moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Clinical Development of a Tool for Optimized Self- and Hetero-diagnosis of Stroke Using Artificial Intelligence: Stage1- Collection of Video-clinical Data in a Pragmatic Situation.
Trial ID: NCT05959746
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2023-08-28
Completion date: 2024-12
Sponsor: Centre Hospitalier Universitaire de Nīmes
Collaborators: Société par Action Simplifiée AI-Stroke
Conditions: Stroke, Cerebrovascular
Interventions: AI-STROKE application
Locations: Centre Hospitalier Universitaire
Countries: France
Enrollment: 300
Age range: 18 Years - None
Sex: ALL

Summary:
The study authors aim to form a collection of video-clinical data in a pragmatic situation to enable the development of relevant AI algorithms (for both hetero- and self-diagnosis modes). The aim is to optimize management through early diagnosis (self- and hetero-diagnosis) and thus to reduce sequelae disability.

The study authors hypothesize that some stroke patients will be able to successfully perform a self-test consisting of a few exercises dictated by an application on a smartphone or tablet and recorded on video.

Eligibility criteria:
Inclusion Criteria:

* Patients treated in the emergency department or hospitalized in the NICU at the CHU de Nîmes for suspected stroke or transient ischemic attack in the acute phase (\<72h), with or without motor deficit
* Patient to be seen again in consultation within 4 months
* Patient has given free and informed consent and signed the consent form. If the patient is not in a position to give consent, it must be obtained, prior to filming the first video, from the designated trusted support person or relatives present. In this case, data will not be used until the patient is able to sign the consent (CNIL).
* Patient affiliated or beneficiary of a health insurance scheme

Exclusion Criteria:

* Patients who do not speak or read French.
* Patient in a period of exclusion determined by another study.
* Patient under court protection, guardianship or curatorship.
* Pregnant, parturient or breast-feeding patients.

Outcomes:
Usable video recording made by the patient; Usable video recording made by the hospital worker
First-in-human Navigation Endoscopic Ultrasound (EUS) System Clinical Study
Trial ID: NCT05515705
Status: TERMINATED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2023-01-10
Completion date: 2024-10-01
Sponsor: IHU Strasbourg
Conditions: Pancreatic Disease
Interventions: EUS navigation system
Locations: Pôle Hépato-Digestif, NHC
Countries: France
Enrollment: 21
Age range: 18 Years - None
Sex: ALL

Summary:
The implementation of Echo-endoscopy (or EUS for: Endoscopic UltraSound) is limited by the difficulty of obtaining quality EUS procedures, since the accuracy of the method relies on the endoscopist's abilities. Indeed, both cognitive and technical skills are required for endoscopic manipulation and image interpretation, as well as a thorough knowledge of intra-abdominal anatomy. The "EUS-GPS" system aims to assist the operator in understanding the orientation and location of the probe tip within the patient. For that, a Global Positioning System (GPS) for EUS procedures is intraoperatively provided, using a 3D-based model based on recorded preoperative data.

In this clinical investigation, the EUS GPS system is intended to help EUS operators to correctly navigate towards the pancreatic gland, perform a comprehensive assessment of the area of interest, and aid in the detection and identification of lesions.

Eligibility criteria:
Inclusion Criteria:

* Man or woman over the age of 18 years old
* Patient having a EUS procedure for pancreas study planned within the frame of his or her clinical care.
* Patient whom pre-operative imaging indicates one lesion with a good definition on the triphasic CT scan done within the 60 days prior to the EUS.
* Patient able to receive and understand information related to the study and give his or her written consent
* Patient affiliated to the French social security system

Exclusion Criteria:

* Patient unable to give informed consent
* Patient with altered anatomy of the upper digestive tract due to previous surgery
* Patient with known abnormal anatomy of the upper digestive tract
* Patient wearing a metal implantable device (pacemaker, etc.)
* Patient presenting, according to the investigator's judgment, a disease which may prevent participation in the procedures provided for by the study
* Patient with an ASA (American Society of Anesthesiologists) score \>2
* Patient with multiple pancreatic lesions.
* Patient unable to tolerate general anaesthesia
* Patient with a body mass index (BMI) \< 23 and \> 40
* Patient with weight variations of \>10% between the date on which the CT scan was performed and the date of the procedure.
* Pregnant or lactating patient
* Patient in exclusion period (determined by a previous or a current study)
* Patient under guardianship or trusteeship
* Patient under the protection of justice of deprives of liberty
* Patient in situation of emergency

Outcomes:
Evaluation of the EUS navigation system's procedure duration compared to standard procedure times
Accuracy of Artificial Intelligence Technology to Detect the Presence of Missed Canals of Endodontically Treated Mandibular Second Molar : Diagnostic Accuracy Experimental Study
Trial ID: NCT07077694
Status: ENROLLING_BY_INVITATION
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-12-25
Completion date: 2025-10-29
Sponsor: Future University in Egypt
Conditions: Presence of Missed Canals; Morphological Variation
Interventions: Root Canal Retreatment
Locations: Faculty of Oral and Dental Medicine future university
Countries: Egypt
Enrollment: 3
Age range: 18 Years - 50 Years
Sex: ALL

Summary:
Aims to detect the Accuracy of Artificial Intelligence Technology in Detecting the Presence of Missed Canals of Endodontically Treated Mandibular Second Molar. Will Artificial intelligence software, CBCT and Clinical visual examination with dental operating microscope differs in the detection of presence of missed canals of endodontically treated human mandibular second molar ?

Eligibility criteria:
Inclusion Criteria:

\- 1) Patients must be medically free from any systemic disease that can affect root canal treatment.

2\) Age range of the patient should be between 18 - 50 years old. 3) No sex predilection. 4) Patients must have good oral hygiene. 5) Patients with root canal-treated teeth. 6) Restorable teeth. 7) Positive patient acceptance for participating in the study. 8) Patients can sign informed consent.

Exclusion Criteria:

\- 1) Patients with very poor oral hygiene. 2) Patients above 50 years and below 18 years. 3) Pregnant women after taking detailed history and pregnancy test must be in the first visit.

4\) Psychologically disturbed patients. 5) Tooth periodontally affected with grade 2 or 3 mobility. 6) Non restorable teeth. 7) Tooth with abnormal anatomy and calcified canals.

Outcomes:
Presence of missed canals
Project Survival-Prospective Biomarker Discovery to Transform Diagnosis and Treatment for Patients With Pancreatic Diseases and Cancer
Trial ID: NCT02781012
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2016-03
Completion date: 2022-02
Sponsor: Pancreatic Cancer Research Team
Collaborators: BPGbio
Conditions: Pancreatic Neoplasms; Pancreatic Cancer; Pancreatitis; Pancreatic Adenocarcinoma; Metastatic Pancreatic Cancer
Locations: Banner Health - MD Anderson Cancer Center; Mayo Clinic - Arizona; Beth Israel Deaconess Medical Center; Mercy Health - St. Mary's; Virginia Piper Cancer Institute - Allina Health; Washington University School of Medicine
Countries: United States; United States; United States; United States; United States; United States
Enrollment: 436
Age range: 18 Years - None
Sex: ALL

Summary:
This sample-collection study is open to participants in several categories: healthy volunteers (with or without a family history of pancreatic cancer) and individuals diagnosed with pancreatitis or any stage of pancreatic cancer. All participants will submit urine, saliva and blood samples; pancreatitis and pancreatic cancer patients will also submit tissue samples if biopsy/ies or surgery is part of the care being provided by their doctor. In partnership with Berg Health, LLC, biomarkers will be investigated for potential use in early detection of pancreatic cancer, to determine prognosis of patients, and to find the most appropriate treatments for patients.

Eligibility criteria:
Inclusion Criteria:

1. Subject is over 18 years of age and has been evaluated at a participating Pancreatic Cancer Research Team (PCRT) study site, AND
2. Subject has been recently diagnosed with pancreatic cancer (any stage) OR
3. Subject has been recently (within 12 months) diagnosed with acute or chronic pancreatitis OR
4. A healthy individual, with no known pancreatic disease (cancerous or otherwise), AND with one first-degree relative with pancreatic cancer, OR two second-degree relatives with pancreatic cancer OR one who has undergone surgery for suspected pancreatic cancer, and who is found to have a non-pancreatic cancer pathology upon final local site or central pathology review, OR
5. A healthy individual with no known benign or malignant pancreatic disease, and no family history of pancreatic cancer.

Exclusion Criteria:

A subject will not be eligible for inclusion in this study if any of the following criteria apply:

* Subject is \< 18 years old.
* Subject has been diagnosed with a primary cancer other than pancreas in the past 5 years, and is currently receiving treatment for this cancer.
* Subject is a pregnant or lactating female;
* Subject is not willing or is unable to give informed consent to this study.

Outcomes:
Berg's Interrogative Biology® Platform
A Study to Assess the Effect and Safety of Artificial Intelligence Assisted Insulin Titration System on Glucose Control in Type 2 Diabetes Mellitus Patients: A Single-center, Open-labeled, Parallel, Randomized Controlled Trial
Trial ID: NCT04053959
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: PHASE4
Start date: 2019-09-27
Completion date: 2020-04-15
Sponsor: Shanghai Zhongshan Hospital
Conditions: Type 2 Diabetes
Interventions: AI assisted insulin system; Physician based insulin regime
Locations: 180 Fenglin Road
Countries: China
Enrollment: 46
Age range: 18 Years - 75 Years
Sex: ALL

Summary:
This is a single-center, open-labeled, parallel group, randomized controlled trial to access the effect and safety of the Artificial Intelligence Assisted Insulin Titration System in patients with Type 2 Diabetes Mellitus.

Eligibility criteria:
Inclusion Criteria:

* Men or women aged 18-75 years old;
* Subjects who had been diagnosed with type 2 diabetes;
* Subjects who are on treatment with insulin for at least 3 months;
* HbA1c: 7.0%-11.0%.

Exclusion Criteria:

* Subjects with acute complications of diabetes such as ketoacidosis or hyperglycemic hyperosmolar state;
* Subjects who change the insulin regimens during hospitalization;
* BMI ≥ 45kg/m2;
* Women who are pregnant or nursing;
* Subjects with severe cardiac, hepatic, renal or general diseases;
* Subjects with psychiatric disorders or impaired cognitive function;
* Subjects with severe edema, infections or peripheral circulation disorders;
* Patients treated with surgery during hospitalization;
* Subjects that are, in the judgement of the investigator, unlikely to comply with the protocol.

Outcomes:
Proportion of time in target range
Comparison of QT Interval Readings Between Smartwatch Combined With Cardiologs Artificial Intelligence and 12-lead ECG in Subjects Hospitalized for Antiarrhythmic Drug Initiation and Follow-up
Trial ID: NCT05161065
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2022-02-12
Completion date: 2023-02-28
Sponsor: Cardiologs Technologies
Conditions: Atrial Fibrillation
Interventions: Cardiologs
Locations: Massachusetts General Hospital
Countries: United States
Enrollment: 50
Age range: 22 Years - None
Sex: ALL

Summary:
The WatchQT study will compare the performance of a smartwatch combined with Cardiologs ECG AI Analysis system in monitoring corrected QT (QTc) intervals with that measured on a manually read 12-lead ECG in subjects with Normal Sinus Rhythm (NSR) during antiarrhythmic drug (AAD) initiation and follow-up at the hospital.

Eligibility criteria:
Inclusion Criteria:

1. Subjects over 22, able and willing to participate in the study
2. Subjects who are admitted to the hospital in NSR or AF and are eligible for AAD (class Ia or class III) dose initiation or re-initiation.
3. Subjects having read the patient information letter and provided his/her consent to participate in writing, by dating and signing the informed consent prior to any trial-related procedure being conducted

Exclusion Criteria:

1. Subjects with Cardiac Implantable Electronic Devices (CIED)
2. Pregnant or breast-feeding subjects

Outcomes:
Concordance between QTc intervals (in ms) measured by smartwatch combined with Cardiologs AI and that measured by manually read 12-lead ECG
Apply Machine Learning to the Interpretation of Urinary Crystal Morphology.
Trial ID: NCT06178575
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2024-01-01
Completion date: 2024-12-31
Sponsor: Yi-Shiou Tseng
Conditions: Kidney Calculi
Enrollment: 200
Age range: 20 Years - None
Sex: ALL

Summary:
The goal of this observational study is to developing an image-based artificial intelligence software that can automatically interpret the types and sizes of crystals in urine. The main question\[s\] it aims to answer are:

* Allowing healthcare professionals to input urine images and receive real-time reading results on crystal types and sizes.
* This aims to provide a faster, more objective, and accurate analysis of crystals.

We anticipate delivering an image AI software suitable for practical applications, promoting the automation and accuracy of urine crystal analysis.

Eligibility criteria:
Inclusion Criteria:

* Retrospectively analyze the urine crystal images preserved from the previous study 107123-E for crystal type analysis. Subsequently, conduct image preprocessing and label categorization for machine software learning and inference. The interpreted results will then be assessed for accuracy using statistical analysis software.

Exclusion Criteria:

* Not applicable

Outcomes:
Kappa statistics
The Effect of Human-AI Uncertainty Calibration vs. AI Uncertainty Alone on the Diagnostic Accuracy of Human Experts for Skin Lesions - a Randomized Controlled Trial.
Trial ID: NCT07468357
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-03-01
Completion date: 2026-11
Sponsor: Copenhagen Academy for Medical Education and Simulation
Collaborators: Technical University of Denmark
Conditions: Skin Lesions
Interventions: Base Model; FDM
Enrollment: 50
Age range: None - None
Sex: ALL

Summary:
The goal of this randomized controlled study is to compare the effect of a new, personalized uncertainty-aware decision model (FDM) to a standard image recognition model in improving the diagnostic accuracy while reducing diagnostic uncertainty in experienced dermatologists tasked with differentiating between melanomas, moles and other benign skin lesions. The main question it aims to answer: Is the FDM a feasible method for an improved human AI partnership in which trust is build, misdiagnoses are avoided, and uncertainty is duly introduced or reduced.

The investigators expect to see only a slight increase in collective diagnostic accuracy for both interventions as the the human participants are skilled dermatologist and thus have high accuracies pre-intervention.

The investigators expect to see a higher increase in diagnostic certainty for the FDM intervention compared to the diagnostic certainty in the Base Model intervention.

The investigators expect to see a higher amount of diagnosis changes from incorrect to correct in the FDM group compared to the Base Model group.

The investigators do not expect any learning effect during the study.

Participants will start by answering a series of training cases consisting of images of skin lesions. These are used to train their individual FDM (only for the FDM-intervention group). From here, the participants will be randomized into two arms determining which of the two interventions they are exposed to. The participants will solve each case withouth any intervention first, and this reply will act as a control.

Eligibility criteria:
Inclusion Criteria:

* Board certified dermatologists with clinical experience in dermoscopic diagnosis.

Exclusion Criteria:

* Doctors who have not yet finished their specialization and dermatologists.
* Dermatologists without clinical experience in dermoscopic diagnosis.

Outcomes:
Accuracy
Effect of the Implementation of Telerehabilitation-based Exercises Compared to the Standard Use of Exercise Dossier and Intervention Diary in a Home Therapeutic Exercise Intervention in Patients With Low Back Pain
Trial ID: NCT06446596
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-11-04
Completion date: 2026-12
Sponsor: University of Valencia
Conditions: Low Back Pain
Interventions: Exercise supported by the digital physiotherapy and telerehabilitation software TRAK; Standard home-based exercise
Locations: University of Valencia
Countries: Spain
Enrollment: 30
Age range: 18 Years - 65 Years
Sex: ALL

Summary:
The objective of the study is to compare the effect of a telerehabilitation-based exercise program, versus a home therapeutic exercise program scheduled through a dossier and an exercise diary, with respect to disability (measured using the OSWESTRY questionnaire) and other variables (range of motion, strength, fatigue, histological changes, pain, medication intake, psychosocial factors and adherence), in patients with low back pain.

Eligibility criteria:
Inclusion Criteria:

* Nonspecific subacute or chronic low back pain

Exclusion Criteria:

* Neuropathic pain
* Pain below the knees
* Pain in legs with tingling, sensation of current or sensory alterations
* Positive neurological tests. Lack of strength, sensitivity or altered reflexes
* Canal stenosis
* Nociplastic pain (fibromyalgia, generalized pain, emotional lability, affective and cognitive implications)
* Previous lumbar surgery
* Specific pathology (rheumatic, spondylolisthesis, oncological, fractures...)
* Pregnancy
* Performing medium-high intensity exercise of more than 100 minutes a week
* Pathology that prevents exercise (decompensated cardiac pathology, decompensated respiratory pathology, significant mobility difficulties...)
* Lack of digital skills
* Visual problems that prevent exercise with the phone screen

Outcomes:
Disability
Effect of Artificial Intelligence-Augmented Human Instruction on Surgical Simulation Performance: A Randomized Controlled Trial
Trial ID: NCT06273579
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-03-09
Completion date: 2024-09-14
Sponsor: McGill University
Conditions: Surgical Education
Interventions: Expert instruction using AI tutor script; AI-augmented personalized expert instruction
Locations: Neurosurgical Simulation and Artificial Intelligence Learning Centre
Countries: Canada
Enrollment: 88
Age range: 18 Years - None
Sex: ALL

Summary:
At the Neurosurgical Simulation and Artificial Intelligence Learning Centre, we seek to provide surgical trainees with innovative technologies that allow them to improve their surgical technical skills in risk-free environments, potentially improving patient operative outcomes. The Intelligent Continuous Expertise Monitoring System (ICEMS), a deep learning application that assesses and trains neurosurgical technical skill and provides continuous intraoperative feedback, is one such technology that may improve surgical education.

In this randomized controlled trial, medical students from four Quebec universities will be blinded and randomized to one of three groups (one control and two experimental). Group 1 (control) will be provided with verbal AI tutor feedback based on the ICEMS error detection. Group 2 will be tutored by a human instructor who will receive ICEMS error data and deliver verbal instruction using the same words as the ICEMS. Group 3 will be tutored by a human instructor who will be provided with ICEMS data and will then deliver personalized feedback.

The aim of this study is to determine how the method of delivery of verbal surgical error instruction influences trainee technical skill acquisition and transfer. Evaluating trainee responses to AI instructor verbal feedback as compared to feedback from human instructors will allow for further development, testing, and optimization of the ICEMS and other AI tutoring systems.

Eligibility criteria:
Inclusion Criteria:

* Medical students who are actively enrolled in their preparatory, first, second year of medical school at any Quebec institution who do not fit the exclusion criteria.

Exclusion Criteria:

* Prior use of the NeuroVR (CAE Healthcare) simulator.

Outcomes:
Intelligent Continuous Expertise Monitoring System (ICEMS) expertise score - Technical skill acquisition across practice tasks on NeuroVR simulator; Intelligent Continuous Expertise Monitoring System (ICEMS) expertise score - Technical skill transfer during complex realistic task on NeuroVR simulator
Safety and Accuracy of Artificial Intelligence-aided Precision MRI Assessment for the Optimization of Prostate Biopsy in Men With Suspicion of Prostate Cancer: a Multicenter Randomized Controlled Trial.
Trial ID: NCT05513638
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2022-08-22
Completion date: 2026-08-22
Sponsor: The First Affiliated Hospital with Nanjing Medical University
Collaborators: The First Affiliated Hospital of Soochow University
Conditions: the Application of Artificial Intelligence in the Diagnosis of Prostate Cancer
Interventions: the clinical use of artificial intelligence in the diagnosis of prostate cancer
Locations: Yu-Dong Zhang
Countries: China
Enrollment: 2000
Age range: 60 Years - 90 Years
Sex: MALE

Summary:
One-fifth of all men will develop clinically significant prostate cancers (CsPC) in their lifetime. An estimated 268,490 new prostate cancer (PCa) cases and 34,500 deaths are expected in the United States during the year 2022, making PCa the second most common cause of cancer-related deaths in men. MRI with the Prostate Imaging Reporting and Data System (PI-RADS) is a current widely used communicative tool for both CsPC detection and guiding targeted prostate biopsy. The high level of expertise required for accurate interpretation and persistent inter-reader variability has limited consistency and it has hindered the widespread adoption of PI-RADS. Artificial intelligence (AI) shows a broad prospect for medical interpretation and triage in various challenging tasks , including the PCa detection and staging with MRI. While rapid technical advances are furthering the application of AI medical imaging, their implementation in clinical practice remains a major hurdle. Besides, the prospect of data-derived AI tool is to assist human experts rather than replace them, and whether AI can match or exceed the human experts is still a matter of debate. Therefore, despite strong potential, there is urgent need for research to better quantify the accuracy, generalizability and clinical applicability before the clinical use of an AI in a real-world clinical setting.

Eligibility criteria:
Inclusion Criteria:

* Clinical suspicious of prostate cancer, presenting with an elevated prostatic specific antigen and/or abnormal digital rectal examination

Exclusion Criteria:

* (1) \<60 years of age; (2) a previous surgery, radiotherapy or drug therapy for prostate cancer (interventions for benign prostatic hyperplasia or bladder outflow obstruction were deemed acceptable); (3) incomplete mp-MRI examination or artifacts of the images.

Outcomes:
biopsy or surgery confirmed newly-diagnosed prostate cancer and clinically significant prostate cancer