moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Evaluation of the Contribution of Artificiel Inteligence on a Dermatologist Tele -Expertise Request Made by General Praticioner : a Retrospective and Single-center Study.
Trial ID: NCT07341906
Status: ENROLLING_BY_INVITATION
Study type: OBSERVATIONAL
Start date: 2026-01-01
Completion date: 2026-06-30
Sponsor: Centre Hospitalier Universitaire de Nice
Conditions: Skin Diseases; Artificial Intelligence
Interventions: The diagnosis made by Chat Gpt
Locations: CHU de NICE
Countries: France
Enrollment: 1000
Age range: 18 Years - None
Sex: ALL

Summary:
The First part of the study will be the inclusion of our patients (those for whom a tele-expertise request has been made by their general practitioner on the OMNIDOC platform between the 1st of May 2024 and the 31st of December 2025. ) After that, the team will investigate the non opposition patient to the use of their personal information.

Subsequently, the clinical study will focus on comparing the diagnosis made by chat gpt expert and a dermatologist from the University Hospital of Nice to a request of dermatology tele-expertise made by general praticioner on OMNIDOC

Eligibility criteria:
Inclusion Criteria:

* Adult with a request on OMNIDOC made by their general practitioner.
* Adult not opposed Social Security affiliation.
* Diagnosis made by a dermatologist.

Exclusion Criteria:

* Pediatric case.
* Unusable image.
* Absence of diagnosis made by a dermatologist.

Outcomes:
Compare the diagnoses offered by artificial intelligence to those proposed by the experts on requests for tele-expertise.
The Development, Implementation, and Evaluation of a Social Engagement Support System
Trial ID: NCT06913049
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-06
Completion date: 2029-04
Sponsor: University of Maryland, Baltimore County
Collaborators: National Institute on Minority Health and Health Disparities (NIMHD)
Conditions: Social Determinants of Health (SDOH)
Interventions: Social Engagement Support System
Locations: University of Maryland, Baltimore County
Countries: United States
Enrollment: 249660
Age range: 18 Years - 64 Years
Sex: ALL

Summary:
The goal of this clinical trial is to determine if artificial intelligence and machine learning (AI/ML) models can help address social needs in Medicaid enrollees. The main questions it aims to answer are:

Can AI/ML models accurately identify social needs from administrative healthcare data?

Can AI/ML models accurately predict which people will engage with social supports?

Researchers will compare individuals who live in different regions to see if AI/ML models perform better than the status quo.

Eligibility criteria:
Inclusion Criteria:

* Members of partner health plan aged 18-64

Exclusion Criteria:

\-

Outcomes:
Change in the prevalence of Health Related Social Needs (HRSNs) as assessed by the Maryland Department of Health HRSN Screening Tool
Use of a Mobile Food Record, Enhanced by a Novel Artificial Intelligence-Based Informatics Framework to Expand Capture of Dietary Intake
Trial ID: NCT05228171
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2022-03-14
Completion date: 2023-12-31
Sponsor: University of Minnesota
Conditions: Healthy
Interventions: mCC app
Locations: University of Minnesota
Countries: United States
Enrollment: 10
Age range: 18 Years - 65 Years
Sex: ALL

Summary:
Human dietary habits remain challenging to measure. Even the "gold-standard" interviewer-administered 24 hour dietary recall has established underreporting issues especially in high-risk populations, such as patients with obesity. Performance of precision nutrition research requires state of the art tools to capture individual dietary patterns. Given the widespread availability of smart phones, mobile-phone based food records present a prime opportunity to capture "in the field" dietary intake. Mobile food records, however, are not yet widely used in nutrition research. Several reasons exist. First, the mobile food record needs to be validated relative to the "gold standard" of dietary intake, the 24-hour dietary recall. Second, many available mobile food records are commercially based, with caveats about data availability and meeting research-quality data security practices (HIPAA compliance).

In response, this study includes using a freely available, research-focused, HIPAA compliant, mobile food record (mCC: my Circadian Clock app) actively used in research studies (including our own ongoing work) to test the hypothesis that a mobile food record can expand capture of dietary intake. The proposed aims include the following: Using a randomized, cross-over design, evaluation of augmenting interviewer-administered 24-hour dietary recalls by a mobile food record (Augmented Recall) results in less energy intake underreporting than a standard (un-augmented) interviewer administered 24-hour dietary recall (Standard Recall).

Eligibility criteria:
Inclusion Criteria:

* Adults between 18-65 years old
* BMI ≥30 kg/m2
* Without a 4 year (BS/BA) college degree
* Owns a smart phone
* Able to read and speak English

Exclusion Criteria:

• Pregnant or lactating women

Outcomes:
Caloric intake
To Investigate the Predictive Efficiency of Staging by Processing Tomography Images in Esophageal and Stomach Malignancies
Trial ID: NCT05648084
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2022-12-15
Completion date: 2024-07-30
Sponsor: Sebahattin Celik MD
Conditions: Gastrointestinal Cancer
Locations: Van Yuzuncu Yil University
Countries: Turkey (Türkiye)
Enrollment: 50
Age range: 18 Years - None
Sex: ALL

Summary:
Esophageal and stomach cancers, which constitute cancers of the upper region of the digestive system, are cancers that are frequently observed and unfortunately have a low rate of cured patients. In these cases, the stage of cancer at diagnosis is very important for two reasons; First, the stage of the cancer is directly related to the survival time. Secondly, treatment is planned according to the stage. Different treatments are applied to patients at different stages. Currently, the TNM staging (Tumor, Lymph Node and Metastases) system is the accepted one worldwide. Despite many advanced technology tools used in staging (Computed Tomography, Magnetic Resonance Imaging, Endoscopic Ultrasonography), there are still difficulties in correct staging before surgery or before-after neoadjuvant therapy. Artificial intelligence techniques are increasingly used in the field of health, especially in the diagnosis and treatment of cancers. Obtaining cancer details in radiological images, which cannot be noticed by the human eye, by analyzing big data with the help of algorithms gave rise to the application area of "radiomics". It is stated that with Radiomics, there will be improvements in both the diagnosis and staging of cancers and, accordingly, in the treatment. While there are studies on the use of endoscopic methods with artificial intelligence for the early diagnosis of esophageal cancers, a limited number of studies have been conducted on stage estimation from radiological images. In particular, there are not enough studies on the investigation of changes in tumor size after chemotherapy with artificial intelligence and the estimation of staging. In this study, it was aimed to investigate the predictive efficiency of staging and the accuracy of the algorithm developed with artificial intelligence by processing tomography images in a region where esophageal cancers are endemic as a primary outcome and to evaluate the post-treatment mortality, morbidity rates and complication rates of the patients as a secondary outcome.

Eligibility criteria:
Inclusion Criteria:

1. Being diagnosed with esophageal cancer (adenocarcinoma or squamous cancer)
2. Being over 18 years old
3. Having a tomography image before or after chemotherapy.
4. Giving informed consent to participate in the study.
5. Having final pathological staging after surgery.

Exclusion Criteria:

1. Previous thoracic surgery.
2. Having a recurrent tumor
3. Inability to perform clinical staging due to technical reasons
4. Drawings cannot be made due to poor tomography quality.

Outcomes:
Artificial intelligence's sensitivity and accuracy to predict the stage of the cancer
Validation on Clinical Adaptability of the Foundation Model Specific to Neuroimaging Diagniosis
Trial ID: NCT07471984
Status: ENROLLING_BY_INVITATION
Study type: OBSERVATIONAL
Start date: 2025-05-01
Completion date: 2030-12-01
Sponsor: Yaou Liu
Conditions: Central Nervous System Disease; MRI; CT; AI (Artificial Intelligence); Diagnose Disease; Classification
Interventions: Foundation Model Specific to Neurological Diagnosis
Locations: Beijing Tiantan Hospital
Countries: China
Enrollment: 50000
Age range: None - None
Sex: ALL

Summary:
This clinic trial aims to investigate whether artificial intelligence (AI) diagnostic tools at neurological diseases diagnosis on brain CT/MRI can improve the work efficiency of specialized neuroimaging physicians, with a specific focus on its clinical value in distinguishing normal from abnormal findings, critical value identification, and neurological disease classification. Using pathological and/or discharge diagnoses of neurological diseases as the gold standard, an AI model will be trained on over 10,000 CT/MRI cases to achieve diagnostic performance comparable to that of neurological radiologists before being transformed and putted to use. Furthermore, clinical trials will be conducted in sub-studies (abnormal cases identification, critical value assessment, and neurological disease classification) to validate the clinical utility of AI and human-AI collaboration in the precise diagnosis of neurological disorders. The expected outcomes include reducing missed and misdiagnosis rates, enabling rapid screening of critical conditions, and achieving precise imaging-based diagnosis by using AI tools.

Eligibility criteria:
Inclusion Criteria:

* For MRI: patients suspected of harboring ischemic, hemorrhagic, brain tumors, degenerative brain disease, or traumatic brain injury at initiating or other institution, who subsequently underwent brain MRI;
* For CT: patients with or without neurological symptoms, suspected of harboring ischemic, hemorrhagic, space-occupying, degenerative brain disease, or traumatic brain injury, who subsequently underwent brain CT.

Exclusion Criteria:

Exclusion Criteria:

* Patients who opted-out or did not give permission to reuse clinical data.
* Patients with a history of prior brain surgery.
* Patients whose brain CT or MRI exhibit severe artifacts (e.g. heavy warping due to air, metal artifacts, heavy motion artifacts), thereby impeding the usage of the data.

Outcomes:
The diagnostic performance of human-AI collaborative approach in identifying abnormal case is not inferior to that of human-only and AI-only; The diagnostic performance of human-AI collaborative approach in critical value judgment is not inferior to that of human-only and AI-only
A Study to Evaluate the Ability of Speech- and Language-based Digital Biomarkers to Detect and Characterise Prodromal and Preclinical Alzheimer's Disease in a Clinical Setting - FUTURE Extension Study.
Trial ID: NCT04846426
Status: TERMINATED
Study type: OBSERVATIONAL
Start date: 2020-11-19
Completion date: 2024-06-07
Sponsor: Novoic Limited
Conditions: Alzheimer Disease; Preclinical Alzheimer's Disease; Prodromal Alzheimer's Disease; Alzheimer's Disease (Incl Subtypes); Mild Cognitive Impairment
Locations: Re:Cognition Health; Re:Cognition Health; Re:Cognition Health; Re:Cognition Health
Countries: United Kingdom; United Kingdom; United Kingdom; United Kingdom
Enrollment: 67
Age range: 50 Years - 85 Years
Sex: ALL

Summary:
The primary objective of the study is to evaluate whether a set of algorithms analysing acoustic and linguistic patterns of speech, can predict change in PACC5 between baseline and +12 month follow up across all four Arms, as measured by the coefficient of individual agreement (CIA) between the change in PACC5 and the corresponding regression model, trained on baseline speech data to predict it. Secondary objectives include (1) evaluating whether similar algorithms can predict change in PACC5 between baseline and +12 month follow up in the cognitively normal (CN) and MCI populations separately; (2) evaluating whether similar algorithms trained to regress against PACC5 scores at baseline, still regress significantly against PACC5 scores at +12 month follow-up, as measured by the coefficient of individual agreement (CIA) between the PACC5 composite at +12 months and the regression model, trained on baseline speech data to predict PACC5 scores at baseline; (3) evaluating whether similar algorithms can classify converters vs non-converters in the cognitively normal Arms (Arm 3 + 4), and fast vs slow decliners in the MCI Arms (Arm 1 + 2), as measured by the AUC, sensitivity, specificity and Cohen's kappa of the corresponding binary classifiers. Secondary objectives include the objectives above, but using time points of +24 months and +36 months; and finally to evaluate whether the model performance for the objectives and outcomes above improved if the model has access to speech data at 1 week, 1 month, and 3 month timepoints.

Eligibility criteria:
Inclusion Criteria:

* Subjects are fully eligible for and have completed the AMYPRED (Amyloid Prediction in early stage Alzheimer's disease from acoustic and linguistic patterns of speech) study.

(See https://clinicaltrials.gov/ct2/show/NCT04828122)

* Subject consents to take part in FUTURE extension study.

Exclusion Criteria:

* Subject hasn't completed the full visit day in the AMYPRED study.

Outcomes:
The agreement between the change in the PACC5 composite between baseline and +12 months and the corresponding regression model, trained on baseline speech data, predicting in all four Arms, as measured by the coefficient of individual agreement (CIA).
Rapid Histologic Assessment for Stereotactic Breast Biopsy: Enhancing Early Detection With Miniature Mass Spectrometry and AI
Trial ID: NCT06663228
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2024-07-17
Completion date: 2025-12-31
Sponsor: National Taiwan University Hospital
Conditions: Breast Cancer
Interventions: MMS
Locations: Jo-Yu Chen
Countries: Taiwan
Enrollment: 100
Age range: 20 Years - 99 Years
Sex: FEMALE

Summary:
Breast cancer is the leading cancer among Taiwanese women, and it is the second leading cause of cancer-related deaths in women. The five-year survival rate for early-stage breast cancer (Stage 0 to II) is over 90%, significantly better than the survival rates for Stage III and Stage IV breast cancer, which are approximately 70% and 25%, respectively. As a result, breast cancer screening and early diagnosis have always been of great importance. Breast cancer screening relies on imaging examinations, and the diagnosis depends on imaging-guided tissue confirmation.

However, when a patient undergoes stereotactic breast biopsy due to suspicious lesions found in mammography, they typically have to wait about a week to receive the pathology results to determine whether the lesion is benign or malignant. For the patient, this waiting period can be agonizing, and for clinicians, earlier knowledge of pathology results would facilitate prompt staging evaluation and treatment planning for cancer.

The investigators use a special technique-paper spray ionization miniature mass spectrometry (PSI-MMS). According to preliminary research, with the assistance of AI, the miniature mass spectrometer can detect the benign or malignant nature of breast tissue within minutes and with decent accuracy. The investigators hope to continue research to further improve accuracy to meet clinical needs and aspire to have the opportunity to apply the miniature mass spectrometer to rapidly differentiate the molecular subtypes of breast cancer.

Eligibility criteria:
Inclusion Criteria:

* Those who come for receiving vacuum-assisted stereotactic breast biopsy

Exclusion Criteria:

* Those who cannot or are unwilling to sign the informed consent.

Outcomes:
breast cancer
Muscle Imaging Project Using ANatomopathology and Full Field Optical Coherence Tomography
Trial ID: NCT07552220
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2026-04
Completion date: 2028-12
Sponsor: Centre Hospitalier de Mâcon
Conditions: Myositis; Health Adult Subjects
Locations: Centre Hospitalier de Mâcon
Countries: France
Enrollment: 20
Age range: 18 Years - None
Sex: ALL

Summary:
Myositis is an inflammatory disease of the skeletal muscles that can be caused by infections, autoimmune diseases, or medications. Early diagnosis of these conditions is essential for optimal treatment. Anatomic pathology is the conventional method used to analyze muscle lesions, but it requires several weeks to obtain results. Full-field optical coherence tomography (OCT), a non-invasive imaging technique, could offer advantages in terms of speed and resolution for visualizing muscle inflammation.

This research project compares histological pathology and full-field OCT for the analysis of muscle biopsies from healthy individuals and patients suspected of having myositis. The aim of this study is to establish an OCT reference standard by analyzing control muscle biopsies and then comparing muscle inflammation in confirmed cases of myositis. The advantages and limitations of both methods will also be evaluated.

Eligibility criteria:
Inclusion Criteria:

* Patient \> 18 years old ans has given his oral consent
* Patient who biospies are performed as part of care

Exclusion Criteria:

* Patient \< 18 years old

Outcomes:
Evaluation of the spatial resolution of the two methods; Evaluation of inflammation detection of the two methods
A Study to Collect Data to Build Artificial Intelligence Derived Algorithms for Estimating Iron Status of Children (FERROUS 2)
Trial ID: NCT06577727
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2025-01-15
Completion date: 2025-09-30
Sponsor: Danone Asia Pacific Holdings Pte, Ltd.
Collaborators: KK Women's and Children's Hospital; Srinagarind Hospital, Khon Kaen University
Conditions: Iron Deficiency
Interventions: Gold standard blood test; Imaging and videography of anatomical sites
Locations: KK Women's and Children's Hopsital; Srinagarind Hospital, Khon Kaen University
Countries: Singapore; Thailand
Enrollment: 250
Age range: 6 Months - 72 Months
Sex: ALL

Summary:
An exploratory study to collect data to build artificial intelligence derived algorithms for estimating iron status in children

Eligibility criteria:
Inclusion Criteria:

1. Children above 6 months of age and below 6 years of age
2. Written consent from parent(s) or legally acceptable representative(s), who themselves are aged ≥ 21 years old, to consent and comply with the terms and conditions of the study
3. Parent(s) should have access to the internet and a smartphone or tablet to complete study questionnaires
4. Parent(s) should be able to comprehend the content of the study and complete the study questionnaires
5. Parents(s) must be comfortable with their child having blood draws
6. Subject has a Hb level corresponding to a Hb group with open slots for participation

Exclusion Criteria:

1. Children with any medical condition that in the opinion of the investigator would not be suitable for enrolment.
2. Children with nail bed obstructions, nail polish, nails discolouration (e.g., leukonychia, melanonychia, bruising, etc.), or scarring of the lips, pigmentations on lips for thyroid dysfunction (e.g., Puettz-jeghers syndrome).
3. Children who are unable to cooperate with study procedures.
4. Employees and/or children/family members or relatives of employees of Danone Global Research \&amp;amp; Innovation Center or the participating sites.

Outcomes:
Accuracy of the Iron AI in a clinic setting
MEasuring the Accuracy of an Artificial Intelligence Tool That Establishes Polyp Size in Real-time
Trial ID: NCT06073405
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2023-01-01
Completion date: 2024-09-30
Sponsor: University of Roma La Sapienza
Conditions: Colon Polyp; Artificial Intelligence
Interventions: Colonic Polyp Measurement Assessment Via Artificial Intelligence
Locations: Ospedale dei Castelli
Countries: Italy
Enrollment: 225
Age range: 18 Years - None
Sex: ALL

Summary:
Primary objective of the study is to evaluate if the novel virtual polyp sizing tool accuracy in determining the size class of polyps among diminutive (0-5 mm), small (6-9 mm) and large (10 mm and above).

Eligibility criteria:
Inclusion criteria:

* Patients aged 18 years or older undergoing colonoscopy
* Ability to provide written, informed consent (approved by EC) and understand the responsibilities of trial participation.

Exclusion criteria:

* Patients with known contra-indications to colonoscopy
* Patients who perform an emergency colonoscopy.

Outcomes:
Accuracy for categorical distribution of polyp size
Observational Analysis and Elaboration of a Predictive Model on the Effects of Physical Activity and Individual Lifestyles in the Management of Diabetic Disease Type 2 as Part of the GATEKEEPER H2020 Project.
Trial ID: NCT05261841
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2021-06-26
Completion date: 2023-06
Sponsor: Casa Sollievo della Sofferenza IRCCS
Conditions: Type 2 Diabetes
Interventions: Standard care for T2D patients, Smartwatch
Locations: Casa Sollievo della Sofferenza IRCCS
Countries: Italy
Enrollment: 100
Age range: 55 Years - 100 Years
Sex: ALL

Summary:
This study aims to produce new evidence on the positive effects of physical activity and certain individual lifestyles in the control of type 2 diabetic disease. The goal is to build and evaluate the effectiveness of a new parsimonious risk prediction model based on the use of classical variables (blood exams), already used in other models for predicting the risk related to the disease, together with measures obtained from the use of wearable devices (steps count, sleep hours, heart rate).

Eligibility criteria:
Inclusion Criteria:

* Age ≥ 55 years old
* Affected by Type 2 Diabetes and monitored by Casa Sollievo della Sofferenza Hospital
* Basic skills in using a smartphone and its apps
* Available to use the device and its applications
* Ability to understand the informed consent

Exclusion Criteria:

* Inability to understand the informed consent
* Low life expectancy
* Type 1 Diabetes
* Secondary Diabetes

Outcomes:
Change from Baseline HbA1c at 6 and 12 months
Automated Detection, Characterization, Triage, and Recruitment of ICH Subjects Using Artificial Intelligence in the ENRICH Trial
Trial ID: NCT03865979
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2020-01-23
Completion date: 2023-04-28
Sponsor: Viz.ai, Inc.
Collaborators: Nico Corporation
Conditions: Intracerebral Hemorrhage
Interventions: Viz RECRUIT
Locations: UAMS Medical Center; Northshore University Health System; Cooper University Hospital; SUNY Buffalo; Montefiore Medical Center; Geisinger Medical Center
Countries: United States; United States; United States; United States; United States; United States
Enrollment: 10
Age range: 18 Years - None
Sex: ALL

Summary:
To evaluate the performance of the Viz RECRUIT software in subjects identified as symptomatic of a stroke event as determined by standard of care imaging assessments and interpretation.

Eligibility criteria:
Inclusion Criteria:

* Subjects with stroke symptoms who undergo CT brain imaging.

Exclusion Criteria:

* Subjects with poor or incomplete CT brain imaging.

Outcomes:
Performance