moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Development of a Decision Support Algorithm (DSA) for Detecting the Sciatic Nerve in Safe Injection Practices
Trial ID: NCT06636604
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2022-05-01
Completion date: 2023-11-01
Sponsor: Sakarya University
Collaborators: The Scientific and Technological Research Council of Turkey
Conditions: Nurses; Injection Site; Sciatic Nerve; Pain; Satisfaction, Patient
Interventions: DSA(Decision Support Algorithm)
Locations: Sakarya University
Countries: Turkey (Türkiye)
Enrollment: 60
Age range: 18 Years - None
Sex: ALL

Summary:
This project aims to develop an artificial intelligence-based decision support algorithm (DSA) for the detection of the sciatic nerve in safe intramuscular injection applications.

In the first phase of the project,developed an artificial intelligence-based decision support algorithm (DSA) for the detection of the sciatic nerve in safe intramuscular injection applications.

In the second phase of the project, with the integration of the Decision Support Algorithm (DSA) on a computer accompanied by a doctor and an engineer, the presence of the sciatic nerve was tested on 30 volunteers. The test resulted in achieving a 100% sciatic nerve image, confirming the reliability of the DSA.

Subsequently, information about the algorithm was provided to volunteers and nurses in the emergency department of Sakarya Training and Research Hospital who applied for intramuscular injection.

Eligibility criteria:
Inclusion Criteria:

* The participant volunteered to take part in the research and is over 18 years of age.
* They have applied to the emergency department for an intramuscular injection and have not experienced any injection-related complications before.
* There is no risk of developing complications during the injection phase, such as bleeding, coagulation disorders, or drug allergy.
* Additionally, there is no scarring or abscess at the injection site.
* There should be no communication barriers, such as a psychological disorder or inability to speak Turkish.
* To participate in this study, it is necessary to be able to read and sign the informed consent form in Turkish.

Exclusion Criteria:

* Excluded were individuals who did not meet the inclusion criteria.

Outcomes:
Pain conditions of the patients after the injection will be determined.; Pain conditions of the patients after the injection will be determined.
Artificial Intelligence Driven Exercises Versus Conventional Exercises Therapy for Patellofemoral
Trial ID: NCT07405034
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-02-26
Completion date: 2027-01-01
Sponsor: Cairo University
Conditions: Patello Femoral Pain Syndrome
Interventions: conventional exercises therapy program; Ai driven EX
Locations: the out-patient clinic faculty of physical therapy, Cairo University,; the out-patient clinic faculty of physical therapy, Cairo University,
Countries: Egypt; Egypt
Enrollment: 40
Age range: 18 Years - 40 Years
Sex: ALL

Summary:
This study aims to investigate and compare the effect of AI driven exercises program versus the conventional exercises therapy program on pain, function and dynamic knee valgus angle in treatment of patellofemoral pain syndrome.This study is randomized controlled trial and includes 40 participants .These participants divided into 2 groups (A, B), each group will be assessed with 3 measurments (vas ,dynamic knee valgus, Kujala Anterior Knee Pain Scale) and each group will receive difeerent treatments.

The individuals in group A(study group) will preceive AI driven exercises program while the individual in group B will receive conventional exercises therapy program. Participants in the both groups are prescribed a 6-week comprehensive exercises program (3 sessions per week) day after day and assessment procedure will be performed pre and post treatmeant .

Eligibility criteria:
Inclusion Criteria:

* Their ages range from 18- 40 years old, including both genders (male \& female).
* Their BMI will be ≤ 29 kg/m2.
* A traumatic knee pain persisting for at least three months.

Exclusion Criteria:

* Any congenital deformity of the lower limb,
* Any recent injuries and surgeries
* severe foot deformities such as hallux valgus and claw toe
* Neuromuscular and neurological disorders.- history of patellofemoral dislocation, subluxation .

Outcomes:
Kujala Anterior Knee Pain Scale
Development and Assessment of Artificial Intelligence (AI)-Enhanced Pretreatment Peer-review Process to Improve Patient Safety in Radiation Oncology
Trial ID: NCT07463833
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-07
Completion date: 2027-07
Sponsor: UNC Lineberger Comprehensive Cancer Center
Collaborators: Agency for Healthcare Research and Quality (AHRQ)
Conditions: Cancer; Prostate Cancer
Interventions: The Artificial Intelligence (AI)/ Machine Learning (ML) contribution to treatment planning
Locations: University of North Carolina at Chapel Hill, Department of Radiation Oncology
Countries: United States
Enrollment: 207
Age range: 18 Years - None
Sex: ALL

Summary:
This prospective study will test artificial intelligence (AI) and machine learning (ML) decision support tools. This tool is designed to help doctors, physicists and other staff during pre-treatment peer review, a step where treatment plans are checked before a patient begins care.

The system highlights summaries showing how different providers may vary in their treatment planning (provider-variability summaries) and points out the best signals or warning signs to look for (optimal cues). By drawing attention to these patterns and cues, the tool aims to help reviewers spot possible treatment-planning mistakes earlier, reduce the chance of errors, and improve overall patient safety.

Eligibility criteria:
In order to participate in this study a subject must meet all of the eligibility criteria outlined below.

Inclusion Criteria:

Providers only

* ≥18 years
* Peer-review attendees at participating clinics

Patients only

* ≥18 years
* All patients with prostate cancer radiation therapy cases treated at participating sites (no intervention delivered to patients)

Exclusion Criteria:

Providers only

• Providers unwilling/unable to comply with study procedures; sites unable to implement the workflow or provide required outcomes.

Patients and Providers

• Has dementia, altered mental status, or any psychiatric or co-morbid condition prohibiting the understanding or rendering of informed consent

Outcomes:
Percentage of patients with changes nodal volume contours
The Validation of a Coronary Artery Disease Screening Algorithm Based on Facial Photos in Community Population
Trial ID: NCT06850233
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2025-03-04
Completion date: 2025-06-24
Sponsor: China National Center for Cardiovascular Diseases
Conditions: Coronary Artery Disease
Interventions: No intervention
Locations: People's Road Community Health Service Center
Countries: China
Enrollment: 100
Age range: 30 Years - 80 Years
Sex: ALL

Summary:
The purpose of this study is to evaluate the effectiveness of a coronary heart disease screening algorithm based on facial photos in community population.

Eligibility criteria:
Inclusion Criteria:

* Community population with a pre-test probability greater than 5% for coronary artery disease

Exclusion Criteria:

* Prior percutaneous coronary intervention (PCI)
* Prior coronary artery bypass graft (CABG)
* The history of heart failure
* With artificially facial alteration (i.e. cosmetic surgery, facial trauma or make-up)
* Other situations which make patients fail to be photographed

Outcomes:
Sensitivity of the algorithm; Specificity of the algorithm
Investigating the Efficacy of Mobile Health Monitoring and Self-care to Improve Obesity Outcomes in Hispanic Adolescence Populations
Trial ID: NCT06959901
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-04-30
Completion date: 2026-05-01
Sponsor: Baylor College of Medicine
Collaborators: U.S. National Science Foundation
Conditions: Obesity and Diabetes Mellitus, Type 2; Diabetes Prevention
Interventions: PATHS-UP Mobile Phone Application
Locations: Children's Nutrition Research Center
Countries: United States
Enrollment: 30
Age range: 12 Years - 18 Years
Sex: ALL

Summary:
Hispanic adolescents in the U.S. are disproportionately burdened by type 2 diabetes (T2D) compared to non-Hispanic white youth (0.079% vs. 0.017%) contributing to higher rates of T2D-related vascular complications, cardiovascular disease, and mortality, among this population. Disparities in T2D are driven in part by independent, modifiable risk factors including low levels of physical activity, sleep, and poor diet. Lifestyle interventions are the cornerstone for maintaining glucose control and managing T2D. However, few studies have developed and tested lifestyle interventions for Hispanic youth with T2D. Digital health interventions that promote healthy lifestyle behaviors like physical activity, sleep, and diet, have demonstrated effectiveness among adults. Studies that use health-based smartphone applications have demonstrated preliminary efficacy for improving health-related lifestyle behaviors as these digital tools leverage behavior change techniques (e.g. self-monitoring, goal-setting, feedback) that have proven effective. Use of digital technology allows for the continuous delivery of intervention content into the home environment extending the reach of clinical care while engaging youth in a format that is age-appropriate given that today's youth are digital frontrunners. Unfortunately, while the use of digital health interventions have increased, few studies have focused on adolescents with overweight and obesity who are at high risk for T2D. The purpose of this study is to 1) develop a mobile health platform for remote and continuous monitoring of activity, sleep, and nutrition and 2) conduct a pilot study (30 days) to evaluate the efficacy of a novel digital health platform in improving obesity-related health outcomes outcomes in Hispanic adolescents (12-18 years; N=30) population.

Eligibility criteria:
Inclusion Criteria:

* self-identify as Hispanic or Latino
* 12-18 years
* BMI% in the 85th-95th range,
* Owns their own iphone

Exclusion Criteria:

* Currently enrolled in a health program
* Diagnosed with T2D
* Present with a disease or condition that would prevent one from engaging in activity

Outcomes:
Rate of Recruitment; Feasibility of Technology; Usability; Acceptability; Acceptability
Revolutionizing Patient Care and Lifestyle Through Predictive Algorithms in Parkinson's Disease
Trial ID: NCT06755645
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2026-09
Completion date: 2028-09
Sponsor: University Ramon Llull
Collaborators: University of Deusto; Hospital Universitari Vall d'Hebron Research Institute
Conditions: Parkinson
Interventions: Lifestyle intervention
Locations: Ramon Llull University
Countries: Spain
Enrollment: 200
Age range: None - None
Sex: ALL

Summary:
Lifestyle interventions can alleviate Parkinson's Disease (PD) symptoms and delay disease progression. The novelty of this project lies in the development of an innovative smart platform that utilizes artificial intelligence (AI) and predictive models to offer a groundbreaking solution that not only prevents disease progression but also significantly improves the well-being of patients with PD. For this, the technological smart platform will encompass 50 clinical variables, and a comprehensive range of other 50 supplementary variables, validated in a database with more than 1500 patients. The smart platform will include a user-friendly interface with different user profiles and a scalable back end with AI-based monitoring and prediction modules. This will offer primary prevention, early detection, and ongoing monitoring by specialized medical professionals at home and in hospitals.

Eligibility criteria:
Inclusion Criteria:

* Patients with Parkinson's disease

Exclusion Criteria:

* N/A

Outcomes:
Movement Disorder Society-Sponsored Revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS)
Genomic and Personalized Medicine for All (GENOMED4ALL): Application of Artificial Intelligence to Improve Disease Classification and Prognosis in Myelodysplastic Syndrome.
Trial ID: NCT04889729
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2021-03-15
Completion date: 2024-12-31
Sponsor: Istituto Clinico Humanitas
Conditions: Myelodysplastic Syndromes
Locations: Istituto Clinico Humanitas
Countries: Italy
Enrollment: 13284
Age range: 18 Years - None
Sex: ALL

Summary:
Myelodysplastic syndromes (MDS) typically occur in elderly people. Current disese classifcation system and prognostic scores (International Prognostic Scoring System, IPSS) present limitations and in most cases fail to capture reliable prognostic information at individual level. Study of MDS has been rapidly transformed by genome characterization and there is increasing evidence that mutation screening may add significant information to currently available prognostic scores. The project will aim to develop artificial intelligence (AI)-based solutions to improve MDS classification and prognostication, through the implementation of a personalized medicine approach. In close collaboration with the European Reference Network on Rare Hematological Diseases (ERN-EuroBloodNet, FPA 739541), GENOMED4ALL involves multiple clinical partners from the network, while leveraging on healthcare information and repositories that will be gathered incorporating interoperability standards as promoted by ERN-EuroBloodNet central registry, the European Rare Blood Disorders Platform (ENROL, GA 947670).

Eligibility criteria:
Inclusion Criteria:

* Patients affected by MDS according WHO criteria \> 18 years old
* Avaliability of clinical and hematological information
* Availability of information on targeted mutation screening

Exclusion Criteria:

* none of the above

Outcomes:
Improving MDS classification; Prediction of probability of overall survival (months between diagnosis and death or end of follow up) for patients with MDS; Prediction of probability of leukemia free surivival (months from diagnosis to progression to acute leukemia or end of follow up) for patients with MDS
Validation of a Lab-free Low-cost Screening Test for Prevention of Cervical Cancer: Automated Visual Evaluation
Trial ID: NCT06815939
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-02-12
Completion date: 2026-12-31
Sponsor: DL Analytics
Collaborators: Basic Health International, Inc.; National Cancer Institute (NCI)
Conditions: Human Papillomavirus (HPV); Cervical Intraepithelial Neoplasia; Uterine Cervical Neoplasms; Cervical Cancers
Interventions: HPV Test; EVA System; Visual Inspection with Acetic Acid (VIA); Automated Visual Evaluation (AVE); Image Capture with Mobile Phone; Pregnancy test; ScreenFire HPV test; Colposcopy with biopsy; Thermal Ablation; Loop electrosurgical excision procedure (LEEP)
Locations: Ministerio de Salud
Countries: El Salvador
Enrollment: 10000
Age range: 30 Years - 59 Years
Sex: FEMALE

Summary:
The purpose of this study is to validate Automated Visual Evaluation (AVE), specifically the CINFinder version developed by DL Analytics, a point-of-care screening and triage diagnostic tool for cervical cancer based on the assessment of digital images through artificial intelligence. Several teams around the world have developed versions of AVE as a triage technology but none as a screening tool.

Eligibility criteria:
Inclusion Criteria:

* Women between 30 and 59 years of age

Exclusion Criteria:

* Pregnancy at the time of colposcopy/biopsy
* Hysterectomy with surgically absent cervix
* HPV test in the last 5 years independently of negative or positive result
* Previous cervical cancer diagnosis or treatment in the last 5 years
* Lack of willingness or capacity to provide informed consent

Outcomes:
Sensitivity; Positive Predictive Value (PPV)
Large Language Model-Generated Messages to Improve Guideline-Directed Medical Therapy in Heart Failure
Trial ID: NCT07337577
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-06-01
Completion date: 2027-12-01
Sponsor: Brigham and Women's Hospital
Conditions: Heart Failure
Interventions: LLM-GDMT Clinical Decision Support Tool
Locations: Mass General Brigham
Countries: United States
Enrollment: 500
Age range: 18 Years - 85 Years
Sex: ALL

Summary:
This study is an investigator-initiated, cluster-randomized implementation trial evaluating a large language model (LLM)-based clinical decision support (CDS) tool designed to improve guideline-directed medical therapy (GDMT) for adult patients with heart failure seen in outpatient cardiology clinics at Mass General Brigham.

For eligible heart failure encounters, the CDS tool reviews existing electronic health record (EHR) data, including diagnoses, medications, vital signs, laboratory results, and recent notes, and generates brief, clinician-facing messages suggesting opportunities to initiate or optimize GDMT and highlighting relevant safety considerations. Messages are delivered to cardiology providers via Epic InBasket and/or institutional email prior to scheduled visits. The tool is advisory only and cannot place orders or change medications automatically; all treatment decisions remain at the discretion of the treating clinician and patient.

Cardiology providers are assigned at the provider/clinic level to early implementation of the CDS tool versus usual care (no messages) during the initial phase. The primary outcome is GDMT optimization within 30 days of an index visit. Secondary outcomes include feasibility of CDS generation and delivery and a 30-day safety composite (e.g., heart failure hospitalization, acute kidney injury, hyperkalemia, hypotension or bradyarrhythmia plausibly related to GDMT).

Eligibility criteria:
Inclusion Criteria:

* Age ≥18 years
* Scheduled outpatient visit with a participating cardiology provider in an MGB outpatient cardiology clinic
* At least one prior cardiology clinic visit in the MGB system within the past 2 years
* Diagnosis of heart failure by ICD code within the past 2 years
* Heart failure diagnosis supported by at least one of the following:
* Current or recent use of a loop diuretic
* Left ventricular ejection fraction ≤40% on the most recent echocardiogram
* Explicit documentation of heart failure diagnosis or heart failure signs/symptoms in a prior cardiology note

Exclusion Criteria:

* Systolic blood pressure \<90 mmHg on the most recent recorded measurement
* Heart rate \<50 beats per minute on the most recent recorded measurement
* eGFR \<20 mL/min/1.73 m² on the most recent laboratory assessment
* Known cardiac amyloidosis or hypertrophic cardiomyopathy
* History of heart transplant or presence of a left ventricular assist device
* Severe aortic stenosis, severe aortic insufficiency, or severe mitral stenosis on the most recent echocardiogram
* Encounter occurs in an adult congenital heart disease clinic

Outcomes:
Any GDMT optimization within 30 days of index visit
Interest of Using Deep Learning Algorithm for Otosclerosis Detection on Temporal Bone High Resolution CT
Trial ID: NCT05987215
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2022-07-01
Completion date: 2023-10-01
Sponsor: Hospices Civils de Lyon
Conditions: Otosclerosis
Interventions: Radiologic diagnosis; Artificial intelligence diagnosis
Locations: Hospices Civils de Lyon, Centre Hospitalier Lyon sud, Service d'ORL, d'otoneurchirurgie et de chirurgie cervico-facaile
Countries: France
Enrollment: 240
Age range: 18 Years - 110 Years
Sex: ALL

Summary:
Otosclerosis is a relatively frequent pathology, of multifactorial origin with genetic and hormonal part, predominantly in women. This disease causes a disorder of the bone metabolism of the middle and inner ear, responsible for a progressive deafness, which can become severe.

Several elements are necessary to make the diagnosis of otosclerosis: the clinical examination and questioning, the audiometric assessment, and finally the temporal bone CT.

The CT scan allows to detect foci of otosclerosis within the bone of the middle or inner ear. This diagnosis is sometimes difficult and requires interpretation by a trained radiologist.

The investigators would like to evaluate the ability of a deep learning algorithm to detect these foci of otosclerosis, and to compare its diagnostic performance with a trained radiologist.

Eligibility criteria:
* Inclusion Criteria \* :

  * age over 18
  * high resolution temporal bone CT scan available for analysis
  * for the "case" group : surgical confirmation of positive diagnosis for otosclerosis
  * for the "control" group : a first radiological analysis in favor of a normal temporal bone CT scanner and an initial radiologic report considered normal as well
* Exclusion Criteria \* :

  * age under 18
  * no high resolution temporal bone CT scan available for analysis
  * unwillingness to participate in the study

Outcomes:
Diagnostic performance of the artificial intelligence algorithm compared to the diagnostic performance of the radiologist : sensitivity, specificity, positive and negative predictive value, area under the ROC curve