moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
The Effects of Chiropractic Care on Neurophysiological Function Immune Markers and Health Related Quality of Life in Children With Subclinical Spinal Pain Using Artificial Intelligence Modelling
Trial ID: NCT05369143
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2022-05-30
Completion date: 2022-12-01
Sponsor: Riphah International University
Conditions: Subclinical Spinal Pain
Interventions: Chiropractic care; Control Group
Locations: Mera Ghar Orphan House
Countries: Pakistan
Enrollment: 107
Age range: 10 Years - 18 Years
Sex: ALL

Summary:
There is growing evidence that chiropractic care positively impacts various aspects of central and autonomic nervous system function.This study aims to investigate short term and long-term effects of Chiropractic care (CC) on neurological, behavioral, immunological functions and health-related quality of life in children with subclinical spinal pain.

Eligibility criteria:
Inclusion Criteria:

* aged between 10 and 18 years
* have subclinical spinal pain

Exclusion Criteria:

* no evidence of spinal dysfunction is present
* they are in current pain (above 3/10 on VAS)
* have sought previous treatment for their spinal issues
* are unable to perform the assessment procedures due to contraindications or movement limitations
* diagnosed immune dysfunction
* utilizing a prescribed immunosuppressive medication
* have uncontrolled asthma
* nasal polyps
* use of an intranasal steroid spray one month or less before the study
* are HIV-positive
* are participating in another research study at the time of data collection
* have any diagnosed comorbidity or concomitant disease
* have allergies to yeast or yeast-derived products
* have chronic sinusitis or recent (within the last six weeks) episode of acute sinusitis.

Outcomes:
Functional near-infrared spectroscopy (fNIRS); Functional near-infrared spectroscopy (fNIRS); Functional near-infrared spectroscopy (fNIRS); Functional near-infrared spectroscopy (fNIRS); Heart rate variability; Whole head EEG(sub-cohort of participants); Whole head EEG(sub-cohort of participants); Whole head EEG(sub-cohort of participants); Whole head EEG(sub-cohort of participants)
Build-up Computed Assisted History Taking, Physical Examination and Diagnosis System of Emergency Patient Through Machine Learning (II)
Trial ID: NCT05596929
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2022-12-12
Completion date: 2023-03-30
Sponsor: National Taiwan University Hospital
Conditions: Internal Disease
Interventions: Artificial intelligence
Locations: National Taiwan University Hospital
Countries: Taiwan
Enrollment: 3000
Age range: 20 Years - None
Sex: ALL

Summary:
In emergency department（ED）, physicians need to complete patient evaluation and management in a short time, which required different history taking, and physical examination skill in healthcare system.

Natural language processing（NLP） became easily accessible after the development of machine learning（ML）. Besides, electronic medical record（EMR） had been widely applied in healthcare systems. There are more and more tools try to capture certain information from the EMR help clinical workers handle increasing patient data and improving patient care.

However, to err is human. Physicians might omit some important signs or symptoms, or forget to write it down in the record especially in a busy emergency room. It will lead to an unfavorable outcome when there were medical legal issue or national health insurance review. The condition could be limited by a EMR supporting system. The quality of care will also improve.

The investigators are planning to analyze EMR of emergency room by NLP and machine learning. To establish the linkage between triage data, chief complaint, past history, present illness and physical examination. The investigators will try to predict the tentative diagnosis and patient disposition after the relationship being found. Thereafter, the investigators could try to predict the key element of history taking and physical examination of the patient and inform the physician when the miss happened. The investigators hope the system may improve the quality of medical recording and patient care.

Eligibility criteria:
Inclusion Criteria:

* Over twenty years old
* Non-traumatic patient

Exclusion Criteria:

* Excluding the patients for administration reasons (issuing a medical certificate)
* Excluding the patients for non-emergency reasons like simply acupuncture, virus screening and prescription for medication.
* Excluding Patients who allocated to critical care station

Outcomes:
Senior doctor appraisal
CORONABED.BOT: an Automation Project Using Artificial Intelligence for Early Diagnosis of Clinical Evolution From SARS-CoV-2"
Trial ID: NCT05787405
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2020-12-01
Completion date: 2024-01-10
Sponsor: Fondazione Policlinico Universitario Agostino Gemelli IRCCS
Conditions: SARS-CoV-1 Infection
Interventions: Artificial Intelligence methods
Locations: Fondazione Policlinico Gemelli
Countries: Italy
Enrollment: 8000
Age range: 18 Years - None
Sex: ALL

Summary:
The aim of the CORONA.BOT project is to exploit the Artificial Intelligence methods of Generator Real World Data Facility to automatically extract structured and unstructured data from hospital databases and to implement an early risk assessment (warning system) regarding the negative outcome for patients infected with SARS-CoV-2.

The objective of CORONABED.BOT is to analyze the care pathways of patients from the same cohort as CORONA.BOT, in order to identify the total length of stay, intensive care occupations and flows between departments, based on variables demographics and first entry clinics Early identification of patients with symptoms compatible with SARS-CoV-2 infection will enable more rapid activation of isolation procedures, contact monitoring/contact history and decisions on the most appropriate clinical pathway in terms of type of treatment and unit. Similarly, the identification of factors correlated with worse outcomes will allow more effective planning for the use of critical resources (such as intensive care and others).

Eligibility criteria:
Inclusion Criteria:

* adult patients (18 years of age or older)
* admitted to the Gemelli and Columbus Polyclinic
* diagnosis of SARS-CoV-2 infection (suspected cases will also be included).

Exclusion Criteria:

* aaaa

Outcomes:
the implementation of prediction models
A Comparison of the Accuracy of an Artificially-Intelligent Stethoscope Versus Pediatric Cardiologists in the Assessment of Pediatric Patients Referred to a Cardiologist for the Assessment of a Heart Murmur
Trial ID: NCT00564122
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2007-12
Completion date: 2008-06
Sponsor: Akron Children's Hospital
Collaborators: Thomas C. Dispenza, M.D.; John R. Bockoven, M.D. M.B.A.
Conditions: Heart Murmurs; Congenital Heart Disease; Structural Heart Disease
Interventions: Artificially-Intelligent Stethoscope; Physical Examination
Locations: Akron Children's Hospital's Heart Center; Akron Children's Hospital's Heart Center; Akron Children's Hospital's Heart Center
Countries: United States; United States; United States
Enrollment: 300
Age range: None - None
Sex: ALL

Summary:
This study will characterize the accuracy of a commercially available artificially-intelligent stethoscope in determining which childhood murmurs suggest underlying congenital structural heart disease and therefore warrant diagnostic echocardiograms.

Eligibility criteria:
Inclusion Criteria:

* The patient must present to a consulted cardiologist for a 1st non-primary care murmur evaluation.

Exclusion Criteria:

* Non English-speakers
* At least moderately agitated or disruptive patients
* An echocardiogram or a Cardiology evaluation was done previously
* The patient no longer has an ausculable murmur at presentation.
* The patient didn't have a transthoracic echocardiogram with Color Doppler done.
* Patients admitted to the NICU before protocol completes.
* Protocol can't be completed on the day of presentation.

Outcomes:
Sensitivity of the Artificially-Intelligent Stethoscope as compared with that of Pediatric Cardiologists in determining which patients needed echocardiograms because they have congenital structural heart disease
Non-invasive Differentiation of Uterine Sarcomas From Uterine Fibroids Using Multiparametric MRI Radiomics
Trial ID: NCT07129005
Status: ENROLLING_BY_INVITATION
Study type: OBSERVATIONAL
Start date: 2025-01-01
Completion date: 2025-12-30
Sponsor: Tongji Hospital
Conditions: Uterine Fibroid; Uterine Sarcoma; Diagnose Disease; AI (Artificial Intelligence)
Interventions: No intervention (observational study)
Locations: Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology
Countries: China
Enrollment: 520
Age range: 18 Years - None
Sex: FEMALE

Summary:
This retrospective case-control study aims to develop and validate a diagnostic model based on multimodal big data and artificial intelligence to differentiate uterine leiomyoma from uterine sarcoma. Investigators will extract historical case data from existing inpatient and outpatient records, including medical history, physical and gynecological examination findings, MRI imaging data, laboratory results, and pathological records. The study seeks to address the question of whether integrating diverse retrospective clinical data with advanced AI techniques can accurately classify uterine tumors as benign leiomyomas or malignant sarcomas, thereby supporting clinical decision-making and optimizing diagnostic workflows.

Eligibility criteria:
Inclusion Criteria:

1. Histopathological confirmation of uterine sarcoma or leiomyoma.
2. Availability of preoperative MRI, includingT2WI and DWI, performed within 2 months of the surgery.

Exclusion Criteria:

1. Tumors smaller than 2 cm. Small tumors may be difficult to accurately perform segmentation and feature extraction, which may affect the accuracy and reliability of the model.
2. Non-primary uterine sarcomas. Sarcomas from other sites with metastasis to the uterus were excluded because the biological characteristics and imaging findings of these tumors may differ from those of primary uterine sarcomas and may lead to bias in the diagnostic model.
3. Concurrent pelvic malignancies. To avoid the influence of other types of tumors on the imaging features of uterine sarcoma and leiomyoma, and to ensure the pertinence and accuracy of the model.

Outcomes:
AUC; Sensitivity; Specificity; Positive Predictive Value (PPV); Negative Predictive Value (NPV)
Predictive Performance of a Generative Model for Corneal Tomography After Implantable Collamer Lens Implantation
Trial ID: NCT07146737
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-07-01
Completion date: 2028-12-31
Sponsor: Second Affiliated Hospital of Nanchang University
Conditions: ICL; Vault; Deep Learning; AI (Artificial Intelligence)
Interventions: Corneal tomography generation model after ICL surgery
Locations: The Second Affiliated Hospital of Nanchang University
Countries: China
Enrollment: 818
Age range: 18 Years - 45 Years
Sex: ALL

Summary:
To evaluate the efficacy of a corneal tomography Imaging model in predicting postoperative vault based on preoperative corneal topography in Implantable Collamer Lens (ICL) surgery.

Eligibility criteria:
Inclusion Criteria:(1) stable myopia (≤0.50D/year change for 2 years), (2) ACD ≥2.80mm, (3) intact corneal endothelium (≥2000 cells/mm²), and (4) no confounding ocular/systemic conditions.

Exclusion Criteria:(1) glaucoma-spectrum disorders or retinal vasculopathies, (2) prior corneal/intraocular surgery, (3) compromised corneal endothelium, (4) uncontrolled systemic diseases, and (5) pregnancy/lactation.

Outcomes:
AUROC of convolutional neural network in predicting vault after ICL surgery
Protocol for the Digital, Individualized, and Collaborative Treatment of Type 2 Diabetes in General Practice Based on Decision Aid (DICTA) - A Randomized Controlled Trial
Trial ID: NCT04880005
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2021-06-01
Completion date: 2025-12-30
Sponsor: University of Southern Denmark
Collaborators: Steno Diabetes Center Odense; Odense University Hospital; Steno Diabetes Center Sjaelland; Aalborg University; Holbaek Sygehus; Slagelse Sygehus
Conditions: Diabetes type2
Interventions: Clinical decision support; Digital Lifestyle Coaching; Integration to Standard Electronic Health Record
Locations: Research Unit for General Practice, Department for Public Health, University of Southern Denmark
Countries: Denmark
Enrollment: 400
Age range: 18 Years - 80 Years
Sex: ALL

Summary:
The purpose of this project is to improve life of patients with type 2 diabetes through an IT-supported lifestyle and treatment intervention.

The intervention is based on combining and adapting existing and effective elements into the IT system of the general practitioner. In this way we will integrate specialist supervised treatment in general practice, individual patient coaching, and improved information exchange and data mining.

The DICTA intervention consists of two integrated components: a patient-directed eHealth lifestyle coaching program delivered via the LIVA application, and a clinician-directed CDS tool embedded into the EPJ system.

1. eHealth lifestyle coaching: Individuals with T2D in the intervention group will receive individualized digital coaching from a health coach through the LIVA application. PROs are shared with GPs and health staff via the EPJ, enabling tailored, data-driven lifestyle support.
2. CDS: GPs receive real-time, individualized, algorithm-based pharmacological treatment recommendations for managing T2D, hypertension, and hypercholesterolemia, as appropriate.

This is expected to facilitate use, assure individually tailored solutions, optimize treatment effects, and strengthen patient engagement.

The study is a randomized controlled trial (RCT). It will include 400 patients with newly diagnosed type 2 diabetes. The patients will receive either treatment based on the intervention or usual care. After one year, we will assess quality of life and cardiovascular risk factors in both groups and evaluate if one group has improved management of their type 2 diabetes compared to the other.

If the intervention proves effective, implementation on a national scale is highly feasible, and the intervention could probably be adapted to other lifestyle-related chronic diseases in Denmark and in other countries.

Eligibility criteria:
Inclusion Criteria:

* Diabetes type 2 in up to 10 years

Exclusion Criteria:

* Fails to complete the initial questionnaire
* No Internet access in own home through computer or smart phone
* Is pregnant or actively trying to get pregnant
* Has a serious or life-threatening disease

Outcomes:
Change in a binary indicator, composed by a composite endpoint of HbA1c, systolic blood pressure, low-density lipoprotein cholesterol, no smoking, and normal albuminuria
Feasibility Evaluation of ONCOhabitats for Supporting Surgical and Therapeutic Planning in Patients With IDH-Wildtype Glioblastoma (SINUE)
Trial ID: NCT07111195
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2024-07-16
Completion date: 2026-09
Sponsor: Juan M Garcia-Gomez
Collaborators: Hospital Clínico Universitario de Valencia; Hospital Universitario de Canarias; Hospital General Universitario de Alicante; Hospital Universitario Virgen de la Arrixaca; Hospital Vall d'Hebron
Conditions: Glioblastoma IDH (Isocitrate Dehydrogenase) Wildtype
Interventions: The ONCOhabitats software for MRI-based habitat segmentation
Locations: Hospital General Universitario Dr. Balmis; Hopsital Universitari Vall d'Hebron; Hospital Clínico Universitario Virgen de la Arrixaca; Hospital Universitario de Canarias; Hospital Clínic i Universitari de València
Countries: Spain; Spain; Spain; Spain; Spain
Enrollment: 140
Age range: 18 Years - None
Sex: ALL

Summary:
The goal of this clinical trial is to validate ONCOhabitats, an advanced imaging software, as a medical device for the clinical management of IDH-wildtype glioblastoma.

The study aims to evaluate whether imaging biomarkers derived from pre-surgical MRI using ONCOhabitats can predict overall survival and support clinical decision-making.

The primary research questions are:

* Can ONCOhabitats identify vascular and molecular characteristics within the peritumoral infiltrated edema (IPE) that are associated with patient prognosis?
* Can these imaging biomarkers aid in stratifying patients according to their response to treatment, including temozolomide and immunotherapy?

Participants will:

* Be adults diagnosed with high-grade glioma who are scheduled for surgical tumor resection
* Undergo preoperative MRI processed with ONCOhabitats to segment the tumor into four biological habitats (HAT, LAT, IPE, and VPE)
* Provide tissue samples from each habitat when feasible, based on surgical and clinical considerations

Researchers will analyze:

* Imaging biomarkers (e.g., relative cerebral blood volume, rCBV)
* Molecular and histopathological features (e.g., MGMT promoter methylation, gene expression profiles associated with immunosuppression)
* Clinical and survival outcomes

This study seeks to enhance glioblastoma characterization and support personalized treatment strategies through the clinical validation of a software platform.

Eligibility criteria:
Inclusion Criteria:

* Adults (≥18 years old) at the time of diagnosis
* Radiological diagnosis of high-grade glioma
* Candidates for surgical resection
* Availability of complete preoperative MRI studies, including:
* T1-weighted MRI (pre- and post-gadolinium)
* T2-weighted MRI
* FLAIR (Fluid-Attenuated Inversion Recovery)
* T2\*-weighted DSC perfusion MRI
* Signed informed consent to participate in the clinical study

Exclusion Criteria:

* Patients who do not provide informed consent
* Patients deemed inoperable

Withdrawal criteria:

* MRI data that cannot be processed using ONCOhabitats
* Patient withdraws informed consent at any time

Outcomes:
Association of ONCOhabitats-Derived Vascular Biomarkers in the IPE Region With Overall Survival
Testing an Artificial Intelligence Chatbot for Mental Health
Trial ID: NCT06013137
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-03-11
Completion date: 2024-08-30
Sponsor: Dartmouth-Hitchcock Medical Center
Conditions: Depression; Anxiety; Eating Disorders
Interventions: Therabot
Locations: Dartmouth College
Countries: United States
Enrollment: 210
Age range: 18 Years - None
Sex: ALL

Summary:
The research team would like to test how efficacious an artificial intelligence chatbot is in delivering supportive behavioral interventions in populations with anxiety, depression, or eating concerns.

Eligibility criteria:
Inclusion Criteria:

* We will recruit participants online using Facebook, Google Ads, Reddit, MTurk, listservs, and other online platforms.
* These individuals must meet the criteria for anxiety, depression or disordered eating, according to the PHQ-9, GADQ-IV, and SWED 3.0.

Exclusion Criteria:

* Individuals currently being treated for active suicidality (measured with C-SSRS), Bipolar I/II, and active psychosis will be excluded from the study.

Outcomes:
Anxiety Symptoms; Depressive Symptoms; Eating Disorders Symptoms
Multi-center Application of an Artificial Intelligence System for Automatic Real-time Diagnosis of Cervical Lesions Based on Colposcopy Images
Trial ID: NCT05281939
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2021-08-01
Completion date: 2024-09-01
Sponsor: Fujian Maternity and Child Health Hospital
Conditions: Artificial Intelligence; Colposcopy; Cervical Lesions; Image
Interventions: Artificial intelligence diagnosis
Locations: Fujian Maternity and Child Health Hospital; Mindong Hospital of Ningde City; Jianou Maternal and child Health Care Hospital; Ningde Hospital affiliated to Ningde Normal University; Quanzhou First Hospital
Countries: China; China; China; China; China
Enrollment: 10000
Age range: 18 Years - None
Sex: FEMALE

Summary:
The application of artificial intelligence in image recognition of cervical lesions diagnosis has become a research hotspot in recent years. The analysis and interpretation of colposcopy images play an important role in the diagnosis，prevention and treatment of cervical precancerous lesions and cervical cancer. At present, the accuracy of colposcopy detection is still affected by many factors. The research on the diagnosis system of cervical lesions based on multimodal deep learning of colposcopy images is a new and significant research topic. Based on the large database of cervical lesions diagnosis images and non-images, the research group established a multi-source heterogeneous cervical lesion diagnosis big data platform of non-image and image data. Research the lesions segmentation and classification model of colposcopy image based on convolutional neural network, explore the relevant medical data fusion network model that affects the diagnosis of cervical lesions, and realize a multi-modal self-learning artificial intelligence cervical lesion diagnosis system based on colposcopy images. The application efficiency of the artificial intelligence system in the real world was explored through the cohort, and the intelligent teaching model and method of cervical lesion diagnosis were further established based on the above intelligent system.

Eligibility criteria:
Inclusion Criteria:

* Married woman
* Woman aged 18 and over
* Woman with an intact cervix
* Patients with abnormal results in cervical cancer screening
* Be able to understand this study and have signed a written informed consent

Exclusion Criteria:

* Woman with acute reproductive tract inflammation
* History of pelvic radiotherapy surgery
* Woman with mental disorder
* Patients with history of other malignant tumors
* Refuse to participate in this study

Outcomes:
HPV testing; Cervical cytology testing; Cervical histopathological examination; Accuracy of CIN2+ diagnosis; Accuracy of CIN3+ diagnosis
Using Explainable AI Risk Predictions to Nudge Influenza Vaccine Uptake
Trial ID: NCT05009251
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2021-09-09
Completion date: 2022-07-31
Sponsor: National Bureau of Economic Research, Inc.
Collaborators: Geisinger Clinic; Massachusetts Institute of Technology; National Institute on Aging (NIA)
Conditions: Influenza; Vaccination; Health Promotion; Health Behavior; Risk Reduction
Interventions: Reminder; Risk reduction; Medical records-based recommendation; Algorithm-based recommendation
Locations: Geisinger Clinic
Countries: United States
Enrollment: 45061
Age range: 18 Years - None
Sex: ALL

Summary:
The study team previously demonstrated that patients are more likely to receive flu vaccine after learning that they are at high risk for flu complications. Building on this past work, the present study will explore whether providing reasons that patients are considered high risk for flu complications (a) further increases the likelihood they will receive flu vaccine and (b) decreases the likelihood that they receive diagnoses of flu and/or flu-like symptoms in the ensuing flu season. It will also examine whether informing patients that their high-risk status was determined by analyzing their medical records or by an artificial intelligence (AI) / machine-learning (ML) algorithm analyzing their medical records will affect the likelihood of receiving the flu vaccine or diagnoses of flu and/or flu-like symptoms.

Eligibility criteria:
Inclusion Criteria:

* Aged 18 or older
* Current Geisinger patient at the time of study
* Falls in the top 10% of patients at highest risk, as identified by the flu-complication risk scores of machine learning algorithm (which operates on coded EHR data)

Exclusion Criteria:

* Has contraindications for flu vaccination
* Has opted out of receiving communications from Geisinger via all of the modalities being tested

Outcomes:
Flu Vaccination at 2 Weeks After Final Outreach Date