moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Lung and Diaphragm-Protective Ventilation by Means of Assessing Respiratory Work
Trial ID: NCT03612583
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: PHASE1
Start date: 2019-02-01
Completion date: 2022-11-30
Sponsor: University Health Network, Toronto
Conditions: Diaphragm Injury; Lung Injury; Respiratory Insufficiency; Mechanical Ventilation Complication
Interventions: Lung- and Diaphragm-Protective Ventilation
Locations: University Health Network
Countries: Canada
Enrollment: 23
Age range: 18 Years - None
Sex: ALL

Summary:
This study is designed to test a proposed strategy for lung- and diaphragm-protective ventilation (LDPV) in patients with acute hypoxemic respiratory failure. Ventilation and sedation will be titrated to evaluate whether it is feasible and safe within this patient population.

Eligibility criteria:
Inclusion Criteria:

* Diagnosed with acute hypoxemic respiratory failure
* PaO2:FiO2 ratio less than or equal to 300 mm Hg at time of screening
* Oral endotracheal intubation and mechanical ventilation
* Bilateral airspace opacities on chest radiograph or chest CT scan

Exclusion Criteria:

* Liberation from mechanical ventilation is anticipated within 24 hours
* Intubated for traumatic brain injury or stroke
* Contraindication to esophageal catheterization
* Intracranial hypertension

Outcomes:
Rate of patients achieving and maintaining LDPV targets
Multicenter Diagnostic Clinical Performance Study For Automated Detection of Diabetic Retinopathy
Trial ID: NCT05471986
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2024-08-01
Completion date: 2024-12-01
Sponsor: Ural Telekomunikasyon Sanayi Ticaret Anonim Sirketi
Conditions: Diabetic Retinopathy
Interventions: Color Fundus Photography/Non-Mydriatic Opthalmic Cameras
Enrollment: 900
Age range: 22 Years - None
Sex: ALL

Summary:
This study is a retrospective study to assess the efficacy and accuracy of the EyeCheckup software in screening for diabetic retinopathy in healthcare settings.

Eligibility criteria:
Inclusion Criteria:

* A diagnosis of either diabetes type 1 or diabetes type 2
* 22 years of age or older
* Patients Fundus images must include at least 1 macula centered and 1 optic disc nerve centered image
* All the subjects must be sequentially photographed meaning no omissions of data between a certain time frame by the sites providing images to ensure consecutive selection
* No history of any other retinal vascular disease, glaucoma, or another disease that may affect the appearance of the retina or optic disc (refractive error and ocular surface disease are allowed)

Subject with image taken by color fundus photography that meets the following requirements:

* The resolution of image is 1024×1024 pixels or higher
* The ability to provide a 7 standard or 4 wide field of view
* Must obtained from either Topcon NW400 or Cannon Model CR-2 AF or CR-2 Plus AF or Optomed Aurora Cameras along with the following data: their age, ethnicity and gender, patient ocular history as evaluated of verified by the clinical sites where data is obtained

Exclusion Criteria:

* No diagnosis of either diabetes type 1 or diabetes type 2
* The macula, optic nerve, or other part in the image of color fundus photography is unclear to determine the disease condition as judged by the certified ophthalmologist who is grading the images.

Outcomes:
Sensitivity and Specificity of EyeCheckup to detect more than mild diabetic retinopathy; Sensitivity and Specificity of EyeCheckup to detect vision-threatening diabetic retinopathy
SETİNT AI-Diseases
Trial ID: NCT05241483
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2020-03-30
Completion date: 2022-12-30
Sponsor: Setint AI Robotic Sistem Egitim ve Danısmanlık San. Tic. A.S.
Conditions: Predictive Value of Tests; Vital Signs; Laboratory Critical Values; Relative Value Scales; Reference Values
Interventions: mesure valition
Locations: Setint Ai
Countries: Turkey (Türkiye)
Enrollment: 1000
Age range: None - None
Sex: ALL

Summary:
Remote patient monitoring and detection of possible diseases with Artificial Intelligence Telemedicine System

Eligibility criteria:
Inclusion Criteria:

* No Eligibility Criteria

Exclusion Criteria:

\-

Outcomes:
Patient symptoms and disease confirmation
Surgeon's Performance in Predicting Postoperative Infections
Trial ID: NCT05961930
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2023-02-01
Completion date: 2023-09-10
Sponsor: Leiden University Medical Center
Conditions: Postoperative Infection
Interventions: Questionnaire
Locations: Sesmu Arbous
Countries: Netherlands
Enrollment: 594
Age range: 18 Years - None
Sex: ALL

Summary:
Post-surgical (bacterial) infections are the most frequent post-surgical complications, including deep or superficial wound infections, urinary tract infections, pneumonia, and even sepsis. Approximately 6.5-25% of all surgical patients will develop any type of bacterial infection. To personalize surgical infection management, (Artificial Intelligence) models are in the making to predict which patients are at high or low risk of developing a post-surgical infection. In order to benchmark these prediction models to the predictive capabilities of surgeons, the investigators aim to investigate the performance of surgeons in predicting the risk of a patient developing (any type) of post-surgical infection within 30 days.

Eligibility criteria:
Predictions are made for patients with the following inclusion and exclusion criteria:

Inclusion Criteria:

* Adult patients (\>18 years old)
* Acute or elective surgery
* Invasive or minimally invasive surgical procedures

Exclusion Criteria:

* Outpatient procedures or procedures not requiring any form of monitoring/anesthesia
* Procedures for which the primary indication is (treatment for) an infection
* Radiological procedures
* Cardiological catheterization procedures
* Psychiatric treatment under anaesthesia (i.e. electroconvulsive therapy)
* Sole anaesthetic procedures except for implantation of a neurostimulator
* Brachytherapy procedures
* Endoscopic procedures for diagnostic purposes only
* Procedures that only entail the taking of a biopsy for diagnostic purposes
* Patients that are pregnant
* Procedures out of office hours (before 8.00 am or after 5.00 pm)

Outcomes:
The discriminative predictive performance of surgeons with respect to estimating the risk of developing (any type) bacterial post-surgical within 30 days of surgery; The calibration properties of surgeons with respect to estimating the risk of developing (any type) bacterial post-surgical within 30 days of surgery
Optimization of Medical Time in the Emergency Department: Impact of an AI-Based System on Prescription Entry
Trial ID: NCT07312019
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-01
Completion date: 2027-01
Sponsor: Centre Hospitalier Universitaire, Amiens
Collaborators: Centre Hospitalier Public du Cotentin; University Hospital, Bordeaux; University Hospital, Strasbourg, France; Clinique de l'Estrée; CHU Aix-en-Provence
Conditions: Drug-related Iatrogenesis; Emergency Department; Artificial Intelligence; Clinical Decision Support; Prescription; Transcription; Medication; Reconciliation
Interventions: current hospital-standard databases; Posos
Locations: CHU Amiens
Countries: France
Enrollment: 770
Age range: 18 Years - None
Sex: ALL

Summary:
Drug-related iatrogenesis is a major public health issue, accounting for a significant proportion of adverse events and hospitalizations in emergency departments. Optimizing prescription management in this context is critical to improve both patient safety and physician efficiency This study aims to evaluate the impact of the POSOS AI-driven device on the medical time required for prescription management in polymedicated patients admitted to emergency departments. The main objective is to establish whether the use of POSOS can reduce transcription time compared to standard electronic management.

Eligibility criteria:
Inclusion Criteria:

* Age ≥18 years
* Admission to emergency department at a participating center
* Polymedicated patients with prescriptions including ≥8 medication lines (including those for long-term illnesses)
* Signed informed consent

Exclusion Criteria:

* Patient under legal protection/judicial measures (guardianship/custody)
* Lack of signed informed consent

Outcomes:
Medical time required for the transcription of prescriptions
Decreasing Irrational Antimicrobial Use in Bangladesh: A Digital Intervention Program
Trial ID: NCT06313619
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2023-10-23
Completion date: 2024-10-31
Sponsor: Bangabandhu Sheikh Mujib Medical University, Dhaka, Bangladesh
Conditions: Resistance Bacterial; Increased Drug Resistance; Drug Resistance, Microbial
Interventions: SmartAMR
Locations: Bangladesh Medical University
Countries: Bangladesh
Enrollment: 27
Age range: 18 Years - None
Sex: ALL

Summary:
We developed 'SmartAMR', a digital pharmacy system featuring real-time records, symptom checks, telemedicine, counselling, and medication reminders. This study aims to assess its feasibility, acceptability, and effectiveness within community pharmacy settings in Bangladesh.

A before-after designed pilot quasi experimental study will be conducted in urban areas. Data will be collected in both qualitative and quantitative methods. Multi-cluster sampling method will be used to select study locations. Pharmacists or drug sellers and patients aged more than 18 years and located within selected intervention areas will be our target population. A baseline survey will be conducted among the pharmacy volunteers to evaluate their knowledge, perception, and practice of rational use of anti-microbials prior to any intervention. Additionally, a household survey will be conducted among community members applying systematic random sampling where one adult from every selected household will be approached maintaining gender equality. After collecting the baseline data, intervention will be given to the community people through awareness building campaign and distributing leaflets and posters on rational use of anti-microbials. After three months, a follow-up survey will be conducted with the same questionnaire. For quantitative portion of the study, all the medicine shops in the selected study areas will be included in the study sample. For qualitative part, the sample will be selected purposively until data saturation and a total of 8 experts will be interviewed. Two sets of questionnaires for pharmacy volunteers and the community dwellers will be prepared based on literature review that will include health literacy questions regarding anti-microbial use. The questionnaire for the follow-up data collection will include some additional questions regarding effectiveness, acceptability, adoption, feasibility, and cost of the intervention.

Eligibility criteria:
Inclusion Criteria:

* Aged 18 years and above
* Registered drug seller/pharmacist
* Permanent residents of the study areas
* Willing to participate

Exclusion Criteria:

* Disoriented or critically ill

Outcomes:
Irrational usage of anti-microbials; Acceptance rate; Completion rate; AMR Knowledge; AMR attitude; AMR practice
The Impact of Artificial Intelligence-Assisted Case Discussion on Artificial Intelligence Attitude, Usage and Proficiency Among Midwifery Students
Trial ID: NCT07490665
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-03-16
Completion date: 2026-07-20
Sponsor: Fenerbahce University
Conditions: Healthy; Student
Interventions: Experimental AI Case Group
Locations: Fenerbahce University
Countries: Turkey (Türkiye)
Enrollment: 81
Age range: None - None
Sex: FEMALE

Summary:
This study aimed to examine the effect of artificial intelligence-assisted case discussions on midwifery students' use and proficiency of artificial intelligence technologies and their clinical competency levels. With the rapid development of artificial intelligence, its integration into healthcare education has become increasingly important. Supporting case-based learning with AI tools may enhance students' clinical decision-making, problem-solving, and critical thinking skills. Therefore, this study evaluates the contribution of AI-assisted educational approaches to the professional development of midwifery students.

Eligibility criteria:
Inclusion Criteria:

* Being a 3rd/4th year student in the Midwifery department
* Having previously taken courses on Healthy and High-Risk Pregnancy
* Having prepared and presented at least one midwifery care plan

Exclusion Criteria:

* Using more than 20% absenteeism in field applications

Outcomes:
Level of artificial intelligence usage; Usage and proficiency level
Utilization and Effectiveness of Ultra-low-dose Chest Computed Tomography Using Innovative CT Denoising Solution Based on Deep Learning Technology
Trial ID: NCT05398887
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2022-06-15
Completion date: 2022-10-01
Sponsor: Intermed Hospital
Conditions: Lung Diseases
Interventions: Low radiation dose CT; Underwent ultra dose chest CT; Artificial Intelligence based model
Enrollment: 200
Age range: 18 Years - None
Sex: ALL

Summary:
The main objective of the study is to evaluate the detection rate of pulmonary conditions, percentage of ionizing radiation dose reduction, and state of image quality of ULDCT coupling with innovative vendor-neutral CT denoising solution based on deep learning technology.

Eligibility criteria:
Inclusion Criteria:

* Patients aged over 18-year-old
* Patients undergoing CT Chest for all purpose

Exclusion Criteria:

* Age less than 18 years
* Any suspicion of pregnancy
* History of thoracic surgery or placement of the metallic device in the thorax
* An inability to hold respiration during CT

Outcomes:
Detection rate of pulmonary conditions; Contrast media dose
Deep Learning ECG Evaluation and Clinical Assessment for Competitive Sport Eligibility
Trial ID: NCT06285084
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2024-02-02
Completion date: 2027-02-02
Sponsor: I.R.C.C.S Ospedale Galeazzi-Sant'Ambrogio
Conditions: Sports Cardiology; Preventive Cardiology; Electrocardiogram; Artificial Intelligence
Locations: Ospedale Galeazzi-Sant'Ambrogio
Countries: Italy
Enrollment: 531
Age range: 18 Years - 60 Years
Sex: ALL

Summary:
The goal of this observationl study is to evaluate the possibility of building a Deep Learning (DL) model capable of analyzing electrocardiographic traces of athletes and providing information in the form of a probability stratification of cardiovascular disease.

Researchers will enroll a training cohort of 455 participants, evaluated following standard clinical practice for eligibility in competitive sports. The response of the clinical evaluation and ECG traces will be recorded to build a DL model.

Researchers will subsequently enroll a validation cohort of 76 participants. ECG traces will be analyzed to evaluate the accuracy of the model to discriminate participants cleared for sports eligibility versus participants who need further medical tests

Eligibility criteria:
Inclusion Criteria:

* Athletes in need of cardiac or sports medical evaluation for the issuance of competitive eligibility.
* Enlisted athletes involved in sports like soccer or those with mixed or aerobic cardiovascular demands according to the COCIS 2017 classification.
* Aged 18 years or older but not exceeding 60 years.
* No history of cardiovascular disease.
* Signed Informed Consent.

Exclusion Criteria:

* Athletes engaging in skill-based sports as per the COCIS 2017 classification.
* High clinical probability of cardiovascular disease, such as typical angina or heart failure.
* Pregnancy and/or breastfeeding (confirmed through self-declaration).

Outcomes:
DL model accuracy
Accuracy of Computer-aided Evaluation of the Relationship Between Mandibular Third Molar and Mandibular Canal on CBCT Images Using Deep Learning Model (Artificial Intelligence): Diagnostic Accuracy Study.
Trial ID: NCT05350228
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2022-05
Completion date: 2023-12
Sponsor: Cairo University
Conditions: Artificial Intelligence
Interventions: CNN based model
Locations: Faculty of dentistry cairo university
Countries: Egypt
Enrollment: 50
Age range: 25 Years - 65 Years
Sex: ALL

Summary:
Convolutional neural network (CNN) are computer applications that assist in the detection and/or diagnosis of diseases by providing an unbiased "second opinion" to the image interpreter10, aiming at improving accuracy and reducing time for analysis. With the rapid growth of Deep Learning (DL) algorithms in image-based applications, CAD systems can now be trained by DL to provide more advanced capability (i.e., the capability of artificial intelligence \[AI\]) to best assist clinicians).

Eligibility criteria:
Inclusion Criteria:

* • CBCT Scans showing Mandibular third molar of patients aging from 25 to 65 years old

  * The FOV should clearly show the third molar completely with its roots and the IAN.
  * Voxel size of 0.2mm.
  * Mandibular third molars. Absence of artifacts, dental implants in the adjacent teeth.

Exclusion Criteria:

* • CBCT images of sub-optimal quality or artifacts/high scatter interfering with proper assessment.

Outcomes:
Accuracy of the automatic evaluation of the relationship between mandibular third molar and the mandibular canal.
Improving Polyp Detection Rate by Artificial Intelligence in Colonoscopy
Trial ID: NCT05322993
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2021-11-19
Completion date: 2025-06-30
Sponsor: Haraldsplass Deaconess Hospital
Collaborators: Haukeland University Hospital; European Society of Gastrointestinal Endoscopy; Kanalspesialistene AS
Conditions: Colonic Polyp
Interventions: GI Genius; Standard white light colonoscope
Locations: Haraldsplass Deaconess Hospital; Haukeland University Hospital; Kanalspesialistene AS
Countries: Norway; Norway; Norway
Enrollment: 5034
Age range: None - None
Sex: ALL

Summary:
The aim of this study is to investigate if the use of artificial intelligence (AI) in colonoscopy improves the polyp detection rate, and if the use of AI has a learning effect.

Eligibility criteria:
Inclusion Criteria:

* Patients coming to outpatient clinics to perform colonoscopies

Exclusion Criteria:

* Total colectomy
* Reservation against registration in Gastronet, the national quality register for colonoscopy in Norway

Outcomes:
Polyp detection rate (PDR) with and without artificial intelligence (AI); PDR after the use of AI, is there a learning effect?
The Accuracy of Computer Aided Detection of Periapical Radiolucencies on Cone -Beam Computed Tomography Images Using Artificial Intelligence: Diagnostic Accuracy Study.
Trial ID: NCT05538104
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2022-09
Completion date: 2023-12
Sponsor: Cairo University
Conditions: Periapical Lesions
Enrollment: 50
Age range: None - None
Sex: ALL

Summary:
A diagnostic accuracy study to assess the accuracy of a newly developed deep learning model in the automatic detection of periapical radiolucent lesions of upper and lower jaws by comparing it with experienced radiologists' opinion, which represents the ground truth.

Hypothesis: The null hypothesis is that the results of the deep learning model are as accurate as the radiologists' opinion.

Eligibility criteria:
Inclusion Criteria:

* • CBCT scans of maxilla and mandible with good quality free pf periapical radiolucent lesions .

  * CBCT scans of maxilla and mandible with good quality showing periapical radiolucent lesions

Exclusion Criteria:

* • CBCT images of sub-optimal quality or artifacts / high scatter interfering with proper assessment.

Outcomes:
• Accuracy of automatic detection of periapical radiolucent lesions on CBCT images.
Feasibility of Non-Invasive Detection and Preservation of Neurocognitive Signals in the Peri-Death Period Using Brain-Computer Interface and Artificial Intelligence: A Prospective Observational Study (NeuroCogPresv)
Trial ID: NCT07477028
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2026-09-01
Completion date: 2035-09-30
Sponsor: Noah Tech, Corp.
Collaborators: Columbia University; Massachusetts Institute of Technology; Stanford University; City of Hope Medical Center
Conditions: Terminal Illness; End-of-Life Care; Death; Brain Death; Death Anxiety; Consciousness; Electroencephalography; Gamma Oscillations; Near-Death Phenomena; Cognitive; Cognition; Memory; EEG; Severe Acute Trauma
Enrollment: 20
Age range: 18 Years - None
Sex: ALL

Summary:
Background: Recent electroencephalography (EEG) data indicate that the transition from clinical death to cellular death is marked by highly organized neurophysiological events, including significant surges in gamma-band power, cross-frequency coupling, and distinct spreading depolarization waves. This prospective, observational feasibility study utilizes rapid-deployment, high-density, noninvasive BCI hardware paired with proprietary AI analytics to detect, classify, and securely archive these terminal neurocognitive signals.

Objectives: (1) Quantify transient gamma-band activity and cross-frequency connectivity post-clinical death; (2) Validate the efficacy of machine learning models for real-time signal classification in high-noise clinical environments; (3) Establish a highly secure, encrypted bio-informational archive of peri-life EEG data.

Design: Prospective, open-label, multicenter, observational cohort (n\>20).

Eligibility criteria:
Inclusion Criteria:

1. Adults ≥18 years with terminal illness or severe acute trauma
2. Do-not-resuscitate (DNR/DNI) order in place
3. Surrogate decision-maker available and willing to provide informed consent
4. Expected survival ≤7 days (physician estimate)

Exclusion Criteria:

1. Brain death already declared \> 24 hours prior to enrollment
2. Contraindication to EEG/BCI headset placement (e.g., severe scalp injury)
3. Patient lacks a legally authorized representative

Outcomes:
Detection of Neurocognitive Signals After Clinical Death Prior to Brain Death
Development of Avatar-based Intervention to Support Patients Undergoing Reduced-Intensity Allogenic Stem Cell Transplantation
Trial ID: NCT05600101
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2022-12-06
Completion date: 2023-05-16
Sponsor: Dana-Farber Cancer Institute
Collaborators: National Cancer Institute (NCI)
Conditions: Hematopoietic Cell Transplantation
Interventions: Care.Coach
Locations: Brigham and Women's Hospital; Dana-Farber Cancer Institute
Countries: United States; United States
Enrollment: 18
Age range: 18 Years - None
Sex: ALL

Summary:
The goal of this research study is to provide an avatar-based technology during a subject's stay for participants who have been admitted to the hospital for reduced-intensity conditioning (RIC) allogeneic transplant. The intervention received will be the care.coach technology.

Eligibility criteria:
Inclusion Criteria for HCT focus group:

* Age 18+.
* DFCI/HCC HCT nurse navigators, inpatient HCT nurses, and HCT physicians and extenders (NP/PA).

Inclusion Criteria for care.coach pilot:

* Age 18+.
* Admitted for reduced-intensity conditioning (RIC) allogeneic HCT

Exclusion Criteria for care.coach pilot:

Deemed by clinical staff or RA to be unable to converse with an avatar, due to:

* Severe, uncorrectable hearing impairment and simultaneous severe, uncorrectable vision impairment.
* Severe speech impairment that precludes understanding by staff (and by extension, by the avatar).
* Not fluent in English.

Outcomes:
Number of Participants Retained