moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Impact of Real-time Automatic Quality Control System on Colorectal Adenoma Detection: a Multicenter Randomized Controlled Trial
Trial ID: NCT04901130
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2021-05-27
Completion date: 2022-01-31
Sponsor: Shandong University
Collaborators: Binzhou Medical University; Shengli Oilfield Hospital; Linyi People's Hospital; Zibo Municipal Hospital; The People's Hospital of Zhaoyuan City
Conditions: Colonoscopy
Interventions: Computer-aided Real-time Automatic Quality Control System (AQCS)
Locations: Binzhou Medical University Hospital; Linyi People's Hospital; Central Hospital of Shengli Oilfield; Qilu Hospital of Shandong University; The People's Hospital of Zhaoyuan City; Zibo Municipal Hospital
Countries: China; China; China; China; China; China
Enrollment: 1254
Age range: 18 Years - 80 Years
Sex: ALL

Summary:
The purpose of this study was to assess the performance of automatic quality-control system (AQCS) in real-time quality control of colonoscopy.

Eligibility criteria:
Inclusion Criteria:

* Consecutive patients (aged 18-80 years) scheduled for colonoscopy examination.

Exclusion Criteria:

* Patients with a history of inflammatory bowel disease, advanced colorectal cancer, polyposis syndromes or known colorectal polyps without complete removal previously.
* Patients with a history of colorectal surgery.
* Patients with a contraindication for biopsy.
* Patients with prior failed colonoscopy.
* Patients with known stenosis or obstruction.
* Patients in pregnancy or lactation phase.
* Patients refused to participate in the trial.

Outcomes:
Colorectal adenoma detection rate (ADR)
Artificial Intelligence Identified Dyskalemia Using Electrocardiogram (AIDE) Prompts Immediate Treatment
Trial ID: NCT05118022
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2022-01-01
Completion date: 2023-02-28
Sponsor: National Defense Medical Center, Taiwan
Conditions: Hyperkalemia; Hypokalemia
Interventions: Artificial Intelligence identified Dyskalemia using Electrocardiogram (AIDE) system
Locations: National Defense Medical Center
Countries: Taiwan
Enrollment: 14989
Age range: 18 Years - None
Sex: ALL

Summary:
This is a randomized controlled trial (RCT) to test a novel artificial intelligence (AI)-enabled electrocardiogram (ECG)-based screening tool for improving the diagnosis and management of potassium abnormalities.

Eligibility criteria:
Inclusion Criteria:

* Patients in emergency department.
* Patients recieved at least 1 ECG examination.

Exclusion Criteria:

* Patients recieved dyskalemia-related treatment before ECG examination.
* The patients recieved ECG at the period of inactive AI-ECG system.

Outcomes:
Cumulative proportion of hyperkalemia treatment; Cumulative proportion of hypokalemia treatment
AI and Fluorescence Help Junior Surgeons Identify the Bile Duct During Laparoscopic Cholecystectomy
Trial ID: NCT07188181
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2024-08-01
Completion date: 2025-07-31
Sponsor: Chang Gung Memorial Hospital
Collaborators: National Science and Technology Council, Taiwan
Conditions: Surgical Education and Anatomical Identification in Laparoscopic Cholecystectomy
Locations: Shih Min Yin
Countries: Taiwan
Enrollment: 8
Age range: None - None
Sex: ALL

Summary:
The present study evaluates whether PGY trainees and surgical residents, with or without AI assistance, could accurately identify the presence and anatomical location of the CBD, as well as delineate intraoperative danger zones during LC.

Eligibility criteria:
Inclusion Criteria:

* Postgraduate-year (PGY) trainees with fewer than 20 laparoscopic cholecystectomy (LC) cases.
* Junior residents with 20-50 LC cases.
* Senior residents with more than 50 LC cases.

Exclusion Criteria:

* Participants not available for the one-week washout and repeat assessment.

Outcomes:
CBD Recognition
Exploring Nursing Students' Ability to Create Artificial Intelligence-Enhanced Patient Education Materials on Infection Care: A Randomized Controlled Trial
Trial ID: NCT06983236
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-05-16
Completion date: 2025-06-25
Sponsor: Çankırı Karatekin University
Conditions: Nursing Students; Artificial Intelligence (AI); Patient Education Materials; Randomized Controlled Trial
Interventions: AI-enabled patient education material; Traditional patient education material
Enrollment: 110
Age range: 18 Years - 38 Years
Sex: ALL

Summary:
The purpose of this study is to explore the ability of nursing students to create artificial intelligence-supported patient education materials regarding infection care. This study will be conducted as a randomized controlled experimental study. Data of the study will be collected with the Descriptive Characteristics Form, Patient Education Information Form and Patient Education Materials Assessment Tool (HEMDA).

Eligibility criteria:
Inclusion Criteria:

* To take Basic Principles and Practices in Nursing-II for the first time
* Not to work as a nurse in a hospital or any health institution

Exclusion Criteria:

* Having previously prepared patient education materials;
* Retaking the Basic Principles and Practices in Nursing-II course
* Having difficulty speaking and understanding Turkish;
* Not attending class

Outcomes:
Patient Education Information Form
Development and Evaluation of an AI-empowered Voice-interactive Chatbot for Migraine Management Among Hong Kong Women: a Randomized Crossover Trial
Trial ID: NCT06920602
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-07-01
Completion date: 2025-11-01
Sponsor: The Hong Kong Polytechnic University
Collaborators: Nethersole Institute of Continuing Holistic Health Education (NICHE)
Conditions: Migraine
Interventions: AI-empowered voice-interactive chatbot; Paper migraine diary; Paper migraine diary; AI-empowered voice-interactive chatbot
Locations: The Hong Kong Polytechnic University
Countries: Hong Kong
Enrollment: 49
Age range: 18 Years - None
Sex: FEMALE

Summary:
The goal of this randomized crossover trial is to examine whether the AI-empowered chatbot will effectively collect comprehensive migraine feature data and demonstrate higher compliance, accuracy, utilization, acceptability, feasibility, and validity in migraine feature tracking compared to traditional paper-based migraine diaries among Hong Kong women with migraine.

Participants will use different tools (AI-empowered voice-interactive chatbot or paper migraine diary) for migraine tracking. Participants will be randomly allocated to one of two intervention sequences: (1) AI-empowered voice-interactive chatbot followed by paper migraine diary, or (2) paper migraine diary followed by AI-empowered voice-interactive chatbot. The study will consist of two intervention periods, each lasting one month, with a 1:1 allocation ratio.

Eligibility criteria:
Inclusion Criteria:

* Women aged 18 years or older
* Meet diagnostic criteria for migraine according to ICHD-3
* Experiencing at least 2 headache days per month
* Living in Hong Kong
* Fluent in Cantonese
* Owning a smartphone with internet access
* Willing to provide informed consent

Exclusion Criteria:

* Individuals with severe cognitive impairments or communication difficulties
* Those unable to use a smartphone or the chatbot due to physical or mental limitations
* Using another alternative electronic migraine diary during recruitment or in the past 12 weeks

Outcomes:
timeliness; Completeness; Missed episodes; Accuracy
Predicting Episodes of Intracranial Hypertension in Neuro-injured Patients: Development of a Decision Algorithm Using Artificial Intelligence
Trial ID: NCT06555900
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2020-12-17
Completion date: 2025-06-30
Sponsor: University Hospital, Brest
Conditions: Creation of a Warehouse of Clinical Data and Physiological Signals at the Patient's Bedside
Locations: CHU de Brest
Countries: France
Enrollment: 500
Age range: 18 Years - 99 Years
Sex: ALL

Summary:
The investigators wish to build up a database of clinical data and physiological signals with a view to developing a predictive algorithm based on continuous analysis of the intracranial pressure waveform and other parameters commonly used in intensive care to predict the occurrence of an episode of intracranial hypertension (HTIC). This algorithm will be designed using supervised learning statistical methods based on innovative statistical analysis methods (artificial intelligence). These methods are classically used to exploit massive data such as sensor data.

Eligibility criteria:
Inclusion Criteria:

* More than 18 years
* admission to intensive care for less than 3 days for a neurological lesion
* Sensor placement for intracranial pressure monitoring

Exclusion Criteria:

* patient under judicial protection
* refusal to participate
* patients under 18 years of age

Outcomes:
Creation of a database of clinical data and physiological signals to develop a predictive algorithm based on continuous analysis of the intracranial pressure waveform to predict the occurrence of an episode of intracranial hypertension.
A Randomized Controlled Study on the Improvement of Medical Experience for Patients With Radiation-induced Oropharyngeal Mucositis Based on the RIS System of the Internet of Things
Trial ID: NCT07126457
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-09-01
Completion date: 2026-12-01
Sponsor: Nanfang Hospital, Southern Medical University
Conditions: Head and Neck Cancer; Radiation-Induced Mucositis
Interventions: RIS system
Locations: Nanfang hospital, Southern medical university; Huizhou Central People's Hospital; Jieyang people's hospital; Meizhou People's Hospital, Meizhou Academy of Medical Sciences Meizhou
Countries: China; China; China; China
Enrollment: 110
Age range: 18 Years - 65 Years
Sex: ALL

Summary:
This study is a researcher-initiated, multi-center, prospective, two-arm interventional randomized controlled study across the country. It aims to explore whether an Internet of Things-based RIS system can enhance the medical experience of patients with radiation-induced oropharyngeal mucositis during the diagnosis and treatment process among head and neck cancer patients undergoing radiotherapy.

Eligibility criteria:
Inclusion Criteria:

1. Voluntarily sign the informed consent form;
2. Reached the age of 18 on the date of signing the informed consent form, and under the age of 65;
3. Patients diagnosed with head and neck tumors and receiving radiotherapy as per the 2017 WHO standards and guidelines.
4. According to the doctor's assessment, patients may experience radiation-induced oropharyngeal mucositis during radiotherapy.

Exclusion Criteria:

* Have other uncontrolled serious diseases, such as unstable heart diseases requiring treatment, poorly controlled diabetes (fasting blood sugar \> 1.5 times the upper limit of the normal value), mental disorders, and a severe history of allergies.

Outcomes:
the satisfaction of patients
Development of an Artificial Intelligence-Based Model for Predicting Difficult Intubation Using Video Laryngoscopic Images and Cormack-Lehane Classification
Trial ID: NCT07152093
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2025-05-01
Completion date: 2025-10-01
Sponsor: Duzce University
Conditions: Difficult Airway Intubation
Locations: Duzce University Faculty of Medicine, Department of Anesthesiology and Reanimation
Countries: Turkey (Türkiye)
Enrollment: 132
Age range: 18 Years - 65 Years
Sex: ALL

Summary:
This prospective observational study aims to develop an artificial intelligence model that can automatically determine the Cormack-Lehane classification from video laryngoscopy images in patients undergoing elective surgery. It also aims to predict the risk of difficult intubation based on this classification. The resulting data will evaluate the applicability of AI-supported decision support systems in clinical airway management.

Eligibility criteria:
Inclusion Criteria:

* 18-65 years

Elective surgery

ASA I-II

No upper airway pathology

Exclusion Criteria:

* Known history of difficult intubation

Morbid obesity (BMI \> 40)

Pregnancy

History of upper airway surgery

Outcomes:
Accuracy of Machine Learning Model in Predicting Difficult Intubation Based on Video Laryngoscopy Images
The Feasibility of Motivational Interviewing on Emotional Intelligence, Dispositional Optimism, and Adherence to Care Practices Among Patients With Permanent Pacemaker
Trial ID: NCT05883514
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2023-04-15
Completion date: 2023-09-15
Sponsor: Alexandria University
Conditions: Pacemaker DDD
Interventions: Motivational interviewing
Locations: Faculty of Nursing, Alexandria university
Countries: Egypt
Enrollment: 70
Age range: 20 Years - 65 Years
Sex: ALL

Summary:
A cardiac pacemaker is an artificial apparatus that stimulates the myocardium electrically to depolarize, to begin a contraction when the heart's natural pacemaker does not work properly. Pacemakers are essential when dysrhythmias or conduction defects compromise the electrical system and the heart's hemodynamic response.

The current status aimed to assess the feasibility of motivational interviewing on emotional intelligence, dispositional optimism, and adherence to care practices among patients with permanent pacemaker.

Eligibility criteria:
Inclusion Criteria:

* Patients with permanent cardiac pacemaker implantation aged from 20 up to 65 years old.
* Both sexes (male \& female).
* Able to communicate verbally.
* Free from psychotic disorders.
* Not scheduled for other surgeries.
* Agree to participate in the current study.

Exclusion Criteria:

* Patients with severe mental problems

Outcomes:
Adherence with Pacemaker Care Practices; Emotional Intelligence; Dispositional optimism
Using Machine Learning With Heart Rate Variability Signals to Detect Delayed Cerebral Ischemia After Subarachnoid Hemorrhage
Trial ID: NCT04415736
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2015-10-01
Completion date: 2020-05
Sponsor: Göteborg University
Conditions: Subarachnoid Hemorrhage, Aneurysmal
Interventions: No intervention, observational study
Locations: Sahlgrenska University Hospital
Countries: Sweden
Enrollment: 64
Age range: 16 Years - 100 Years
Sex: ALL

Summary:
The overall aim of this study is to, with the help of computer/data scientist and machine learning processes, analyse collected heart rate variability data in order to evaluate whether specific patterns could be found in patients developing delayed cerebral ischemia after subarachnoid hemorrhage.

Eligibility criteria:
Inclusion Criteria:

* Adult patients
* aneurysmal subarachnoid hemorrhage
* admitted to Neurointensive care unit at Sahlgrenska University Hospital, Gothenburg, Sweden

Exclusion Criteria:

* cardiac arrythmias
* use of pacemaker

Outcomes:
HRV data analysed by artificial intelligence for detection of DCI
Conversations With AI Chatbots Increase Short-Term Vaccine Intentions But Do Not Outperform Standard Public Health Messaging
Trial ID: NCT07132125
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-03-17
Completion date: 2025-05-22
Sponsor: University of Pennsylvania
Conditions: HPV Vaccination Intent
Interventions: Default-style LLM chatbot; Conversational-style LLM chatbot; Public health informational materials
Locations: Participants are recruited online via Prolific
Countries: United States
Enrollment: 930
Age range: 18 Years - None
Sex: ALL

Summary:
The goal of this clinical trial is to test whether short conversations with large language model (LLM) chatbots can persuade vaccine-hesitant parents to vaccinate their children against human papillomavirus (HPV). The study compares two chatbot styles to official public health information and to a no-message control. Parents of HPV-eligible children first complete a survey about their attitudes toward the HPV vaccine and their main concerns. They are then randomly assigned to read public health materials, have a three-minute conversation with either a default-style chatbot or a conversational-style chatbot tailored to their concern, or receive no message. The main outcome is change in intent to vaccinate immediately after the intervention, with follow-up surveys at 15 and 45 days.

Eligibility criteria:
Inclusion Criteria:

* Adults aged 18 years and up
* Living in Canada, the UK, or the USA
* At least one child eligible for HPV vaccine based on country-specific guidelines who has not received any doses of the vaccine or whose vaccination status is unknown

Exclusion Criteria:

* Failure to pass attention checks
* Identified as non-human respondents by reCAPTCHA or other automated bot detection

Outcomes:
Change in intent to vaccinate child against HPV within the next 12 months
Development and Validation of Gastroscopy Surveillance Recommendations Based on Natural Language Processing for Patients With Gastric Cancer and Precancerous Diseases
Trial ID: NCT05631015
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2012-01-01
Completion date: 2023-12-31
Sponsor: Xiuli Zuo
Conditions: Helicobacter Pylori Infection; Atrophic Gastritis; Intestinal Metaplasia; Low Grade Intraepithelial Neoplasia; High Grade Intraepithelial Neoplasia; Early Gastric Cancer; Gastric Cancer
Interventions: AI recongnize disease and generate recommendations
Locations: Qilu Hospital, Shandong University
Countries: China
Enrollment: 2000
Age range: 18 Years - 80 Years
Sex: ALL

Summary:
The purpose of this study is to develop and validate a clinical decision support system based on automated algorithms. This system can use natural language processing to extract data from patients' endoscopic reports and pathological reports, identify patients' disease types and grades, and generate guidelines based follow-up or treatment recommendations

Eligibility criteria:
Inclusion Criteria:

* Patients aged 18 - 80 years
* Patients underwent endoscopic examination

Exclusion Criteria:

* Patients with the contraindications to endoscopic examination
* Patients with imcomplete examination information
* Patients undergo endoscopy for therapy
* Patients have history of upper gastrointestinal surgery
* Patients with duodenal or Laryngeal neoplasms
* Patients with gastrointestinal submucosal tumor

Outcomes:
The diagnostic accuracy of gastric diseases with deep learning algorithm; The accuracy of recommentions for different disease with deep learning algorithm
Pivotal Prospective Clinical Trial to Demonstrate the Efficacy and Safety of AEYE-DS Software Device for Automated Diabetic Retinopathy Detection From Digital Fundoscopic Images
Trial ID: NCT04612868
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2020-10-16
Completion date: 2021-12-26
Sponsor: AEYE Health Inc
Conditions: Diabetes Mellitus; Diabetic Retinopathy
Interventions: AEYE Software Device
Locations: The Eye Care Institute
Countries: United States
Enrollment: 531
Age range: 22 Years - None
Sex: ALL

Summary:
AEYE-DS is a software device developed to increase compliance with diabetic retinopathy screening by automatically detecting more-than-mild diabetic retinopathy from digital fundus images using Artificial Intelligence (AI)-based software. This study has been designed to validate the safety and efficacy of the device at primary care and other point of care sites.

Eligibility criteria:
Inclusion Criteria:

* Age ≥22
* Male or female
* Documented diagnosis of diabetes mellitus, meeting the criteria established by the American Diabetes Association (ADA) and World Health Organization (WHO).
* Understand the study and volunteer to sign the informed consent

Exclusion Criteria:

* Uncorrectable vision loss (e.g., with the use of eyeglasses), blurred vision, or floaters.
* Diagnosed with macular edema, severe non-proliferative retinopathy, proliferative retinopathy, radiation retinopathy, or retinal vein occlusion.
* History of laser treatment of the retina or injections into either eye, or any history of retinal surgery.
* Currently participating in another investigational eye study and actively receiving investigational product for Diabetic Retinopathy (DR) or Diabetic Macular Edema (DME).
* Participant has a condition that, in the opinion of the investigator, would preclude participation in the study (e.g., unstable medical status including blood pressure or glycemic control, microphthalmia or previous enucleation).
* Participant is contraindicated for imaging by fundus imaging systems used in the study:

  1. Participant is hypersensitive to light
  2. Participant recently underwent photodynamic therapy (PDT)
  3. Participant is taking medication that causes photosensitivity
  4. Participant has a history of angle-closure glaucoma or narrow anterior chamber angles

Outcomes:
Sensitivity and Specificity, Based on Two Macula-centered Images (One Image From Each Eye of the Patient)
The Impact of Artificial Intelligence Dietary Smartphone Application on Nutritional Status Among Cancer Patients
Trial ID: NCT04643626
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2021-05-01
Completion date: 2022-03-31
Sponsor: Universiti Putra Malaysia
Collaborators: National Cancer Institute, Malaysia
Conditions: Malnutrition, Calorie; Malnutrition; Protein; Weight Change, Body
Interventions: artificial intelligence dietary android mobile application
Locations: National Cancer Institute
Countries: Malaysia
Enrollment: 56
Age range: 18 Years - None
Sex: ALL

Summary:
Multi-center prospective pilot trial study

Eligibility criteria:
Inclusion Criteria:

* Possessing smartphone and is willing to use smartphone application
* Been referred to dietitian in outpatient clinic
* At least 18 years of age
* Malaysian

Exclusion Criteria:

* Inability to use the smartphone application (eg. due to vision problems)
* Not been referred to dietitian in outpatient clinic
* Patients who participate in other study at the same time
* On nutrition support (enteral / intra-venous)
* Vulnerable subjects

Outcomes:
weight changes; daily energy intake; daily protein intake
Effect of the Computer Aided Diagnosis with Explainable Artificial Intelligence for Colon Polyp on Optical Diagnosis and Acceptance of Technology
Trial ID: NCT06617468
Status: ACTIVE_NOT_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-09-20
Completion date: 2024-12-31
Sponsor: Seoul National University Hospital
Conditions: Colon Polyp
Interventions: computer-aided diagnosis with explainable AI; computer-aided diagnosis with deep learning
Locations: Healthcare System Gangnam Center, Seoul National University Hospital
Countries: South Korea
Enrollment: 120
Age range: None - None
Sex: ALL

Summary:
The goal of this clinical trial is to learn if computer-aided diagnosis with deep learning and computer-aided diagnosis with explainable AI work to optical diagnosis performance and acceptance of technology in endoscopists. The main questions it aims to answer are:

Do computer-aided diagnosis with deep learning and computer-aided diagnosis with explainable AI improve optical diagnosis performance in endoscopists?

Does experience using deep learning-based computer-assisted diagnosis and explainable AI-based computer-assisted diagnosis improve endoscopists' acceptance of computer-aided diagnosis as a technology?

Participants will:

Conduct a survey on acceptance and use of technology about computer-aided diagnosis.

Perform a test to estimate the pathologic diagnosis on 200 NBI still images without the aid of computer-aided diagnosis.

More than 1 month later, perform a same test to estimate the pathologic diagnosis on 200 NBI still images with computer-aided diagnosis with deep learning or explainable AI.

Conduct a survey on acceptance and use of technology about computer-aided diagnosis.

Eligibility criteria:
Inclusion Criteria:

* Endoscopists with colonoscopy experience

Exclusion Criteria:

* Who can not perform colonoscopy

Outcomes:
Accuracy of optical diagnosis