moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Whole Slide Image Based Deep Learning for Diagnosing the International Association for the Study of Lung Cancer Proposed Grading System of Lung Adenocarcinoma
Trial ID: NCT05925764
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2024-10-15
Completion date: 2024-12-31
Sponsor: Shanghai Pulmonary Hospital, Shanghai, China
Conditions: Lung Adenocarcinoma; Whole Slide Image; IASLC Grading System; Artificial Intelligence
Interventions: Whole Slide Image based Deep Learning
Locations: Affiliated Hospital of Zunyi Medical University; The First Affiliated Hospital of Nanchang University; Ningbo HwaMei Hospital
Countries: China; China; China
Enrollment: 200
Age range: 18 Years - 85 Years
Sex: ALL

Summary:
The purpose of this study is to evaluate the performance of a whole slide image based deep learning model for diagnosing the IASLC grading system in resected lung adenocarcinoma based on a multicenter prospective cohort.

Eligibility criteria:
Inclusion Criteria:

1. Age ranging from 18-85 years old;
2. Pathological confirmation of primary lung adenocarcinoma after surgery;
3. Obtained written informed consent.

Exclusion Criteria:

1. Multiple lung lesions;
2. Poor quality of whole slide images;
3. Mucinous adenocarcinomas and variants;
4. Participants who have received neoadjuvant therapy.

Outcomes:
Agreement rate of the IASLC grading system
Multimodal Artificial Intelligence-Based Fall Risk Prediction in Patients With Parkinson's Disease: Single vs. Dual-Task Conditions
Trial ID: NCT07058714
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2025-07-01
Completion date: 2025-09-15
Sponsor: Biruni University
Conditions: Parkinson Disease
Locations: Biruni University
Countries: Turkey (Türkiye)
Enrollment: 30
Age range: 40 Years - 75 Years
Sex: ALL

Summary:
Parkinson's disease (PD) is characterized by motor symptoms such as bradykinesia, tremor, rigidity, and postural instability, often leading to gait disturbances and a high risk of falls. Dual-task walking assessments-requiring simultaneous motor and cognitive engagement-have gained importance in evaluating real-life mobility impairments in PD, as they more accurately reflect challenges faced during daily activities. While clinical tools such as the Timed Up and Go (TUG), Four Square Step Test (FSST), and Mini-BESTest are widely used, their in-person application may not always be feasible for individuals with mobility or access limitations. Telehealth-based assessment methods, therefore, offer practical alternatives. Recently, the integration of artificial intelligence (AI), particularly machine learning (ML), into clinical assessments has opened new possibilities for fall risk prediction by enabling the simultaneous analysis of motor, cognitive, and balance-related parameters. This study aims to predict fall risk in individuals with PD using AI-based models that incorporate multiple data sources. Furthermore, it compares the predictive accuracy of models derived from single-task and dual-task conditions, with the goal of developing a more precise and clinically useful decision-support tool for early intervention.

Eligibility criteria:
Inclusion Criteria:

Clinical diagnosis of idiopathic Parkinson's disease

Hoehn and Yahr stage between 1 and 3

A score of at least 21 on the Montreal Cognitive Assessment (MoCA)

Stable medication regimen during the past month

Assessment conducted during the patient's "on" period

Ability to walk independently on a flat surface (Functional Ambulation Classification ≥ 3)

Exclusion Criteria:

Severe hearing or visual impairments

Presence of other neurological, cardiovascular, or orthopedic conditions affecting gait

Diagnosis of any other neurological disorder (e.g., dementia, cerebrovascular disease)

Less than 5 years of formal education

Presence of vascular pathology in the lower extremities

Outcomes:
Mini-Balance Evaluation Systems Test (Mini-BESTest); Four Square Step Test (FSST)
Continuous Wearable Monitoring Analytics to Improve Outcomes in Heart Failure - LINK-HF2 Multicenter Implementation Study
Trial ID: NCT04502563
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2021-04-19
Completion date: 2024-10-30
Sponsor: VA Office of Research and Development
Collaborators: George E. Wahlen Department of Veterans Affairs Medical Center; Michael E. DeBakey VA Medical Center; VA Palo Alto Health Care System; Malcom Randall VA Medical Center; Hunter Holmes McGuire VA Medical Center; VHA Innovation Ecosystem
Conditions: Heart Failure
Interventions: Remote monitoring and predictive analytics; Sham comparator
Locations: VA Palo Alto Health Care System, Palo Alto, CA; North Florida/South Georgia Veterans Health System, Gainesville, FL; VA Salt Lake City Health Care System, Salt Lake City, UT; Hunter Holmes McGuire VA Medical Center, Richmond, VA
Countries: United States; United States; United States; United States
Enrollment: 176
Age range: 18 Years - None
Sex: ALL

Summary:
Heart failure (HF) is a type of heart disease that leads to need of admissions to the hospital during worsening of symptoms. These admissions are expensive and very inconvenient for patients. The investigators have previously shown that monitoring of patients with a using a small wearable sensor combined with a mathematical model can detect worsening of HF before the patient needs medical care.

In this study the investigators will test whether the remote monitoring and prediction of HF worsening can be used to find out when patients are at risk, change their treatment and avoid a hospitalization.

The study will enroll 240 Veterans with HF and randomly assign half of them to monitoring and communication of the information on HF worsening to their medical teams. The investigators hope to find our how to best use this approach in routine care of HF. The investigators also plan to determine if this approach will indeed led to less admissions to the hospital among these patients, shorter hospital stays and better quality of life.

Eligibility criteria:
Inclusion Criteria:

* Subject must be 18 years old or older
* New York Heart Association Functional Classification Class II-IV, documented in site's medical record system.
* Subject able and willing to sign Informed Consent Document, and if participating in a patient interview, able to comprehend and agree with items listed in the VA Consent Cover Letter.
* Subject willing and able to perform all study related procedures.

Exclusion Criteria:

* Expected Left Ventricular Assist Device implantation or heart transplantation in the next 30 days.
* Skin damage or significant arthritis, preventing wearing of device.
* Uncontrolled seizures or other neurological disorders leading to excessive abnormal movements or tremors in the upper body.
* Pregnant women or those who are currently nursing.
* Visual/cognitive impairment that as judged by the investigator does not allow the subject to independently follow rules and procedures of the protocol.

Outcomes:
Heart Failure Hospitalization Rate
An Integrated Care Platform Based on the Monitoring of Older Individual Intrinsic Capacity for Inclusive Health (CAREUP)
Trial ID: NCT05963399
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-04-30
Completion date: 2024-12-14
Sponsor: Istituto Nazionale di Ricovero e Cura per Anziani
Collaborators: European Union
Conditions: Old Age
Interventions: CAREUP platform
Locations: EURAG Austria; IRCCS INRCA Hospital; Ana Aslan International Foundation
Countries: Austria; Italy; Romania
Enrollment: 90
Age range: 65 Years - None
Sex: ALL

Summary:
The objective of this study is to assess the usability and acceptance of the CAREUP solution in improving the quality of life at home of the older adults. The CAREUP solution is a platform that collects data from different tools such as tablets, smartwatches, ambient sensors, iHealth weight scale, and Digital Hand Dynamometer.

.

Eligibility criteria:
Inclusion Criteria:

* Live independently in their home in rural or urban areas;
* In good physical and cognitive status;
* Able to stand and walk unaided;
* Availability to sign the Informed Consent;
* Ability to use smartphones/tablets independently.

Exclusion Criteria:

* Use of active implant or not-implant medical devices;
* Allergy to nickel components;
* Lack of written informed consent;
* Acute or untreated medical problems such as history of syncopal episodes, epilepsy and vertigo not controlled pharmacologically; serious dysfunction of the autonomic system; severe behavioural syndromes not compensated by drugs; concurrent neurological diseases; severe systemic diseases with life expectancy \< 1 year;
* A myocardial infarction or stroke within 6 months;
* Painful arthritis, spinal stenosis, amputation, painful foot lesions or neuropathy limiting balance and mobility;
* Uncontrolled hypertension;
* Pacemaker or implantable cardioverter defibrillator;
* Diagnosis of mild and or/advanced Parkinson's disease or other neuromuscular disorder;
* Diagnosis of mild and/or advance cognitive impairments
* Metastatic cancer or immunosuppressive therapy;
* Significant visual or hearing impairment

Outcomes:
Change in acceptability of the CAREUP platform; Change in usability of the CAREUP platform
Impact of an Artificial Intelligence-Based Management Model on Psychological and Behavioral Outcomes in Breast Cancer Patients Undergoing Home CDK4/6 Inhibitor Therapy: A Multicenter Randomized Controlled Trial
Trial ID: NCT07409298
Status: ENROLLING_BY_INVITATION
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-07-05
Completion date: 2026-05-30
Sponsor: Tianjin Medical University Cancer Institute and Hospital
Collaborators: Tianjin Medical University General Hospital; Tianjin First Central Hospital; Tianjin Medical University Second Hospital; Tianjin People's Hospital; The First Affiliated Hospital of Tianjin University of Traditional Chinese Medicine; Tianjin Third Central Hospital; Tianjin Fifth Central Hospital
Conditions: CDK4/6 Inhibitors; Depression Anxiety Disorder; Breast Cancer
Interventions: The "Zhiliao Doctor" AI management platform; Tailored Active Follow-up Strategy
Locations: Tianjin Medical University Cancer Institute & Hospital
Countries: China
Enrollment: 300
Age range: 18 Years - 80 Years
Sex: ALL

Summary:
This version uses full terminology and flows well for a research proposal or paper.

Primary Objective: To compare the intervention effects of an "AI-based management model," a "customized active follow-up model," and a "conventional patient education and follow-up model" on anxiety and depression in patients.

Secondary Objectives: To evaluate the impact of these models on patients' quality of life (QoL), patient-reported adverse events, medication adherence, serological markers, progression-free survival (PFS), and overall survival (OS).

Eligibility criteria:
Inclusion Criteria:

* Females aged ≥ 18 years, regardless of menopausal status.
* Diagnosis of HR+/HER2- breast cancer.
* Currently receiving or scheduled to receive CDK4/6 inhibitor therapy.
* Eastern Cooperative Oncology Group (ECOG) performance status of 0-2.
* No history of severe psychiatric disorders (e.g., bipolar disorder, schizophrenia).
* Baseline psychological state sufficient to understand and participate in psychological interventions.
* Currently engaged in a rehabilitation plan, including exercise therapy and physical therapy.
* Willingness to provide written informed consent.
* Capability to perform online operations via smartphone or computer.

Exclusion Criteria:

* Severe complications (e.g., heart disease, severe hepatic or renal dysfunction) that render the patient unable to tolerate CDK4/6i therapy.
* History of severe psychiatric illness (e.g., severe depression, bipolar disorder, or schizophrenia) that precludes self-assessment.
* Other malignant tumors within the past 5 years (excluding specific cured carcinomas).
* Pregnant or breastfeeding women.
* Positive baseline pregnancy test for women of childbearing potential.
* Inability to adapt to or intolerance of the AI virtual doctor's intervention formats.
* Any other condition that the investigator deems unsuitable for participation in the study.

Outcomes:
Depression; anxiety
Performance Evaluation of the Software Medical Device to Detect the Thyrotoxic State in Patients With Hyperthyroidism
Trial ID: NCT07017907
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-07-01
Completion date: 2026-12-31
Sponsor: THYROSCOPE INC.
Conditions: Graves Disease; Hyperthyroidism/Thyrotoxicosis
Interventions: Heart rate-based AI software for detecting thyrotoxicosis
Enrollment: 15
Age range: 22 Years - None
Sex: ALL

Summary:
This observational study aims to evaluate the performance of a software-based medical device, Glandy HYPER, in detecting the thyrotoxic state in patients with hyperthyroidism. The device utilizes heart rate data collected from commercially available wearable devices and compares it with thyroid function test results. The study will enroll patients diagnosed with Graves' disease, monitoring their heart rate during sleep and correlating these measurements with free T4 levels obtained through serial blood testing. No investigational device output will be disclosed to participants, and the study will not alter standard clinical care.

Eligibility criteria:
Inclusion Criteria:

* Adults aged 22 years or older, regardless of sex.
* Individuals who are newly diagnosed with Graves' disease or currently undergoing treatment for it.
* Individuals who have received sufficient explanation about the investigational software and are able to use it appropriately.
* Individuals who voluntarily agree to participate in the study and have signed informed consent, either directly or via a legally authorized representative.

Exclusion Criteria:

* Individuals with cardiac conditions such as arrhythmia or heart failure.
* Individuals taking medications that significantly affect heart rate, including antiarrhythmics or antihistamines. (Intermittent short-acting beta-blockers are allowed.)
* Pregnant or breastfeeding individuals, or those planning pregnancy during the study period or not using appropriate contraception.
* Individuals with significant comorbidities that interfere with follow-up or study compliance.
* Individuals with severe psychiatric disorders, substance use disorder, or alcohol dependence.
* Individuals deemed ineligible at the discretion of the investigator for safety or ethical concerns.

Outcomes:
F1 Score for Detection of Thyrotoxicosis Using the Investigational Software
Development of Artificial Intelligence Models for Segmentation and Characterization of Prostate Cancer: a Single-center Retrospective Observational Study.
Trial ID: NCT06168864
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2020-01-06
Completion date: 2022-06-01
Sponsor: IRCCS San Raffaele
Conditions: Prostate Cancer
Interventions: Artificial intelligence models for segmentation and characterization of prostate cancer
Locations: Irccs San Raffaele
Countries: Italy
Enrollment: 350
Age range: 18 Years - None
Sex: MALE

Summary:
Prostate cancer is the second most common cancer in the male population. This pathology represents an oncological and public health problem especially in developed countries, due to a greater presence of elderly men in the population.

Medical imaging plays a central role in the staging and restaging of prostate disease. Magnetic resonance imaging (MRI), computed tomography (CT) and positron emission tomography (PET) are among the methods commonly used in normal clinical practice for the characterization of prostate cancer. To date, the study of these images is limited to a qualitative visual analysis, however there is increasing evidence relating to the usefulness of introducing a quantitative (or semi-quantitative) analysis of biomedical images.

The current increase in available imaging data, and their quality, allows the application of artificial intelligence methods also in the medical field for the automation of tasks (e.g. automatic segmentation) and classification (e.g. tumor aggressiveness).

The extraction of quantitative data, and more generally the study of tumor lesions, requires manual segmentation by one or more doctors. This process requires very long times as each image must be processed individually; furthermore, the result also depends on the level of experience of the doctor carrying out the segmentation and this could create a source of heterogeneity, affecting the reproducibility of the segmentation.

AI-based automatic segmentation methods can be applied to medical images for the localization of tumor lesions, thus exceeding the limits of manual segmentation.

Eligibility criteria:
Inclusion Criteria:

* Patients with histological diagnosis of prostate cancer;
* Patients who performed a PET exam with 68 Ga-PMSA.

Exclusion Criteria:

* CT and MR images with artifacts that preclude interpretation of results.

Outcomes:
Artificial intelligence algorithms for the classification of prostate cancer lesions on medical images.
Assessing the Utility of AI Models in MAFLD Diagnosis: Comparison With Traditional Non-Invasive Fibrosis Scores.
Trial ID: NCT07305636
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2025-05-13
Completion date: 2025-11-30
Sponsor: Tanta University
Conditions: MAFLD; AI (Artificial Intelligence)
Locations: Faculty of Medicine
Countries: Egypt
Enrollment: 522
Age range: 18 Days - None
Sex: ALL

Summary:
This study evaluates the accuracy of artificial intelligence (AI) models using FibroScan and clinical data to predict hepatic fibrosis in Egyptian patients with metabolic-associated fatty liver disease (MAFLD). The performance of the AI models will be compared with conventional noninvasive fibrosis scores (FIB-4, APRI, NAFLD fibrosis score, and FAST). The goal is to improve early, noninvasive diagnosis of fibrosis and reduce reliance on liver biopsy.

Eligibility criteria:
Inclusion Criteria:

\- Adults ≥18 years.

Diagnosed with MAFLD according to international criteria (hepatic steatosis with metabolic dysfunction).

Valid FibroScan evaluation with available LSM and CAP values.

Exclusion Criteria:

* Excessive alcohol intake (\>30 g/day for men, \>20 g/day for women).

Chronic viral hepatitis (HBV or HCV).

Autoimmune hepatitis.

Known malignancy.

Pregnancy.

Refusal to participate.

Outcomes:
Measure diagnostic accuracy of AI models in predicting hepatic fibrosis stage (F0-F4)
A Randomized Investigator-blinded Comparative Study to Assess the Efficacy of the Cosmetic Care Product RV4133C - Formula JT2559 in Subjects With Facial Acne, During a 3-month-associated-treatment Phase Followed by a 3-month-maintenance Phase
Trial ID: NCT06716398
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-07-03
Completion date: 2025-07-30
Sponsor: Pierre Fabre Dermo Cosmetique
Conditions: Acne
Interventions: Test product; Comparator product
Locations: UNIFESP
Countries: Brazil
Enrollment: 60
Age range: 14 Years - 24 Years
Sex: ALL

Summary:
In this study, we are interested in the global benefit of the tested product as complementary care to a topical medical treatment during a 3 month-associated treatment phase followed by a 3 month-maintenance phase, compared to a light moisturizing cream, in teenagers and adults with acne. We also want to evaluate the tolerance of the tested product in association and in maintenance of the topical medical treatment.

Eligibility criteria:
Inclusion Criteria:

* Male or Female aged between 14 and 24 years (included)
* Subject with facial acne with score index severity IGA = 2 or 3, assessed on a scale ranged from 0 to 4.
* Subject for whom a prescription of 12 weeks of a reference topical medical treatment is required from the day of the inclusion visit, for mild to moderate face acne

Exclusion Criteria:

Criteria related to the disease:

* Facial skin disease other than acne, skin abnormalities, or dermatological condition on the face liable to interfere with the study assessments
* Acne conglobata, Acne fulminans, nodulocystic acne or acneiform eruptions, according to investigator's assessment
* Other type of pigmentation disorder than acne-related PIH liable to interfere with the study assessments according to the investigator

Criteria related to treatments and/or products:

\- Topical or oral treatment established or modified during the previous weeks or planned to be established or modified during the study, liable to interfere with the evaluation of the efficacy or cutaneous tolerance of the investigational products

Outcomes:
Assessment of number of total acne lesions on the face
Interpretation of Uroflowmetry Samples from Pediatric Patients by Clinicians and Introduction to Artificial Intelligence, and Interpretation of the Samples by Artificial Intelligence
Trial ID: NCT06814847
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2024-10-01
Completion date: 2025-02-01
Sponsor: Marmara University
Conditions: Voiding Dysfunction; Voiding Disorders; Machine Learning
Locations: Marmara University School of Medicine, Urology Department
Countries: Turkey (Türkiye)
Enrollment: 500
Age range: 4 Years - 17 Years
Sex: ALL

Summary:
Uroflowmetry is the one of the most commonly used non-invasive test for evaluating children with lower urinary tract symptoms (LUTS). However, studies have highlighted a weak agreement among experts in interpreting uroflowmetry patterns. This study aims to assess the impact of machine learning models, which have become increasingly prevalent in medicine, on the interpretation of uroflowmetry patterns.

Eligibility criteria:
Inclusion Criteria:

* Aged between 4 and 17 years with LUTS
* Urinate more than 50% of the expected bladder capacity on UF

Exclusion Criteria:

* Patients who were unable to cooperate with the voiding command
* Had neurological disorders
* Urinate less than 50% of the expected bladder capacity on UF
* Under 4 years of age, and were over 18 years of age

Outcomes:
Performance of Machine Learning Models in Evaluating Voiding Patterns