moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
A Novel Wearable Digital Biomarker for Detecting Changes in Multiple Sclerosis (MS) Disease Condition Through Home Monitoring of MS Patients
Trial ID: NCT05781113
Status: ACTIVE_NOT_RECRUITING
Study type: OBSERVATIONAL
Start date: 2023-07-01
Completion date: 2026-12-31
Sponsor: Celestra Health Systems
Collaborators: University of Ottawa; Barts & The London NHS Trust; The Ottawa Hospital; Brigham and Women's Hospital
Conditions: Multiple Sclerosis
Locations: Brigham and Women's Hospital; The Ottawa Hospital; The Royal London Hospital
Countries: United States; Canada; United Kingdom
Enrollment: 90
Age range: 18 Years - 60 Years
Sex: ALL

Summary:
To measure the effectiveness of a Remote Patient Monitoring solution based on the use of a smart insole wearable device (and associated smart phone app), for monitoring MS patients' condition on a day-to-day basis. The main focus is the objective measurement of gait, given that 75% of people with MS display clinically significant gait impairments. Initial gait lab "gold standard" data indicate that the Artificial Intelligence (AI)-based digital biomarker will prove to be highly effective at detecting changes in the MS patient's condition.

Eligibility criteria:
Inclusion Criteria:

* Participants must have a diagnosis of Multiple Sclerosis (MS) based on the McDonald criteria, within an age range of 18 to 60.
* The participant must have an Extended Disability Status Scale (EDSS) score at screening less than or equal to 6.5, inclusive.
* The participant cohort will include at least 3 participants at each site exhibiting one of the following gait phenotypes: ataxic, hemiplegic and spastic. (Some participants may exhibit more than one phenotype).
* The participant cohort will include at least 3 participants at each site with a progressive form of MS.

Exclusion Criteria:

* Participants that are currently suffering from a musculoskeletal injury (e.g., sprain, fracture, strain, etc.) that limits their ability to use their full range of motion of any joint at the time of recruitment.
* Inability to provide informed consent.

Outcomes:
Participant Adherence; Clinician Acceptance
Prospective Single Blinded Randomized Control Trial on the Effectiveness of Using Large Language Model Artificial Intelligence Chatbot to Improve Boston Bowel Preparation Score (BBPS) for Colonoscopy Preparation
Trial ID: NCT06905782
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-04-01
Completion date: 2026-02-02
Sponsor: National University of Malaysia
Conditions: Colorectal Cancer Screening; Colon Polyps and Adenomas; Bowel Preparation Quality; Artificial Intelligence (AI)
Interventions: AI chatbot study arm; Traditional counselling study arm
Locations: Faculty of Medicine, The National University of Malaysia
Countries: Malaysia
Enrollment: 96
Age range: 18 Years - 75 Years
Sex: ALL

Summary:
Traditional pre-colonoscopy counselling requires significant time from healthcare workers to explain procedures, limiting efficiency and patient turnover. Inadequate bowel preparation exacerbates this issue, leading to repeat procedures and increased costs. However, no study has yet evaluated the effectiveness of AI in improving the Boston Bowel Preparation Scale (BBPS) for colonoscopy preparation. By addressing this gap, AI chatbots could provide personalized guidance, reduce healthcare worker burden, improve preparation quality, and enhance patient experience.This research attempts to evaluate the effectiveness of using Artificial intelligence (AI) chat bot to improve bowel preparation, anxiety level and patient's satisfaction among colonoscopy patients in Hospital Tuanku Muhriz (HCTM), compared to conventional instructions

Eligibility criteria:
Inclusion Criteria:

* All scheduled colonoscopy with indication
* Adequate digital literacy
* Adequate language literacy with Malay and English language

Exclusion Criteria:

* Patients with memory impairment due to previous stroke, dementia or Alzheimer's disease
* Diagnosed with clinical anxiety

Outcomes:
To determine the effectiveness of artificial intelligence (AI) chat bot in improving bowel preparation among colonoscopy patients in Hospital Tuanku Mukhriz (HCTM), compared to conventional instructions
Using Artificial Intelligence on Mobile Devices to Automate Directly Observed Therapy (DOT), Confirm Medication Ingestion, and Optimize Treatment in Patients on Anticoagulation Therapy.
Trial ID: NCT02599259
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2015-03
Completion date: 2016-04
Sponsor: AiCure
Collaborators: Montefiore Medical Center
Conditions: Stroke
Interventions: Monitored (M+)
Locations: Montefiore Medical Center
Countries: United States
Enrollment: 28
Age range: 18 Years - None
Sex: ALL

Summary:
AiCure uses artificial intelligence and visual recognition technology to confirm medication ingestion. The software is available as an app and downloaded onto a smart phone. The single-site, parallel-arm, randomized controlled trial will test the feasibility and impact of using the platform in a stroke population. End points: usability, consistent use of the device, and optimization of treatment.

Eligibility criteria:
Inclusion Criteria:

* Is male or female at least 18 years of age.
* Having a diagnosis of ischemic stroke.
* Has a score between 1 and 20 on the NIH Stroke Survey (NIHSS) at admission and upon enrollment at discharge from the index admission to the hospital or at first encounter in the outpatient stroke center.
* Is taking any one of the monitored drugs- Coumadin®, Pradaxa®, Xarelto® or Eliquis®.
* Is going home or to acute outpatient rehabilitation after discharge.
* Has sufficient capacity to provide consent or agree to assent.
* Has at least minimal mental capacity and motor skills.

Exclusion Criteria:

* Has poor fine motor skills, to preclude him/her from holding a pill steady in front of a camera.
* Has impaired visual or auditory faculties.
* Is being released to a nursing home, hospice or any other inpatient care facility.
* Has stable, therapeutic INRs on warfarin for at least one year.
* Has a mechanical mitral valve or left ventricular assist device.

Outcomes:
To evaluate acceptability and likability of the platform to patients and providers in providing real-time adherence monitoring.
PAtient Similarity for Decision-Making in Prevention of Cardiovascular Toxicity (PACT): A Feasibility Study
Trial ID: NCT05377320
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2026-12
Completion date: 2028-12
Sponsor: Medical College of Wisconsin
Conditions: Heart Failure; Coronary Artery Disease; Peripheral Artery Disease; Ischemia; Hypertension; Diabetes Mellitus; Cardiomyopathies; Cardiotoxicity
Interventions: Clinical Decision Aid; Standard Care
Locations: Froedtert & the Medical College of Wisconsin
Countries: United States
Enrollment: 60
Age range: 18 Years - None
Sex: ALL

Summary:
This is a single-center, double-arm, open-label, randomized feasibility study that will determine whether a novel clinical decision aid accessed via the electronic health record will be acceptable to both cancer survivors and their cardiologists, will favorably impact appropriate medication use and cardiac imaging surveillance, and will improve clinician and patient decision-making, perception, and behavior towards cardioprotective medication usage and cardiovascular disease imaging utilization.

Eligibility criteria:
Inclusion Criteria:

1. Patients ≥18 years with a history of cancer.
2. Have not previously visited a cardiologist to assess cardiovascular risk after cancer diagnosis.
3. Clinically at intermediate, high, or very high risk for cardiovascular diseases determined based on imprecise clinical risk models, such as those used for cardiac dysfunction.
4. Ability to understand a written informed consent form, and willing to sign it prior to study registration.

Exclusion Criteria:

1. Patient \<18 years.
2. Without a personal history of cancer.
3. Existing cardiomyopathy diagnosed after cancer diagnosis.
4. Documented cognitive impairment.
5. Patient or patient representative who is unable and unwilling to sign the informed consent form.

Outcomes:
Medication use; Medication use; Medication use; Imaging surveillance; Imaging surveillance; Imaging surveillance
Accurate Prediction and Treatment of Prostate Cancer by Artificial Intelligence Model-based Whole Slide Images and MRIs
Trial ID: NCT06662708
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-12-01
Completion date: 2030-12-31
Sponsor: Shao Pengfei
Collaborators: Institute of Automation, Chinese Academy of Sciences
Conditions: Prostate Cancer; Prostate Intraductal Carcinoma; Prostate Cancer Aggressiveness; Prostate Cancer Stage; Pathology
Interventions: Accurate Prediction Artificial Intelligence Models
Locations: The First Affiliated Hospital of Nanjing Medical University (Jiangsu Provincial People's Hospital)
Countries: China
Enrollment: 200
Age range: 30 Years - None
Sex: MALE

Summary:
The aim of this clinical trial is whether artificial intelligence models can be used for accurate clinical preoperative diagnosis and postoperative diagnosis of pathological findings, and will also measure the accuracy of the predictions made by the artificial intelligence models.The main target questions addressed by the model building are:

1. whether the AI model can learn from preoperative MRI and postoperative Whole Slide Images so as to accurately predict information such as benignness or malignancy, aggressiveness, grading, subtypes, genes, etc. for participants suspected of having prostate cancer preoperatively/puncturally.
2. whether the AI model is capable of learning postoperative macropathology slides to enable outcome diagnosis of surgical pathology slides in new participants.

Participants will:

1. complete an MRI examination and have their MRI images analysed by the established AI model to make an accurate diagnosis of them.
2. Based on the diagnosis, if prostate cancer is predicted, they will undergo radical prostate cancer surgery and refine their surgical pathology.

Eligibility criteria:
Inclusion Criteria:

* Patients with suspected PCa (elevated PSA or suspicious positive lesions on ultrasound or MRI results);

Exclusion Criteria:

* Previous treatment of the prostate in any form, including surgery, radiotherapy/chemotherapy, endocrine therapy, targeted therapy and immunotherapy;
* Patients with any item missing from the baseline clinical and pathological information;
* Patients with a history of other malignancies, serious comorbidities or other health problems;
* Unable to provide/sign an informed consent form;
* Patients who, in the judgement of the investigator, are deemed unfit to participate in this clinical trial;

Outcomes:
Prediction of postradical prostate cancer pathology after radical prostatectomy using the 'AUC' comprehensive assessment model; Predicting the performance of post-radical pathology by the 'AUC' comprehensive assessment model; 'F1 Score' to assess performance of preoperative 3D modelling
Personalized Warfarin Dosing Using Genomics and Computational Intelligence
Trial ID: NCT00872079
Status: TERMINATED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2008-09
Completion date: 2011-09
Sponsor: US Department of Veterans Affairs
Conditions: Venous Thrombosis; Atrial Fibrillation; Myocardial Infarction
Interventions: Genomics
Locations: VA Medical Center, Louisville
Countries: United States
Enrollment: 175
Age range: 18 Years - None
Sex: ALL

Summary:
This study will create a computer program that can be used to help dose a drug called warfarin for the prevention of blood clotting. The study will collected specific information about those patients receiving this drug and use that information to create a computer program that will predict the effects of the drug. With this prediction program in place, the investigators can perform a series of "what if I gave this amount of drug" simulations to determine the best dose of drug for that patient. Once the computer programs are developed, the investigators will test the program in patients that actually need this drug. They will also include genetic information into the prediction since it has been shown that this information can affect how well the drug works. Patients will have this genetic information determined during this study.

Eligibility criteria:
Inclusion Criteria:

* Warfarin therapy
* Attend anticoagulation clinic
* Warfarin therapy for 6 months

Exclusion Criteria:

* History of non-compliance

Outcomes:
Patient Genomics
Construction and Clinical Application of an X-ray AI-Aided Diagnosis System: A Randomized Controlled Trial
Trial ID: NCT07497243
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2026-05-01
Completion date: 2026-11-30
Sponsor: Union Hospital, Tongji Medical College, Huazhong University of Science and Technology
Conditions: Chest X-ray for Clinical Evaluation
Interventions: AI-assisted radiologist diagnostic group; Radiologist diagnostic group
Locations: Wuhan Union Hospital; Wuhan Union Jinyin Lake Hospital; Wuhan Union West Hospital; The First Affiliated Hospital of Zhengzhou University
Countries: China; China; China; China
Enrollment: 16000
Age range: None - None
Sex: ALL

Summary:
X-ray examination is one of the most commonly used imaging modalities, especially chest X-ray, which is routinely performed for hospitalized patients. However, due to the low density resolution of X-ray images, radiologists' ability to diagnose diseases-particularly small lesions-is often affected. Studies have shown that the diagnostic accuracy of radiologists using chest X-rays is only around 70%, which does not meet clinical demands.

Based on this, we developed an artificial intelligence model to assist radiologists in interpreting X-ray images and generating reports, with the aim of improving diagnostic accuracy and reducing interpretation time.

Eligibility criteria:
Inclusion Criteria:

* Clinically suspected thoracic diseases (such as pneumonia, tuberculosis, or lung cancer) requiring X-ray diagnosis；
* Patients providing written informed consent for research data use；
* Complete clinical records (including chief complaints, medical history, and laboratory test results)

Exclusion Criteria:

* Substandard X-ray image quality (including severe motion artifacts, over-/underexposure, or missing anatomical structures)
* Pregnant or lactating women

Outcomes:
Area Under the Curve
Performance Evaluation of CAD-EYE and SCALE-EYE for Detection, Classification, and Measurement of Colorectal Polyps: a Prospective Study
Trial ID: NCT05236790
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2022-06-20
Completion date: 2023-06-20
Sponsor: Centre hospitalier de l'Université de Montréal (CHUM)
Conditions: Colorectal Polyp; Colorectal Adenoma
Interventions: Detection and classification and size measurement of polyp by Artificial Intelligence (Cad eye) and the Scale Eye
Locations: Centre Hospitalier Universitaire de Montréal
Countries: Canada
Enrollment: 348
Age range: 45 Years - 80 Years
Sex: ALL

Summary:
The investigators hypothesize that the clinical implementation of an AI system is an optimal tool to monitor, audit and improve the detection and classification of polyps during colonoscopy. The purpose of this prospective clinical cohort study is to evaluate the performance of the SCALE-EYE virtual scale for measuring polyp size when used during live colonoscopies. The investigators also wish to evaluate CAD-eye for detection and classification of polyp histology. It is hypothesized that CAD-eye and SCALE-EYE can function in real-time practice with high accuracy.

Eligibility criteria:
Inclusion Criteria:

* indication of undergoing a screening, surveillance, or diagnostic colonoscopy
* Age 45-80 years
* Signed informed consent

Exclusion Criteria:

* Known inflammatory bowel disease (IBD)
* Active Colitis
* Poor general health, defined as an American Society of Anesthesiologists (ASA) physical status class \>3
* Coagulopathy
* Familial polyposis syndrome
* Emergency colonoscopies
* Poor bowel preparation score defined as the total Boston bowel preparation score \<6 and the right-segment score \<2.

For per-polyp analysis:

1. For optical diagnosis (CADx) we will exclude all polyps \>5mm. We will also exclude sessile serrated polyps and traditional serrated adenomas from the analysis as CADx can only differentiate between adenomas and hyperplastic polyps.
2. For polyp size measurement (SCALE-EYE), we will exclude all polyps resected in several pieces (piecemeal resection), or specimens damaged (e.g., fractured) during the suction and retrieval process, submillimeter polyps too small for measurement after resection. Polyps after the 5th resected polyp in a single piece will be excluded to reduce the time associated with the procedure.
3. For polyp detection (CADe) we will not exclude any polyp from the analysis.

Outcomes:
Evaluation of the Scale-Eye; Evaluation of CADe function; Evaluation of CADx function
Evaluation of a Soft Tissue Biopsy System for Trans-rectal and Trans-perineal Biopsy of the Prostate Using Standard of Care and Artificial Intelligence (AI) Analysis
Trial ID: NCT06550960
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-08-05
Completion date: 2025-02-01
Sponsor: Uro-1 Medical
Conditions: BPH
Interventions: Evaluation of two biopsy systems
Enrollment: 30
Age range: 18 Years - 80 Years
Sex: MALE

Summary:
This is a post-market study of a cleared biopsy system. The quality of tissue cores varies with standard of care biopsy needles. The SureCore Plus biopsy system is being evaluated as to quality and volume of tissue from prostate biopsy as compared to standard of care biopsy needles.

Eligibility criteria:
Inclusion Criteria:

* Patient has BPH requiring a prostate biopsy
* Able and willing to provide consent

Exclusion Criteria:

* Active infection
* Subject participating in an other device study of the prostate

Outcomes:
Procedure success; Quality of biopsy tissue
Vital Sign Comparison Between Lifelight and Standard of Care - Development
Trial ID: NCT04003662
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2018-05-01
Completion date: 2021-05-01
Sponsor: Xim Limited
Conditions: Vital Signs
Interventions: Vital Signs Measurement
Locations: Portsmouth Hospitals NHS Trust
Countries: United Kingdom
Enrollment: 9000
Age range: 3 Years - None
Sex: ALL

Summary:
Patients and volunteers both with and without medical problems will be recruited; vital sign measurements are taken twice with normal equipment and while recording video data at the same time. The data collected will allow the Artificial Intelligence to develop the LifeLight algorithm to to improve measurement accuracy of its video data based vital signs monitor.

Eligibility criteria:
Inclusion Criteria:

* I1. Sufficiently conversant in the English language to satisfy I3.
* I2. Able and willing to comply with all study requirements.
* I3. Able and willing to provide written informed consent to participate (including by parent or legal guardian if under 16 years old).

Exclusion Criteria:

* There are no exclusion criteria.

Outcomes:
Blood Pressure Readings; Oxygen Saturation Readings; Heart Rate (pulse) Readings; Respiratory Rate
Comparison of Artificial Intelligence and Ultrasonographers With Different Skills in Diagnosing Intussusception in Children and Identifying Indications for Surgery
Trial ID: NCT05823792
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2023-04-09
Completion date: 2023-05-10
Sponsor: Hongkui Yu， MD
Conditions: Ileocolic Intussusception
Interventions: Non-invasive diagnosis of ileocolic intussusception by ultrasound with indication for surgery
Locations: Hongkui Yu， MD
Countries: China
Enrollment: 250
Age range: 3 Months - 36 Months
Sex: ALL

Summary:
Artificial intelligence versus different skilled sonographers.

Eligibility criteria:
Inclusion Criteria:

* Children aged 3-36 months with suspected ileocolic intussusception

Exclusion Criteria:

* Children younger than 3 months or older than 36 months with suspected ileocolic intussusception

Outcomes:
Ultrasound diagnosis of ileocolic intussusception
Development and Clinical Validation of an Artificial Intelligence-Based Interpretation System for Newborn Screening of Inherited Metabolic Disorders
Trial ID: NCT07368504
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2027-01-01
Completion date: 2028-11-30
Sponsor: The Children's Hospital of Zhejiang University School of Medicine
Conditions: Inherited Metabolic Disorders
Interventions: Artificial intelligence-based interpretation system for newborn genetic metabolic disease screening
Locations: The Children's Hospital, Zhejiang University School of Medicine
Countries: China
Enrollment: 200000
Age range: 2 Days - 28 Days
Sex: ALL

Summary:
The goal of this clinical trial is to evaluate whether an artificial intelligence (AI)-based interpretation system can accurately diagnose inherited metabolic disorders in newborns undergoing routine screening. The main questions it aims to answer are:

What is the sensitivity and specificity of the AI system compared to standard manual interpretation? Does the AI system reduce variability in screening results? Researchers will compare the AI interpretation results with those from standard manual review by trained laboratory staff to assess diagnostic performance.

Participants will:

Have their routine newborn screening blood samples analyzed using both the AI system and standard manual interpretation Be followed according to national newborn screening guidelines if either method indicates a positive result

Eligibility criteria:
Inclusion Criteria:

* Newborns who underwent routine newborn screening for inherited metabolic disorders at the Zhejiang Provincial Newborn Screening Center between May 2025 and December 2027
* Blood samples collected between 2 and 28 days of age
* Availability of complete newborn screening test data and essential clinical information

Exclusion Criteria:

* Missing, incomplete, or poor-quality screening data
* Duplicate samples from the same newborn

Outcomes:
Sensitivity of the AI interpretation system for detecting inherited metabolic disorders; Specificity of the AI interpretation system for detecting inherited metabolic disorders
Development and Prospective Validation of a Digital Pathology-based Artificial Intelligence Diagnostic Model for Pan-cancer Lymphatic Metastasis
Trial ID: NCT06517979
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2024-07-26
Completion date: 2027-06-30
Sponsor: Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
Conditions: Cancer; Lymphatic Metastasis
Interventions: Artificial intelligence (AI)-based diagnostic model
Locations: Sun Yat-sen Memorial Hospital of Sun Yat-sen University
Countries: China
Enrollment: 10000
Age range: None - None
Sex: ALL

Summary:
The goal of this diagnostic test is to develop an artificial intelligence (AI)-based pan-cancer universal diagnostic model for detecting pathological lymph node metastasis (LNM), and prospectively evaluate its apllication value in the real-world clinical practice.

Investigators will compare the diagnostic performance (sensitivity, specificity, etc.) of the AI model and routine pathological report issued by pathologists, to see if the AI model can improve the clinical workflow of pathological evaluation of cancer LNM in in the real world.

Eligibility criteria:
Inclusion Criteria:

* Patients with cancer, undergoing radical tumor resection and lymph node dissection.
* Patients with complete clinical and pathological information.

Exclusion Criteria:

* The patient refused to participate in this diagnostic test.

Outcomes:
sensitivity