moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Airway Coach Project: Prediction of Videolaryngoscopy Strategy With Clinical and Ultrasound Parameters (Multicentric)
Trial ID: NCT06881589
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-01-27
Completion date: 2026-09-01
Sponsor: Clinica Universidad de Navarra, Universidad de Navarra
Collaborators: County Clinical Emergency Hospital Cluj-Napoca; Hospital Ancona; Hospital CUF Porto
Conditions: Difficult Videolaryngoscopy
Interventions: Ultrasound mesurements
Locations: Clinica Universidad de Navarra
Countries: Spain
Enrollment: 210
Age range: 18 Years - 90 Years
Sex: ALL

Summary:
This multicentre observational study will collect clinical parameters, demographic data, and point-of-care airway ultrasound measurements from adult patients undergoing videolaryngoscopy across participating centres. The collected data will be analysed using artificial intelligence-based analytical approaches, including statistical and machine-learning techniques, to examine their association with predefined videolaryngoscopy-related outcomes.

Eligibility criteria:
Inclusion Criteria:

* Patients (male or female) ASA I-III, aged between 18 and 90 years, undergoing scheduled surgery requiring orotracheal intubation. The signature of the informed consent is required authorizing its inclusion in the study.

Exclusion Criteria:

* Obesity class II defined as a BMI greater than 35.
* Pregnant.
* Cervical tumors, goiter or patients who have required radiotherapy at the cervical level
* Abnormalities that condition anatomy alterations such as facial / cervical fractures.
* Maxillofacial abnormalities
* People who cannot give their consent

Outcomes:
Distance from skin to Hyoid bone measured with lineal ultrasound probe; Distance from skin to epiglottis measured with lineal ultrasound probe; Tongue Thickness measured with convex probe; Distance from Jaw to Hyoid bone distance measured with convex probe
iMatter2: An Artificial Intelligence (AI)-Driven Approach to Supercharge a Novel Digital Patient-reported Outcomes Tool for Diabetes Management
Trial ID: NCT05884775
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-11-04
Completion date: 2028-05-31
Sponsor: NYU Langone Health
Conditions: Type 2 Diabetes
Interventions: Mobile Health (mHealth) Patient-Reported Outcome (PRO) tool
Locations: NYU Langone Health
Countries: United States
Enrollment: 353
Age range: 18 Years - None
Sex: ALL

Summary:
Investigators will conduct a hybrid type 1 effectiveness-implementation randomized controlled trial (RCT) to evaluate the effectiveness of iMatter2 versus usual care on reduction in HbA1c at 12-months (primary outcome) among patients with Type 2 diabetes (T2D).

Eligibility criteria:
Inclusion Criteria:

PCP Group:

* Fulltime primary care provider (MD/DO, NP) practicing at the participating family health centers or faculty group practices and
* Provide care to at least five patients with a diagnosis of T2D

Patient Group:

* Have a diagnosis of T2D for ≥6 months;
* Have uncontrolled T2D defined as HbA1c \>7% documented in the EHR on at least two visits in the past year;
* Fluency in English or Spanish;
* Be willing to send/receive text messages; and
* Be \> 18 years of age.

Exclusion Criteria:

Patient Group:

* Refuse or are unable to provide informed consent;
* Have acute renal failure, end stage renal disease (ESRD) or evidence of dialysis, renal transplantation, or other ESRD-related services documented in the EHR;
* Have significant psychiatric comorbidity or reports of substance abuse (as documented in the EHR);
* Are pregnant or planning to become pregnant within 12 months;
* Currently participate in another T2D study; or
* Plan to discontinue care at the clinic within the next 12 months.

Outcomes:
Change in Hemoglobin A1C (HbA1c)
Effect of Music on Surgical Performance During Artificial Intelligence Based Simulation Training: A Randomized Controlled Trial
Trial ID: NCT07111481
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-08-05
Completion date: 2026-03-14
Sponsor: McGill University
Conditions: Surgical Education
Interventions: Mozart music
Locations: Neurosurgical Simulation and Artificial Intelligence Learning Centre
Countries: Canada
Enrollment: 97
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.

Previous research has found that music can impact cognitive performance and learning outcomes. However, the effects of music on neurosurgical simulation performance-along with the associated affective-cognitive responses-remain largely unexplored.

In this randomized controlled trial, medical students from four Quebec universities will be blinded and randomized to one of two groups. The control group will undergo a simulation training session without music, while the intervention arm will listen to a Mozart piano sonata during their session. The aim of this study is to determine how listening to Mozart music during surgical simulation training influences learner technical skill acquisition and transfer, as well as their emotions and cognitive load.

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
Data Collection for a Surface Caries Detection Software
Trial ID: NCT07048444
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-07-22
Completion date: 2026-03-01
Sponsor: 3Shape A/S
Collaborators: New York University
Conditions: Caries Active; Caries Root; Caries,Dental; Caries; Initial; Caries Assessment
Interventions: N/A as observational study
Enrollment: 88
Age range: 18 Years - None
Sex: ALL

Summary:
Dental caries (dental cavity) is very common and can impact oral health and oral health-related quality of life. Dental caries is when the tooth surfaces break down, and if left untreated, can cause tooth pain. In routine clinical practice, dental professionals typically assess caries lesions by looking at patients' teeth, touching the areas of interest with a dental probe, and running a probe over the teeth to check the buildup of bacteria.

Dentists often use x-rays to help find cavities, and they can also take pictures of the teeth to show what the problem looks like.

New tools called intraoral scanners are becoming more useful for finding cavities. These scanners do not use radiation and do not hurt. Some computer programs that use artificial intelligence (AI) can also help dentists find cavities. AI can study the spots on patients' teeth and help make better, faster decisions. The purpose of this study is to collect data that will be used to develop and test an AI software that can help dentists in the detection of caries.

Eligibility criteria:
Inclusion Criteria:

* Willing and able to give informed consent for participation in the study
* Male or female, aged 18 years or above
* Patients looking for dental treatment
* Good general health (i.e. absence of any condition that the Principal Investigator evaluates as a risk either to the subject or to the data quality)
* Able (in the Investigator's opinion) and willing to comply with all study requirements.
* Minimum of 15 natural teeth without crowns
* At least one cavitated caries lesion

Exclusion Criteria:

* Patients with orthodontic appliances and permanent retainers
* Any other significant disease or disorder which, in the opinion of the Investigator, may either put the participants at risk because of participation in the study, or may influence the result of the study, or the participant's ability to participate in the study)
* Patients who are not able or willing to sign the Informed Consent
* Patients who are currently pregnant. A urine pregnancy test will be administered during the study visit for woman who could be pregnant

Outcomes:
Diagnostic accuracy for surface caries detection software (SCDS) in surface caries detection on visible tooth surfaces compared to clinical visual tactile examination using the International Caries Detection and Assessment System (ICDAS)
Volatolomic and Proteomic Profile; Breath, Urine and Serum as Non-invasive Tools for Early Diagnosis of Lung Cancer
Trial ID: NCT06341387
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2023-05-05
Completion date: 2024-07-31
Sponsor: European Institute of Oncology
Collaborators: University of Rome Tor Vergata
Conditions: Lung Cancer; Non-small Cell Lung Cancer Stage I; Non-small Cell Lung Cancer Stage II; Lung Cancer Diagnosis
Interventions: Breath, urine and blood analysis
Locations: Europen insitute of Oncology- Division of Thoracic Surgery
Countries: Italy
Enrollment: 200
Age range: 50 Years - 80 Years
Sex: ALL

Summary:
The goal of this prospective, case-control study is to discover the specific "omics" biomarkers of early stage of lung cancer using the non-invasive samples (breath, urine and serum) in a total of 200 subjects (100 healthy controls and 100 lung cancer patient). The main questions it aims to answer are:

* Which are the "omics" biomarkers that characterize the early stage of lung cancer?
* How to Translate Laboratory Data into Clinical Data?

For each participant we will collected the breath, urine and blood samples. In lung cancer patients group the samples will be sample before lung cancer resection. The samples of Breath, urine and serum will be analysed using different type of analysis: eNose and the Gas Chromatography combined with Ion Mass Spectrometry (GC/IMS). Moreover, Serum will be analyzed by mass-spectrometry-based proteomics. The purpose of these analyses will be to find biomarkers capable of distinguishing the early-stage of lung cancer from the healthy group. Followup will be performed to evaluate the possible change of the volatolomic and proteomic profile.

Eligibility criteria:
Inclusion Criteria:

1. Lung cancer group

   * Diagnosis of early stage - lung cancer
   * Signed Informed Consent
   * Completed questionnaire
2. Healthy subjects

   * high risk individuals (heavy smokers, subjects with pulmonary disease non-cancer related or with a familiar history of lung cancer disease)
   * Recent (within 6 months) negative Chest X-ray or CT scan

Exclusion Criteria:

Both groups

* No previous chemo or radiotherapy for lung cancer
* No previous malignancies within last 5 years
* No abuse of alcohol (no more than 1 litre of wine for day).
* No patients with psychiatric, addictive, or any disorder, which compromises ability to give informed consent for participation in this study

Outcomes:
Comparison of the proteomic and volatolomic signature in samples of respiratory exhalation, serum, and urine from patients with lung cancer (stage I/II) and healthy individuals at high risk.
Effectiveness of Artificial Intelligence-assisted Recovery Assessment After Outpatient Gastrointestinal Endoscopy. A Pilot Double-blind Randomized Controlled Trial.
Trial ID: NCT06923059
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-04-01
Completion date: 2027-06-30
Sponsor: Chinese University of Hong Kong
Conditions: Artificial Intelligence Assistance in Endoscopy Recovery; AI Validation
Interventions: AI-assisted endoscopy recovery assessment
Locations: Alice Ho Miu Ling Nethersole Hospital
Countries: Hong Kong
Enrollment: 460
Age range: 18 Years - None
Sex: ALL

Summary:
We have developed and validated an AI model to assess endoscopy recovery status based on 400 voice recordings from 200 patients. This model has a mean accuracy of 84.14% with a mean area under the curve (AUC) of 0.91.

To further enhance the performance of this AI model, we plan to collect additional voice recordings to retrain it. We also plan to develop a mobile application of this AI model for effectiveness evaluation in a pilot randomized controlled trial (RCT) setting. Endoscopy nurses in Hong Kong were invited to participate in a survey study. Therefore, we believe implementation of AI model in clinical practice will be well accepted by endoscopy nurses in Hong Kong.

Eligibility criteria:
Inclusion Criteria:

* speak Cantonese;
* aged ≥18 years;
* undergoing outpatient sedated gastrointestinal endoscopy of any indication in Combined Endoscopy Unit at Alice Ho Miu Ling Nethersole Hospital

Exclusion Criteria:

* patients who are unable to provide consent or communicate verbally

Outcomes:
Recovery time
A Comparative Single-centre Study to Evaluate an Enhanced Artificial Intelligence Breast MRI Interpretation System in Women Over 20 With Breast Lesions
Trial ID: NCT03829423
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2019-04
Completion date: 2020-07
Sponsor: Jamil Kanfoud
Collaborators: Brunel University London; First Option Software Ltd.
Conditions: Breast Cancer
Interventions: Breast MRI interpretation
Enrollment: 1526
Age range: 20 Years - None
Sex: FEMALE

Summary:
Interpretation of breast MR images is a very time-consuming process and places a great burden on breast radiologists. This project aims to develop a technical solution that addresses this healthcare challenge by developing a system that is able to automatically interpret breast MR images in order to aid the radiologist in their diagnosis.

Eligibility criteria:
Inclusion Criteria:

* Breast MRI scans
* MRI examinations undertaken at partner NHS Trust in the UK
* MRI examinations undertaken on the MRI system currently installed at partner NHS Trust site (since 2008)

Exclusion Criteria:

* Incomplete breast MRI datasets
* Breast MRI without lesions
* Breast lesion on MRI not biopsied

Outcomes:
Sensitivity/specificity of breast interpretation algorithm
Assessing Psoriasis Treatment Outcomes: a Single-center, Prospective Study Evaluating Patient Outcomes in Psoriasis
Trial ID: NCT07391423
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-07
Completion date: 2029-03
Sponsor: Wake Forest University Health Sciences
Conditions: Psoriasis
Interventions: Artificial intelligence; Human; Standard of Care
Locations: Wake Forest University Health Sciences Department of Dermatology
Countries: United States
Enrollment: 15
Age range: 18 Years - None
Sex: ALL

Summary:
To assess the feasibility of AIGPP video interactions with psoriasis patients. To explore patient attitudes, trust, and comfort levels concerning AIGPP interactions in a healthcare setting, and to understand their influence on the patient-physician dynamic. To evaluate medication adherence over a 4-week period in patients with psoriasis randomized to weekly AI-generated automated physician providers (AIGPP) video interactions, weekly human video calls, or standard care (single follow up visit after 4 weeks). Adherence rates will be evaluated via a cap designed to record adherence attached to the study medication container.

Eligibility criteria:
Inclusion Criteria:

* Patients ≥18 years of age
* Patients with a current diagnosis of psoriasis suitable for treatment with topical Betamethasone (BD)
* Patients who live in the United States
* Patients with a sufficient command of the English language
* Patients with a mobile device able to at least operate at 25 frames per second (FPS).

Exclusion Criteria:

* Patients less than the age of 18
* Patients with severe or unstable comorbid conditions
* Patient who are pregnant or breastfeeding
* Patients with any other skin conditions that prohibit or confound the ability of the investigator to interpret skin findings
* Patients that are taking concomitant topical or systemic therapy for the treatment of psoriasis and/or phototherapy
* Patients without mobile devices able to at least operate at 25 frames per second (FPS).
* Patients without a current diagnosis of psoriasis suitable for treatment with topical BD

Outcomes:
Ability to implement AIGPP system and Rate of dropout in AIGPP trial arm; Thematic analysis of qualitative interviews regarding psoriasis patients' attitudes, trust, and comfort levels with AIGPP interactions in a healthcare setting
Manual Versus AI-Assisted Clinical Trial Screening Using Large-Language Models
Trial ID: NCT06588452
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2024-03-04
Completion date: 2024-09-13
Sponsor: Brigham and Women's Hospital
Conditions: Comparing Manual and AI Patient Screening in Heart Failure
Interventions: RECTIFIER - a generative artificial intelligence screening tool; Manual clinical trial screening by study staff
Locations: Brigham and Women's Hospital
Countries: United States
Enrollment: 4500
Age range: 18 Years - 90 Years
Sex: ALL

Summary:
A prospective randomized controlled trial comparing manual review and AI screening for patient eligibility determination and enrollments. A structured query will identify potentially eligible patients from the Mass General Brigham Electronic Data Warehouse (EDW), who will then be randomized into either the manual review arm or the AI-assisted review arm.

Eligibility criteria:
Inclusion Criteria:

* Documented diagnosis of heart failure (e.g., ICD-9 codes 428 ICD-10 codes I50 or Problem list in the electronic health record)
* Most recent left ventricular ejection fraction (LVEF) assessed within the past 24 months
* Seen Mass General Brigham provider within the last 24 months

Exclusion Criteria:

* LVEF \<50% currently prescribed or intolerant to an evidence-based beta-blocker, ARNI, MRA, and SGLT2i at least 50% goal dose
* LVEF\>50% currently prescribed or intolerant to SGLT2i
* Systolic blood pressure (SBP) \<90 mmHg at last measure

Outcomes:
Determine study eligibility, analyzed using a survival analysis framework, specifically the Fine-Gray subdistribution hazards model, to account for competing risks.
Validation of Inspiratory Muscle Pressure Estimation and Automated Detection of Asynchronies in Patients Under Assisted Mechanical Ventilation
Trial ID: NCT05820347
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2022-08-26
Completion date: 2023-07-18
Sponsor: University of Sao Paulo General Hospital
Collaborators: Magnamed Tecnologia Medica S/A
Conditions: Respiratory Failure
Interventions: Artificial Intelligence Estimation of Muscle Pressure during Mechanical Ventilation
Locations: Heart Institute, University of São Paulo
Countries: Brazil
Enrollment: 50
Age range: 18 Years - None
Sex: ALL

Summary:
The goal of this diagnostic study is to validate estimation of inspiratory muscle pressure by an artificial intelligence algorithm compared to the gold standard, the measure from an esophageal catheter balloon, in patients under assisted mechanical ventilation. The main questions it aims to answer are:

• Are inspiratory muscle pressure estimates from an artificial intelligence algorithm accurate when compared to the direct measure from an esophageal balloon?

Participants will be monitored with an esophageal balloon and with an artificial intelligence algorithm simultaneously, with inspiratory muscle pressure estimation during assisted mechanical ventilation with decremental levels of pressure support.

Eligibility criteria:
Inclusion Criteria:

* Patients under assisted or assist-control mechanical ventilation

Exclusion Criteria:

* Contraindication to esophageal catheter insertion (esophageal cancer or bleeding, esophageal fistula, skull base fracture, uncontrolled coagulopathies)
* Contraindication to transient neuromuscular blockade
* Bronchopleural fistula (persistent air leak)
* Hemodynamic instability (norepinephrine \> 1mcg/kg/min)
* Gestation
* Current sinus infection
* Refusal from patient's family of attending physician
* Palliative care

Outcomes:
Concordance between muscle pressure amplitude (in cmH2O) estimation by artificial intelligence and esophageal balloon; Correlation between muscle pressure amplitude estimation (in cmH2O) by artificial intelligence and esophageal balloon; Detection of initiation time and ending time of a spontaneous breathing cycle by artificial intelligence compared with esophageal balloon
Assessing Demographic Biases in Deep Learning Model for Fetal Growth Estimation in Clinical Practice
Trial ID: NCT06314178
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2024-07-01
Completion date: 2024-11-30
Sponsor: Copenhagen Academy for Medical Education and Simulation
Conditions: Pregnancy Complications
Locations: Copenhagen University Hospital, Rigshospitalet
Countries: Denmark
Enrollment: 185
Age range: None - None
Sex: FEMALE

Summary:
The goal of this observational study is to compare a new artificial intelligence (AI) feedback tool with the traditional method for estimating fetal weight during ultrasound scans on pregnant women between 24-42 weeks of gestation. The study aims to investigate the presence of demographic bias in the AI model. The demographic factors examined in the study include Body Mass Index (BMI), the number of births, fetal age, mother\&#39;s age, fetal sex, and the presence of preeclampsia. Moreover, the study will compare the accuracy of the AI model and the Hadlock model, a fetal growth formula, in estimating fetal weight. Participants will have their ultrasound scans pseudonymized and securely stored on password-protected removable drives, ensuring their identity and privacy are maintained. Afterward, the ultrasound data will be sent to the Technical University of Denmark (DTU), where the AI model will analyze the images to estimate fetal weight.

Eligibility criteria:
Inclusion Criteria:

* Women with gestational age between 24-42 weeks undergoing a third-trimester growth scan.

Exclusion Criteria:

* Women with multiple pregnancies.

Outcomes:
Demographic biases