moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Artificial Intelligence Based Autonomous Socket Proposal Program: Socket Design Experiences
Trial ID: NCT05341674
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2020-01-01
Completion date: 2022-03-01
Sponsor: Hasan Kalyoncu University
Conditions: Artificial Intelligence; Prosthetic
Interventions: the stumps of all patients were scanned with the Artec Eva Lite brand 3D scanner.
Locations: Hasan Kalyoncu University
Countries: Turkey (Türkiye)
Enrollment: 101
Age range: 18 Years - 65 Years
Sex: ALL

Summary:
The aim of this study is to develop an artificial intelligence-based autonomous socket recommendation program that will provide a more comfortable and easier test socket production with high time-cost efficiency and to share experiences about socket designs in these processes.

Eligibility criteria:
Inclusion Criteria:

\- Conscious patients \>18 years old having undergone amputation surgery

Exclusion Criteria:

* • Severe visual and perception impairment

  * Surgical intervention with functional sequelae in the extremities
  * Pain that does not allow tests to be done
  * Patients with diseases with neurological dysfunction (stroke, multiple sclerosis, etc.)

Outcomes:
Software ( Artificial Intelligence Based Autonomous Socket Proposal Program)
Artificial Intelligence Detection of Bladder Tumors Under Endoscopy
Trial ID: NCT06474338
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2024-01-01
Completion date: 2034-12-31
Sponsor: Peking Union Medical College Hospital
Conditions: Bladder Tumor
Interventions: detection by artificial intelligence; detection by urologists
Locations: Peking Union Medical College Hospital
Countries: China
Enrollment: 1000
Age range: 18 Years - 100 Years
Sex: ALL

Summary:
The goal of this clinical trial is to learn if the AI algorithm can detect bladder tumors better than urologists under cystoscopy. It will also train the AI algorithm for bladder tumor detection. The main question it aims to answer is:

Can AI algorithm achieve IOU value, precision, recall, false negative rate of bladder tumor detection similar to that of urologists? The cystoscopy video will be annotated by AI and urologists. Researchers will compare AI algorithm to urologists to see if Al algorithm has a similar capability as urologists do.

Eligibility criteria:
Inclusion Criteria:

The patient who receives cystoscopy and the cystoscopy video is available, and one or multiple bladder lesions can be observed in the cystoscopy.

Exclusion Criteria:

The patient whose cystoscopy is not clear enough to analyze.

Outcomes:
intersection over union
Development and Validation of a Peripheral Venous Waveform-based Pulse Pressure Variation Calculating Deep Learning Model
Trial ID: NCT06734650
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2024-12-28
Completion date: 2026-11-28
Sponsor: Seoul National University Bundang Hospital
Conditions: Peripheral Vein; Arterial Wave Reflections; Pulse Pressure Variation; Stroke Volume Variation; Deep Learning Model
Interventions: peripheral waveform collection
Locations: Seoul National University Bundang Hospital
Countries: South Korea
Enrollment: 150
Age range: 19 Years - 80 Years
Sex: ALL

Summary:
Pulse pressure variation is a monitoring index that indicates the response to fluid therapy in patients receiving mechanical ventilation, and is used as a reference for patients with unstable hemodynamic conditions. However, it is invasive because it requires arterial puncture to collect it. In a previous study by the investigators, the investigators developed and verified an artificial intelligence model that predicts stroke volume variation, in real time using only the central venous pressure waveform. However, since a large vein such as the jugular vein must be punctured to collect the central venous pressure waveform, it is still invasive, and its clinical utility is low. Therefore, in this study, the investigators collected waveforms from peripheral veins that are less invasive and can be a wide range of applications because all surgical patients have them. The investigators aimed to develop and verify an artificial intelligence model that predicts pulse pressure variation obtained from peripheral venous waveforms .

Eligibility criteria:
Inclusion Criteria:

* Patients who voluntarily agreed and signed the written informed consent form before participating in this study
* Adult aged 19 years or older
* American Society of Anesthesiologists physical class (ASA) 1-3
* Patients scheduled for elective hepatectomy under general anesthesia
* Patients who require arterial pressure monitoring and additional peripheral venous access for routine anesthesia preparation
* Non-smokers with normal pulmonary function

Exclusion Criteria:

* Patients with abnormal findings on electrocardiogram before surgery
* Patients who cannot undergo peripheral venous puncture

Outcomes:
Pulse pressure variation
Cardio-Oncologie: étude Pilote évaluant la faisabilité, Utilité Concrète, Interdisciplinaire d'Une Unité Spécialisée
Trial ID: NCT04407780
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2017-01-01
Completion date: 2027-06-01
Sponsor: European Georges Pompidou Hospital
Conditions: Cancer; Cardiac Disease; Metabolic Syndrome; Arrhythmia; Heart Diseases
Interventions: No interventions are planned
Locations: Assistance Publique Hôpitaux de Paris - Centre Université de Paris
Countries: France
Enrollment: 5000
Age range: 18 Years - None
Sex: ALL

Summary:
Cardio-oncology is an emerging field. Most of the data available have been issued from either retrospective analysis, industry data or pharmacovigilance data. These data sources include a number of bias.

CONFUCIUS is a single tertiary centre prospective registry including all patients who have been referred for cardio-oncology assessemnt.

The objectives are to provide a comprehensive vue of cardoi-oncology, enable to detect early signals of cardiotoxicity and enhance ancillary projetcts aiming at specific populations (e.g., type of cancer) and/or drugs.

Eligibility criteria:
Inclusion Criteria:

* referred to cardio-oncology

Exclusion Criteria:

* refusal to participate

Outcomes:
Incidence of cardio-toxicty
DECIDE-AI:The Impact of Artificial Intelligence on Dentists' Decision-Making Process During Caries Detection: A Randomized Controlled Study
Trial ID: NCT07027189
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-10-02
Completion date: 2026-06-02
Sponsor: Radboud University Medical Center
Collaborators: Prime Dental Alliance Eindhoven
Conditions: Artificial Intelligence (AI) in Diagnosis; Artificial Intelligence Supported Image Reviewing
Interventions: Artificial intelligence in diagnosis
Locations: Department of Dentistry Radboud Uniersity Medical Center
Countries: Netherlands
Enrollment: 25
Age range: None - None
Sex: ALL

Summary:
This study aims to evaluate the influence of artificial intelligence (AI) on the decision-making process for intervention after caries lesion detection. Participants will be dentists working in the Netherlands randomly divided into two groups. Dentists will be divided into two groups and receive a set of bitewing radiographs, which first will be evaluated with or without AI support according to their group. Participants will examine caries lesions on the radiographs and formulate treatment plans accordingly. Then, after a wash-out period of one month, the same radiographs, but in the opposite condition of AI support and again formulate treatment suggestions according to the present caries lesions.

Eligibility criteria:
Inclusion Criteria:

1. Graduated, practising dentists.
2. At least three years of experience

Exclusion Criteria:

1. Retired dentists.
2. Specialized practitioners (e.g., orthodontists and oral surgeons) if their typical practice does not involve routine caries diagnostics and treatment planning.

Outcomes:
Treatment decisions: Compare the treatment recommendations of dentists for caries lesions detected with and without AI support.
Artificial Intelligence to Improve Cardiometabolic Risk Evaluation Using CT Scans
Trial ID: NCT05058690
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-02-15
Completion date: 2025-02-28
Sponsor: Caristo Diagnostics Limited
Collaborators: University of Oxford; University of Leeds; Milton Keynes University Hospital NHS Foundation Trust
Conditions: Pre-diabetes; Diabetes Mellitus
Interventions: Oral Glucose Tolerance Test
Enrollment: 180
Age range: 18 Years - 80 Years
Sex: ALL

Summary:
To validate the ability of the FatHealth algorithm to identify individuals with pre-diabetes and with type 2 diabetes mellitus

Eligibility criteria:
Inclusion Criteria:

* Participant is willing and able to give informed consent for participation in the study. Male or Female, aged 18 to 80 years.
* Body mass index (BMI) ≥ 25kg/m2
* FatHealth status assessed as the following:

  * Elevated FatHealth status (50% of participants)
  * Non-elevated FatHealth status (50% of participants)

Exclusion Criteria:

* Participant is unable or unwilling to give informed consent
* Participant is unable to understand English language
* Confirmed diagnosis of diabetes mellitus treated with oral medication or Insulin

Outcomes:
Number of participants identified with pre-diabetes/type 2 diabetes mellitus when fasting blood sample test results are compared against FatHealth algorithm results
Prospective Realworld Cohort Study to Validate Effectiveness of an Artificial Intelligence Defined Lung Nodule Malignancy Score in Patients With Pulmonary Nodule Multicentric, Multinational, Prospective, Observational Study.
Trial ID: NCT05817110
Status: ACTIVE_NOT_RECRUITING
Study type: OBSERVATIONAL
Start date: 2023-04-20
Completion date: 2026-12-06
Sponsor: AstraZeneca
Conditions: Lung Malignancy
Interventions: Participant Cohort
Locations: Research Site; Research Site; Research Site; Research Site; Research Site; Research Site; Research Site; Research Site; Research Site; Research Site; Research Site; Research Site; Research Site; Research Site; Research Site; Research Site; Research Site; Research Site; Research Site; Research Site; Research Site; Research Site
Countries: Egypt; Egypt; India; India; India; India; India; India; India; India; India; India; India; India; India; India; India; Indonesia; Mexico; Mexico; Turkey (Türkiye); Turkey (Türkiye)
Enrollment: 712
Age range: 35 Years - None
Sex: ALL

Summary:
Artificial intelligence (AI) based algorithms have demonstrated increased accuracy in predicting the risk of Lung Cancer among patients with an incidental pulmonary nodule (IPN) on chest radiographs. Qure.ai, an AI company specializing in the reading of chest X- Rays (CXRs) by a proprietary algorithm and has developed a new model, qXR, that can report the lung nodule malignancy score (LNMS) based on lung nodule features.

Our study aims to prospectively validate the lung nodule malignancy score against radiologist assessment of CT scans and Lung CT Screening Reporting and Data System score (Lung-RADS).(lung RADS score explained below) Thus, lung nodule malignancy score (interpreted by qXR as a high or low category) will be compared with radiologist-based assessment probability of CT scan and Lung-RADS assessment. The results of this prospective observational study will pave the way for improved nodule management, leading to better clinical outcomes in patients with incidental pulmonary nodule (IPNs), especially concerning malignancy assessment.

Eligibility criteria:
Inclusion Criteria:

* Male or female patients aged \>35 years
* Patients diagnosed with incidental pulmonary nodule (IPN) on CXR (chest x-ray) by qXR and confirmed by the radiologist at the site with nodule size ≥8 and ≤30 mm.

Exclusion Criteria:

* Any medical or other contraindications for a CT scan
* Nondigital (chest x-ray)CXR
* CT scan is done more than 6 months after (chest x-ray) CXR
* Patients with already diagnosed lung cancer
* The patients referred for an X-Ray for a suspicious Lung cancer
* A patient who already participated in the study.

Outcomes:
To estimate the positive and negative predictive values of qXR LNMS (lung nodule malignancy score ) in a multi-centre real-world setting.
Optimization of the Diagnosis of Bone FRACtures in Patients Treated in the Emergency Department by Using Artificial Intelligence for Reading Radiological Images in Comparison With Traditional Reading by the Emergency Doctor.
Trial ID: NCT06051682
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2023-09-11
Completion date: 2025-10-11
Sponsor: Elsan
Collaborators: Clinique Esquirol Saint Hilaire
Conditions: Artificial Intelligence; Bone Fracture
Interventions: Artificial intelligence; Emergency physician
Locations: Clinique Esquirol Saint Hilaire
Countries: France
Enrollment: 1500
Age range: 18 Years - None
Sex: ALL

Summary:
As part of the management of a patient with suspected bone fractures, emergency physicians are required to make treatment decisions before obtaining the imaging reading report from the radiologist, who is generally not available only a few hours after the patient's admission, or even the following day. This situation of the emergency doctor, alone interpreting the radiological image, in a context of limited time due to the large flow of patients to be treated, leads to a significant risk of interpretation error. Unrecognized fractures represent one of the main causes of diagnostic errors in emergency departments.

This comparative study consists of two cohorts of patients referred to the emergency department for suspected bone fracture. The first will be of interest to patients whose radiological images will be interpreted by the reading of the emergency doctor systematically doubled by the reading of the artificial intelligence. The other will interest a group of patients cared for by the simple reading of the emergency doctor.

All of the images from both groups of patients will be re-read by the establishment's group of radiologists no later than 24 hours following the patient's treatment.

A centralized review will be provided by two expert radiologists. Also, patients in both groups will be systematically recalled in the event of detection of an unknown fracture for hospitalization.

Eligibility criteria:
Inclusion Criteria:

* Major Subject
* Patient admitted to the emergency department for suspected peripheral fractures in the extremities of the upper limb and/or lower limb (wrist/hand and ankle/foot).
* Patient affiliated to or entitled to a social security system
* Patient having received written and informed information about the study and having signed a free and informed consent to participate in the study.

Exclusion Criteria:

* Patient previously admitted to the emergency room for suspicion of fractures and not included in the study
* Patient admitted to the emergency room with suspicion of multiple fractures
* Refusal to participate in the study
* Protected patient: adult under guardianship, curatorship or other legal protection, deprived of liberty by judicial or administrative decision and under judicial protection
* Pregnant, breastfeeding or parturient patient

Outcomes:
Patient readmission rate for failure to diagnose fracture during initial treatment.
To Develop and Validate a Nasoendoscopic Intelligent Diagnostic System for Nasopharyngeal Carcinoma
Trial ID: NCT04547673
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2020-06-20
Completion date: 2026-12-31
Sponsor: First Affiliated Hospital, Sun Yat-Sen University
Collaborators: Sun Yat-sen University
Conditions: Otolaryngological Disease; Artificial Intelligence
Interventions: rigid nasal endoscopes
Locations: First Affiliated Hospital of Sun Yat-sen University; Sun Yat-Sen University Cancer Center; Kiang Wu Hospital
Countries: China; China; China
Enrollment: 1000
Age range: 18 Years - None
Sex: ALL

Summary:
Nasopharyngeal carcinoma (NPC) occurs at a high frequency in southern China, northern Africa, and Alaska, with a reported incidence of 30 cases per 100 000 in Guangdong Province. Endoscopic examination and biopsy are the main methods used for detection and diagnosis of NPC. Early NPC patients achieve favourable prognoses after concurrent radiotherapy and chemotherapy in compassion with advanced NPC patients.

Here, the investigators focused on the utility of artificial intelligence to detect early NPC, which based on white light imaging (WLI) and Narrow-band imaging (NBI) nasoendoscopic examination. Having access to this unique population provides an unprecedented opportunity to investigate the effect of intelligent system on diverse nasopharyngeal lesions detection and develop a novel Computer-Aided Diagnosis System.

Eligibility criteria:
Inclusion Criteria:

* Older than 18 years of age

Exclusion Criteria:

* Refuse to sign the informed consent statement
* Patients who have contraindications, e.g. coagulation dysfunction, drug allergy.

Outcomes:
Pathological diagnosis
Posture Sensing Method Validation at the Office Workstation
Trial ID: NCT04122326
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2019-05-01
Completion date: 2020-01-15
Sponsor: University of Southern California
Collaborators: U.S. National Science Foundation
Conditions: Musculoskeletal Pain; Musculoskeletal Injury; Musculoskeletal Strain
Interventions: Workstation Modification
Locations: University of Southern California
Countries: United States
Enrollment: 20
Age range: 18 Years - None
Sex: ALL

Summary:
Office workers spend 11.6 hours per day in sedentary activities during the day, leading to increased health risks. Although there is literature on prevalence of musculoskeletal disorders and work station modification for office workers, successful implementation and follow-through of evidence-based recommendations in office environments is extremely challenging. This study aims to validate various techniques for evaluating posture, particularly in the neck, shoulder, and trunk, in order to develop evidence-based feedback for the office desk user. This study will include up to 40 volunteers over the age of 18 who regularly work on computer-based tasks at a desk. Participants will be observed working in a provided work station in a variety of different working postures. Data are collected using electromyography sensors, Kinect camera, and at least two video recording cameras to obtain postural information. Measurement of joint positioning and posture will be completed using an ergonomic screening tool from images collected throughout the testing. Participants will provide information on their level of comfort throughout various body regions in response to working in various positions. This will be an iterative study with multiple positions used to test the positioning of sensors, and the development of suitable algorithms to evaluate posture. The collected data will be used to inform sensing methods for office work posture.

Eligibility criteria:
Inclusion Criteria:

* 18 years old or older
* Ability to read, speak, and converse in English

Exclusion Criteria:

* Severe limitations in range of motion through the upper extremity, trunk or neck
* Significant musculoskeletal disorders (e.g., low back pain, carpal tunnel syndrome, cervical radiculopathy)
* Neurological conditions that affect sensation

Outcomes:
Change in Pain on the Visual Analog Pain Scale from baseline
Assessment of the Contribution of an Artificial Intelligence Tool to Help the Diagnosis of Limb Fractures in Pediatric Emergencies : an Interventional, Prospective, Single-center Study
Trial ID: NCT05187585
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2022-02-10
Completion date: 2024-02-17
Sponsor: Fondation Lenval
Conditions: Fractures, Bone
Interventions: radiograph interpretation without the support of the RAYVOLVE app; radiograph interpretation with the support of the RAYVOLVE app
Locations: Hopitaux Pediatriques de Nice Chu-Lenval
Countries: France
Enrollment: 1200
Age range: None - 17 Years
Sex: ALL

Summary:
Limb fracture is a common pathology in children. It represents the first complaint in traumatology among children in developed countries. Failure to diagnose a fracture can have severe consequences in pediatric patients with growing bones, that can lead to delayed treatment, pain and poor functional recovery.

X-ray is the first tool used by doctors to diagnose a fracture. However, the diagnosis of fracture in the emergency room can be challenging. Most images are interpreted and processed by emergency pediatricians before being reviewed by radiologists (most often the day after).

Previous studies have reported the rate of misdiagnosis in fracture by emergency physicians from 5% to 15%.

A tool to investigate in diagnosing limb fractures could be helpful for any emergency physicians exposed to this condition

Eligibility criteria:
Inclusion Criteria:

* Children under 18
* Showing signs that may suggest a limb fracture and justifying the realization of an X-ray (trauma with pain, deformation, edema, wound)
* Written informed consent from one of the two parents or the holder of parental authority signed
* Beneficiaries or members of a Health Insurance scheme

Exclusion Criteria:

* A sign (s) of vital distress
* Any other reason than that of a suspected limb fracture
* A diagnosis of a limb fracture before its management in the emergency room (x-ray made in pre-hospital)

Outcomes:
Diagnosis fracture with Rayvolve app compare to gold standard
Accuracy of Artificial Intelligence Technology in Detecting Periapical Lesions in Human Teeth. Diagnostic Accuracy Experimental Study.
Trial ID: NCT07319182
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-12-06
Completion date: 2025-12-21
Sponsor: Future University in Egypt
Conditions: Periapical Lesion
Interventions: Cone beam computed tomography; artificial intelligence software
Locations: Future University in Egypt
Countries: Egypt
Enrollment: 100
Age range: 18 Years - 50 Years
Sex: ALL

Summary:
Aims to evaluate the accuracy of using Artificial intelligence software in detecting the presence of periapical lesion compared to CBCT imaging.

Eligibility criteria:
Inclusion Criteria:

1. All patients must be medically free from any systemic disease that can affect root canal treatment.
2. 18 to 50 years old patients with permanent teeth presenting with periapical pathosis.
3. No sex predilection.
4. All patients must have good oral hygiene.
5. Restorable teeth
6. Positive patient's acceptance for participating in the study.
7. Patients able to sign informed consent

Exclusion Criteria:

1. Patients above 50 years or patients below 18 years.
2. Patients with very poor oral hygiene.
3. Pregnant women after taking detailed history and pregnancy test must be in the first visit.
4. Psychologically disturbed patients.

Teeth that have:

* Periodontally affected with grade 2 or 3 mobility.
* Not restorable teeth.
* Abnormal anatomy and calcified canals

Outcomes:
CBCT scans