moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Examining the Effects of Virtual Reality on Early Vital Variables and Spillovers in Intensive Care Patients
Trial ID: NCT06442254
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-06-08
Completion date: 2024-09-15
Sponsor: Istinye University
Conditions: Virtual Reality; Intensive Care Unit Acquired Weakness
Interventions: Virtual Reality
Locations: İstinye Üniversitesi
Countries: Turkey (Türkiye)
Enrollment: 34
Age range: 18 Years - None
Sex: ALL

Summary:
Weakness of respiratory muscles delays weaning from the ventilator, prolongs hospital stay and increases treatment costs. Conventional treatments for respiratory muscles reverse these negative effects. İntensive care services are a set of services that have a very important place in public health care due to the vital support and they provide to all critically ill patients. This care services are constantly improving with the use of technological innovations. With the development of technology, virtual reality application has begun to be used therapeutically in the field of physiotherapy. Virtual reality is the combination of reality and imagination with fictions created using technology. Today, developers can surprisingly create realistic worlds filled with artificial intelligence that behaves believably. Studies have shown positive effects of virtual reality on acute respiratory frequency, pain and fatigue, and in light of this, it was predicted that it would be beneficial to apply to intensive care patients. This study will shed light on the rehabilitation of patients in intensive care and contribute to the literature.

Eligibility criteria:
Inclusion Criteria:

* Individuals receiving inpatient treatment in intensive care who volunteer to participate in the research or who are allowed by their first-degree relatives if they are unconscious,
* Being over 18 years of age
* Being eligible to receive physiotherapy and rehabilitation from an intensive care physician
* Are in clinically stable condition

Exclusion Criteria:

* Patients with coagulation disorders (PT (Prothrombin Time); INR (International Normalized Ratio) value higher than 1.5 and platelet amount less than 50,000 m³)
* Patients with signs of increased intracranial pressure
* Skin wounds, ulcerations, allergic reactions
* Patients in contact isolation due to infection
* In shock
* Having malignancy
* Having multiple organ failure
* Having visual impairment
* Patients who are unconscious

Outcomes:
Respiratory Muscle Strength (MIP); Respiratory Muscle Strength (MEP); Richmond Agitation-Sedation Scale; SOFA (Sequential Organ Failure Assessment); Visuel Anaolog Scale; The Glasgow Coma Scale; APACHE II Score; Chelsea Critical Care Physical Assessment Tool (CPAx); Body temperature; Blood pressure; Respiratory rate; Pulse rate; The Physical Function in ICU Test
Artificial Intelligence-Assisted Evaluation of Pulmonary Hypertension
Trial ID: NCT05566002
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2022-06-01
Completion date: 2025-12-31
Sponsor: Chinese Pulmonary Vascular Disease Research Group
Conditions: Pulmonary Hypertension; Pulmonary Arterial Hypertension
Interventions: Right heart catheterization
Locations: Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College
Countries: China
Enrollment: 2000
Age range: 18 Years - None
Sex: ALL

Summary:
Pulmonary hypertension represents a challenging and heterogeneous condition that is associated with high mortality and morbidity if left untreated. Artificial intelligence is used to study and develop theories and methods that simulate and extend human intelligence, which is being applied in fields related to cardiovascular diseases. The study intends to combine multimodal clinical data of patients who undergo right heart catheterization at Fuwai Hospital with artificial intelligence techniques to create programs that can screen and diagnose pulmonary hypertension.

Eligibility criteria:
Inclusion Criteria:

* Age ≥18 years old
* Patients previously received chest X-ray, electrocardiography, echocardiography, other routine examinations, and RHC at the Fuwai Hospital, CAMS \& PUMC, Beijing, China

Exclusion Criteria:

* Patients without RHC
* The quality of routine examinations and RHC cannot meet the requirement for further analysis
* Severe loss of results of routine examinations (chest X-ray, electrocardiography, echocardiography, etc.)

Outcomes:
Accuracy of diagnosis by artificial intelligence-assisted algorithm
Artificial Intelligence-augmented Perioperative Clinical Decision Support
Trial ID: NCT05284227
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2023-04-26
Completion date: 2024-01-01
Sponsor: Wuerzburg University Hospital
Collaborators: Goethe University; Charite University, Berlin, Germany; Asklepios Kliniken Hamburg GmbH; Fraunhofer Institute for Digital Medicine MEVIS; Technical University of Munich; RWTH Aachen University
Conditions: ASA Class III/IV Patients; Surgery; Clinical Decision Support Systems
Interventions: Electronic Clinical Decision Support Application; Standard procedures of the hospital and Sham Clinical Decision Support Application
Locations: University Hospital Wuerzburg
Countries: Germany
Enrollment: 210
Age range: 18 Years - None
Sex: ALL

Summary:
The study investigates a novel anaesthesiological clinical decision support (CDS) application, that integrates risk evaluation tools and updated clinical guidelines guided by artificial intelligence in the setting of preoperative anaesthesiological assessment. It will be compared to the current standard preoperative assessment workflow with participants being actual patients. 480 participants will be randomly assigned to either the CDS group (preoperative assessment using the CDS application) or the Control group (standard preoperative assessment workflow).

Eligibility criteria:
Inclusion Criteria:

* ASA (American Society of Anaesthesiologists)-Class III and IV
* non-cardiac surgery

Exclusion Criteria:

* none

Outcomes:
Number of clinically not indicated preoperative diagnostic procedures ordered by the anaesthesiologist
Assessment of Diagnostic Adequacy of Artificial Intelligence-assisted Point-of-Care Echocardiography Among Anesthesiology Trainees
Trial ID: NCT06548373
Status: ACTIVE_NOT_RECRUITING
Study type: OBSERVATIONAL
Start date: 2024-08-13
Completion date: 2026-12
Sponsor: Loma Linda University
Conditions: Anesthesia
Interventions: Artificial Intelligence-assisted Point-of-Care Echocardiography
Locations: Loma Linda University Troesch Medical Center
Countries: United States
Enrollment: 16
Age range: 18 Years - 65 Years
Sex: ALL

Summary:
This pilot study serves to assess the diagnostic adequacy of Artificial Intelligence (AI)-guided point-of-care. Focused echocardiography examinations will be obtained by Anesthesiology trainees and assessed by an expert Cardiologist Echocardiographer. AI-guided echocardiography examinations will be compared to each other. AI-guided and non-AI-guided echocardiography examinations will also be compared.

Eligibility criteria:
Inclusion Criteria:

* Loma Linda University resident in Anesthesiology
* Post-Graduate Year 2

Exclusion Criteria:

* Residents not a part of a Loma Linda University residency program
* Residents not a part of an Anesthesiology residency program
* Loma Linda University Anesthesiology Post-Graduate Year 1 Residents
* Loma Linda University Anesthesiology Post-Graduate Year 3 Residents
* Loma Linda University Anesthesiology Post-Graduate Year 4 Residents

Outcomes:
Echocardiogram Evaluation
An AI Large Language Model Based on Multi-task and Multimodal Data Fusion Accurately Predicts the Efficacy of Neoadjuvant Chemotherapy for Breast Cancer: a Multicenter, Bidirectional Cohort Study
Trial ID: NCT06510127
Status: ENROLLING_BY_INVITATION
Study type: OBSERVATIONAL
Start date: 2024-08-01
Completion date: 2026-01-31
Sponsor: Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
Collaborators: First Affiliated Hospital of Jinan University
Conditions: Breast Cancer
Locations: Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
Countries: China
Enrollment: 840
Age range: None - None
Sex: FEMALE

Summary:
Neoadjuvant chemotherapy is an important part of the systematic treatment of breast cancer, and it is of great clinical significance to predict the efficacy of neoadjuvant chemotherapy in early stage. The emergence of multi-modal artificial intelligence model has brought new ideas for it. However, the limited ability of artificial intelligence to integrate multi-modal data, the lack of multi-modal models, and the insufficient level of evidence in clinical promotion of artificial intelligence are all scientific problems that need to be solved. In the early stage of the study, a variety of artificial intelligence accurate prediction and auxiliary diagnosis and treatment models for breast cancer were constructed based on magnetic resonance imaging and pathomics, etc., and the effectiveness of the models in predicting the curative effect of neoadjuvant chemotherapy for breast cancer was explored. In order to further improve the predictive efficiency of the model and fill the gap in the systematic study of multi-modal data fusion model, this clinical study intends to combine pathological images, magnetic resonance imaging, diagnostic report text and clinical variables to establish an artificial intelligence large language model based on multi-task and multi-modal data fusion to accurately predict the efficacy of neoadjuvant chemotherapy for breast cancer. A multicenter, bidirectional cohort study was conducted to explore the predictive effectiveness of the model.

Eligibility criteria:
Inclusion Criteria:

* Women
* Pathological diagnosis of non-metastatic invasive breast cancer (stage II-III)
* At least 4 cycles of neoadjuvant chemotherapy
* Radical surgery was performed after neoadjuvant chemotherapy
* There are pathological images and reports of breast puncture specimens before neoadjuvant chemotherapy
* 'There are MRI images and reports of breast MRI within 2 weeks before neoadjuvant chemotherapy
* There are standard clinical records

Exclusion Criteria:

* Inflammatory breast cancer
* Bilateral breast cancer
* Newly diagnosed stage IV breast cancer
* Other tumors have not been completely removed or less than 3 years after surgery
* Treatment other than neoadjuvant therapy had been performed before surgery

Outcomes:
Predictive ability of the model for pCR after neoadjuvant chemotherapy in breast cancer patients
Developing a Novel Artificial Intelligence Patient App to Recognize Squamous Cell Carcinoma (SCCs) in Recessive Dystrophic Epidermolysis Bullosa (RDEB): Image Collection
Trial ID: NCT05843994
Status: ACTIVE_NOT_RECRUITING
Study type: OBSERVATIONAL
Start date: 2023-01-30
Completion date: 2028-11-30
Sponsor: Northwestern University
Collaborators: Epidermolysis Bullosa Research Partnership
Conditions: Epidermolysis Bullosa Dystrophica
Interventions: online survey
Locations: Department of Dermatology, Northwestern University Feinberg School of Medicine and Lurie Children's Hospital
Countries: United States
Enrollment: 20
Age range: 12 Years - None
Sex: ALL

Summary:
In this study, an artificial intelligence model to detect squamous cell carcinomas (SCC) on photos of recessive dystrophic epidermolysis bullosa (RDEB) skin is developed. The ultimate goal is to integrate this model into an app for patients and physicians, to help detect SCCs in RDEB early.

SCCs which rapidly metastasize are the main cause of death in adults with RDEB. The earlier an SCC is recognized, the easier it can be removed and the better the outcome. AI leverages computer science to perform tasks that typically require human intelligence and has recently been used to identify skin cancers based on images. We are currently developing an AI approach for early detection of SCC and distinction of malignancy from chronic wounds and other RDEB skin findings. The aim is to create a web application for patients with RDEB to upload images of their skin and get an output as to SCC present/ no SCC. This will be especially valuable for patients with difficult access to medical expertise and those who are hesitant to allow full skin examination at each visit, often because of fear of biopsies. Thus, this project will directly benefit patients by allowing early recognition of SCCs and will empower patients and their families by providing a home use tool.

So far, the study team has mainly used professional images (photographs taken in hospital settings by physicians, nurses, and clinical photographers) of both SCCs in RDEB and images of RDEB skin without SCC to develop and train the AI model. The images that are expected in a real-life setting will mostly be pictures taken by patients or family members with their phones or digital cameras. These images have different properties regarding resolution, focus, lighting, and backgrounds. Incorporating such images will be crucial in the upcoming phases of model development-testing and validation-for the web application be a success for patients.

Eligibility criteria:
Inclusion Criteria:

* patient with recessive dystrophic epidermolysis bullosa
* patient with history of cutaneous squamous cell carcinoma
* patient consent for upload and use of clinical data and photographs

Exclusion Criteria:

* Patients who do not agree to upload and use of photographs and clinical data

Outcomes:
Percent agreement of the presence or absence of squamous cell carcinoma (SCC) on the skin in photographs as detected by the App versus confirmed physician diagnosis
Development and Validation of a Machine Learning Model to Differentiate Drug-induced Liver Injury and Autoimmune Hepatitis
Trial ID: NCT05532345
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2022-07-01
Completion date: 2023-05-31
Sponsor: Beijing Friendship Hospital
Conditions: Drug-induced Liver Injury; Autoimmune Hepatitis
Locations: Beijing Friendship Hospital, Capital Medical University
Countries: China
Enrollment: 2583
Age range: None - None
Sex: ALL

Summary:
A retrospective, multi-center, non-interventional cohort study has been going to explore whether artificial intelligence can discriminate Drug-induced liver injury and Autoimmune hepatitis.

A machine learning-based tool will be developed and validated to help clinicians to differentiate between Drug-induced liver injury and Autoimmune hepatitis

Eligibility criteria:
Inclusion Criteria:

* Drug-induced liver injury

  1. RUCAM ≥6 and met one of the following biochemical conditions: a.) ALT≥5 ULN, b.) or ALP ≥2 ULN, iii) or ALT≥3 ULN and TBil≥2 ULN.
  2. RUCAM was between 3-5, the medical records were further reviewed by the three authors to determine the eligibility.
* Autoimmune hepatitis

  1. The revised International Autoimmune Hepatitis Group (IAIHG) diagnostic score≥6 points.
  2. Liver biopsy available, which is compatible with typical features of AIH.
  3. If liver biopsy was unavailable, patients who achieved biochemical resolution after sustained immunosuppressive therapy.

Exclusion Criteria:

* Drug-induced liver injury

  1. Hepatotropic viral infection: hepatitis A, B, C, D and E.
  2. Non-hepatotropic viral infection: cytomegalovirus (CMV) and Epstein-Barr virus (EBV), etc.
  3. Hypoxic ischemic hepatitis and congestive liver disease.
  4. Alcohol consumption: male \>40g/d, female \>20g/d, and ≥5 years.
  5. Biliary obstruction, primary biliary cholangitis; primary sclerosing cholangitis.
  6. Autoimmune hepatitis.
  7. Parasitic infection.
  8. Sepsis.
  9. Previous liver transplantation or bone marrow transplantation.
  10. Pregnancy or lactation.
  11. Genetic and metabolic liver diseases.
* Autoimmune hepatitis

  1. Hepatotropic viral infection: hepatitis A, B, C, D and E.
  2. Non-hepatotropic viral infection: cytomegalovirus (CMV) and Epstein-Barr virus (EBV), etc.
  3. Hypoxic ischemic hepatitis and congestive liver disease.
  4. Alcohol consumption: male \>40g/d, female \>20g/d, and ≥5 years.
  5. Biliary obstruction, primary biliary cholangitis; primary sclerosing cholangitis.
  6. Drug-induced liver injury.
  7. Parasitic infection.
  8. Sepsis.
  9. Previous liver transplantation or bone marrow transplantation.
  10. Pregnancy or lactation.
  11. Genetic and metabolic liver diseases.

Outcomes:
Accuracy of the model in the differential diagnosis of DILI and AIH; The confidence of the model in the differential diagnosis of DILI and AIH
Automated Structured Education Intervention Based on an App and Artificial Intelligence in Chinese Patients With Type 1 Diabetes
Trial ID: NCT04016987
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2020-09-08
Completion date: 2023-12
Sponsor: Second Xiangya Hospital of Central South University
Conditions: Type 1 Diabetes
Interventions: Automated structured education intervention based on an app and artificial intelligence
Locations: Institute of Metabolism and Endocrinology, Second Xiangya Hospital, Central South University
Countries: China
Enrollment: 138
Age range: 18 Years - 50 Years
Sex: ALL

Summary:
In recent years, more and more attention has been paid to diabetes self-management. Glycemic control and self-management skills of patients with type 1 diabetes (T1DM) in China are poor. Artificial intelligence (AI) and the Internet offer a new way to improve the self-management skills of patients with chronic diseases. Few studies have combined AI technology with structured education intervention of type 1 diabetes. This study is innovative in that it compares the effectiveness of smartphone app between usual care, as well as automatic and individualized app education and standardized app education to explore whether the individualized treatment advocated by the latest guideline will bring any additional benefit to T1DM patients. The ultimate goal is to provide an effective and convenient approach for glycemic control of type 1 diabetes and reduce related disease burden in China.

Eligibility criteria:
Inclusion Criteria:

* Individuals diagnosed with Type 1 Diabetes according to the 1999 World Health Organization report
* Insulin dependence from disease onset
* Aged 18-50 years
* With a disease duration over 6 months
* With a HbA1c level over 7%
* Treated T1DM with multiple daily injections or insulin pump
* Individuals who own smartphone and are capable of using wechat or apps

Exclusion Criteria:

* Age below 18 years or above 50 years
* Being pregnant
* With mental disorders
* Have any other condition or disease that may hamper from compliance with the protocol or complication of the trial
* Already using a smartphone app for managing diabetes
* Having chronic complications including diabetic retinopathy, diabetic nephropathy or diabetic foot, diabetic neuropathy

Outcomes:
changes in serum hemoglobin A1c level
Technology Revising Ultrasound Solutions for Tomorrow - Anticancer-treatment Cardiotoxicity Identification by Echocardiography
Trial ID: NCT06310330
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-03-08
Completion date: 2026-12-31
Sponsor: Norwegian University of Science and Technology
Collaborators: St. Olavs Hospital
Conditions: Breast Neoplasm Malignant Primary
Interventions: Comprehensive echocardiographic follow-up; Simplified echocardiographic follow-up
Locations: St. Olavs hospital
Countries: Norway
Enrollment: 250
Age range: 18 Years - None
Sex: ALL

Summary:
TRUST-ACE will compare a simplified echocardiographic protocol focusing on ventricular function with the guideline recommended comprehensive echocardiographic examination using a randomised design in follow-up of breast-cancer patients with respect to identification of cancer treatment related cardiac dysfunction (CTRCD).

Secondly, the study will evaluate whether novel tools used to improve standardization of recordings as well as automated measurements of central measurements, e.g. ejection fraction (EF) and global longitudinal strain (GLS) can improve the precision of echocardiography in daily clinical practice.

Eligibility criteria:
Inclusion Criteria:

* Breast cancer and referral for follow-up to identify potential CTRCD

Exclusion Criteria:

* Not willing or able to consent

Outcomes:
Proportion of patients correctly classified with cancer therapy related cardiac dysfunction (CTRCD)
Prospective Validation of Pathology-based Artificial Intelligence Diagnostic Model for Lymph Node Metastasis in Prostate Cancer
Trial ID: NCT06253065
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2024-01-12
Completion date: 2025-12-31
Sponsor: Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
Conditions: Prostatic Neoplasms; Lymphatic Metastasis
Interventions: Artificial intelligence (AI)-based diagnostic model (developed)
Locations: Sun Yat-sen Memorial Hospital of Sun Yat-sen University
Countries: China
Enrollment: 225
Age range: None - None
Sex: MALE

Summary:
The goal of this diagnostic test is to prospectively test the performance of pre-developed artificial intelligence (AI) diagnostic model for detecting pathological lymph node metastasis (LNM) of prostate cancer. Investigators had developed this AI model based on deep learning algorithms in preliminary research, and it performed well in retrospective tests.

Investigators will compare the diagnostic performance (sensitivity, specificity, etc.) of the AI model and routine pathological report issued by pathologists, to see if the AI model can improve the clinical workflow of pathological evaluation of LNM in prostate cancer in the real world.

Eligibility criteria:
Inclusion Criteria:

* Patients with prostate cancer, undergoing radical prostatectomy and pelvic lymph node dissection.
* Patients with complete clinical and pathological information.

Exclusion Criteria:

* Patients with other tumors that metastasized to pelvic lymph nodes.
* The patient refused to participate in this diagnostic test.

Outcomes:
sensitivity
Novel 3-dimensional Echocardiographic Quantification of Mitral Regurgitant Volume
Trial ID: NCT07528781
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2026-03-17
Completion date: 2028-04-01
Sponsor: Germans Trias i Pujol Hospital
Conditions: Moderate or Severe Mitral Regurgitation
Interventions: analysis of the performance of an artificial intelligenced based software for analysis of 3D tranesophageal echocardiography data
Locations: Hospital University Germans Trias i Pujol
Countries: Spain
Enrollment: 200
Age range: 18 Years - 95 Years
Sex: ALL

Summary:
The goal of this observational study is demonstrate that a novel artificial intelligence based software for the quantification of the mitral regurgitation on 3-dimensional transesophageal echocardiography (3D CFQ) is more reproducible and accurate than the conventional assessment of mitral regurgitation performed with 2-dimensional echocardiography. The main question aims to answer is to demonstrate the agreement between 3D-CFQ measurement of the mitral regurgitant volume and the cardiac magnetic resonance measurement of the regurgitant volume is better than the agreement between 2-dimensional echocardiography and cardiac magnetic resonance. If this is demonstrated, we would like to test the accuracy of this tool to be applied in acute settings such as transcatheter interventions to decide the therapy to follow.

Participants will undergo clinically indicated transesophageal echocardiography to characterize the mechanism and severity of mitral regurgitation and to cardiac magnetic resonance to be used as reference stadard to define the mitral regurgitation severity.

Eligibility criteria:
Inclusion Criteria:

* Patients with at least moderate mitral regurgitation of any etiology
* Patients with good transesophageal echocardiography image quality

Exclusion Criteria:

* Patients with prior transcatheter or surgical mitral valve intervention
* Patients with contraindications for transesophageal echocardiography or cardiac magnetic resonance

Outcomes:
Accuracy of 3DCFQ to quantify mitral regurgitation