moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Value of Some Risk Scores in Predicting Cardiovascular Events After Gastrointestinal Surgery
Trial ID: NCT07539532
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2026-01-01
Completion date: 2026-07-31
Sponsor: Bach Mai Hospital
Conditions: Postoperative Complications; Cardiovascular Diseases; Digestive System Surgical Procedures
Locations: Bach Mai hospital
Countries: Vietnam
Enrollment: 5000
Age range: 16 Years - None
Sex: ALL

Summary:
The goal of this observational study is to develop and evaluate an artificial intelligence (AI)-based multimodal model for predicting major cardiovascular events within 30 days after gastrointestinal surgery in adults at Bach Mai Hospital. The study will also compare the predictive performance of this AI-based model with commonly used traditional risk scores.

The main questions it aims to answer are:

Can an AI-based multimodal model predict major cardiovascular events within 30 days after gastrointestinal surgery? Does the AI-based model show better predictive performance than the Revised Cardiac Risk Index (RCRI), the American College of Surgeons National Surgical Quality Improvement Program Myocardial Infarction or Cardiac Arrest calculator (ACS NSQIP MICA), and the ACS NSQIP Surgical Risk Calculator (ACS NSQIP SRC)? Researchers will compare the AI-based multimodal model with traditional risk scores using measures of predictive performance, including discrimination, calibration, net reclassification improvement, and integrated discrimination improvement.

Participants will be adults undergoing gastrointestinal surgery. Researchers will review medical record data from patients treated in 2025 and will also collect the same types of clinical data prospectively in 2026. The clinical outcome being predicted is the occurrence of major cardiovascular events within 30 days after surgery. The study will not change routine clinical care.

Eligibility criteria:
Inclusion Criteria:

* Adults aged 18 years or older.
* Undergoing gastrointestinal surgery at Bach Mai Hospital between January 2025 and December 2026.
* Available preoperative, intraoperative, and postoperative data sufficient for analysis.

Exclusion Criteria:

* Death within 24 hours after surgery due to a clearly non-cardiovascular cause.
* Incomplete data required for analysis.

Outcomes:
Area under the receiver operating characteristic curve of the AI-based multimodal model for predicting 30-day major adverse cardiovascular events after gastrointestinal surgery
Artificial Intelligence Screening on Patients With Primary Angle Closure Glaucoma
Trial ID: NCT03611387
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2018-04-01
Completion date: 2019-12-31
Sponsor: Sun Yat-sen University
Collaborators: University of California
Conditions: Ophthalmopathy
Interventions: CASIA 2 OCT
Locations: Zhognshan Ophthalmic Center, Sun Yat-sen University
Countries: China
Enrollment: 1500
Age range: 18 Years - None
Sex: ALL

Summary:
The Tomey CASIA (Tomey Corporation, Nagoya, Japan) is a novel rapid imaging device that captures high-quality imaging of the entire anterior chamber of the eye over detailed imaging of a single section. In this prospective study, we want to investigate and compare the anatomical structure of the drainage angle of the eye in patients with different types glaucoma using Tomey machine. This study gives us a better understanding of the predictability, validity and accuracy of Tomey machine in the diagnosis of different types of glaucoma. Moreover, the data collected here will be used to create an artificial intelligence (AI) platform to screen certain type of glaucoma.

Eligibility criteria:
Inclusion Criteria:

* Patient with or without glaucoma

Exclusion Criteria:

* Patients with any history of eye surgery (except cataract surgery) including laser procedure.
* Age \< =18 years

Outcomes:
The accuracy of the CASIA 2 OCT; The sensitivity and specificity of the AI
Artificial Intelligence Versus Sonographer Echocardiogram Analysis and Reporting in Patients With Heart Failure: A Randomized Controlled Trial
Trial ID: NCT07021599
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-07-01
Completion date: 2028-12-03
Sponsor: Prince of Wales Hospital, Shatin, Hong Kong
Conditions: Heart Failure
Interventions: Tailored AI echocardiogram analysis and reporting system
Locations: Prince of Wales Hospital
Countries: Hong Kong
Enrollment: 514
Age range: 18 Years - None
Sex: ALL

Summary:
This is a non-inferiority, three-year, multicenter, double-blinded randomized controlled study of an AI versus experienced sonographer echocardiogram analysis in HF patients. Consecutive patients presented for echocardiogram examination with new or worsening HF symptom and positive HF blood markers will be recruited. A target of 514 patients will be randomized 1:1 to receive either AI or sonographer echocardiogram analysis. The primary endpoint of diagnostic accuracy is the complete agreement of disease grading with an experienced cardiologist (American Society of Echocardiography level III) using a standardized grading chart. Important secondary endpoints include the time used for echocardiogram report drafting and report endorsement, 6-month heart failure symptom and hospitalization, and the cost-effectiveness of AI to increase echocardiogram service. Clinical, biochemical and echocardiographic predictors of worsening of heart failure and hospitalization will be identified.

Eligibility criteria:
Inclusion Criteria:

* Aged 18 years or above
* Has new or worsening of heart failure symptoms
* Elevated heart failure blood markers (N-terminal prohormone of brain natriuretic peptide, "NTproBNP") within 3 months from enrolment or by point-of-care blood test, to ensure that the patient's symptoms are cardiac origin
* Provision of written informed consent

Exclusion Criteria:

* Known severe valvular heart disease
* Prior prosthetic valve implantation
* Previously known or suspected \>=severe tricuspid regurgitation, \>=moderate aortic regurgitation, \>=moderate mitral stenosis or pericardial disease during detected during image acquisition
* Insufficient image quality for proper analysis determined by the scanning sonographer (estimated to be 15% of all echocardiograms screened)

Outcomes:
Rate of complete agreement in LVEF assessments; Rate of complete agreement in assessments of AS severity; Rate of complete agreement in assessment of MR severity
Valutazione Delle Pressioni di Riempimento Del Ventricolo Sinistro Attraverso l'Impiego di un Software di Intelligenza Artificiale Applicato Alla Stima Dello Strain Atriale Sinistro Con Ecocardiografia Speckletracking: Studio di Validazione Mediante Cateterismo Cardiaco
Trial ID: NCT05768698
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2021-07-20
Completion date: 2024-06-11
Sponsor: Istituto Auxologico Italiano
Collaborators: Philips Healthcare
Conditions: Heart Failure, Diastolic; Echocardiography
Interventions: Software algorithm
Locations: Philips Healthcare
Countries: United States
Enrollment: 20
Age range: 18 Years - None
Sex: ALL

Summary:
The goal of this single-center, prospective, observational study is to validate a software package based on an artificial intelligence algorithm for automated non-invasive estimation of LVFP against invasively measured ones by left and right heart catheterization; In addition, the added value of this new automated software to detect increased LVEDP \> 15 mm Hg compared to the current ASE/EACVI algorithm for assessing diastolic dysfunction and longitudinal left atrial strain during the reservoir phase will be evaluated.

Eligibility criteria:
Inclusion Criteria:

* ≥ 18 yo,
* Signed informed consent to be part of this study,
* No pregnancy,
* Good acoustic window and patient cooperation to obtain good 2- and 3-dimensional datasets focused on left atrium (LA),

Exclusion Criteria:

* Unwillingness to be part of the study,
* Patients with poor 2-dimensional imaging quality in ≥ 1 LA segment or lacking of a discernible R-R interval, which might preclude accurate strain measurements,
* Patients with more than mild mitral valve heart disease, aortic regurgitation, left side valvular prosthesis or mitral valve repair, mitral annulus calcification (≥ 5 mm),
* Patients with history or presence of atrial fibrillation (AF), supraventricular arrhythmias, ventricular paced rhythm, left bundle branch block (LBBB),
* Patients taking diuretics or vasodilators (nitrates) on the day of the examination, which could alter loading conditions.

Outcomes:
Left ventricular filling pressure
Artificial Intelligence Enables Precision Diagnosis of Cervical Cytology Grades and Cervical Cancer
Trial ID: NCT04551287
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2019-07-01
Completion date: 2020-12-14
Sponsor: Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
Collaborators: Guangzhou Women and Children's Medical Center; The Third Affiliated Hospital of Guangzhou Medical University
Conditions: Cervical Cancer; Diagnostic Platform; Diagnosing Cervical Cytology Grades and Cancer; Artificial Intelligence
Locations: Guangzhou Women and Children's Medical Center; The Third Affiliated Hospital of Guangzhou Medical University; Sun Yat-Sen Memorial Hospital of Sun Yat-sen University
Countries: China; China; China
Enrollment: 16164
Age range: 25 Years - 65 Years
Sex: FEMALE

Summary:
Cervical cancer, the fourth most common cancer globally and the fourth leading cause of cancer-related deaths, can be effectively prevented through early screening. Detecting precancerous cervical lesions and halting their progression in a timely manner is crucial. However, accurate screening platforms for early detection of cervical cancer are needed. Therefore, it is urgent to develop an Artificial Intelligence Cervical Cancer Screening (AICS) system for diagnosing cervical cytology grades and cancer.

Eligibility criteria:
Inclusion Criteria:

1. Women Aged 25-65 years old.
2. Availability of confirmed diagnostic results of the cervical liquid-based cytological examination, and satisfactory digital images from the liquid-based cytology pap test: at least 5000 uncovered and observable squamous epithelial cells, samples with abnormal cells (atypical squamous cells or atypical glandular cells and above).

Exclusion Criteria:

1. Unsatisfactory samples of cervical liquid-based cytological examination: less than 5000 uncovered, observable squamous epithelial cells, and more than 75% of squamous epithelial cells affected because of blood, inflammatory cells, epithelial cells over-overlapping, poor fixation, excessive drying, or contamination of unknown components.
2. Women diagnosed with other malignant tumors other than cervical cancer.

Outcomes:
Area under ROC curve (AUC)
An Investigation on Artificial Intelligence Literacy and E-Health Literacy of Patients With Fibromyalgia Syndrome
Trial ID: NCT07020845
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2024-12-16
Completion date: 2025-05-30
Sponsor: Gulseren Demir Karakilic
Conditions: Fibromyalgia Syndrome; Healthy Controls Group - Age and Sex-matched
Interventions: Observation only
Locations: Yozgat Bozok University Faculty of Medicine, Department of Physical Medicine and Rehabilitation
Countries: Turkey (Türkiye)
Enrollment: 212
Age range: 18 Years - 50 Years
Sex: ALL

Summary:
This cross-sectional observational study aimed to assess electronic health literacy and artificial intelligence literacy among individuals diagnosed with fibromyalgia syndrome, with particular focus on artificial intelligence literacy. Although general health literacy has been investigated in this patient population, digital and artificial intelligence-related competencies have not been sufficiently studied. The research included 106 individuals diagnosed with fibromyalgia syndrome and 106 age- and sex-matched healthy volunteers, recruited between December 2024 and May 2025. All participants completed a series of standardized questionnaires, including the Revised Fibromyalgia Impact Questionnaire, the Beck Depression Inventory, the Beck Anxiety Inventory, the Electronic Health Literacy Scale, and the Artificial Intelligence Literacy Scale. The study explored demographic, clinical, and psychological factors associated with digital and artificial intelligence literacy levels among both patient and control groups.

Eligibility criteria:
Inclusion Criteria:

Age between 18 and 50 years

Diagnosis of Fibromyalgia Syndrome according to 2016 American College of Rheumatology criteria (for patient group)

Adequate cognitive function

Literacy

Willingness to participate and provide informed consent

For control group: Healthy individuals matched by age and sex, with no reported health issues

Exclusion Criteria:

Cognitive impairment preventing questionnaire completion

Illiteracy

Presence of severe comorbidities or neurological/psychiatric disorders that may affect participation

Refusal or inability to provide informed consent

Outcomes:
E-Health Literacy; Artificial Intelligence Literacy
Effectiveness of a Therapy Chatbot on Anxiety and Depressive Symptoms in Subclinical Young Adults: A Randomized Controlled Trial
Trial ID: NCT05762939
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2022-05-03
Completion date: 2022-08-02
Sponsor: University of Social Sciences and Humanities, Warsaw
Collaborators: Ministry of Science and Higher Education, Poland
Conditions: Anxiety; Depression
Interventions: Chatbot; Control (book)
Locations: Laboratory of Affective Neuroscience in Poznań, SWPS University of Social Sciences and Humanities
Countries: Poland
Enrollment: 81
Age range: 18 Years - 35 Years
Sex: ALL

Summary:
This clinical trial compares the effects of using an artificial intelligence based therapy chatbot and a self-help book to lower anxiety and depressive symptoms. The therapy chatbot (named Fido) is an application delivering support for mental health via a conversation-like interface in a highly inflected language (Polish).

The team will recruit young people (aged 18-35) who currently struggle with anxiety and/or depression. The participants will use the chatbot or self-help materials for two weeks. Then, the researchers will check if using the chatbot or the book improved several aspects of mental health and the quality of life. Moreover, they will compare the groups for any differences in outcomes.

Eligibility criteria:
Inclusion Criteria:

* declaring at least mild depressive or anxiety symptoms by achieving a total score of at least 16 points on Center for Epidemiologic Studies Depression Scale Revised or at least 50 points on Penn State Worry Questionnaire
* being able to visit the study site in Poznań (Poland) to complete all measurements

Exclusion Criteria:

* undergoing psychotherapy, coaching or psychopharmacological treatment
* being diagnosed with a neurological disorder

Outcomes:
CESD-R Score Change; PHQ-9 Score Change; PSWQ Score Change; SWLS Score Change; PANAS Score Change; STAI Score Change; R-UCLA Score Change
Gastrointestinal Endoscopy Artificial Intelligence Cloud Platform in Gastrointestinal Endoscopy Screening: a Prospective Multi-center Real-world Study
Trial ID: NCT05435872
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2022-07-09
Completion date: 2024-07-01
Sponsor: Peking Union Medical College Hospital
Collaborators: Beijing Aerospace General Hospital; Beijing Fangshan District Liangxiang Hospital; People's Hospital of Beijing Daxing District; Gucheng County Hospital of Hebei Province; Nanhe County Hospital of Hebei Province
Conditions: Diagnoses Disease; Quality Control; Endoscopy; Artificial Intelligence
Interventions: The Artificial intelligence Cloud Platform
Locations: Peking Union Medical College Hospital
Countries: China
Enrollment: 2000
Age range: 18 Years - 80 Years
Sex: ALL

Summary:
Study objective: To establish a quality control system for gastrointestinal endoscopy based on artificial intelligence technology and an auxiliary diagnosis system that can perform lesion identification, improving the detection rate of early gastrointestinal cancer while standardizing, normalizing, and homogenizing the endoscopic treatment in primary hospitals (including some of the primary hospitals, which are participating in Beijing-Tianjin-Hebei Gastrointestinal Endoscopy Medical Consortium) under Gastrointestinal Endoscopy Artificial Intelligence Cloud Platform as the hardware base.

Study design: This study is a prospective, multi-center, real-world study.

Eligibility criteria:
Inclusion Criteria:

* From the beginning to the end of the study, patients who received gastroscopy and colonoscopy due to confirmed clinical indications were admitted to Beijing Aerospace General Hospital, Beijing Fangshan District Liangxiang Hospital, People's Hospital of Beijing Daxing District, Gucheng Country Hospital of Hebei Province, and Nanhe Country Hospital of Hebei Province.
* After fully informing and answering the questions, the endoscopic examination with Gastrointestinal Endoscopy Artificial Intelligence Cloud Platform can be accepted, and a signed informed consent form can be provided.

Exclusion Criteria:

* Patients who refuse to participate in this study;
* Patients with intolerance or contraindications to endoscopic examination, such as severe cardiopulmonary diseases, coagulation disorders, or a total of platelet less than 50\*10\^9/L.

Outcomes:
Diagnosis rate of early gastrointestinal cancer.
Analysing the Link Between Tongue Signs and Bile Reflux by Artificial Intelligence
Trial ID: NCT05369572
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2022-06-30
Completion date: 2025-06-30
Sponsor: Shandong University
Conditions: Artificial Intelligence; Tongue; Bile Reflux
Locations: Qilu hosipital
Countries: China
Enrollment: 1500
Age range: 18 Years - 80 Years
Sex: ALL

Summary:
By introducing artificial intelligence into Chinese medicine tongue diagnosis, we collated and collected tongue images, anxiety and depression scales and gastroscopy reports, mined and analysed the correlation between tongue images and bile reflux and anxiety and depression and constructed a prediction model to analyse the possibility of predicting bile reflux and anxiety and depression in patients based on tongue images.

Eligibility criteria:
Inclusion Criteria:

* Patients aged 18 to 80 years who wish to undergo gastroscopy.
* Patients have given their informed consent and signed the informed consent form.

Exclusion Criteria:

* Serious heart, liver, kidney or other underlying illness, or mental illness.
* Patients taking anti-anxiety or depression medication within 3 months.
* Current H. pylori infection.
* History of surgery on the digestive or biliary tract.
* Peptic ulcer, malignant tumour of the digestive tract, etc.
* Patients taking bismuth or other staining medications.
* Pregnant or lactating women.

Outcomes:
Sensitivity; Specificity; Positive predictive values(PPV); Negative predictive values (NPV); AUC (95% CI); Accuracy
Comparing Physician and Artificial Intelligence Chatbot Responses to Frequently Asked Questions From Osteoarthritis Patients: a Prospective Cross-sectional Study
Trial ID: NCT07202286
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2024-05-22
Completion date: 2024-06-05
Sponsor: Canisius-Wilhelmina Hospital
Conditions: Osteo Arthritis Knee and Hip; Artificial Intelligence (AI); Acceptability; Accuracy; Chatbot
Locations: Canisius Wilhelmina Hospital
Countries: Netherlands
Enrollment: 286
Age range: None - None
Sex: ALL

Summary:
This study aimed to compare the patient acceptability (preference, length, and difficulty) and accuracy of Chat-Generative Pre-Trained Transformer (ChatGPT) responses to questions from people with osteoarthritis (OA) with physician responses.

Eligibility criteria:
Inclusion Criteria:

* all individuals visiting the Department of Orthopedics in the Canisius Wilhelmina Hospital, a district general hospital in Nijmegen, The Netherlands, diagnosed with knee or hip OA between March 2023 and March 2024

Exclusion Criteria:

* partly completion of the questionnaire

Outcomes:
Preferred response
Efficacy of Real-Time Computer Aided-Detected of Colonic Neoplasia in an Underserved Population, A Randomized Controlled Trial
Trial ID: NCT05963724
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2022-09-01
Completion date: 2023-05-27
Sponsor: Riverside University Health System Medical Center
Conditions: Colorectal Cancer; Colorectal Neoplasms
Interventions: Real-Time Computer Aided Detection; Stanford Colonoscopy
Locations: Riverside University Health System
Countries: United States
Enrollment: 1100
Age range: 30 Years - 100 Years
Sex: ALL

Summary:
This study assesses the sensitivity and added benefits of computer-aided detection compared to standard care (white-light) in detecting colon polyps in patients undergoing colonoscopy.

Eligibility criteria:
Inclusion Criteria:

* Undergoing colonoscopy at RUHS
* Age \> 45 years
* No contraindications to colonoscopy

Exclusion Criteria:

* Prior history of subtotal colectomy

Outcomes:
Adenoma Detection Rate
Artificial Intelligence in Assessing Gastric Intestinal Metaplasia Via the EGGIM Score
Trial ID: NCT07257796
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-11-01
Completion date: 2027-12-31
Sponsor: Qilu Hospital of Shandong University
Conditions: Intestinal Metaplasia of Gastric Mucosa; Artificial Intelligence; Endoscopy
Interventions: The diagnostic performance of AI system and endoscopists
Locations: Qilu Hospital of Shandong University; Shandong First Medical University First Affiliated Hospital; Shandong Second Provincial General Hospital
Countries: China; China; China
Enrollment: 3000
Age range: 40 Years - 75 Years
Sex: ALL

Summary:
The endoscopic grading system (EGGIM) has been widely used to assess the extent of gastric intestinal metaplasia during endoscopy. Investigators developed an artificial intelligence (AI) system to automatically evaluate the extent of gastric intestinal metaplasia (GIM) and calculate the EGGIM scores in endoscopy examination. This study is a prospective, multi-center study aimed at exploring the performance and reliability of AI-EGGIM scoring.

This is a prospective study designed to validate the AI-EGGIM system in a larger cohort. The study protocol was developed based on preliminary experience from a prior investigation (NCT05464108).

Eligibility criteria:
Inclusion Criteria:

* patients aged 40-75 years who undergo the IEE examination
* patients who voluntarily sign the informed consent form

Exclusion Criteria:

* patients with severe cardiac, cerebral, pulmonary or renal dysfunction or psychiatric disorders who cannot participate in gastroscopy
* patients with previous surgical procedures on the stomach

Outcomes:
Performance of AI system to diagnose the grade of intestinal metaplasia by calculating the EGGIM score