jiao Tong University
Countries: China
Enrollment: 208
Age range: 16 Years - 92 Years
Sex: ALL

Summary:
The goal of this observational retrospective study is to evaluate artificial intelligences (AI)'s proficiency in identifying and annotating brain bleeds in computed tomography (CT) images.

The main questions it aims to answer are:

* Whether AIs at present are capable of analyzing and recognizing cerebral traumas in CT images?
* If they are, how competent are they and how can humans take advantages of that? CT images were selected during the normal diagnosis and treatment process of patients, and no one needed to undergo any additional procedures connected to the study.

Eligibility criteria:
Inclusion Criteria:

* People who had cerebral traumas with brain bleedings shown in CT images.

Exclusion Criteria:

* Brain bleedings were not clear or representative in CT images.

Outcomes:
The identification completeness of the annotated images.
Early Detection of Advanced Liver Disease Via Artificial Intelligence-Enabled Electrocardiogram (Advance): A Pragmatic, Cluster-Randomized Clinical Trial
Trial ID: NCT05782283
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2023-04-18
Completion date: 2025-09-01
Sponsor: Mayo Clinic
Conditions: Cirrhosis
Interventions: ACE (AI-Cirrhosis-ECG) 2.0
Locations: Mayo Clinic Minnesota
Countries: United States
Enrollment: 279
Age range: 18 Years - None
Sex: ALL

Summary:
The overall objectives of this study are to determine the effectiveness of ACE 2.0 model in early detection of advanced liver fibrosis, and to determine the acceptance and barriers for use of an AI-enabled algorithm for prediction of liver disease in primary care.

Eligibility criteria:
Criteria:

Inclusion Criteria:

* Primary care clinicians (physicians, nurse practitioners, and physician assistants).
* Part of a team that cares for adult patients (≥18 years).
* Have the ability to order ECG.
* Consent will be obtained from primary care clinicians.
* Patients' data will be collected from electronic medical records (EMR).
* Adult patients (≥ 18 years) undergoing an ECG for any indication over a period of 6 months will be included.

Exclusion Criteria:

* Patients with known cirrhosis based on noninvasive fibrosis assessment tests, liver biopsy or complications of decompensated disease, or with a documented history of cirrhosis identified by clinical notes.

Outcomes:
The primary objective of this pragmatic trial is to validate a deep learning-based artificial intelligence (AI) model for early detection of cirrhosis-associated signals on digitized ECG.
AI Based Multi-modal Parameter of Peripheral Blood Cells (MMPBC) Predicts Survival Risk in Critically Ill Children: a Multicenter, Retrospective Cohort Study
Trial ID: NCT06034639
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2023-03-01
Completion date: 2023-09-30
Sponsor: Zhujiang Hospital
Conditions: Children
Locations: Zhujiang Hospital of Southern Medical University
Countries: China
Enrollment: 3
Age range: 1 Day - 18 Years
Sex: ALL

Summary:
This study aims to investigate whether an AI prediction model based on blood cell multi-modal data can achieve early warning of survival risk in critically ill children through a large-scale multi-center cohort of critically ill children.

Eligibility criteria:
Inclusion Criteria:

1. Children who were admitted to NICU and PICU from January 1, 2018, to March 31, 2023.
2. Age \<18 years, gender not limited.
3. Blood routine tests were performed using Mindray Medical's five-category blood cell analyzer (including BC6000, BC6000PLUS, BC6800PLUS, and BC7500 series), and the instrument or computer system retained relatively complete blood cell multi-modal data.
4. Detailed clinical medical records related to this study can be obtained.
5. Patients who were repeatedly admitted to NICU or PICU and had different conditions, causes, and outcomes each time were counted as new cases.

Exclusion Criteria:

1. Children with congenital immunodeficiency.
2. Children with blood diseases, including iron-deficiency anemia, macrocytic anemia, hereditary spherocytosis, glucose-6-phosphate dehydrogenase deficiency, thalassemia, autoimmune hemolytic anemia, aplastic anemia, immune thrombocytopenia, acute lymphoblastic leukemia, acute non-lymphoblastic leukemia, multiple myeloma, allergic purpura, myelodysplastic syndrome, etc.
3. Children with genetic metabolic diseases, including galactosemia, mucopolysaccharidosis, glycogen storage disease, phenylketonuria, albinism, alkaptonuria, hypoxanthine-guanine phosphoribosyltransferase deficiency, chromhidrosis, Goucher disease, Tay-Sachs disease, etc.
4. Children with chromosomal diseases, including Down syndrome, trisomy 18, etc.
5. Children who received blood products within six months, including transfused blood components, human immunoglobulin, etc.

Outcomes:
death; multiple organ dysfunction syndrome(MODS); sepsis
Study on the Effectiveness of Gastroscope Operation Quality Control Based on Artificial Intelligence Technology
Trial ID: NCT04384575
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2020-02-22
Completion date: 2022-05-01
Sponsor: Peking University
Conditions: Gastric Cancer
Interventions: blind spots
Locations: Beijing Cancer Hospital
Countries: China
Enrollment: 1570
Age range: 18 Years - 75 Years
Sex: ALL

Summary:
This study aims to construct a real-time quality monitoring system based on artificial intelligence technology.

Eligibility criteria:
Inclusion Criteria:

1. Patiens aged 18 years or above undergoing gastroscopy；
2. Be able to read, understand and sign informed consent；

Exclusion Criteria:

1. Patients with absolute contraindications to endoscopy examination;
2. pregnant women;
3. previous history of gastric surgery;
4. the researcher considers that the subject is not suitable for clinical trial.

Outcomes:
Accuracy; Sensitivity; Specificity
Impact of AI (Artificial Intelligence) on Adenoma Detection During Colonoscopy in FIT+ Patients: a Prospective Randomized Controlled Trial
Trial ID: NCT04691401
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2020-12-20
Completion date: 2021-12-31
Sponsor: Valduce Hospital
Conditions: Polyp of Colon
Interventions: Artificial Intelligence System (CAD EYE, Fujifilm Co.)
Locations: Gastroenterology Unit, Valduce Hospital
Countries: Italy
Enrollment: 750
Age range: 50 Years - 74 Years
Sex: ALL

Summary:
The Italian screening program invites the resident population aged 50-74 for Fecal Immunochemical Test (FIT) every 2 years. Subjects who test positive are referred for colonoscopy. Maximizing adenoma detection during colonoscopy is of paramount importance in the framework of an organized screening program, in which colonoscopy represent the key examination. Initial studies consistently show that Artificial iIntelligence-based systems support the endoscopist in evaluating colonoscopy images potentially increasing the identification of colonic polyps. However, the studies on AI and polyp detection performed so far are mostly focused on technical issues, are based on still images analysis or recorded video segments and includes patients with different indications for colonoscopy. At the best of our knowledge, data on the impact on AI system in adenoma detection in a FIT-based screening program are lacking. The present prospective randomized controlled trial is aimed at evaluating whether the use of an AI system increases the ADR (per patient analysis) and/or the mean number of adenomas per colonoscopy in FIT-positive subjects undergoing screening colonoscopy. Therefore Patients fulfilling the inclusion criteria are randomized (1:1) in two arms: A) patients receive standard colonoscopy (with high definition-HD endoscopes) with white light (WL) in both insertion and withdrawal phase; all polyps identified are removed and sent for histopathology examination; B) patients receive colonoscopy examinations (with HD endoscopes) equipped with an AI system (in both insertion and withdrawal phase); all polyps identified are removed and sent for histopathology examination. In the present study histopathology represents the reference standard.

Eligibility criteria:
Inclusion Criteria:

* Consecutive adult (50-74 yrs.) outpatients undergoing colonoscopy in the frame of the FIT-based screening program.

Exclusion Criteria:

* patients with CRC history or hereditary polyposis syndromes or hereditary non-polyposis colorectal cancer
* patients with inadequate bowel preparation
* patients in which cecal intubation was not achieved or scheduled for partial examinations
* patients with gastrointestinal symptoms
* polyps could not be resected due to ongoing anticoagulation preventing resection and pathological assessment

Outcomes:
ADR; APC
Prospective Real-World Study of a Multimodal Artificial Intelligence Model Across Disease Types
Trial ID: NCT07269236
Status: ENROLLING_BY_INVITATION
Study type: OBSERVATIONAL
Start date: 2025-10-01
Completion date: 2030-09
Sponsor: Nanfang Hospital, Southern Medical University
Conditions: Pancancer
Locations: Nanfang Hospital, Southern Medical University; Zhongshan City People's Hospital
Countries: China; China
Enrollment: 2000
Age range: 18 Years - None
Sex: ALL

Summary:
This project proposes to collect prospective multimodal data-such as pathology, imaging, and clinical information-and to perform integrative analyses. AI technologies can offer novel solutions for disease classification, tumor grading, histological subtyping, molecular subtyping, selection of chemotherapy regimens, risk stratification, treatment response prediction, report generation, and intelligent question-answering. This research provides important support for precision medicine and individualized treatment and has significant theoretical and practical implications. Conducting a prospective randomized controlled study better aligns with clinical application requirements and can accelerate the comprehensive deployment of AI systems.

Eligibility criteria:
Inclusion Criteria:

1. Age 18 years or older.
2. Patients with available digitized pathology slides, radiological imaging, and corresponding clinical data.

Exclusion Criteria:

1.Patients with missing data or specimens not meeting quality control requirements for analysis.

Outcomes:
Area under ROC curve (AUC)
Application Evaluation Research on the Artificial Intelligence-assisted Support System for the Diagnosis of Colorectal Tubular Adenoma Lesions
Trial ID: NCT07073430
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2023-11-28
Completion date: 2026-10-31
Sponsor: Renmin Hospital of Wuhan University
Collaborators: Beijing Friendship Hospital, Captial Medical University; Air Force Military Medical University, China; The Sixth Affiliated Hospital, Sun Yat-sen University; Army Medical University, China; Guizhou Provincial People's Hospital; Shengjing Hospital; The Second Medical Center, Chinese PLA General Hospital; Zhejiang University; Shandong University
Conditions: Colorectal Adenoma; Artificial Intelligence (AI)
Interventions: AI models with NBI
Locations: Renmin Hospital of Wuhan University
Countries: China
Enrollment: 4000
Age range: 18 Years - None
Sex: ALL

Summary:
This study is a prospective,multi-center and observational clinical study.Investigators would like to innovatively construct a "trinity" database of colorectal tubular adenomas based on white light - magnifying chromo - pathological images.It simulates the decision - making logic of doctors, and based on the multimodal endoscopic LAFEQ method previously proposed, develop a multimodal deep - learning diagnostic model for colon adenomas and an interpretable risk prediction model for intestinal adenomas. While achieving high - precision auxiliary treatment decisions, clearly present the decision - making basis, and break through the limitation of poor interpretability of previous medical imaging AI models.

Eligibility criteria:
Inclusion Criteria:

* Patients aged ≥ 18 years, who need to undergo colonoscopy, regardless of gender.
* Voluntarily sign the informed consent form
* Promise to abide by the research procedures and cooperate in the implementation of the entire research process.

Exclusion Criteria:

* Patients who has a history of abdominal or pelvic surgery or radiotherapy in the past;
* Patients who has definite active lower gastrointestinal bleeding.
* Existing or suspected hereditary colorectal polyposis, inflammatory bowel disease;
* Uncontrolled hypertension (systolic blood pressure \> 160 mmHg or diastolic blood pressure \> 95 mmHg after standardized treatment)
* There is a history of stroke, coronary artery disease, or vascular disease;
* Pregnant;
* Intestinal preparation cannot be carried out.

Outcomes:
The accuracy rate of diagnosing adenomas
Optimising a Digital Diagnostic Pathway for Heart Failure in the Community
Trial ID: NCT04724200
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2021-01-05
Completion date: 2023-12-31
Sponsor: NHS Greater Glasgow and Clyde
Collaborators: University of Glasgow
Conditions: Heart Failure
Interventions: Echocardiogram
Locations: NHS Louisa Jordan
Countries: United Kingdom
Enrollment: 867
Age range: 18 Years - None
Sex: ALL

Summary:
This is a prospective, observational study. The primary aim is to assess the diagnostic accuracy of Artificial Intelligence (AI) analysis of Point of Care (POC) handheld transthoracic echocardiogram images (POC handheld echocardiogram) compared to the current gold-standard of transthoracic echocardiogram images acquired and analysed by a British Society of Echocardiography (BSE) accredited operator, using a standard echocardiogram machine (standard TTE), for the diagnosis of Heart Failure with reduced Ejection Fraction (HFrEF), in patients referred from the community for investigation of suspected new Heart Failure (HF).

Eligibility criteria:
Inclusion Criteria:

* Adult individuals referred from the community who are awaiting HF diagnostic investigations, including echocardiography, to investigate for a suspected new diagnosis of HF

Exclusion Criteria:

1. Individuals less than 18 years of age
2. Individuals who have had an interim echocardiogram for another clinical reason, whilst on the outpatient echocardiogram waiting list for investigation of HF
3. Individuals with a previous ICD-10 diagnosis of HF in any coding position
4. Inability of the participant, in the opinion of the investigators, to understand and/or comply with study procedures, or any conditions that, in the opinion of the investigators, may render the participant unable to understand, attend and/or undergo study visit investigations
5. Inability to read and understand the PIS (provided in English only), and understand the research team

Outcomes:
Negative and positive predictive value (NPV and PPV); False positive; False negative; sensitivity; specificity; Area under curve
A Machine Learning Approach to Infantile Spasms Recognition in Video Recordings
Trial ID: NCT06315829
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2024-08-26
Completion date: 2026-05-06
Sponsor: Johns Hopkins University
Conditions: Infantile Spasms
Interventions: Spasm Vision
Locations: Johns Hopkins Hospital
Countries: United States
Enrollment: 61
Age range: None - 2 Years
Sex: ALL

Summary:
Infantile spasms are a type of seizure linked to developmental issues. Unfortunately, they are often misdiagnosed, causing delays in treatment. The purpose of this study is to develop a computer program that can reliably differentiate infantile spasms from similar, yet benign movements in videos. This computer program will learn from videos taken by parents of study participants. Quickly recognizing and treating infantile spasms is crucial for ensuring the best developmental outcomes.

Eligibility criteria:
Inclusion Criteria:

* Participant age less than 24 months
* Participant evaluated in the Johns Hopkins Outpatient Center, Johns Hopkins Pediatric Emergency Department or Johns Hopkins Inpatient Units due to spells of abnormal movement or seizure
* Participant evaluated by a pediatric neurologist during the outpatient or inpatient visit at Johns Hopkins Hospital
* At least one video recording of the spell of abnormal movement produced by the parent/guardian available for provider review

Exclusion Criteria:

* Poor video recording quality
* Entire patient is not in frame

Outcomes:
Model Sensitivity (Recall); Model Specificity; Model Positive Predictive Value (Precision); Model Negative Predictive Value