moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Screening Test Accuracy of a Gynocular™, HR-HPV Testing, and VIA for Detection of Cervical Intraepithelial Neoplasia, Grade Two and Above, in Women Living With HIV in Lusaka, Zambia
Trial ID: NCT03931083
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2019-05-08
Completion date: 2022-12-01
Sponsor: University of Bern
Collaborators: Centre for Infectious Disease Research in Zambia; Cervical Cancer Prevention Program in Zambia; International Agency for Research on Cancer
Conditions: Uterine Cervical Neoplasms; HIV/AIDS
Interventions: Screening for CIN2+/HSIL
Locations: Centre for Infectious Disease Research in Zambia
Countries: Zambia
Enrollment: 450
Age range: 18 Years - 65 Years
Sex: FEMALE

Summary:
Cervical cancer in HIV-positive women is largely preventable through regular screening. The World Health Organization (WHO) recommends cervical screening for HIV-positive women every three years. Currently the least costly method for screening and the most viable option for many countries is visual inspection after application of acetic acid (VIA). Alternative testing methods are HPV testing and assessment with a portable magnification device. The investigators plan to assess and compare the screening test accuracy of these screening tests in women living with HIV. All women will receive histopathology reference standard.

Eligibility criteria:
Inclusion Criteria:

1. HIV-infected women confirmed through medical records
2. Women residing within Lusaka district and plans to stay in this area for the next 6 months
3. Women between 18 and 65 years of age (age bracket as per Zambian guidelines for cervical cancer screening)
4. Able and willing to consent
5. Willing to undergo a pelvic examination and cancer screening
6. Has had sexual intercourse before
7. Agrees to have follow-up appointment in 6 months

Exclusion Criteria:

1. Women with a history of cervical cancer or previous hysterectomy (where the cervix was also removed)
2. Pregnant women or women who plan to get pregnant within the next 6 months
3. Women who have been vaccinated against HR-HPV

Outcomes:
Test accuracy (sensitivity, specificity) of the Gynocular™ when used as stand-alone tests to detect CIN2+; Test accuracy (sensitivity, specificity) of HR-HPV when used as stand-alone tests to detect CIN2+; Test accuracy (sensitivity, specificity) of VIA when used as stand-alone tests to detect CIN2+
Ultrasound Assessment of the Larynx and Trachea in the Neonatal Period
Trial ID: NCT05636410
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2022-06-14
Completion date: 2023-07-31
Sponsor: Princess Anna Mazowiecka Hospital, Warsaw, Poland
Conditions: Larynx; Trachea
Interventions: Larynx and trachea ultrasound
Locations: Department of Neonatology, Ujastek Medical Center; Department of Neonatology and Neonatal Intensive Care Warsaw Medical University
Countries: Poland; Poland
Enrollment: 300
Age range: 0 Days - 28 Days
Sex: ALL

Summary:
Diseases of the larynx and trachea are a heterogenous group of disorders. Witch may include congenital anatomical disorders, neoplastic changes, vocal cord paralysis of varied aetiology or narrowing of the larynx associated with long-term intubation. The multitude of disorders of these organs necessitates the continuing search for diagnostic methods which will not only provide answers to clinical questions but will also be safe and with the least level of interference with the wellbeing of the patient, which is of particular importance in the neonate.

In recent decades only a few studies have been reported which described the ultrasound anatomy of the larynx prior to and following intubation or that of the mobility of the vocal cords. To date, no standards have been published concerning the size of the structures of the larynx and trachea or the mobility of the vocal cords on ultrasound examination in the neonate. Additionally, there are no recommendations which include ultrasound examination as a reliable component of the diagnosis of congenital disorders of the larynx or other diseases of this organ.

Ultrasound is a non-invasive, repeatable and safe diagnostic method, which has recently, thanks the development of technology, provided for the very accurate imaging of even small structures, as well as their assessment on dynamic examination. Furthermore, the easy availability of this examination may in the future contribute to the early diagnosis of diseases of the larynx and trachea without the need to prolong neonatal hospitalization or anaesthesia.

In addition, the use of neural networks to analyse the ultrasound images obtained will provide for the development of algorithms which could become an irreplaceable tool, not only in the diagnosis of the disorders described, but also in predicating disorders affecting their further development or functional disorders in infancy.

Eligibility criteria:
Inclusion Criteria:

\- gestational age ≥32 weeks

Exclusion Criteria:

* gestational age \< 32 weeks,
* presence of significant congenital anomalies,
* intubated neonate,
* unstable neonate.

Outcomes:
Larynx and trachea anatomy description.; Ultrasound assessment of the mobility of the vocal cords; Artificial intelligence algorithms based on artificial neural networks
Predicting the Development of Bone Cement Implantation Syndrome in Arthroplasty Operations Using Artificial Intelligence Methods
Trial ID: NCT06777160
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2024-09-08
Completion date: 2025-04-15
Sponsor: Ataturk University
Conditions: Bone Cement Implantation Syndrome
Interventions: Collecting data from patients who underwent arthroplasty and cement was used
Locations: Ataturk University Faculty of Medicine
Countries: Turkey (Türkiye)
Enrollment: 250
Age range: 18 Years - None
Sex: ALL

Summary:
This study aims to predict the development of bone cement syndrome, which may develop due to polymethylmethacrylate cement used to adhere the prosthesis to the bone in arthroplasty surgeries, which may cause intraoperative and postoperative mortality and morbidity, using artificial intelligence methods and to provide a sustainable life comfort to patients in the postoperative period with the standardization predicted in the long term.

Eligibility criteria:
Inclusion Criteria:

1. Total knee arthrplasties
2. Partial hip arthrplasty using cement
3. Total hip arthrplasty using cement
4. Patients ≥18 years old.

Exclusion Criteria:

Patients who do not want to participate in the study

Outcomes:
collection of data-1; collection of data-2; collection of data-3; artificial intelligence modeling
Feasibility of Gadolinium Contrast Reduced Brain MRI: the Potential of Deep Learning
Trial ID: NCT06462924
Status: WITHDRAWN
Study type: INTERVENTIONAL
Phase: PHASE1
Start date: 2024-08-30
Completion date: 2024-09-15
Sponsor: DeepMeds
Conditions: Artificial Intelligence; Deep Learning
Interventions: DeepGad
Enrollment: 0
Age range: 18 Years - 80 Years
Sex: ALL

Summary:
MRI scans were performed using 3 different 1.5T scanners with an eight-channel head coils. Following a 3D pre-contrast T1w scan, a low-dose contrast-enhanced 3D T1w scan was obtained using 20% (0.02 mmol/kg) of the standard dosage of gadoterate meglumine.

The subjects were immediately administered the remaining 80% (0.08 mmol/kg) of the contrast agent to reach the standard dose of 0.1 mmol/kg, which served as a training ground truth for further quantitative assessment. All three acquisitions were performed during a single imaging session, with no additional gadolinium dose administered above the standard protocol.

Eligibility criteria:
Inclusion Criteria:

1. Clinical indications for imaging with a contrast-enhanced 3D-T1w MRI sequence including tumor suspicion, postoperative tumor follow-up, multiple sclerosis, routine brain imaging, etc.,
2. no plan for dynamic contrast administration or deviation from the standard dose of 0.1 mmol/kg body weight (e.g., sella imaging, magnetic resonance angiography),
3. no clinical contraindications to imaging prolongation (i.e., emergency, poor patient condition).

Exclusion Criteria:

1. prominent image artifacts,
2. incomplete study sequences (e.g., early termination)
3. errors related to contrast agent administration.

Outcomes:
Image quality; Vessel conspicuity
Investigation of Learning Effectiveness and Self-efficacy of Caregivers in Pressure Injury Wound Care Before and After Virtual Reality and AI Wound-detecting System Intervention
Trial ID: NCT06367179
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-03-22
Completion date: 2025-02-04
Sponsor: National Taiwan University Hospital
Conditions: Pressure Injury
Interventions: Virtual reality and AI use; Health education leaflet
Locations: National Taiwan University Hospital
Countries: Taiwan
Enrollment: 92
Age range: 20 Years - None
Sex: ALL

Summary:
This study is an experimental study. The main caregivers of pressure injury patients in the plastics surgery ward and general medicine ward of the hospital in Taipei City who are over 20 years old, have good communication skills in Chinese and Taiwanese or who can read Chinese are the research objects. During the study process, pre-tests will be given to the accepted subjects, which are the correctness evaluation scale of caregiver's pressure injury wound dressing change and caregiver self-efficacy scale, and then the accepted subjects will be divided into experimental group and control group. The experimental group will receive interventions of virtual reality and artificial intelligence wound detecting system, while the control group maintained the traditional pressure injury health education with oral introduction and health education leaflet. After the intervention measures are given, a post-test (same as the pre-test content) will be conducted within two days. Finally, analyze the effectiveness of the intervention of virtual reality pressure injury education video and artificial intelligence wound detecting system on caregivers of pressure injury improve wound care correctness and increase self-efficacy.

Eligibility criteria:
Inclusion Criteria:

1. Actual age is over 20 years old.
2. Those who can communicate smoothly in Mandarin and Taiwanese, or those who have the ability to read Chinese.
3. Caregivers of patients with pressure injuries.

Exclusion Criteria:

1. Those who suffer from mental or intelligence-related diseases or whose cognitive abilities are insufficient to answer the questionnaire questions.
2. Those who are unable to complete basic demographic information.

Outcomes:
Accurate dressing change rate of pressure injury
Artificial Intelligence for Chest Radiography: Impact on Economics, Patient Outcomes and Radiology Service Delivery
Trial ID: NCT06456203
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-10
Completion date: 2025-12
Sponsor: Duke-NUS Graduate Medical School
Conditions: Pneumonia; Lung Cancer
Interventions: AI
Enrollment: 10000
Age range: 14 Years - 130 Years
Sex: ALL

Summary:
Randomized Clinical Trial of the impact of Chest radiograph AI-assisted triage and report generation upon clinical outcomes and an economic analysis of impact of AI decision support on radiology service delivery.

Eligibility criteria:
Inclusion Criteria:

* All patients attending radiography to have chest radiographs during the study period

Exclusion Criteria:

* age below 14
* deceased before discharge
* chest radiograph performed in non-standard projections

Outcomes:
Report generation time; Turnaround Time; Time to discharge
Building Research With Artificial Intelligence in Neuro-Ophthalmology
Trial ID: NCT06390579
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2023-10-01
Completion date: 2024-02-01
Sponsor: Fondation Ophtalmologique Adolphe de Rothschild
Conditions: Optic Neuropathy; Optic Neuropathy, Ischemic; Optic Neuritis; Optic Nerve Diseases; Papilledema; Optic Atrophy; Brain Tumors; Retinal Photograph; Artificial Intelligence (AI); Machine Learning; Deep Learning
Interventions: Deep learning algorithm applied on retrospectively collected color fundus photographs
Locations: Hopital Fondation Adolphe de Rothschild
Countries: France
Enrollment: 693
Age range: None - None
Sex: ALL

Summary:
The research team, recognized as a world leader in Artificial Intelligence for neuro-ophthalmology, has shown that it is possible to diagnose certain neuro-ophthalmologic or neurologic disorders from a single retinal fundus image (Milea et al, New England Journal of Medicine, 2020). However, clinical practice requires identifying a broader spectrum of diseases (inflammatory, ischemic, hereditary, neurodegenerative) within the same analysis.

The main objective is to develop, through a new algorithm capable of classifying multiple disorders from a smaller set of conventional retinal images.

This project meets a significant public health need: the global shortage of neuro-ophthalmologists. It aims to provide healthcare professionals with a rapid triage tool to detect serious and treatable conditions, enabling timely intervention.

The study will include patients with clearly defined neuro-ophthalmologic or neurologic conditions, confirmed diagnoses, and retinal imaging. Clinical, paraclinical, and imaging data collected during standard care will be used, with strict anonymization according to legal and institutional requirements.

Specific Objectives :

1. Evaluate the performance of a diagnostic classification algorithm trained on retinal images.
2. Assess the ability to detect multiple pathologies from a single retinal image.
3. Support the development of advanced computer vision tools for medical diagnostics.

Eligibility criteria:
Inclusion Criteria:

* Patients with well-defined neuro-ophthalmologic or neurologic conditions, including different forms of optic neuropathies and various neurodegenerative diseases.
* Patients with a robust reference diagnosis confirmed by clinical experts.
* Patients with available retinal fundus images collected during routine care.

Exclusion Criteria:

* Patients without a confirmed diagnosis or unclear clinical classification.
* Patients without retinal fundus images or with images that are completely unreadable.
* Patients whose data cannot be anonymized according to legal and institutional protocols.

Outcomes:
Diagnostic performance of the Artificial Intelligence algorithm in detecting multiple neuro-ophthalmologic and neurologic conditions from retinal imaging.
Early Prediction of Acute Exacerbations of COPD Using Wearable and Portable Remote Monitoring Technology With AI/ML Empowered Platforms: A Prospective Clinical Study
Trial ID: NCT06802003
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-05-22
Completion date: 2026-08-31
Sponsor: McGill University Health Centre/Research Institute of the McGill University Health Centre
Collaborators: Restech Srl
Conditions: COPD; AE COPD
Interventions: Biometric wearable and handheld devices
Locations: McGill University Health Centre
Countries: Canada
Enrollment: 50
Age range: 40 Years - None
Sex: ALL

Summary:
This study is aimed to collect real-time physiological data using two wearable devices (a biometric ring and a biometric wristband), daily lung mechanical measurements by a handheld oscillometer, and participant-reported symptoms in patients with COPD remotely from their home environment. The data will be used to train and validate artificial intelligence and machine learning (AI/ML) models to predict COPD exacerbations in advance of their actual occurrence. The data will also be used to test the new severity classification system for exacerbations of COPD, as well as to determine important relationships between physiological measurements from the wearable devices, the handheld oscillometer, the self-reported symptoms, and the tests performed at the baseline visit.

Eligibility criteria:
Inclusion Criteria:

* Males/females, age ≥ 40, former/current smokers with ≥10 pack-year smoking history
* FEV1/FVC \< 0.7, with 80% \< FEV1 ≤50% (moderate, 'GOLD 2') 50% \< FEV1 ≤ 30% (severe, 'GOLD 3') or FEV1 \< 30% (very severe, 'GOLD 4') COPD
* History of 2 or more exacerbations in the preceding 12 months requiring corticosteroids, antibiotics, or both
* Ability to provide informed consent
* Ability to access internet at least once daily

Exclusion Criteria:

* No existing COPD diagnosis
* Any medical/cognitive/functional condition which renders inability to operate research equipment/devices, and/or to complete daily symptom response

Outcomes:
Respiration; Cardiovascular; Oxygen level; Step count; Sleep duration; Rapid eye movement (REM) sleep; Deep sleep; Body temperature; Resistance at 5 Hz (R5); Reactance at 5 Hz (X5); Intra-breath difference between expiratory and inspiratory reactance (ΔXrs); Tidal volume (Vt); Respiratory flows; Respiratory rate (RR); Minute ventilation (Ve); Heart rate (HR) prior oscillometry test; Blood oxygen saturation (SpO2) prior oscillometry test; Daily symptom questionnaire; Weekly exacerbation questionnaire; Calories; Metabolic equivalents; Movement intensity; Sleep efficiency; Sleep oncet; Sleep disturbance
Point-of-Care Echocardiography in Patients with Known or Suspected Aortic Stenosis
Trial ID: NCT06717373
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-02-07
Completion date: 2025-04-01
Sponsor: Caption Health, Inc.
Conditions: Patients with an Indication for an Echocardiographic Examination with Suspicion of Valcular Heart Disease
Interventions: AutoAS
Locations: Northwestern University
Countries: United States
Enrollment: 200
Age range: 18 Years - None
Sex: ALL

Summary:
This is a prospective clinical trial intended to capture echocardiographic images in patients clinically indicated for an echocardiographic examination using an additional Point of Care Ultrasound device . The images collected will be used to test and possibly validation an AI base Aortic Valve Stenosis algorithm

Eligibility criteria:
Inclusion Criteria:

* Patients scheduled for an echocardiographic examination
* Patients ≥18 years old
* Patient with known or suspected valve disease based on medical history

Exclusion Criteria:

* Patients with artificial aortic valves
* Unable to lie flat for study
* Patients experiencing a known or suspected acute cardiac event
* Patients unwilling or unable to give written informed consent

Outcomes:
Sensitivity and Specificity
Predicting Outcomes in Intermediate and High-risk Non-muscle Invasive Bladder Cancer Using Automated Analysis of Digital Pathology Data
Trial ID: NCT05825950
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2024-01-25
Completion date: 2026-09
Sponsor: University of Texas Southwestern Medical Center
Collaborators: Valar Labs
Conditions: Non-muscle-invasive Bladder Cancer
Locations: University of Texas Southwestern Medical Center; University of Texas Health Science Center at San Antonio
Countries: United States; United States
Enrollment: 600
Age range: 18 Years - 99 Years
Sex: ALL

Summary:
This is a multi-center study and the aim is to develop and validate an Artificial Intelligence (AI) -based histologic analysis tool to predict responsiveness to intravesical Bacillus Calmette-Guérin (BCG) and intravesical chemotherapy in intermediate and high-risk non-muscle invasive bladder cancer patients.

Eligibility criteria:
Inclusion Criteria:

* Diagnosis of intermediate or high-risk non-muscle invasive bladder cancer as defined by AUA/SUO criteria (Intermediate-risk: recurrence within 1 year low grade Ta, solitary low grade Ta \>3 cm, multifocal low grade Ta, high grade Ta ≤3 cm, low grade T1; High risk: high grade T1, recurrent high grade Ta, high grade Ta \>3 cm, multifocal high grade Ta, any CIS, any BCG failure in high grade disease, any variant histology, any lymphovascular invasion) following pathologic evaluation of tissue specimens from TURBT.
* Intravesical therapy within 6 months from enrollment including patients treated with BCG, mitomycin C, or Gemcitabine/Docetaxel.
* English or Spanish speakers
* ≥ 18 years of age
* Ability to understand and the willingness to provide an informed consent

Exclusion Criteria:

* Inadequate tissue from TURBT
* ≥ T2 bladder cancer
* Systemic therapy
* Inability to read or write English or Spanish
* Unwilling to sign written informed consent

Outcomes:
Recurrence free survival (RFS) at 3 months after TURBT in participants with HR NMIBC; Recurrence free survival (RFS) at 6 months after TURBT in patients with HR NMIBC; Recurrence free survival (RFS) at 12 months after TURBT in patients with HR NMIBC; Recurrence free survival (RFS) at 24 months after TURBT in patients with HR NMIBC
Early Pre-hospital Thrombolysis Guided by Artificial Intelligence Assisted Mobile Application in Patients With ST-elevation Myocardial Infarction: A Multi-center Cluster Randomized Controlled Trial
Trial ID: NCT07160491
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-09-08
Completion date: 2029-07-31
Sponsor: Shenyang Northern Hospital
Conditions: STEMI - ST Elevation Myocardial Infarction
Interventions: A novel artificial intelligence assisted mobile application
Locations: No. 83, Wenhua Road, Shenhe District, Shenyang City
Countries: China
Enrollment: 3356
Age range: 18 Years - 80 Years
Sex: ALL

Summary:
The aim of the study is to elucidate whether guiding by a novel artificial intelligence assisted mobile application can improve the clinical outcomes of patients in whom "guide wire passing through the lesion" could not be achieved within 120 min after diagnosis of STEMI, compared to conventional treatment strategies. With concerns of the inadequate use of thrombolysis in patients with STEMI in China, this study applies a new artificial intelligence assisted mobile application to guide the process of thrombolysis combined with PCI treatment, in order to accomplish the rapid coordination and cooperation of the whole medical network during re-perfusion treatment in different regions and different medical institutions in China, increases the proportion of early thrombolysis in pre-hospital setting, shortens the time from STEMI onset to reperfusion, and provides a reliable, effective and replicable new strategy for promoting and optimizing early reperfusion.

Eligibility criteria:
Inclusion Criteria:

* (the following inclusion conditions shall be met at the same time):

  1. age: ≥ 18 years and ≤ 80 years;
  2. Chest pain lasted for more than 30 min, and the onset time of chest pain was ≤ 12 hours;
  3. ECG: ST segment elevation after J-point in 2 or more adjacent leads: limb leads ≥ 0.1 mv or chest leads ≥ 0.2 mv;
  4. it is expected that "guide wire passing through the lesion" could not be achieved within 120 min after the diagnosis of STEMI;
  5. Signed informed consent .

Exclusion Criteria:

* (1) Cardiac rupture; (2) Complete left bundle branch block (LBBB) or ventricular pacing; (3) There are contraindications to thrombolysis; (4) Have serious comorbidities; (5) Have complex heart disease; (6) There are situations that are not suitable for clinical trials.

Outcomes:
Incidence of major adverse events (MAEs) at 1 year