moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Development of Artificial Intelligence Innovation for Assessing Fall Risk Using Clinical Neurological Studies With Transcranial Magnetic Stimulation and Quantitative Sensory Testing in the Thai Elderly
Trial ID: NCT06480279
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2024-05-16
Completion date: 2025-08-16
Sponsor: Chulalongkorn University
Collaborators: King Chulalongkorn Memorial Hospital; National Research Council of Thailand
Conditions: Fall
Locations: Department of Medicine, Faculty of Medicine, Chulalongkorn University.
Countries: Thailand
Enrollment: 150
Age range: 65 Years - 80 Years
Sex: ALL

Summary:
The elderly are at high risk of falls due to neurophysiological changes, such as motor and sensory impairment, that occur with aging. The incidence of falls in older adults is high, with approximately 30% of adults over the age of 65 falling at least once per year. Falls can lead to serious consequences such as fractures, hospitalization, and loss of independence. The changes in neurophysiology have also been found to decrease balance control in aging people, leading to postural instability. It is well-known that postural balance is influenced by sensory inputs, including touch, proprioception, vision, and vestibular, as well as motor output such as motor controls. Impairments in these systems contribute to postural instability and an increased risk of falling in the elderly.

Eligibility criteria:
Inclusion Criteria:

1\) Male and female individuals aged 65-80 years.

Exclusion Criteria:

1. History of previous lower limb surgery, such as total knee replacement or ankle surgery.
2. History of a neurological disorder, such as stroke or Parkinson's disease.
3. History of vestibular disease, such as Meniere's disease.
4. History of seizures or epilepsy.
5. The presence of metal implanted devices in or around the head area
6. MoCA score \< 26

Outcomes:
Montreal Cognitive Assessment (MoCA); Body weight; Muscle mass; Body mass index (BMI); Percent body fat; The duration during Timed Up and Go Test; The repetition of Chair Stand Test; Muscle evoke potential (MEP); Cortical silent period (CSP); Cold temperature threshold; Vibration threshold
Evaluation of the PROSTia Test in Patients Undergoing Prostate Biopsy
Trial ID: NCT06931496
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-04-15
Completion date: 2025-11-01
Sponsor: Central Hospital, Nancy, France
Conditions: Prostate Cancer (Adenocarcinoma)
Interventions: PROSTia test
Locations: CHRU Nancy
Countries: France
Enrollment: 150
Age range: 18 Years - None
Sex: MALE

Summary:
The investigators are interested here in the contribution of a new prostate cancer screening method and, more specifically, in the new and somewhat futuristic approach of artificial intelligence in the development of new, more accurate algorithms that make it possible to rethink the benefits of mass generalisation of prostate biopsies.

Main objective The main objective of this research is to use artificial intelligence and an associated algorithm to identify new indicators that would make it possible to avoid a prostate biopsy in patients with an initial suspicion of prostate cancer.

Eligibility criteria:
Inclusion criteria:

* Patients followed up at the Nancy CHRU in the Urology Department and eligible for a prostate biopsy in search of prostate cancer, taking into account an increase in PSA, PSA density, lesion on MRI.
* Person who has received full information about the organisation of the research and has not objected to their participation and the use of their data.
* Adult male over 18 years of age - Patient affiliated to or benefiting from a social security scheme - Patient who understands French and is able to fill in a self-administered questionnaire or who has the possibility to be assisted in filling it in.

Exclusion criteria:

* Patients who have already had a prostate biopsy
* Patients under court protection, guardianship or trusteeship
* Patients deprived of liberty by judicial or administrative decision

Outcomes:
sensitivity; specificity; positive likehood ratio; negative likehood ratio; disease prevalence; positive predictive value; negative predictive value; accuracy
National Colorectal Polyp Care for Diagnosis, Classification and Resection
Trial ID: NCT03712059
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2018-11-01
Completion date: 2020-09-30
Sponsor: Changhai Hospital
Collaborators: 175 medical centers in China
Conditions: Colorectal Polyp
Interventions: FIT test and colonoscopy; Polypectomy and biopsy; Classification
Locations: Changhai Hospital, Second Military Medical University
Countries: China
Enrollment: 12000
Age range: 18 Years - 75 Years
Sex: ALL

Summary:
This study has three main purposes：screening: the first purpose is to evaluate the diagnostic value of combintion of the life risk factors and immunochemical fecal occult blood test (FIT) on detection of colorectal neoplasia in Chinese population; resection: the second objective is to investigate the complete resection rate of colorectal adenoma and risk factors of incomplete resection in China; identification and classification: the third objective is to initially establish an artificial intelegence-assissted recognition and classification system of polyp based on deep learning.

Eligibility criteria:
Inclusion Criteria:

1. Age between 18 to 75 years old and patients with or without alarming gastrointestinal symptoms were analyzed separately.
2. 3-4 L polyethylene glycol and foaming agent are used for bowel preparation.
3. Withdrawal time ≥6mins (excluding the time of biopsy)

Exclusion Criteria:

1. A history of acute myocardial infarction (within 6 months), severe heart, liver, kidney dysfunction, or mental illness.
2. Patients taking anticoagulants such as aspirin and warfarin, or who have coagulopathy.
3. Patients with inflammatory bowel disease and colon polyposis.
4. History of colonic procedure (including surgery, polypectomy, EMR, and ESD) in the screening section
5. Diameter of polyp greater than 1cm, lateral developmental lesions (LST), colon cancer, lesions requiring ESD and surgery
6. Patients participating in other clinical trials now or within 60 days.
7. Intestinal obstruction.

Outcomes:
Detection of colorectal neoplasia
A Randomized Controlled Trial to Evaluate an Artificial Intelligence-enabled Clinical Assistant for Thyroid Cancer Staging and Risk Stratification Among Medical Students and Clinicians
Trial ID: NCT07234539
Status: ENROLLING_BY_INVITATION
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-10-02
Completion date: 2026-04-30
Sponsor: The University of Hong Kong
Collaborators: Innovation and Technology Commission, Hong Kong
Conditions: Thyroid Cancer; Large Language Models
Interventions: AI-enabled clinical assistant
Locations: Department of Surgery, School of Clinical Medicine, The University of Hong Kong; School of Public Health, The University of Hong Kong
Countries: Hong Kong; Hong Kong
Enrollment: 70
Age range: 18 Years - None
Sex: ALL

Summary:
This study aims to evaluate the clinical feasibility of adopting artificial intelligence (AI)-based models to improve clinical management of thyroid cancer.

Eligibility criteria:
Inclusion Criteria:

* medical students
* clinicians (including but not limited to surgeons, oncologists, pathologists)

Exclusion Criteria:

* medical students and clinicians who had reviewed the clinical notes or were involved in the processing of the clinical notes prior to the commencement of clinical experimental studies

Outcomes:
Accuracy of Cancer Staging and Risk Stratification by Participants Compared with Ground Truth across Intervention and Non-intervention Groups; Participants' Confidence in Cancer Staging and Risk Stratification as Assessed by a 0-10 Scale Questionnaire; Efficiency
OPTimising a Screening Program to Detect Pacemaker-associated Heart Failure Using Artificial Intelligence
Trial ID: NCT07372651
Status: ENROLLING_BY_INVITATION
Study type: OBSERVATIONAL
Start date: 2025-10-07
Completion date: 2026-09
Sponsor: University of Leeds
Collaborators: Leeds Hospitals Charity
Conditions: Heart Failure; Pacemaker
Locations: Cardiovascular Research Facility, Leeds General Infirmary
Countries: United Kingdom
Enrollment: 150
Age range: 18 Years - None
Sex: ALL

Summary:
Pacemakers are an effective treatment for slow heart rates which improve symptoms and save lives. However, for some people pacemakers can cause heart failure (HF) because of the unnatural way in which they stimulate heart beats. In several studies conducted in West Yorkshire we showed that \~1/3 of patients with pacemakers have undiagnosed HF. We also showed that where HF is discovered, treating it with safe and inexpensive medications reduces the chances of being admitted to hospital or dying. However, detecting HF requires an echocardiogram (a heart ultrasound scan) which takes \~45 minutes, requires a skilled technician, and costs £120; or, to put it another way \~£540,000 to assess the \~4,500 patients cared for at our hospital. A new approach is needed. We think that using new technologies can improve our ability to screen for HF in people with pacemakers. We will test two approaches. First, we will assess whether a hand-held echocardiogram can measure heart function using artificial intelligence (AI) as accurately as a standard echocardiogram done by a skilled technician. Second, we will assess whether a finger-prick blood test can detect the presence of abnormal function as accurately as a standard echocardiogram.

Eligibility criteria:
Inclusion Criteria:

* Adult patients aged ≥18 years
* Patients with existing pacemakers who have a right ventricular pacing burden ≥20%.
* Ability to provide informed consent

Exclusion Criteria:

* Patients who are unwilling or unable to provide informed consent.
* Patients known to have heart failure
* Patients with any previous measurement of left ventricular ejection fraction \<50%.
* Patients with conduction system pacemakers.
* Patients with cardiac resynchronisation therapy pacemakers/defibrillators.
* Patients with implantable cardioverter defibrillators.
* Patients with leadless pacemakers.

Outcomes:
The accuracy of artificial intelligence derived left ventricular ejection fraction (LVEF) compared to expert sonographer measured LVEF
Investigating the Relationship Between Nocturnal Glucose Levels and Motion During Sleep - The Moonwalk Pilot-Study
Trial ID: NCT05609175
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2023-02-14
Completion date: 2024-09-26
Sponsor: University of Bern
Collaborators: Insel Gruppe AG, University Hospital Bern; DCB Research AG
Conditions: Diabetes; Hypoglycemia; Hyperglycemia
Locations: Department of Diabetes, Endocrinology, Nutritional Medicine and Metabolism, Inselspital Bern, University and University Hospital Bern
Countries: Switzerland
Enrollment: 37
Age range: 18 Years - None
Sex: ALL

Summary:
In this study the investigators will explore whether the motion during sleep of people with diabetes changes as a function of the blood glucose levels. The motion will be assessed with a radar sensor, a thermal camera, and wrist worn smartwatches. Additionally, participants will answer a short daily questionnaire. Data will be collected for 10 days and analyzed at the end of the study.

Eligibility criteria:
Inclusion Criteria:

* Informed consent as documented by signature
* Age ≥ 18 years
* Patients at the outpatient clinics or from a referring diabetologist with a diagnostic CGM according to best medical practice
* Patients with diabetes mellitus on an insulin therapy or with non-insulin antidiabetic medication with the potential to induce hypoglycemia
* HbA1c value between 6% and 10% (inclusive) within 6 months prior to inclusion

Exclusion Criteria:

* Wearables cannot be attached around the wrist of the patient
* Known allergies to components of the wearables
* Female subjects: pregnancy, lactation period, lack of a negative urine pregnancy test (except in case of menopause, sterilization or hysterectomy), or unwilling to use a contraception during the study (for sexually active subjects of childbearing potential)
* Known cardiac arrhythmia (e.g., atrial flutter or fibrillation, Atrioventricular-reentry tachycardia, Atrioventricular block \> grade 1)
* Pacemaker or ICD (implantable cardioverter defibrillator)
* Treatment with antiarrhythmic drugs or beta-blockers
* Drug or alcohol abuse
* Inability to follow the procedures of the study, e.g. due to language problems, psychological disorders, dementia, etc. of the participant
* Physical or psychological disease likely to interfere with the normal conduct of the study and interpretation of the study results as judged by the investigator
* Any illness or use of medications that could affect sleep patterns (including sleep apnea)
* Dependency from the sponsor or the clinical investigator
* Participation in another investigation with an investigational drug or a pre-market medical device within the 30 days preceding and during the present investigation

Outcomes:
Area under the receiver operator characteristics curve (AUROC) in detecting nocturnal level 1 hypoglycemia based on motion data obtained by radar signals
Prospective Analysis of Tumor Tissues From the Oral Cavity by SpiderMassTM Spectroscopy
Trial ID: NCT05104619
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2021-10-01
Completion date: 2023-10
Sponsor: Centre Hospitalier Universitaire, Amiens
Collaborators: Université de Lille
Conditions: Mass Spectroscopy; Molecular Profile; Surgical Margins; Squamous Cell Carcinoma; Oral Cavity; Tongue
Interventions: tongue surgery
Locations: Centre Hospitalier Universitaire d'Amiens
Countries: France
Enrollment: 14
Age range: 18 Years - None
Sex: ALL

Summary:
In squamous cell carcinoma of the tongue, complete surgical resection of the tumor generally represents the primary treatment strategy. Among factors determining the disease prognostic, the surgical margins status is essential for the continued management of the patient, with or without complementary therapies. Generally, surgical margins are defined using visual and tactile elements. Unfortunately, in 15 to 30 percent of cases, they are defined as positive after histopathological examination, on average 5 to 7 days post-surgery.

Techniques have recently been developed to analyze the cancerous and non-cancerous nature of tissues, using the spectral properties of tumor cells. SpiderMass™ is an instrument equipped with a probe for tissue micro-sampling and real-time molecular profile analysis. It is thus ultimately a tool for anatomopathological decision support, for diagnosis and prognosis of the disease. Used in the operating room, SpiderMass™ could also become an innovative surgical tool to precisely define cancerous areas in situ and the margins status in intraoperative conditions.

Based on the principle of artificial intelligence, the SpiderMass™ machine must initially learn to recognize pathological tissues versus healthy. This project aims to accomplish this first phase of machine learning for squamous cell carcinoma of the tongue.

Eligibility criteria:
Inclusion Criteria:

* age : 18 years and older
* squamous cell carcinoma of the tongue
* surgical resection as first-line treatment
* tumor classification T1 to T4.

Exclusion Criteria:

* metastatic disease
* neoadjuvant therapies

Outcomes:
Tumor cells detection in tongue tumor with SpiderMass technique
The Role of Artificial Intelligence in Predicting Stage and Survival in Non-Small Cell Lung Cancer
Trial ID: NCT07068139
Status: ACTIVE_NOT_RECRUITING
Study type: OBSERVATIONAL
Start date: 2010-01-01
Completion date: 2025-09-01
Sponsor: Hilkat Fatih Elverdi
Conditions: Non-Small Cell Lung Cancer; Artificial Intelligence (AI) in Diagnosis
Interventions: AI-Based Predictive Modeling
Enrollment: 150
Age range: 18 Years - None
Sex: ALL

Summary:
This study aims to evaluate the role of artificial intelligence (AI) in predicting disease stage and survival in patients diagnosed with non-small cell lung cancer (NSCLC). Using a retrospective design, the research will analyze radiologic imaging data (PET-CT and chest CT) and corresponding histopathological results of patients who underwent lung cancer surgery at Ondokuz Mayis University Hospital.

The goal is to develop and validate a deep learning-based AI model that can automatically assess preoperative radiologic features and estimate postoperative tumor stage and survival outcomes. By integrating radiologic data with confirmed pathological diagnoses, the AI system is expected to provide clinical decision support that can improve diagnostic speed, reduce human error, and help clinicians predict prognosis more accurately.

This study does not involve any experimental treatment or prospective follow-up of patients. All data will be collected from existing medical records. The findings may contribute to the digital transformation of healthcare and promote the use of AI tools in thoracic oncology.

Eligibility criteria:
Inclusion Criteria:

* Age ≥ 18 years
* Diagnosed with non-small cell lung cancer (NSCLC)
* Underwent surgical treatment for NSCLC at Ondokuz Mayis University Hospital
* Available preoperative PET-CT and chest CT imaging
* Available postoperative histopathological diagnosis and staging
* Signed informed consent form for data use in research

Exclusion Criteria:

* Age \< 18 years
* No available PET-CT or chest CT imaging in hospital records
* No available histopathological diagnosis in hospital records
* Diagnosed with a type of lung cancer other than NSCLC
* Patients who did not undergo surgery
* Patients who did not provide informed consent for retrospective data use

Outcomes:
Development of AI Model for Predicting Tumor Stage and Survival
Phase II - Precision Imaging to Evaluation Kaposi Sarcoma (PRIME-KS)
Trial ID: NCT06898203
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-05-31
Completion date: 2029-11-30
Sponsor: Washington University School of Medicine
Collaborators: National Cancer Institute (NCI); Pensievision; Kenya Medical Research Institute; Makerere University
Conditions: Kaposi Sarcoma
Interventions: SkinScan3D
Locations: Kenya Medical Research Institute; Infectious Diseases Institute, Makerere University
Countries: Kenya; Uganda
Enrollment: 300
Age range: 18 Years - None
Sex: ALL

Summary:
AIMS 2 \& 3:

In this study the investigators seek to formally compare reproducibility and accuracy of KS lesion size measurements between SkinScan3D and the current standard of care manual measurement method. The investigators will then test the SS3D device on 100 patients in a variety of real-world practice locations to determine whether the device will be usable, acceptable, appropriate, and feasible in routine care settings.

Eligibility criteria:
Inclusion Criteria - Aim 2 (Patients):

* Adults age ≥18 years old
* Histopathology-confirmed Kaposi Sarcoma
* At least 3 skin lesions
* Capable of informed consent
* On treatment for Kaposi Sarcoma

Exclusion Criteria - Aim 2 (Patients):

* Patients not initiating Kaposi Sarcoma treatment
* Very ill patients requiring hospitalization

Inclusion Criteria - Aim 3 (Patients):

* Adults age ≥18 years old
* Histopathology-confirmed Kaposi Sarcoma
* Capable of informed consent
* Initiating treatment for Kaposi Sarcoma

Exclusion Criteria - Aim 3 (Patients):

* Patients with Kaposi Sarcoma that participated in Aim 1 or Aim 2
* Prior or ongoing Kaposi Sarcoma treatment

Outcomes:
Reproducibility of KS lesion size measurements between SkinScan3D and current standard of care measurement - Aim 2 Only; Accuracy of KS lesion size measurements between SkinScan3D and current standard of care measurement - Aim 2 Only; Device usability - Aim 3 Only; Acceptability - Aim 3 Only; Appropriateness - Aim 3 Only; Feasibility - Aim 3 Only
Belun Ring Platform (BLS-100) Home Sleep Apnea Testing Device With an Improved Algorithm for Assessment of Obstructive Sleep Apnea: A Comparison to In-Lab Polysomnography
Trial ID: NCT04885062
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2021-02-01
Completion date: 2022-06-30
Sponsor: Belun Technology Company Limited
Collaborators: University Hospitals Cleveland Medical Center
Conditions: Sleep-Disordered Breathing; Sleep Architecture
Interventions: Belun Ring; PSG
Locations: University Hospitals Cleveland Medical Center
Countries: United States
Enrollment: 136
Age range: 18 Years - 80 Years
Sex: ALL

Summary:
OSA is commonly diagnosed with either attended in-lab polysomnography (PSG) or unattended home sleep apnea testing (HSAT). The BLS-100 (Belun Technology Company Limited, Hong Kong) is a novel neural network-based HSAT platform consists of a ring shape pulse oximeter sensor, a cradle, and an improved proprietary analytic algorithm. This study investigates the hypothesis that the BLS-100 is a reasonable HSAT device for OSA assessment.

Eligibility criteria:
Inclusion Criteria:

* Provision of signed and dated informed consent form.
* Age 18-80
* Clinically assessed and suspicious for OSA

Exclusion Criteria:

* Full night PAP titration study
* On home O2, noninvasive ventilator, diaphragmatic pacing, or any form of nerve stimulator
* Having atrial fibrillation-flutter, pacemaker/defibrillator, LVEF \< 55%, left ventricular assist device (LVAD), or status post cardiac transplantation
* Patients taking narcotics
* Recent hospitalization or recent surgery in the past 30 days
* Unstable cardiopulmonary status on the night of the study judged to be unsafe for sleep study by the sleep tech and/or the on-call sleep physician

An individual who meets the above criteria but fails to have at least 4 hours of technically valid sleep based on BLS-100 in a diagnostic study or at least 3 hours of technically valid sleep during the diagnostic portion of a split night study will be excluded from statistical analyses.

Outcomes:
To Evaluate the Ability of the Belun Ring Pulse Oximeter to Gauge the Apnea-Hypopnea Index (AHI)
engAGE: Managing cognitivE decliNe throuGh Theatre Therapy, Artificial Intelligence and Social Robots drivEn Interventions
Trial ID: NCT06302686
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2023-10-09
Completion date: 2024-06-24
Sponsor: Istituto Nazionale di Ricovero e Cura per Anziani
Collaborators: European Union
Conditions: Mild Cognitive Impairment; Older Adults
Interventions: engAGE system; Group practicing with only the support of a booklet
Locations: IRCCS INRCA Hospital; Karde A/S; HUG
Countries: Italy; Norway; Switzerland
Enrollment: 78
Age range: 65 Years - None
Sex: ALL

Summary:
The objectives of the engAGE project are to counteract and slow down cognitive decline progression, to enhance the intrinsic capacity of the users, and to support the wellbeing of older persons with mild cognitive impairment (MCI) by providing an ecosystem of services based on an innovative system that integrates social robots.

Eligibility criteria:
Inclusion Criteria:

* mild cognitive impairment (MCI);
* Montreal Cognitive Assessment (MoCA) score 21 - 25;
* Memory Assessment Clinics - Questionnaire (MAC-Q) ≥ 25;
* Reisberg scale 2 - 4;
* Clinical Frailty Scale score 1 - 3;
* 4-items GDS score ≤ 1;
* have an informal caregiver to support in carrying out the main daily activities.

Exclusion Criteria:

* lack of informal caregivers;
* significant visual or hearing impairment.

Outcomes:
Change in perceived stability of cognitive status by the older adults