moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Conversational Artificial Intelligence Simulations to Improve Non-Technical Skills Among Anesthesiology Trainees: A Prospective, Mixed-Methods Study
Trial ID: NCT06459479
Status: WITHDRAWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-08-01
Completion date: 2026-07-31
Sponsor: Stanford University
Conditions: Medical Education
Interventions: Conversational Artificial Intelligence Simulations
Enrollment: 0
Age range: 18 Years - 18 Years
Sex: ALL

Summary:
This is a prospective, collaborative, mixed-methods study which includes anesthesiology trainees receiving one active intervention - a conversational artificial intelligence (CAI) simulation. The goal is to explore the capability of AI to provide high-fidelity simulations that can ultimately improve how healthcare professionals handle difficult conversations such as disclosing medical errors or mistakes.

Eligibility criteria:
Inclusion Criteria:

* Hospital providers, including nurses, physician assistants, physicians, physician trainees, and other healthcare workers at Lucile Packard Children's Hospital (LPCH) and affiliated facilities who communicate with patients daily will be included

Exclusion Criteria:

* Participants with reported severe motion sickness
* Nausea
* Seizure disorder
* Currently using chronotropic heart medications, such as β blockers

Outcomes:
Evaluate acceptance of Conversational Artificial Intelligence Simulations using Guided Discussion Interview Question Guide
Construction and Validation of an Intelligent Ultrasound Diagnostic System for the Spectrum of Neuroblastoma in Children: A Multicenter Study
Trial ID: NCT07549425
Status: ACTIVE_NOT_RECRUITING
Study type: OBSERVATIONAL
Start date: 2026-01-01
Completion date: 2026-09-30
Sponsor: The Children's Hospital of Zhejiang University School of Medicine
Conditions: Neuroblastic Tumors
Locations: Anhui Provincial Children's Hospital; The Children's Hospital Affiliated to Soochow University; Kunming Children's Hospital; The Children's Hospital of Zhejiang University School of Medicine; Zhejiang Cancer Hospital; Wenling Institute of Medical Big Data and Artificial Intelligence
Countries: China; China; China; China; China; China
Enrollment: 300
Age range: None - 18 Years
Sex: ALL

Summary:
The goal of this observational study is to build an intelligent ultrasound diagnostic system that integrates pathological typing, risk stratification and prognosis assessment. The main question it aims to answer is:

1. Can the prediction model of neuroblastoma tumors (NTs) in children based on ultrasound images distinguish each pathological subtype?
2. Can the multimodal fusion model established based on clinical and pathological features identify high-risk patients, predict bone marrow metastasis, and estimate the therapeutic effect?
3. Can this ultrasound diagnostic system achieve a systematic and intelligent assessment of NTs patients to assist in clinical risk stratification and individualized treatment decisions?

Eligibility criteria:
Inclusion Criteria:

1. The diagnosis of NTs was confirmed by surgical resection or biopsy with histopathological examination, and the type was classified as NB, GNB or GN according to the INPC standard.
2. Age ≤ 18 years old, with no gender restrictions.
3. There are complete abdominal (or primary site) ultrasound images archived, in original DICOM or JPG format, with image quality meeting the analysis requirements.
4. Complete clinical and pathological data relevant to the research purpose are available.

Exclusion Criteria:

1. The patient has previously undergone surgical resection treatment in another hospital, but the tumor recurred or remained after the operation.
2. Poor quality of ultrasound images: There are artifacts that seriously affect the identification of tumor contours or feature extraction, image blurring, or incomplete display of the lesion.
3. Severe data deficiency: Key clinical pathological data or imaging data are missing, making it impossible to extract and analyze the required information.

Outcomes:
F1 score; accuracy rate; specificity; sensitivity
Construction of an Integrated Intelligent Model for Spontaneous Intracerebral Hemorrhage Based on Deep Learning
Trial ID: NCT07570680
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2022-09-01
Completion date: 2026-12-30
Sponsor: Second Affiliated Hospital of Nanchang University
Collaborators: First Affiliated Hospital of Gannan Medical University; Zhejiang University; Ganzhou City People's Hospital; The First People's Hospital of Xiushui; Jiujiang No.1 People's Hospital; Renmin Hospital of Wuhan University; First Affiliated Hospital of Zhejiang University
Conditions: Hemorrhage Stroke
Interventions: Large language model
Locations: The Second Affiliated Hospital of Nanchang University
Countries: China
Enrollment: 7100
Age range: 8 Years - None
Sex: ALL

Summary:
Although hemorrhagic stroke also has the characteristics of high mortality and disability rates, and constitutes a major public health problem worldwide, there is a relative lack of in-depth research teams for hemorrhagic stroke in China. The current preoperative imaging evaluation of spontaneous cerebral hemorrhage is still limited to the traditional Tada formula, and there are subjective differences in diagnosis among different doctors, making it difficult to achieve homogenization in clinical decision-making. Hemorrhagic stroke is a common and frequently occurring disease in Jiangxi Province. Therefore, establishing a new diagnosis and treatment system focused on hemorrhagic stroke can not only fill the research gap in this field in China, improve the accuracy and homogeneity of hemorrhagic stroke diagnosis and treatment, but also promote related research progress to reduce the mortality and disability rates of this disease and improve the clinical prognosis of patients.

Eligibility criteria:
Inclusion Criteria:

1. Age \>= 8 years old;
2. Patients diagnosed with spontaneous hemorrhagic stroke based on medical history and auxiliary examinations;
3. Received non-contrast computed tomography (NCCT) in the outpatient or emergency department;
4. Treated in accordance with standard clinical guidelines during hospitalization;
5. Have complete clinical data.

Exclusion Criteria:

1. Had undergone surgical treatment in another hospital before admission;
2. Was in a state of shock upon admission;
3. Had severe heart, liver, or kidney dysfunction or other life-threatening systemic diseases;
4. Died during hospitalization;
5. Had an expected lifespan of less than six months or was unable to complete the study follow-up for other reasons.

Outcomes:
Area Under Curve
A Fast, Quiet, Artificial Intelligence-based PET/MR Solution for Paediatric Diseases
Trial ID: NCT06709690
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2024-10-25
Completion date: 2025-12-31
Sponsor: Ruijin Hospital
Conditions: Paediatric Patients
Locations: China, Shanghai Ruijin Hospital affiliated to Shanghai Jiao Tong University of Medicine
Countries: China
Enrollment: 35
Age range: None - 18 Years
Sex: ALL

Summary:
To investigate the feasibility of deep learning-assisted rapid silent PET/MR in pediatric applications.

Eligibility criteria:
Inclusion Criteria:

1. Children who have clinical symptoms and are suspected of having malignant tumors after being evaluated by clinical pediatricians and require 18F-FDG PET/MR examination to further clarify their conditions.
2. Signed and dated informed consent form
3. Commit to complying with the research procedures and cooperate in the implementation of the entire research process.
4. No gender restriction, aged between 0 and 18 years old.

Exclusion Criteria:

1. Patients with serious illnesses that researchers consider unsuitable for participation in this clinical study. Such as severe heart and lung failure, severe bone marrow suppression, severe liver and kidney dysfunction, etc.
2. Patients with severe claustrophobia，epileptic seizures, respiratory distress who cannot lie down, and patients with mental anomalies who are unable to cooperate.
3. Patients with metal implants such as pacemakers, artificial metal heart valves, and neurostimulators in their bodies.
4. Non-compliant person.

Outcomes:
Lesion detection rate; Completion of PET/MR examinations; Sedation during PET/MR examinations; Qualitative image quality
The Impact of Training Dental Students for Using a Novel Artificial Intelligence-based Platform for Pulp Exposure Prediction Before Deep Caries Excavation: A Randomized Controlled Trial
Trial ID: NCT05912361
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2023-08-20
Completion date: 2024-01-01
Sponsor: University of Copenhagen
Conditions: Artificial Intelligence; Education
Interventions: receiving a one hour theoretical and hands on training session before using an AI-based platform
Locations: University of Copenhagen Department of Odontology Cariology and Endodontics Section for Clinical Oral Microbiology
Countries: Denmark
Enrollment: 20
Age range: 20 Years - None
Sex: ALL

Summary:
The emergence of artificial intelligence (AI) and specifically deep learning (DL) have shown great potentials in finding radiographic features and treatment planning in the field of cariology and endodontics . A growing body of literature suggests that DL models might assist dental practitioners in detecting radiographical features such as carious lesions, periapical lesions, as well as predicting the risk of pulp exposure when doing caries excavation therapy. Although, current literature lacks sufficient research on the effect of sufficient training of dental practitioners for using AI-based platforms. This prospective randomized controlled trial aims to assess the performance of students when using an AI-based platform for pulp exposure prediction with and without sufficient preprocedural training. The hypothesis is that participants performance at group with sufficient training is similar to the group without sufficient training.

Eligibility criteria:
Inclusion Criteria:

* perhaps 4th year and 5th year dental students at the university of Copenhagen who are willing to participate voluntarily and have signed the consent letter.
* Limited or no previous knowledge and experience about AI

Exclusion Criteria:

* None

Outcomes:
Performance of students at pulp exposure prediction in the AI-based platform with and without training session based on their accuracy; Performance of students at pulp exposure prediction in the AI-based platform with and without training session based on their sensitivity; Performance of students at pulp exposure prediction in the AI-based platform with and without training session based on their specificity
Validation of an AI-Assisted Pancreatic EUS System for Training Improvement: a Prospective, Multi-Center, Randomized Trial
Trial ID: NCT06790134
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-01-15
Completion date: 2026-03-30
Sponsor: The Third Xiangya Hospital of Central South University
Collaborators: First affiliated hospital of Nanhua University; the Affiliated Nanhua Hospital, University of South China; ZhuZhou Central Hospital; First People's Hospital of Chenzhou
Conditions: Pancreatic Diseases
Interventions: Artificial intelligence device
Locations: The Third Xiangya Hospital of Central South University
Countries: China
Enrollment: 200
Age range: 18 Years - 80 Years
Sex: ALL

Summary:
The goal of this clinical trial is to verify the auxiliary role of the artificial intelligence (AI) system in pancreatic endoscopic ultrasound (EUS) scans. The main questions it aims to answer are as follows:

1. A comparison of the image recognition accuracy between the AI system and EUS endoscopists.
2. Whether the AI system can improve the quality of scans for EUS endoscopists. Participants will conduct EUS scanning with or without the assistance of the AI system.

Eligibility criteria:
Inclusion Criteria:

1\. Age ≥18 years old, \<80 years old 2.Patients who need endoscopic ultrasonography of pancreas; 3. Agree to participate in this study and sign the informed consent form.

Exclusion Criteria:

Subjects who meet any of the following criteria cannot be selected for this trial:

First. The patient's physical condition does not meet the requirements of conventional endoscopic ultrasonography:

Poor physical condition, including hemoglobin ≤8.0g/dl, severe cardiopulmonary insufficiency, etc. Anesthesia assessment failed Pregnancy or breastfeeding In the acute stage of chemical and corrosive injury, it is very easy to cause perforation Recent acute coronary syndrome or clinically unstable ischemic heart attack Heart disease patients with right-to-left shunt, patients with severe pulmonary hypertension (pulmonary artery pressure\> 90mmHg), patients with uncontrolled systemic hypertension and patients with adult respiratory distress syndrome.

Second. Disagree to participate in this study.

Third. There are other problems that do not meet the requirements of this research or that affect the results of the research:

Pancreatic disease has undergone surgery or radiotherapy and chemotherapy beforehand; Mental illness, drug addiction, inability to express themselves or other diseases that may affect follow-up.

Outcomes:
The completeness for standard station scanning
Smartphone-Based Use of Phonocardiography Technique for Detection of Murmurs: Diagnostic Performance Study Protocol
Trial ID: NCT06070298
Status: ACTIVE_NOT_RECRUITING
Study type: OBSERVATIONAL
Start date: 2023-12-01
Completion date: 2025-05
Sponsor: Wong Chun Ka
Collaborators: Laboratory of Data Discovery for Health; The University of Hong Kong
Conditions: Valvular Heart Disease; Heart Murmurs; Cardiovascular Abnormalities
Interventions: Computer algorithms
Locations: Queen Mary Hospital
Countries: China
Enrollment: 577
Age range: 22 Years - None
Sex: ALL

Summary:
This observational study aims to assess the performance of the software called ausculto™. ausculto™ is a collection of computer algorithms that intend to analyse heart sounds recorded from the built-in microphone of a smartphone for abnormal sounds.

Participants will have their heart sounds recorded during their regular clinic appointment after consenting to participate in this study.

Researchers will manually annotate the recorded heart sounds to create a database for use in future training and testing of artificial intelligence (AI) intended for medical uses.

Eligibility criteria:
Inclusion Criteria:

* Age: ≥22 years (adult)
* Outpatients who have undergone echocardiography within 3 years at their normal non-research follow-up clinic visit appointment

Exclusion Criterion:

* Implanted active medical devices in the torso, such as pacemakers and defibrillators

Outcomes:
Sensitivity of ausculto™ Using Auscultation Paired with Echocardiography as Ground Truth; Specificity of ausculto™ Using Auscultation Paired with Echocardiography as Ground Truth
Artificial Intelligence-Enhanced Medical Data Analysis for Differentiating Inflammatory and Degenerative Joint Diseases and Detecting of Disease Severity in Elderly Patient
Trial ID: NCT07153315
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-09-01
Completion date: 2026-10-01
Sponsor: Assiut University
Conditions: Arthritis, Rheumatoid (RA)
Locations: Assiut University Hospital
Countries: Egypt
Enrollment: 140
Age range: 60 Years - None
Sex: ALL

Summary:
This study aims to evaluate the diagnostic accuracy of AI-assisted imaging analysis in differentiating between inflammatory and degenerative joint diseases in elderly patients. The performance of AI-based analysis will be compared with radiologists' assessments to determine its reliability in clinical practice. In addition, the study will explore imaging features most predictive of each disease type using advanced machine learning techniques. Finally, the feasibility of implementing AI tools in the routine management of geriatric musculoskeletal disorders will be assessed.

Eligibility criteria:
Inclusion criteria :

1. Age ≥ 60 years
2. Clinical suspicion or confirmed diagnosis of inflammatory joint disease (e.g., rheumatoid arthritis, psoriatic arthritis) or degenerative joint disease (e.g., osteoarthritis)
3. Availability of relevant musculoskeletal imaging (X-rays, MRI, or ultrasound) suitable for AI-based analysis
4. Ability to provide informed consent or have a legal representative consent on behalf of the

Exclusion criteria:

1. History of recent joint trauma (within the last 6 months) or previous joint surgery affecting the studied sites
2. Presence of bone or joint malignancy (primary or metastatic)
3. Diagnosis of overlapping rheumatologic syndromes or mixed pathology (e.g., RA with concurrent gout, or OA with inflammatory overlap)
4. Inadequate imaging quality or absence of required imaging modalities
5. Inability or unwillingness to provide informedconsent

Outcomes:
Diagnostic accuracy of AI system
Feasibility and Effectiveness of an Ai-based Endoscopic Ultrasound Navigation System in the Training of Endoscopic ultrasonics-a Prospective, Randomized, Multi-center Clinical Trial
Trial ID: NCT05421520
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2023-10-08
Completion date: 2024-05-31
Sponsor: Renmin Hospital of Wuhan University
Conditions: Endoscopic Ultrasound
Interventions: Artificial intelligence assisted system
Locations: The Eighth Affiliated Hospital, Sun Yat-sen University; Renmin Hospital of Wuhan University
Countries: China; China
Enrollment: 12
Age range: 18 Years - None
Sex: ALL

Summary:
In the early stage, the investigators successfully constructed an artificial intelligence model-based ultrasonic endoscopy-assisted film reading system and named the modified system biliopancreatic Master. The system can realize real-time ultrasonic station recognition and anatomical mark recognition and provide doctors with corresponding operation techniques. This study aimed to verify the feasibility and effectiveness of the biliopancreatic master system developed by our project team in shortening the training period of ultrasound endoscopists through a single-center clinical study.

Eligibility criteria:
Endoscopist:

Inclusion Criteria:

Endoscopists with experience in performing more than 300 gastroscopies; Lack of experience in independently operating an endoscopic ultrasound (EUS); No experience in EUS-guided fine-needle aspiration biopsy (FNA).

Patient:

Inclusion Criteria:

Patient age ≥18 years; Patients who consecutively undergo sedated EUS procedures; Ability to read, understand, and sign the informed consent; Patients suspected of having biliary (both pancreatic and biliary) lesions based on clinical symptoms and/or radiological findings and/or laboratory test results; High-risk patients for pancreatic cancer: known genetic mutations associated with the risk of pancreatic cancer (BRCA2, BRCA1, PALB2, ATM, CDKNA/p16); familial pancreatic ductal adenocarcinoma with no known lineage mutations; Peutz-Jeghers syndrome (STK11); Lynch syndrome (MLH1/MSH2/MSH6, EPCAM, PMS2); familial adenomatous polyposis (APC) etc. Based on the preoperative cholangiopancreatography report, patients are classified into those with or without radiological findings. Patients with radiological findings are categorized by lesion location, such as the pancreas, PD, CBD, and other lesions (like gallbladder, duodenal papilla suspected of biliary invasion).

Exclusion Criteria:

Patients with absolute contraindications to EUS examination; Previous gastric surgery; Pregnancy; Severe internal medical diseases; History of allergy to anesthesia drugs; Esophageal narrowing or obstruction; Upper gastrointestinal anatomical abnormalities caused by advanced tumors.

Outcomes:
Learning cycle for ultrasound endoscopists
Frailty Assessment Tool for Patients Undergoing Cardiac Surgery Based on Artificial Intelligence Multimodal Non-contact Monitoring.
Trial ID: NCT06096922
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2023-05-11
Completion date: 2024-12-31
Sponsor: China National Center for Cardiovascular Diseases
Conditions: Frailty
Interventions: No intervention
Locations: Fuwai hospital
Countries: China
Enrollment: 521
Age range: 18 Years - None
Sex: ALL

Summary:
The main purpose of this study is to establish a frailty automatic evaluation index "AI frailty index" based on artificial intelligence multi-modal non-contact monitoring information analysis. At the same time, the study will explore the correlation between ' AI weakness index ' and perioperative and long-term prognosis and quality of life.

Eligibility criteria:
Inclusion Criteria:

* Elective cardiac surgery

Exclusion Criteria:

* Emergency surgery
* Age ≤ 18 years
* The clinical situation is unstable, such as frequent occurrence of coronary ischemia events, unstable heart failure, or acute events leading to severe discomfort symptoms or changes in vital signs
* Unable to cooperate in completing research data collection due to severe neurological and psychiatric abnormalities or other reasons
* Duration of postoperative ventilation \> 48 hours or inability to obtain scale data on the first day after surgery

Outcomes:
One-dimensional matrix vector composed of five frailty scale results
Hemodynamic Effects of Anesthesia Induction
Trial ID: NCT06421181
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-01-14
Completion date: 2026-06-30
Sponsor: Kliniken Essen-Mitte
Collaborators: Heinrich-Heine University, Duesseldorf
Conditions: Patients Undergoing Anaesthesia Induction
Locations: Evangelische Kliniken Essen-Mitte
Countries: Germany
Enrollment: 30
Age range: 18 Years - None
Sex: FEMALE

Summary:
The idea of that project is to characterize the hemodynamic changes of a daily used clinical intervention (induction of anesthesia) in a highly controlled environment by two hemodynamic monitoring devices. The aim is an advanced hemodynamic profiling of this intervention and additionally screen for changes in flow patterns in an exploratory fashion.

Both devices complement one another in their hemodynamic profiling ability. One device is a continuous monitoring with instant traceable changes and the other an intermittent point-of-care ultrasound/echocardiography device with advanced possibilities for differential diagnostics.

A second purpose is to test the possibility to implement advanced echocardiography in a point-of-care approach during anaesthesia induction and evaluate the time and quality of a comprehensive analysis by a not-certified anaesthetists with an echocardiography device with features of artificial intelligence versus a certified expert.

Eligibility criteria:
Inclusion Criteria:

* Patients undergoing elective laparotomy in the hospital Evangelische Kliniken Essen-Mitte
* Due to the invasiveness of the planned surgery there is an indication for an arterial and central-venous line as well as an peridural catheter

Exclusion Criteria:

* Age \< 18 years
* Lack of written informed consent
* Insufficient language skills
* Unwillingness to have pseudonymized disease data stored at the study site and lack of consent to share anonymized data as part of the clinical trial
* American society of anaesthesiologists physical status higher as grade 3
* congestive heart failure with a grade of 2 or higher according to the New York heart association (NYHA)
* Ischemic cardiopathy with a grade of 2 or higher according to the Canadian cardiovascular society (CCS)
* Known severe valve pathologies of the heart
* Chronic kidney disease with dependency of hemodialysis
* Atrial fibrillation ora trail flutter
* Pulmonary hypertension

Outcomes:
Longitudinal change of heart rate assessed in percentages during the induction of anaesthesia; Longitudinal change of arterial blood pressure assessed in percentaqes during the induction of anaesthesia; Longitudinal change of stroke volume assessed in percentaqes during the induction of anaesthesia; Longitudinal change of myocardial strain imaging assessed by transthoracic echocardiography during the induction of anaesthesia