moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Prospective, Multicenter, Controlled Study on the Impact of Real-time Feedback on Red-out
Trial ID: NCT07273890
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-12-10
Completion date: 2028-04-20
Sponsor: The First Affiliated Hospital of Anhui Medical University
Conditions: Artificial Intelligence; Colonoscopy; Real-time Feedback
Interventions: AI system
Enrollment: 576
Age range: 18 Years - 70 Years
Sex: ALL

Summary:
This study will employ a prospective, multicenter, controlled design. It will be conducted across multiple centers, with participated centers randomly assigned to one of four groups: Group A, Group B, Group C, and Group D.

The research will primarily focus on the AI-based analysis of colonoscopic images to calculate the following metrics: caecal intubation time, red-out percentage, and the AI-based red-out avoiding score. Based on the study's implementation protocol, a decision will be made regarding whether to provide real-time feedback. Additionally, the presence of any complications will be assessed both during and after the colonoscopy procedure.

Eligibility criteria:
Inclusion Criteria:

1. Study Participants (Patients):

   Aged 18 to 70 years, any gender. Individuals scheduled to undergo diagnostic or screening colonoscopy at the investigational site.
2. Colonoscopists:

Expert-level colonoscopists (having performed a total of \>1000 colonoscopy procedures).

Right-handed.

Exclusion Criteria:

1. Study Participants (Patients):

   Individuals undergoing the following procedures:

   cases with a history of colorectal surgery; cases with a history of chemotherapy, raditherapy; cases with a history of abdominal, and/or pelvic surgery; cases with a history of difficult colonoscopies; cases with colorectal tumours and obstructive lesions; cases with colorectal diverticula; cases with ulcerative colitis or Crohn's disease; cases with ischemic bowel disease; cases with colorectal polyposis; cases with melanosis coli; cases undergoing sigmoidoscopy; cases with poor intestional cleanliness (segment Boston bowel preparation scale (BBPS) of \< 2 points, total BBPS of \< 6 points); cases undergoing therapy procedures such as biopsy or CSP during the intubation phase; cases with transparent cap assisted colonoscopy; cases with water-assisted colonoscopy; cases with air insufflation level of M or L; cases failed caecal intubation within 15 min; cases with colonoscope stiffness level \> 0; obese cases or underweight cases; and cases refusing participation.

   Individuals who decline to provide informed consent.
2. Colonoscopists:

Those who have performed fewer than 300 complete colonoscopies in any calendar year within the past three years.

Those who decline to participate in the study.

Outcomes:
Red-out percentage; Caecal intubation time; AI-based red-out avoiding score
Posture and Musculoskeletal System Disorders in Individuals Who Have Had COVID-19
Trial ID: NCT07005908
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2023-09-01
Completion date: 2024-11-01
Sponsor: Izmir Democracy University
Conditions: COVID-19 Respiratory Infection
Interventions: Physical evaluations of post-COVID-19 individuals
Locations: Izmir Democracy University
Countries: Turkey (Türkiye)
Enrollment: 41
Age range: 18 Years - None
Sex: ALL

Summary:
In this study, it was aimed to reveal the results of posture measurement evaluated by artificial intelligence method, pain status and musculoskeletal disorders in individuals who have had COVID-19.

Eligibility criteria:
Inclusion Criteria:

* adult individuals aged 18 and over
* volunteering to participate in the study,
* Individuals who can understand and answer the questionnaires
* Individuals who were diagnosed with COVID-19 (individuals with a positive Polymerase Chain Reaction (PCR) test result, compatible with COVID-19 infection as a result of lung X-ray or lung tomography despite negative PCR test results) and discharged after recovery/home quarantine completed

Exclusion Criteria:

* Individuals with any physical or mental disability/disease and/or cognitive impairment
* Individuals newly diagnosed with COVID-19, therefore in quarantine at home or receiving treatment in hospital
* Individuals with suspected COVID-19
* Pregnant women

Outcomes:
Pain Intensity
Prospective Evaluation of Artificial Intelligence-enabled Screening for Transthyretin Amyloid Cardiomyopathy (ATTR-CM): TRACE-AI Prospective Study
Trial ID: NCT07398950
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2026-03
Completion date: 2026-12
Sponsor: Yale University
Collaborators: Bridgebio Pharma, Inc
Conditions: ATTR Amyloidosis With Cardiomyopathy; Heart Failure
Interventions: ATTR-CM AI-ECG and AI-Echo Model
Locations: Yale New Haven Health System
Countries: United States
Enrollment: 150
Age range: 18 Years - None
Sex: ALL

Summary:
The TRACE-AI Diagnostic Study will evaluate the performance of artificial intelligence (AI) models applied to electrocardiograms (AI-ECG) and echocardiograms (AI-Echo) to identify transthyretin amyloid cardiomyopathy (ATTR-CM) in adults with heart failure. Model performance will be validated through comparison with technetium-99m pyrophosphate (PYP) imaging results.

Eligibility criteria:
Inclusion Criteria:

* Patients 18 years or older with at least one of each component cardiovascular diagnosis testing (ECG and Echo) in the YNHHS.
* Patients with a diagnosis of HFpEF or HFmrEF
* Participant from Phase I TRACE-AI Study with AI-ECG and AI-Echo screen in the preceding 36 months

Exclusion Criteria:

* Patients who have opted out of research studies
* Patients with cardiac amyloid diagnostic test in the past 36 months
* Patients with hypertrophic cardiomyopathy or end-stage renal disease
* Pregnant women
* Patients unable or unwilling to provide informed consent
* Non-English speakers and cognitively impaired individuals who are unable to comprehend the consent and the on-screen instructions on the application, which are in English.

Outcomes:
Performance of the sequential screening approach
The OpRESTORE AI-Patient Navigator Study: Developing and Testing an AI-enhanced Patient Navigator for UK Veterans With Service-related Physical Health Problems
Trial ID: NCT07474584
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-05-01
Completion date: 2027-11-01
Sponsor: Imperial College Healthcare NHS Trust
Conditions: Veteran
Interventions: AI-OpRESTORE healthcare navigator; OpRESTORE healthcare navigation
Locations: OpRESTORE, Imperial College Healthcare NHS trust
Countries: United Kingdom
Enrollment: 1389
Age range: 18 Years - None
Sex: ALL

Summary:
OpRESTORE is a national NHS service that supports UK veterans with complex physical health problems linked to their military service. Veterans referred to OpRESTORE often need care from many different specialists, including surgeons, pain teams, rehabilitation, and mental health services. Currently, decisions about which service is most appropriate are made by a multidisciplinary team (MDT) of clinicians. While effective, this process can be slow, resource-intensive, and sometimes difficult for patients to navigate.

This study will develop and test a new digital "navigator" tool that uses artificial intelligence (AI) to support these referral decisions. The aim is to see whether the tool can safely and accurately match veterans to the right care pathway, while reducing delays and improving patient experience.

The project will be carried out in several stages:

* Reviewing past OpRESTORE records to design the AI model.
* Testing the tool alongside the MDT ("shadow testing") to check whether its recommendations match the clinical decisions.
* Running a case-control study to compare outcomes between patients referred using AI support and those referred by the MDT alone.
* Creating and testing a structured self-referral form to make it easier for veterans to access care directly.

The main outcome will be whether the AI tool makes the same referral decisions as the MDT. Other outcomes include patient satisfaction, quality of life, time taken to reach the right service, and overall costs.

The study will recruit veterans aged 18 or older who are referred to OpRESTORE with a physical health need. It will run for two years. If successful, this approach could free up clinician time, shorten waits for treatment, and improve veterans' health and wellbeing, while laying the foundations for wider use of AI-supported navigation across the NHS.

Eligibility criteria:
Inclusion Criteria:

* Meets criteria for referral to OpRESTORE service
* Age 18 years or older.
* Capacity to consent.
* Have a physical health need (e.g. not purely mental health, or seeking social care advice)

Exclusion Criteria:

* Referrals processed outside standard MDT workflow.
* Patients lacking capacity to consent.
* Prisoners.
* Acute presentation best managed by emergency services and not appropriate for OpRESTORE.

Outcomes:
Algorithm concordance against control
Ted Rogers Understanding of Exacerbations in Heart Failure 2 (TRUE-HF2): Observational Study
Trial ID: NCT07447908
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2026-03-16
Completion date: 2029-09
Sponsor: University Health Network, Toronto
Collaborators: Canadian Institutes of Health Research (CIHR)
Conditions: Heart Failure; Heart Failure - NYHA II - IV; Heart Failure Patients; Cardiopulmonary Exercise Test; Apple Watch
Locations: Southlake Health; 200 Elizabeth Street; Sunnybrook Health Sciences Center
Countries: Canada; Canada; Canada
Enrollment: 360
Age range: 18 Years - None
Sex: ALL

Summary:
Heart Failure (HF) is a complex disease associated with the highest burden of cost to the healthcare system. The cardiopulmonary exercise test (CPET) is instrumental in determining the prognosis of patients with HF. This multicentre study will validate whether aggregate biometric data from the Apple Watch combined with demographic, cardiac, and biomarker testing can improve our ability to predict heart failure outcomes among a diverse outpatient HF population.

Eligibility criteria:
Inclusion Criteria:

* broad age range (\> 18 years of age)
* NT-proBNP \> 400 or 1000 if in AF
* Within 3 months post discharge from HF hospitalization/ HF ED visit/ HF rapid clinic visit with intensification of diuretic therapy
* NYHA functional class I-IV, heart failure with reduced and preserved ejection fraction
* Literacy in English
* Patient provided informed consent

Exclusion Criteria:

* Unable to perform a CPET based on the standard protocol
* End stage renal disease requiring dialysis
* Living with LVAD
* MRP deems patient unfit for the study
* Post heart transplant

Outcomes:
Prediction of CPET parameters; Prediction of worsening heart failure
Pain Assessment in Neurorehabilitation Through Physiological Signals Recorded by Wearable Devices in Real-world Context: an Exploratory Study
Trial ID: NCT05747040
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2023-02-01
Completion date: 2023-10-01
Sponsor: Azienda Usl di Bologna
Conditions: Pain; Multiple Sclerosis
Interventions: monitoring
Locations: Irccs - Istituto Delle Scienze Neurologiche
Countries: Italy
Enrollment: 15
Age range: 18 Years - 75 Years
Sex: ALL

Summary:
This exploratory interventional study aims at exploring the feasibility of using physiological signals recorded through wearable devices, together with artificial intelligence techniques, to assess pain automatically and objectively. Automatic methods to assess presence/absence of pain, discern nociceptive from neuropathic pain, and estimate the intensity of pain will be trained an tested on a population of multiple sclerosis patients undergoing neurorehabilitation.

Eligibility criteria:
Inclusion Criteria:

* Age between 18 and 75 years
* Diagnosis of Multiple Sclerosis for at least three months post-onset
* Prescription of a physiotherapy-based motor rehabilitation program
* Signature of the informed consent to participate in the study

Exclusion Criteria:

* Heart rhythm modifying disease and/or factors such as arrhythmogenic heart disease (e.g., atrial fibrillation), presence of pacemakers and/or use of drugs capable of affecting heart rhythm, such as beta blockers (C07) or other antiarrhythmic drugs (C01)
* Cognitive impairments that preclude the possibility of providing valid informed consent, such as a disorder of consciousness or confusional state, the latter defined by temporal and/or spatial disorientation detected during ordinary conversation. In case of doubt, a simple confusional state assessment test (4AT) will be administered before enrollment
* Language comprehension skills lower than 75% in an ordinary conversation due to aphasic disorder of severe deafness despite the use of a hearing aid. In case of doubt, a simple language comprehension test (token test) will be administered before enrollment
* Linguistic expression less than 75%. In case of doubt, a simple verbal fluency test (verbal fluency by phonemic category) will be administered before enrollment
* Severe psychiatric comorbidity that may interfere with adherence to the study protocol (e.g., severe personality disorders, severe psychomotor agitation)
* History or current use of narcotic drugs (including marijuana)
* Modification in the two weeks prior to enrollment or foreseeable modification during enrollment of any chronic pain management program, both pharmacological (cortisone for systemic use, H02; antirheumatics, M01; analgesics, N02; antiepileptics, N03; antidepressants tricyclics, N06AA; atypical antidepressants such as duloxetine or venlafaxine, N06AX) and non-pharmacological (e.g., acupuncture or other manual therapies, physical therapies, such as tecar therapy)

Outcomes:
Number of registrations
Investigation of a New Stoma Product for People With a Stoma
Trial ID: NCT04101318
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2020-01-22
Completion date: 2020-07-17
Sponsor: Coloplast A/S
Conditions: Peristomal Skin Complication
Interventions: Non-Conformité Européene marked product; Conformité Européene marked competitor products
Locations: Ragene Saetre
Countries: Norway
Enrollment: 79
Age range: 18 Years - None
Sex: ALL

Summary:
The CP288 study is an investigation of a non-Conformité Européene marked stoma product which will be conducted in the UK, Italy, Netherlands, Germany, and Norway. The study will enroll up to 100 subjects. The study is a randomized, open-label, comparative study.

Eligibility criteria:
Inclusion Criteria:

1. Has given informed consent to participate by signing the informed consent signature form.
2. Has an ileostomy or colostomy with liquid output (defined by a 6-7 on the Bristol Scale)
3. Currently using a flat product
4. Be at least 18 years of age and have full legal capacity
5. Have had their stoma for at least three months (90 days)
6. Can use a product with a max cut size of 40 mm
7. Has experienced leakage\* under the baseplate at least three times within the last 14 days. \*leakage defined as output seeping under the baseplate (Appendix 6 - Classification of leakage)
8. Has symptoms of peristomal skin complications or has peristomal skin complications defined by at least one of the below

   1. Has experienced symptoms of skin complications (itching, burning, pain) within the last fourteen days
   2. Has experienced red skin in the inner circle (within three cm from stoma edge) within the last fourteen days
   3. Has skin complication (assessed by Principal Investigator, or delegate) in the inner circle (within three cm from stoma edge) of the peristomal area
9. Is able to handle the electronic diary (questionnaire/ photo) themselves.
10. Is able to handle (apply, remove, cut etc.) the product themselves
11. Is willing to not use barrier film or barrier cream during the investigation
12. Is willing and suitable (determined by Principal Investigator, or delegate) to use a flat custom cut one-piece open or a two-piece open product during the investigation.
13. Is willing to change the product (1pc) or baseplate (2pc) at least every fourth days.

Exclusion Criteria:

1. Is currently receiving or have within the past 60 days received radio-and/or chemotherapy

   \- low doses chemotherapy (assessed by Principal Investigator) is allowed for indications other than cancer.
2. Is currently receiving or have within the past month received topical steroid treatment in the peristomal skin area, e.g. lotion or spray.

   * Low dose systemic steroid treatment (e.g. inhalation) assessed by the Principal Investigator are allowed.
   * Other systemic steroid treatment (e.g. injection, or tablet) are not allowed.
3. Is breastfeeding
4. Is pregnant (based on pregnancy test -urine)
5. Has known hypersensitivity towards any of the products used in the investigation

   \-

Outcomes:
Max Itching Within a Week at Steady State
Boneview-ED - Impact of Artificial Intelligence Detecting Fractures in the Emergence Department : a Pragmatic Prospective Study
Trial ID: NCT06013852
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-07-22
Completion date: 2025-01-21
Sponsor: University Hospital, Angers
Conditions: Fractures, Bone
Interventions: processing the radiography with the "Boneview" software
Locations: Dr Damien COMBES
Countries: France
Enrollment: 1600
Age range: 18 Years - None
Sex: ALL

Summary:
Traumatic emergencies are the primary reason for consultation in emergency departments and standard radiography is the primary imaging exam for osteoarticular trauma. However, with the increase in the number of patients admitted to emergency departments and thus the increased workload for emergency room attendants, Interpretation of radiographs in trauma emergencies is made more difficult, resulting in a high risk of misinterpretation.

The growing presence of artificial intelligence in the medical field, notably through the involvement of diagnostic software on imageries, makes its use more relevant in the aid of the replay of osteoarticular imageries.

A recent meta-analysis of 32 studies evaluating the performance of artificial intelligence in fracture detection found comparable performance between experienced radiologists and AI-based diagnosis. However, these were mainly retrospective studies, and thus more distant from the reality of its use in a care stream such as emergencies.

The objective of this study is therefore to prospectively validate the use of artificial intelligence software during its implementation in an emergency department for patients admitted for a suspicion of osteoarticular trauma.

Eligibility criteria:
Inclusion Criteria:

* Major patient
* Admitted to the emergency department after trauma less than 48 hours
* Patient with an indication on an x-ray of the limbs or/and pelvis
* Express patient consent
* Affiliated patient or social security beneficiary

Exclusion Criteria:

* Polytraumatized patient
* X-ray of the corso-lumbar spine, skull, cervical spine (all parts of the body not affected by the intended use of the software)
* Pregnant, lactating or parturient patient

Outcomes:
Change in ER support between X-ray reading without and with artificial intelligence
Prediction of Non-sentinel Lymph Node Metastatic Status of Breast Cancer Based on Pathology-MRI Images and Artificial Intelligence
Trial ID: NCT06510738
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2024-08
Completion date: 2025-12
Sponsor: Yunnan Cancer Hospital
Collaborators: Affiliated Cancer Hospital of Shantou University Medical College
Conditions: Breast Cancer; Artificial Intelligence; Lymph Node Metastasis; Digital Pathology
Enrollment: 700
Age range: 18 Years - 75 Years
Sex: FEMALE

Summary:
The goal of this observational study is to develop an artificial intelligence model based on pathology and magnetic resonance imaging (MRI) images to predict the metastatic status of non-sentinel lymph nodes in patients with breast cancer sentinel lymph node metastasis. The main questions it aims to answer are:

Can an artificial intelligence model based on MRI images of breast cancer patients predict the non-sentinel lymph node metastatic status in patients with breast cancer sentinel lymph node metastasis?

Can an artificial intelligence model based on intraoperative frozen section images of sentinel lymph nodes in breast cancer patients predict the non-sentinel lymph node metastasis status in patients with sentinel lymph node metastasis from breast cancer?

Can artificial intelligence models based on preoperative MRI and intraoperative frozen section images of sentinel lymph nodes in breast cancer patients predict the non-sentinel lymph node metastatic status in patients with sentinel lymph node metastasis from breast cancer?

Researchers will retrospectively and prospectively collect preoperative MRI and intraoperative sentinel lymph node section images from breast cancer patients.

Eligibility criteria:
Inclusion Criteria:

1. Patients with primary breast cancer
2. Positive sentinel lymph node biopsy and axillary lymph node dissection.
3. No neoadjuvant chemotherapy, radiotherapy or ablation prior to surgery.
4. MRI within 3 weeks prior to surgery.

Exclusion Criteria:

1. recurrent breast cancer or history of surgery or radiation therapy in the axilla
2. Inflammatory breast cancer
3. Bilateral breast cancer or metastatic breast cancer
4. previous or current history of other malignant tumors
5. Sentinel lymph node count \> 5

Outcomes:
Non-sentinel lymph node metastatic status; Accuracy, Sensitivity, Specificity，Area under the curve; Positive predictive value，Negative predictive value
Remote Digital Voice Biomarkers for Central Fatigue Detection in Generalised Myasthenia Gravis: An Online Single-Cohort Observational Study
Trial ID: NCT07033559
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2026-02
Completion date: 2026-12
Sponsor: Thymia Limited
Collaborators: UCB Pharma
Conditions: Myasthenia Gravis Generalised
Enrollment: 240
Age range: 18 Years - None
Sex: ALL

Summary:
The goal of this observational study is to learn if computer analysis of voice recordings can detect a type of exhaustion called "central fatigue" in adults with generalised myasthenia gravis.

The main questions it aims to answer are:

1. Can advanced voice analysis accurately tell when participants are experiencing deep exhaustion based on how they speak?
2. How easy and acceptable is voice-based fatigue monitoring for people with myasthenia gravis?

Participants will:

1. Record themselves reading short passages and answering questions out loud twice daily (morning and evening), twice a week, for 4 weeks.
2. Answer brief questionnaires about their energy levels, mood, and myasthenia gravis symptoms during each session.
3. Use their own devices (computer, tablet, or smartphone) to complete all study activities online from home.

Eligibility criteria:
Inclusion Criteria:

* Adults ≥18 years old
* Self-reported generalised Myasthenia Gravis diagnosis confirmed by healthcare provider for ≥6 months
* Disease stability for ≥6 months (no hospitalisations, medication changes, or significant symptom worsening)
* English as first language
* Residence in US or UK
* Vision adequate for screen reading (with aid or correction if necessary)
* Access to internet-connected device with compatible browser and microphone
* Adequate internet connectivity (≥5 Mbps download, ≥3 Mbps upload)
* Ability to complete twice-daily assessments during specified time windows
* Signed electronic informed consent

Exclusion Criteria:

* Pure ocular Myasthenia Gravis
* Diagnosed mild cognitive impairment or dyslexia
* Speech or hearing impairments affecting voice recording
* Unable to provide credible diagnostic information (healthcare provider diagnosis, antibody test results, current medications)
* Major inconsistencies in reported medical history
* Unsigned informed consent

Outcomes:
Accuracy of AI Model for Binary Central Fatigue Classification as Assessed by Voice Biomarker Analysis