moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Determination of Walking Imitations with Artificial Intelligence Model in Forensic Medicine; Lie Detector for Walking
Trial ID: NCT06057272
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2023-10-30
Completion date: 2024-12-30
Sponsor: Hacettepe University
Conditions: Gait, Stumbling; Fracture Lower Leg; Imitates Movements
Locations: Hacettepe University Physical Therapy and Rehabilitation Faculty
Countries: Turkey (Türkiye)
Enrollment: 60
Age range: 18 Years - 40 Years
Sex: MALE

Summary:
The goal of this clinical trial is to obtain more consistent results with the use of repetitive neural networks in gait models and classification approaches in individuals applying forensic sciences. It was aimed to develop a decision support system for determining the walking imitations of individuals with explainable machine learning approaches on disability compensation in the field of Forensic Medicine.

Participants will be assessed regarding kinematic and temporospatial gait parameters, pain severity, and fatigue level.

Comparison group: Researchers will compare the patients applying to the forensic medicine department to those applying to the orthopedic department, and their healthy counterparts.

Eligibility criteria:
Inclusion Criteria: Grup Forensic Medicine

1. Applying to the Department of Forensic Medicine to determine the disability rate,
2. Antalgic gait evaluation performed within the scope of traffic accident or disability reporting,
3. Having a history of unilateral lower extremity fracture,
4. Not having any orthopedic or neurological problems that may affect walking, other than fractures,
5. At least 6 months after surgical treatment,
6. Scoring 24 or more from the Standardized Mini Mental Test
7. Male individuals between the ages of 18-40

Inclusion Criteria: Grup Orthopedics and Traumatology

1. Having a history of unilateral lower extremity fracture,
2. Not in a position of disability or compensation after the fracture,
3. Treated in the Orthopedics and Traumatology Department
4. Similar to the participants in the Group Forensic Medicine in terms of demographic characteristics,
5. No orthopedic or neurological problems other than fractures that would affect walking,
6. At least 6 months after surgical treatment,
7. Scoring 24 or above from the Standardized Mini Mental Test,
8. Male individuals between the ages of 18-40

Inclusion Criteria: Grup Healthy

1. Healthy participants who have compatible demographic characteristics of the patient groups,
2. Scoring 24 or more from the Standardized Mini Mental Test,
3. Male individuals between the ages of 18-40.

Exclusion Criteria:

1. Having any problems or pain in the upper extremities and/or trunk,
2. Having problems in both lower extremities,
3. Treatment is ongoing,
4. Patients who do not have the ability to walk independently,
5. Having a Body Mass Index of 30 kg/m² and above.

Outcomes:
Kinematic Gait analysis; Temporospatial gait analysis
Computer Aided Polyp Detection (C3PO) Trial: A Multicenter International Trial Evaluating Diagnostic Accuracy of Artificial Intelligence System in Detecting Colon Polyps
Trial ID: NCT04777019
Status: WITHDRAWN
Study type: OBSERVATIONAL
Start date: 2020-06-07
Completion date: 2025-06-06
Sponsor: The University of Texas Health Science Center, Houston
Conditions: Colonic Polyp
Interventions: Not applicable as standard of care Colonoscopy
Enrollment: 0
Age range: 18 Years - None
Sex: ALL

Summary:
Computer aided detection (CADe) algorithms have been developed to overcome human errors and assist endoscopists in detecting more polyps during colonoscopy. The aim of this study was to investigate the accuracy of the novel Pentax Discovery CADe system (Discovery-AI) against pre-recorded videos of colon polyps of various size, shape and pathology while using videos of normal colon segments as controls from two different institutes.

Eligibility criteria:
Inclusion Criteria:

* Patients scheduled for a regular or screening colonoscopy at the institution as standard of care.
* Over 18 years of age

Exclusion Criteria:

* None if they qualify based on inclusion criteria
* Pregnant patients
* Inmate or prisoners
* Unable to provide informed consent

Outcomes:
To investigate the accuracy of the novel Pentax Discovery CADe system (Discovery-AI) against pre-recorded videos of colon polyps of various size, shape and pathology while using videos of normal colon segments as controls
Artificial Intelligence-Assisted Magnetic Resonance Imaging for Quality, Efficiency and Equity in the National Health Service (NHS) Care of Multiple Sclerosis
Trial ID: NCT07111637
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-06-14
Completion date: 2026-04-14
Sponsor: Queen Mary University of London
Collaborators: icometrixLeuven
Conditions: Multiple Sclerosis
Interventions: AI software
Locations: Barts Health NHS Trust
Countries: United Kingdom
Enrollment: 1336
Age range: 18 Years - 99 Years
Sex: ALL

Summary:
Multiple Sclerosis (MS) is a long-term disease that affects over 150,000 people in the UK. Starting treatment early is important for managing Multiple Sclerosis (MS). It is also essential to monitor the treatment to see if it is working and to switch treatments if needed.

Magnetic resonance imaging (MRI) is the only accepted tool to monitor how well the treatment is working. Current evaluation of brain Magnetic resonance imaging (MRI) scans requires visual inspection, of which sensitivity is degraded by human, and technical factors, such as lack of time, fatigue of radiologists, and lack of standardization of image acquisition protocols across the National Health Service (NHS). MRI-readings can be significantly enhanced by artificial intelligence (AI)-assistive software. Evidence suggests the rate of new lesion detection to be 3 - 4 times higher when using assistive software compared to visual inspection of MRI scans.

In this study, an Artificial Intelligence (AI) software called "icobrain ms." developed by the company "icometrix" (Leuven, Belgium) is tested. This tool helps track MS by measuring changes in the brain using MRI scans. The AI can highlight problem areas and create reports that doctors can use to make better decisions about participants' treatment. The aim of the study is to prove that icobrain ms can be used to assist the neuro-radiologist with their visual assessment of MRI scans by a radiologist, and that it will help clinicians make more informed decisions about participants' current MS treatment.

Eligibility criteria:
Inclusion Criteria:

* Clinically Isolated Syndrome suggestive of demyelination (CIS) or definitive diagnosis of MS.
* Undergoing MRI head investigation.
* On an MS DMT pathway.
* Access to a smartphone, tablet or computer.

Exclusion Criteria:

* patients with Multiples Sclerosis participating in a randomised controlled CTIMP (participating in a single arm study may be included, provided this is in line with the other protocol).

Outcomes:
To assess the clinical usefulness of icobrain ms by evaluating the detection of disease activity (as defined by new/expanding lesions).
Genetic Fuzzy Artificial Intelligence Driven Virtual Reality for Prevention of Collision-based Injury
Trial ID: NCT03915119
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2018-01-01
Completion date: 2019-09-30
Sponsor: Children's Hospital Medical Center, Cincinnati
Conditions: Injuries
Interventions: GFT AI training
Locations: Cincinanti Childrens Hospital Medical Center
Countries: United States
Enrollment: 45
Age range: 14 Years - 22 Years
Sex: ALL

Summary:
The purpose of this study is to develop and test a VR training system that integrates GFT AI with virtual obstacle scenarios that, when compared to a sham-VR training system, is hypothesized to increase neuromechanical and perceptual-motor fitness, decrease collision frequency and impact forces for soccer athletes, during a single training session and also when assessed at approximately 1 week and 1 month following training.

Eligibility criteria:
Inclusion Criteria:

* healthy
* varsity high school or collegiate soccer athlete

Exclusion Criteria:

* unable to participate in soccer
* history of congenital or acquired cognitive, ophthalmologic, or neurological disorders including developmental delay, brain tumor, stroke, or known peripheral or central vestibular disorders
* patients who have begun anti-depressant, stimulant or anti-seizure medications for treatment of their symptoms or for other, unrelated reasons within two months of testing will be excluded from testing

Outcomes:
Pro-saccades test
Artificial Intelligence in New Cardiac MR Markers for Congenital Heart Disease -Improving the Assessment, Monitoring and Prediction in Children
Trial ID: NCT05979870
Status: ENROLLING_BY_INVITATION
Study type: OBSERVATIONAL
Start date: 2022-10-01
Completion date: 2028-12-31
Sponsor: Sahlgrenska University Hospital
Conditions: Aortic Valve Disease; Pulmonary Valve Disease; Child, Only; Magnetic Resonance Imaging; Risk Stratification
Interventions: Valvuloplasty,
Locations: Queen Silvia Children'sHospital, Sahlgrenska University Hospital
Countries: Sweden
Enrollment: 45
Age range: 1 Week - 18 Years
Sex: ALL

Summary:
The goal of this study is to investigate children with aortic and pulmonary valve disease treated or untreated longitudinally. Established CMR measures with additional newly developped, promising, highly refined and clinically applicable quantitative imaging biomarkers, will be utilized as compared to the conventional CMR estimates.

The main question\[s\] it aims to answer are:

* \[question 1\]To evaluate risk stratification for surgery and intervention of the aortic- and pulmonary valve
* \[question 2\]Investigate the cardiac and vascular hemodynamic and morphological changes before and after interventional or surgical treatment of the aortic- and pulmonary valve at short and long term.

Participants will undergo cardiac MRI before and after interventional or surgical treatment of the aortic or pulmonary valve Researchers will compare MRI data to an age matched control group established at the department in another study.

Eligibility criteria:
Inclusion Criteria:

* aortic valve disease
* or pulmonary valve disease
* clinical indication for valvular surgery
* or clinical indication for valvular intervention

Exclusion Criteria:

* contraindications for MRI
* need of sedation or general anesthesia, unless there is a clinical indication for a CMR

Outcomes:
CMR measures; Physical capacity
The Use of Remimazolam Versus Dexmedetomidine for Patients Receiving Drug-Induced Sleep Endoscopy
Trial ID: NCT07459322
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-04-15
Completion date: 2027-12-31
Sponsor: Fu Jen Catholic University Hospital
Conditions: OSAS (Obstructive Sleep Apneas Syndrome); DISE; Sedation
Interventions: Remimazolam; Dexmedetomidine
Locations: Fu Jen Catholic University Hosptial
Countries: Taiwan
Enrollment: 60
Age range: 18 Years - None
Sex: ALL

Summary:
This study seeks to compare the clinical efficacy and safety of the novel sedative Remimazolam with Dexmedetomidine in Drug-Induced Sleep Endoscopy (DISE), evaluating their performance in sedation depth stability, incidence of adverse events, and postoperative recovery time, thereby providing evidence-based guidance for sedative selection in OSA patients. Additionally, it aims to develop a model for predicting upper airway obstruction sites by analyzing Polysomnography (PSG) data with artificial intelligence, enhancing diagnostic accuracy and treatment decision-making efficiency for Obstructive Sleep Apnea (OSA) to optimize clinical management.

Eligibility criteria:
Inclusion Criteria:

* Patients age \> 18 years old
* American Society of Ansthesiologists functional class \< III
* Schedule to receive DISE exam for evaluation of OSAS

Exclusion Criteria:

* Patient refusal
* Allergic to experimental medication
* History of central nervous system diseases
* History of psychiatric diseases
* Pregnancy
* Severe, uncontrolled systemic diseases

Outcomes:
Apneic events; Bradycardia; Hypotension; Agitation; Surgeon satisfaction score; Hypoxic event
Artificial Intelligence Enhanced Optical Coherence Tomography (AI-OCT) Imaging for Pre-surgical Margin Detection of Basal Cell Carcinoma
Trial ID: NCT07358637
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2026-09
Completion date: 2028-12
Sponsor: Henry Ford Health System
Conditions: Basal Cell Carcinoma of Skin
Interventions: AI-OCT
Enrollment: 30
Age range: 18 Years - None
Sex: ALL

Summary:
Basal cell carcinomas (BCCs) are the most common human malignancy, affecting about 2 million Americans each year. Mohs micrographic surgery (MMS) removes tissue by sequential excision. Costs for MMS could be reduced if the number of necessary excision stages were decreased by a more accurate initial tumor margin assessment.

The goal of this observational study is to learn if Optical Coherence Tomography (OCT) used in conjunction with artificial intelligence algorithms is accurate in the detection of superficial BCC margins prior to MMS. This study also aims to determine if AI-OCT guided margin delineation can reduce the number of stages in MMS.

Researchers will first focus on validating AI-OCT as a method for accurately detecting BCCs. A follow-up study would then address the guided pre-surgical margin delineation.

Eligibility criteria:
Inclusion Criteria:

* Male or Female, ages 18 or older
* at least one biopsy proven superficial or nodular BCC
* willingness to have photographs taken of the treatment area
* ability to understand and willingness to sign a written informed consent document

Exclusion Criteria:

* infiltrative, micronodular, or morpheaform BCC
* pregnant women
* subjects not willing to have a biopsy taken from the treatment area
* subjects with herpes simplex virus infection in the treatment area

Outcomes:
Validation of AI-OCT as an accurate method for detecting basal cell carcinomas
Development and Validation of an Artificial Intelligence-assisted Strategy Selection System for Colonoscopy Cleaning
Trial ID: NCT04444908
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2020-05-11
Completion date: 2020-11-16
Sponsor: Renmin Hospital of Wuhan University
Conditions: Colorectal Adenoma
Locations: Renmin hospital
Countries: China
Enrollment: 657
Age range: 18 Years - None
Sex: ALL

Summary:
Patients with poor inadequate bowel preparation need to undergo secondary colonoscopy. but the evaluation of intestinal cleanliness is judged by doctors subjectively. there are no objective and effective criteria to guide the evaluation. We use the deep learning technique to develop the EndoAngel with real-time intestinal cleanliness assessment. It can derive a decision curve for bowel cleanliness based on the relationship between the percentage of bowel segments with a Boston score of 1 and the adenoma detection rate. It can help doctors to identify patients who need a second colonoscopy, and provide a new way for artificial intelligence in improving the detection rate of colonoscopic adenomas.

Eligibility criteria:
Inclusion Criteria:

* Male or female aged 18 or above;
* Colonoscopy is needed to further clarify the characteristics of digestive tract diseases;
* Patients able to give informed consent were eligible to participate.
* Able and willing to comply with all study process.
* No intestinal organic disease.

Exclusion Criteria:

* Has participated in other clinical trials, signed informed consent and was in the follow-up period of other clinical trials.
* Has participated in clinical trials of the drug and is in the elution period of the experimental drug or control drug.
* Drug or alcohol abuse or psychological disorder in the last 5 years.
* Patients in pregnancy or lactation.
* Known polyposis syndromes.
* Gastrointestinal Bleeding.
* A history of inflammatory bowel disease, colorectal cancer and colorectal surgery.
* A history of colorectal surgery.
* Patients with a contraindication for biopsy.
* Previous history of allergy to ingredient of bowel cleanser.
* Patients with intestinal obstruction or perforation, toxic megacolon, Colectomy, heart failure (Grade III or IV) , severe cardiovascular disease, severe liver failure or renal insufficiency, etc. .
* Researchers believe that the patient is not suitable to participate in the trial.

Outcomes:
The adenoma detection rate (ADR); Cleanliness assessment of different intestinal segment in the artificial intelligence system
Clinical Validation of RBfracture to Demonstrate Improved Diagnostic Performance of the Intended Users
Trial ID: NCT07421791
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2026-02
Completion date: 2026-04
Sponsor: Radiobotics
Conditions: Fracture; Joint Dislocation; Joint Effusion; Knee Lipohemarthrosis
Interventions: RBfracture
Enrollment: 415
Age range: 22 Years - None
Sex: ALL

Summary:
The goal of this study is to determine if the computer software, RBfracture, developed by Radiobotics, helps primary care, emergency, and radiology clinicians more easily identify bone injuries caused by a traumatic impact (such as a fall or car collision). RBfracture uses artificial intelligence (AI) to analyze X-ray images of patients to identify fractures and joint dislocations visible on the X-ray images. RBfracture also identifies fluid buildup in the elbow and knee joints resulting from a fracture or dislocation.

Sixteen clinicians will review X-ray images from 415 adult patients, who may have sustained a bone injury, to diagnose any injuries visible on their X-ray images. First, the clinicians will review half of the images with and half of the images without the help of the RBfracture software. After a 4-week break, the clinicians will once again review the same images. This time, the software's help will be switched, so it is unavailable for the images the clinicians previously reviewed with it, and available for the images they reviewed without it.

The number of correct and incorrect diagnoses made by the clinicians when they were helped by the software will be compared to the number of correct and incorrect diagnoses made by the clinicians when they did not receive any help from the software. This comparison will reveal if using the software helps clinicians to diagnose more injuries and miss less injuries.

Eligibility criteria:
Inclusion Criteria:

* XR exams of a patient ≥22 years of age following a recent acute musculoskeletal trauma.
* Modality is digitally acquired radiographs (Computed Radiography or Digital Radiography)

Exclusion Criteria:

* XR exam types that are outside of the intended use (e.g., chest, abdomen, facial bones, cervical spine).
* Exams with missing patient age.
* Exams from follow-up patient examinations, e.g., post-surgical controls or evaluation of fracture healing.
* Any exams containing radiographs previously used in software development.
* Exams containing additional radiographs that are incoherent with the XR exam type (e.g., wrist radiograph in a hip and pelvis exam type).
* Radiograph views that are unsupported.
* Poor radiographic image quality, rendering radiograph clinically unsuitable (e.g., inappropriate selection of technical exposure factors, patient motion, presence of artefacts, and improper collimation of the radiographic beam).

Outcomes:
Change in diagnostic accuracy between device-assisted and device-unassisted readers at the exam level.
Artificial Intelligence in the Identification of Lung Contusions Through Chest Radiological Examination in Blunt Thoracic Trauma
Trial ID: NCT06777056
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2024-12-15
Completion date: 2025-06-15
Sponsor: IRCCS Azienda Ospedaliero-Universitaria di Bologna
Conditions: Lung Contusion
Locations: IRCCS Azienda Ospedaliero - Universitaria di Bologna
Countries: Italy
Enrollment: 135
Age range: 18 Years - None
Sex: ALL

Summary:
The observational study focuses on comparing the interpretation of chest radiological examinations performed using a computer-based system with the standard interpretation conducted by a radiologist. The "LUNIT" system serves as a tool designed to assist radiologists in detecting the 10 most common abnormalities visible on chest radiographs, with proven efficacy in large case series.

The investigation addresses the need to evaluate lung injuries resulting from thoracic trauma, which are linked to a higher risk of complications requiring close monitoring to detect potential respiratory failure.

The primary aim of the study is to assess the accuracy of the LUNIT system in interpreting chest radiographs for the identification of lung contusions compared to the standard radiologist-based interpretation.

Eligibility criteria:
Inclusion Criteria:

* Age ≥ 18 years
* Patients presenting to the general Emergency Department of the IRCCS AOU of Bologna between June 1, 2014, and June 1, 2024, for blunt thoracic trauma
* Performance of chest HRCT within 48 hours of standard chest X-ray

Exclusion Criteria:

* none

Outcomes:
Sensitivity of lung contusion detection on chest X-rays, with LUNIT software
Artificial Intelligence-Assisted Real-time Detection of Missed Lesions During Colonoscopy: A Prospective Study
Trial ID: NCT04227795
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2020-01-01
Completion date: 2020-03-01
Sponsor: The University of Hong Kong
Conditions: Colon Adenoma; Colonic Polyp; Colon Cancer
Interventions: Artificial intelligence-Assisted real time colonoscopy
Locations: Queen Mary Hospital
Countries: Hong Kong
Enrollment: 52
Age range: 40 Years - 90 Years
Sex: ALL

Summary:
A prospective validation of real time deep learning artificial intelligence model for detection of missed colonic polyps

Eligibility criteria:
Inclusion Criteria:

* consecutive adult patients, age 40 or above, who were scheduled to have outpatient colonoscopy in the Queen Mary Hospital were invited to participate

Exclusion Criteria:

* Patients were excluded if they were unable to provide informed consent, considered to be unsafe for taking biopsy or polypectomy including patients with bleeding tendency and those with severe comorbid illnesses.
* Also, patients with history of inflammatory bowel disease, familial adenomatous polyposis, Peutz-Jeghers syndrome or other polyposis syndromes were excluded.

Outcomes:
Adenoma miss rate
A Multi-omics Breast Cancer Distant Metastasis Prediction Model Empowered by Artificial Intelligence and Its Clinical Translation Research
Trial ID: NCT07252986
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-12-01
Completion date: 2028-12-30
Sponsor: Second Affiliated Hospital, School of Medicine, Zhejiang University
Conditions: Breast; Breast Cancer; Pathology
Locations: The Second Affiliated Hospital, Zhejiang University School of Medicine
Countries: China
Enrollment: 2000
Age range: None - None
Sex: FEMALE

Summary:
Breast cancer is one of the most common malignancies in women worldwide, and distant metastasis is the main cause of poor prognosis and death. This project aims to construct a precision prediction system based on multi-omics (digital pathology, immunohistochemistry, proteomics, gene sequencing) and artificial intelligence to predict distant and organ-specific metastasis (bone, lung, liver, brain) in breast cancer, analyze its mechanisms, and provide new solutions for precision medicine.

Eligibility criteria:
Inclusion Criteria:

1. Female patients pathologically diagnosed with breast cancer at our hospital or participating centers between January 1, 2000, and December 30, 2028.
2. Must meet one of the following: 1) Initially diagnosed with breast cancer at our hospital, has developed distant metastasis (liver, bone, lung, or brain), and has a biopsy or surgical specimen from the metastatic site stored at our hospital, with complete clinical information available. 2) Primary tumor specimen is not at our hospital, but a biopsy or surgical specimen from a distant metastatic site (liver, bone, lung, brain) is stored at our hospital.

Exclusion Criteria:

1. Concomitant metastatic malignancy from another primary cancer.
2. Incomplete clinical information.

Outcomes:
Metastasis-Free Survival