moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Validation of Three Non-invasive Hemodynamic Monitoring Systems for Determining Measures of Volume Responsiveness, Ventriculo-arterial Coupling and Cardiac Output
Trial ID: NCT06828432
Status: ENROLLING_BY_INVITATION
Study type: OBSERVATIONAL
Start date: 2025-06-03
Completion date: 2027-01-31
Sponsor: Hernando Gomez
Collaborators: United States Department of Defense
Conditions: Open Heart Surgery
Interventions: CRUISE CTICU CareTaker Vitalstream Device; CRUISE CTICU CNAP Device; CRUISE CTICU Edwards Clearsight Device
Locations: University Pittsburgh Medical Center
Countries: United States
Enrollment: 50
Age range: 18 Years - 105 Years
Sex: ALL

Summary:
The goal of this study evaluate three novel, Food and Drug Administration (FDA)-approved devices currently used to monitor blood pressure and the amount of blood that the heart pumps during each beat.

These devices are the ClearSight (Edwards Life Sciences), CareTaker (CareTaker Medical) and CNAP (CNS Systems). These devices measure blood pressure, how much blood the heart is pumping around the body each minute and on a heartbeat to heartbeat basis and other parameters that can tell the doctor if the patient will benefit from specific treatments. The purpose of this study is to determine the accuracy of these devices. This knowledge will help us understand how investigators can use such non-invasive devices to care for patients at less risk than the devices currently used for such purposes.

The main question the study aims to answer is:

• What monitor is the most accurate in capturing non-invasive patient data?

Eligibility criteria:
Inclusion Criteria:

* Adult patients (18 years and older)
* Male and female patients following cardiac surgery admitted to the CTICU with or without a pulmonary artery catheter

Exclusion Criteria:

* Subjects whose sternum remains open post-surgery and/or the presence of a LV ventricular assist device.
* Presence of a any ventricular assist device, ECMO, Impella, durable LVAD, or central L/RVAD.
* Spontaneous breathing activity upon coming out of the operating room (i.e., patients extubated in the operating room).
* Presenting chronic atrial fibrillation, refractory acute atrial fibrillation to medical therapy or frequent extra-systoles.
* Children will be excluded from this study. The hospital unit where all subjects will be recruited is for adults' (ages 18 and up) only. Gender, racial and ethnic sub groups will not be explicitly excluded.

Outcomes:
Stroke Volume Variation; Pulse Pressure Variation (PPV); Arterial Dynamic Elastance (Eadyn)
Evaluation of a Novel CT-based 3D Planning, Computer Navigation and Subsequent Migration Analysis in Total Hip Arthroplasty: A Single Arm Study With 2 Years Follow-up
Trial ID: NCT05159206
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2021-04-01
Completion date: 2024-12-15
Sponsor: Region Skane
Conditions: Hip Osteoarthritis
Interventions: CT group
Locations: Department of Orthopedics, Skåne University Hospital, Lund University
Countries: Sweden
Enrollment: 27
Age range: 40 Years - 75 Years
Sex: ALL

Summary:
The aim of this study is to evaluate a new technique that optimizes the prosthesis position for patients who undergo total hip arthroplasty surgery. Today, most surgeons take a regular X-ray (2D) to plan the surgery. In this study, patients will do a computed tomography (CT) before surgery, and with the use of the new system, the investigators will be able to make a more accurate plan when it comes to prosthetic placement and leg length adjustment in 3D. The software uses artificial intelligence (AI) to generate the information required for clinical conclusions automatically, and it enables surgeons to adapt to each unique case and anticipate situations during surgery. The system also helps the surgeon during operation with a navigation tool to place the implant in the pre-operatively planned position. It guides surgeons in real-time to place the implants accurately. To see whether the plan and navigation were correct, the investigators will do a new computed tomography after surgery to look at the achieved result.

In addition to planning and navigation, the same system can be used to measure the migration of the prosthesis in relation to the bone over time using AI by comparing multiple scans. The investigators aim to follow all patients for 2 years (total of 4 CT scans after surgery) with the intention to be able to foresee implant loosening.

Eligibility criteria:
Inclusion Criteria:

* Primary or secondary hip osteoarthritis
* Qualified for total hip arthroplasty surgery at the regional Hospital

Exclusion Criteria:

* BMI \>35
* Unable to comprehend the patient information
* Other cause for assessing the patient as ineligible (such as: serious medical comorbidity or active drug abuse)

Outcomes:
CT validation; Change in prosthetic migration
A Novel Technique for Estimating Maximal Jaw Movement, Neck and Shoulder Joint Range of Motion Using an Artificial Intelligence Model
Trial ID: NCT06629038
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2024-12-05
Completion date: 2026-12-31
Sponsor: National Taiwan University Hospital
Conditions: Oral Cancer
Interventions: observation alone
Locations: School and Graduate Institute of Physical Therapy, College of Medicine, National Taiwan University
Countries: Taiwan
Enrollment: 40
Age range: 20 Years - 65 Years
Sex: ALL

Summary:
This observational study aims to develop an AI-based system for tracking mandibular and shoulder movements using deep learning techniques. It will compare AI-generated pose estimations with gold standard measurements to assess accuracy, particularly in patients with functional impairments from oral cancer treatment, such as trismus, spinal accessory nerve dysfunction, neck dystonia, and radiation fibrosis.

Eligibility criteria:
Inclusion Criteria:

* Healthy adults without a history of head, neck or shoulder injury or surgery, and without HNC-related radiotherapy or chemoradiotherapy
* Oral cancer patients with trismus, clinical signs of neck or shoulder joint impairment after oral cancer surgery or radiotherapy
* Age between 20 and 65 years

Exclusion Criteria:

* Could not communicate
* Had any disorder that could influence movement performance

Outcomes:
maximal mouth opening; maximal lateral excursion of mandible; range of motion of shoulder abduction; range of motion of neck
The Role and Applications of Artificial Intelligence in Dental Implant Planning
Trial ID: NCT06527378
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2024-05-01
Completion date: 2024-07-25
Sponsor: University of Bari Aldo Moro
Conditions: Edentulous Alveolar Ridge; Edentulous Mouth; Tooth Loss
Interventions: AI in implant planning
Locations: Giuseppe D'Albis
Countries: Italy
Enrollment: 15
Age range: 18 Years - 75 Years
Sex: ALL

Summary:
Artificial intelligence (AI) is revolutionizing the dentistry, offering new opportunities to improve the precision and efficiency of implantology. This study aims to evaluate the current evidence on the use of AI in implant planning assessment

Eligibility criteria:
Inclusion Criteria:

* Patients who underwent 3D or 2D radiological examinations for implant-prosthetic rehabilitation

Outcomes:
Valuation on the diagnosis of anatomical structures
Application of Hyperspectral Imaging in the Diagnosis of Glomerular Diseases
Trial ID: NCT05797051
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2023-05-30
Completion date: 2023-09-20
Sponsor: Qianfoshan Hospital
Conditions: Glomerulonephritis, IGA; Glomerulonephritis, Membranous; Diabetic Nephropathies; Nephrosis, Lipoid; Hyperspectral Imaging
Interventions: Microscopic hyperspectral imaging system
Enrollment: 80
Age range: 18 Years - 90 Years
Sex: ALL

Summary:
Morning urine samples of patients with IgA nephropathy, idiopathic membranous nephropathy, diabetic nephropathy, and minimal degenerative nephropathy confirmed by renal needle biopsy in our hospital from November 2020 to January 2022 were collected. By scanning the morning urine samples of corresponding patients with microhyperspectral imager, machine learning and deep learning were used to classify microhyperspectral images, and the classification accuracy was greater than 85%. Thus, hyperspectral imaging technology could be used as a non-invasive diagnostic means to assist the diagnosis of glomerular diseases.

Eligibility criteria:
Inclusion Criteria:

* Over 18 years old;
* Patients with IgA nephropathy, idiopathic membranous nephropathy, diabetic nephropathy, minimal change nephropathy confirmed by renal biopsy;
* Had not received hormone and/or immunosuppressive therapy before renal biopsy;
* Complete clinical data, all signed the "Admission Certificate of Qianfoshan Hospital of Shandong Province", and agreed to use relevant medical information, biological specimen examination and examination results for scientific research.

Exclusion Criteria:

* There are factors causing secondary membranous nephropathy, such as immune diseases (systemic lupus erythematosus), tumors/infections (viral hepatitis), drugs or poisons, etc.;
* Severe infection: fever, cough and expectoration, sore throat, abdominal pain, diarrhea, carbuncle and furuncle and other clinical manifestations of skin and soft tissue infection, blood routine white blood cell count beyond the normal range (10×109/L);
* Severe cardiovascular disease: including chronic heart failure grade 3 or above and various arrhythmias;
* Infectious diseases: active hepatitis, AIDS, syphilis, etc. ;
* Tumor evidence: it has been found that there is a certain tumor or clinical manifestations, tumor markers, etc., suggesting the possibility of tumor.

Outcomes:
Microhyperspectral image of urine specimen
Effectiveness of Gamification With a Narrative Adapted to the Player's Profile in Obstetric Nursing Competencies: A Cluster Randomized Controlled Pilot Trial Protocol
Trial ID: NCT07434336
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-02-19
Completion date: 2026-04-05
Sponsor: Servicio Canario de Salud
Conditions: Learning Development; Obstetric
Interventions: Adapted Gamified; Generic Gamified
Locations: Universidad del Atlántico Medio
Countries: Spain
Enrollment: 38
Age range: None - None
Sex: ALL

Summary:
This pilot study, conducted at the University of Atlántico Medio, seeks to transform the training of future nurses through a "Precision Education" model, moving away from generic simulations to focus on learning adapted to the student's individual profile. Through a clinical trial with 38 second-year students, the research evaluates whether using Artificial Intelligence to personalize educational narratives (gamification) according to the student's personality enhances academic performance in obstetrics (pregnancy and childbirth). The ultimate goal is to demonstrate that adapting teaching to each student's psychological characteristics is not only a sustainable strategy but also produces better-prepared professionals with higher knowledge retention, directly translating into safer and higher-quality care for patients and their families.

Eligibility criteria:
Inclusion Criteria:

* Students of the bachelor's degree in nursing
* Enrolled in the course "Reproductive Health Nursing and Sex Education
* Active enrollment
* Face-to-face attendance
* Technological access

Exclusion Criteria:

* Students repeating the course
* Students with prior specialized training
* Students with uncorrected sensory impairment

Outcomes:
Learning Gain
THE EFFECTS OF CORRECTIVE EXERCISE AND KİNESİOTAPE APPLICATIONS ON PERFORMANCE AND FUNCTIONAL MOBILITY IN YOUNG BASKETBALL PLAYERS
Trial ID: NCT07358936
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-02-15
Completion date: 2026-07-16
Sponsor: Vedat Göken
Conditions: Youth; Sport Performance
Interventions: Kinesiotape; Corrective Exercise
Locations: Istinye University; Istinye University
Countries: Turkey (Türkiye); Turkey (Türkiye)
Enrollment: 45
Age range: 9 Years - 16 Years
Sex: MALE

Summary:
The main objective of this research is to comparatively examine the effects of corrective exercise programs and kinesio taping (KT) applications on performance parameters and functional mobility in adolescent male basketball players. Kinesio taping is used with the aims of regulating muscle tone, increasing proprioception, improving circulation, and supporting pain management. In this context, corrective exercises are applied to correct movement disorders, eliminate musculoskeletal imbalances, and improve the functional movement quality in athletes. Although the effects of both methods on the performance parameters and functional mobility of athletes have been examined separately in the literature, a direct comparative study in adolescent basketball players is noticeable.

Eligibility criteria:
Inclusion Criteria:

* The male athlete's sport must be basketball.
* The male athlete must be between 9 and 16 years old.

Exclusion Criteria:

* Having undergone any surgical operation within the last 6 months.
* Having suffered a lower/upper extremity injury within the last 3 months.
* The volunteer does not wish to continue the study.
* Athletes for whom the necessary assessments or procedures could not be performed were excluded.

Outcomes:
Y Balance Test; Functional Movement System(FMS); Agility T test; My Jump 2; Seated Medicine Ball Throw Test; The Closed Kinetic Chain Lower Extremity Stability Test
Photoplethysmography (PPG) to Predict Ejection Fraction and Other Echographic Data in the General Population
Trial ID: NCT04843371
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2021-08-20
Completion date: 2026-12
Sponsor: Tulane University
Conditions: Valvular Heart Disease; Pericardial Disease; Cardiomyopathies
Interventions: Photoplethysmography
Locations: East Jefferson General Hospital; University Medical Center; Tulane Doctors - Speciality Care - Napoleon
Countries: United States; United States; United States
Enrollment: 500
Age range: 18 Years - 120 Years
Sex: ALL

Summary:
The investigators are aiming to investigate the association between ejection fraction (EF) determined by echocardiography and signals obtained from Photoplethysmography (PPG) in the general population. The investigators are also aiming to investigate the association between blood pressure and signals obtained from PPG in the general population.

Finally, the investigators are also aiming to investigate the association between signals obtained from PPG in the general population to cardioechographic findings such as, valvular heart disease, structural heart diseases, cardiomyopathies, pericardial disease etc.

Eligibility criteria:
Inclusion Criteria:

* Male or Female aged18 years or older.
* Patients scheduled to undergo an echocardiogram at Tulane Medical Center.

Exclusion Criteria:

* Participants under 18 years of age.
* Participants with cognitive impairments.
* Participants with a physical inability to wear the Biostrap during the echocardiogram.
* Individuals who cannot read, speak, and/or understand English.

Outcomes:
Association between obtained PPG waveforms and recorded ejection (EF) fractions.
Development and Validation of a Multimodal Artificial Intelligence Model for Predicting Lymph Node Metastasis in T1 Gastric Cancer and Its Impact on Physician Diagnostic Performance
Trial ID: NCT07124754
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-01-01
Completion date: 2025-12-30
Sponsor: Qun Zhao
Conditions: T1 Gastric Cancer Lymph Node Metastasis Early Gastric Cancer Artificial Intelligence-Assisted Diagnosis Multimodal Data Integration
Interventions: Multimodal Artificial Intelligence Diagnostic Model for Lymph Node Metastasis in T1 Gastric Cancer
Locations: the Fourth Hospital of Hebei Medical University
Countries: China
Enrollment: 300
Age range: 18 Years - None
Sex: ALL

Summary:
This study aims to develop and validate an artificial intelligence (AI) model that integrates clinical, pathological, and imaging data to predict the presence of lymph node metastasis (LNM) in patients with T1-stage gastric cancer.

The study will also compare the diagnostic performance of physicians with and without AI assistance, including clinicians with varying levels of experience.

The goal is to improve early decision-making and support more personalized treatment strategies for patients with early gastric cancer.

Eligibility criteria:
Inclusion Criteria:

Age 18 years or older

Histologically confirmed primary gastric adenocarcinoma

Clinical stage T1 (T1a or T1b) confirmed by endoscopy and imaging

Undergoing radical gastrectomy with lymph node dissection

Preoperative data available: clinical variables, CT imaging, and pathology slides

Written informed consent provided

Exclusion Criteria:

History of other malignancies within the past 5 years

Received neoadjuvant chemotherapy or radiotherapy

Incomplete clinical or pathological data

Poor quality or missing CT or histopathology images

Patients with distant metastasis (M1) at diagnosis

Inability or refusal to provide informed consent

Outcomes:
Diagnostic Accuracy of the AI Model for Predicting Lymph Node Metastasis in T1 Gastric Cancer; Diagnostic Accuracy of the AI Model for Predicting Lymph Node Metastasis in T1 Gastric Cancer
Retrospective Use of Patient Treatment Data for the Evaluation and Further Development of an Artificial Intelligence-based Algorithm for Clinical Decision Support in Invasive Mechanical Ventilation of Intensive Care Patients
Trial ID: NCT05668637
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2023-01-01
Completion date: 2026-09-01
Sponsor: Technische Universität Dresden
Conditions: Invasive Mechanical Ventilation
Interventions: Artificial Intelligence-based Decision support
Locations: Cleveland Clinic Foundation, Cleveland, USA; University Hospital Carl Gustav Carus Dresden; Institut Fur Angewandte Informatik (Infai) Ev; Institut Mihajlo Pupin; Better Care Sl; Fundacio Parc Tauli; Fundacion Publica Andaluza Progreso Y Salud; Inselspital, Universitätsspital Bern
Countries: United States; Germany; Germany; Serbia; Spain; Spain; Spain; Switzerland
Enrollment: 318542
Age range: 18 Years - None
Sex: ALL

Summary:
Invasive mechanical ventilation is one of the most important and life-saving therapies in the intensive care unit (ICU). In most severe cases, extracorporeal lung support is initiated when mechanical ventilation is insufficient. However, mechanical ventilation is recognised as potentially harmful, because inappropriate mechanical ventilation settings in ICU patients are associated with organ damage, contributing to disease burden. Studies revealed that mechanical ventilation is often not provided adequately despite clear evidence and guidelines. Variables at the ventilator and extracorporeal lung support device can be set automatically using optimization functions and clinical recommendations, but the handling of experts may still deviate from those settings depending upon the clinical characteristics of individual patients. Artificial intelligence can be used to learn from those deviations as well as the patient's condition in an attempt to improve the combination of settings and accomplish lung support with reduced risk of damage.

Eligibility criteria:
Inclusion Criteria:

• Subjects who are 18 years or older and receive invasive mechanical ventilation for \> 4 hours

Exclusion Criteria:

• Patients receiving one-lung ventilation

Outcomes:
Relative time of same device settings of the health care provider and the IntelliLung algorithm
Retrospective Study Comparing Radiologist Diagnostic Performance Versus Artificial Intelligence (AI) for Hip Fracture Suspicion in Elderly Patients
Trial ID: NCT04304742
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2020-03-01
Completion date: 2021-10-30
Sponsor: University Hospital, Montpellier
Conditions: Painful Hip; Fonctional Impotence After Minor Trauma (Fall)
Interventions: hip standard radiography/ CT scan/ MRI
Locations: Uhmontpellier
Countries: France
Enrollment: 1000
Age range: 60 Years - None
Sex: ALL

Summary:
In France, femoral neck fracture is mainly detected with interpretation of pelvis/hip X-ray imaging (French Health Authority recommandation).

However, up to 10% of fractures are not identified or misdiagnosed, especially in patients admitted to the emergency department.

Indeed, radiologists may be subject to excessive work, wich cause the risk of inaccurate on X-rays diagnosis.

The Artificial intelligence (AI) begins study the detection of fratures on medical imaging.

In this retropective study, this technology developed by GLEAMER company is tested to evaluate the detection rate of hip fracture and specifically femoral neck fracture, compared to the radiologist diagnostic, in eldery patients admitted in emergency department.

AI could optimize the diagnostic performance of radiologists (increase of confidence level) and improve the efficiency of suspected fractures sorting from emergency department.

Eligibility criteria:
Inclusion criteria:

* Patients aged 60 and older
* Minor trauma
* Admitted in emergency department for painful hip/fonctional impotence after minor trauma
* Takes at least a hip radiography

Exclusion criteria:

* Painful before the minor trauma
* Important trauma

Outcomes:
Detection rate of femoral neck fracture
A Prospective Study to Evaluate the Effectiveness of Thermalytix (AI-based Thermographic Solution Developed by Niramai) for Breast Cancer Screening as Compared to Standard Screening Modalities for Breast Cancer
Trial ID: NCT04688086
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2018-12-15
Completion date: 2020-01-30
Sponsor: Niramai Health Analytix Private Limited
Collaborators: Max Healthcare Insititute Limited
Conditions: Breast Cancer; Early Detection of Cancer
Interventions: Thermalytix
Locations: Max Healthcare Insititute Limited
Countries: India
Enrollment: 687
Age range: 18 Years - 80 Years
Sex: FEMALE

Summary:
The purpose of this study is to determine the clinical performance of AI-based Thermalytix with the current standard-of-care diagnostic modalities in women.

Eligibility criteria:
Inclusion Criteria:

1. Female subjects equal to and above 18 years
2. Subjects who are willing to give written informed consent for study participation
3. Subjects who are ready to comply with the study related visits and procedures

Exclusion Criteria:

1. Subjects who are pregnant
2. Subjects who are lactating
3. Subjects who have undergone either lumpectomy or mastectomy
4. Subjects who have undergone chemotherapy in the last 2 weeks at the time of study enrollment
5. Any active illness, psychological and/or pathological condition that would interfere with study participation in the opinion of the Investigator

Outcomes:
Sensitivity, Specificity, Positive Predictive Value and Negative Predictive Value of Thermalytix