moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Efficacy of Digital Cognitive Behavioral Therapy for Insomnia (CBT-I) With Artificial Intelligence Chatbot for Youth With Insomnia: An Assessor-blind, Randomized Controlled Trial
Trial ID: NCT06593262
Status: ACTIVE_NOT_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-07-26
Completion date: 2026-08
Sponsor: Chinese University of Hong Kong
Conditions: Insomnia
Interventions: app-based digital cognitive behavioral therapy for insomnia with AI chatbot; app-based digital cognitive behavioral therapy for insomnia without AI chatbot
Locations: Department of Psychiatry, the Chinese University of Hong Kong
Countries: Hong Kong
Enrollment: 168
Age range: 18 Years - 25 Years
Sex: ALL

Summary:
Insomnia is a common sleep problem, which affects 9.4-38.2% of youths worldwide. Youth insomnia is linked to mental health problems, leading to substantial health and economic burden. The project aims to conduct an assessor-blind, randomized controlled trial to evaluate the efficacy of an app-based CBT-I with AI chatbot in reducing insomnia symptoms among youths, compared to an app-based CBT-I without AI chatbot.

Eligibility criteria:
Inclusion Criteria:

i. Youth aged 18 to 25 years (as defined by WHO and evidence on AI mental health chatbots only available for individuals aged 18 years and older)

ii. Online informed consent of participation in the study should be provided

iii. Willing to comply with the requirements of the study protocol

iv. The presence of moderate to severe insomnia measured by a score of 15 or above on the Insomnia Severity Index (ISI)

v. difficulties initiating, maintaining or nonrestorative sleep over a 1-month period

vi. Having access to a smartphone and consistent internet access for their smartphone

vii. Accepting the terms of service and privacy policies of the mobile apps used in the study for the delivery of the intervention

Exclusion Criteria:

i. A history of any sleep disorder(s) (other than insomnia disorder) such as narcolepsy, obstructive sleep apnea, and restless legs syndrome

ii. A history of any mental retardation or neuropsychiatric disorder(s) (other than depression and anxiety disorders as they are the secondary outcomes) such as bipolar disorder, schizophrenia, and substance use disorder

iii. Presence of serious suicidality as evidenced by ideation with a plan or an attempt

iv. Receiving any pharmacological treatment (including sleep promoting agents)

v. Receiving any structured psychotherapy

vi. Having shiftwork and trans-meridian travel in the past 3 months and during the intervention

Outcomes:
Insomnia Severity Index
Development of a Computer-aided Polypectomy Decision Support
Trial ID: NCT04811937
Status: WITHDRAWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2021-12
Completion date: 2023-04
Sponsor: Centre hospitalier de l'Université de Montréal (CHUM)
Conditions: Adenomatous Polyps
Interventions: Computer-aided polypectomy decision support by Artificial Intelligence
Locations: Centre Hospitalier Universitaire de Montréal
Countries: Canada
Enrollment: 0
Age range: 45 Years - 80 Years
Sex: ALL

Summary:
Quality components of colonoscopy include the detection and complete removal of colorectal polyps, which are precursors to CRC. However, endoscopic ablation may be incomplete, posing a risk for the development of "interval cancers". The investigators propose to develop a solution based on artificial intelligence (AI) (CADp computer-aided decision support polypectomy) to solve this problem.This research project aims to develop CADp, a computer decision support solution (CDS) for the ablation of colorectal polyps from 1 to 20 mm.

Eligibility criteria:
Inclusion Criteria:

* Signed informed consent
* Age 45-80 years
* Indication to undergo a lower GI endoscopy.

Exclusion Criteria:

* Known inflammatory bowel disease
* Active colitis
* Coagulopathy
* Familial polyposis syndrome;
* Poor general health, defined as an American Society of Anesthesiologists (ASA) physical status class \>3
* Emergency colonoscopies

Outcomes:
Accuracy of the CADp system; Completeness of polypectomy; Training CADp; Validity of the choice of primary outcome
Normal Reference Values of Cardiac Measurements by Echocardiography in Chinese Han Adults Based on Artificial Intelligence: A Multi-center Study in China
Trial ID: NCT06234241
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2023-06-01
Completion date: 2023-12-31
Sponsor: Tianxin Dong
Conditions: Heart Chambers
Interventions: Echocardiography
Locations: The First Hospital of China Medical University
Countries: China
Enrollment: 2000
Age range: 18 Years - None
Sex: ALL

Summary:
Establish echocardiographic normal reference values of Chinese Han adults based on artificial intelligence.

Eligibility criteria:
Inclusion Criteria:

* Age ≥ 18 years; No history or clinical evidence of cardiac, cardiovascular, lung, or kidney disease; No abnormal findings on physical examination, biochemical examination, electrocardiography, or echocardiography.

Exclusion Criteria:

* Hypertension (blood pressure \[BP\] \> 140/80 mmHg) or receiving medication for hypertension; body mass index \> 30 kg/m2; total cholesterol ≥ 6.2 mmol/L, triglyceride ≥ 2.3 mmol/L, or high-density lipoprotein cholesterol \< 1.0 mmol/L; endocrine diseases, such as diabetes mellitus (fasting blood glucose ≥ 7.0 mmol/L), thyroid disease, pheochromocytoma, adrenal insufficiency, and hyperaldosteronism; anemia; abnormal liver function; connective tissue diseases, such as rheumatoid arthritis, vasculitis, systemic lupus erythematosus, dermatomyositis, and scleroderma; malignancy; current pregnancy or lactation; professional sport activity; and a history of alcoholism.

Outcomes:
Cardiac volumes; Cardiac Function; Cardiac strain
Digital Signals of Stress in Crohn's: Forecasting Symptom Transitions
Trial ID: NCT04809194
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2019-09-01
Completion date: 2022-06-30
Sponsor: 4YouandMe
Collaborators: Evidation Health; Vector Institute of Artificial Intelligence; Icahn School of Medicine at Mount Sinai; Oxford University Hospitals NHS Trust
Conditions: Crohn Disease
Locations: Mount Sinai; Nuffield Department of Population Health of Oxford University
Countries: United States; United Kingdom
Enrollment: 195
Age range: 18 Years - 65 Years
Sex: ALL

Summary:
There is little information on how Crohn's disease progresses in between a patient's clinic visits and how stress impacts symptom change including flare-ups. The purpose of this research study is to see if digital tools like smartphones, and wearable devices are helpful in finding out new information that may explain fluctuation in symptoms. This study is a feasibility study that will try to identify biomarkers, collected through a smartphone app and wearable devices paired with clinical information collected during clinic visits to track participants' overall health for 6 to 12 months. The data collected will be used to identify and predict symptoms associated with Crohn's disease flare ups. The aim of this work is to inform knowledge of what triggers Crohn's disease worsening that might lead to advances in management.

Eligibility criteria:
Inclusion Criteria:

* Diagnosis of Crohn's disease previously established by endoscopy, imaging, and/or histology
* Disease of the small bowel, large bowel, or both large and small bowel
* Needs to have at least one flare in the last 12 months while on current therapy
* Subjects may be on no treatment or may have concurrent treatment with 5-aminosalicylates, antibiotics, corticosteroids, thiopurines, methotrexate or biologic therapies, including infliximab, adalimumab, certolizumab pegol, vedolizumab, or ustekinumab
* Disease level, see stratified enrollment below
* Subjects must have a personal cell phone that is an iPhone SE or newer and be willing to upgrade to the most recent iOS operating system and use their phone for study. This includes a willingness to download and use the study applications and sync their phone with the necessary study devices.

Exclusion Criteria:

* Imminent indication for surgery, such as small bowel obstruction or abscess, or surgery in the past 4 weeks
* More than 100 cm of small bowel resected
* Dependence on chronic enteral supplementation or parenteral nutrition or fluids
* Ileostomy or Colostomy (some of the outcome measures do not apply as cannot count # bowel movements)
* History of asymptomatic Crohn's disease for the last 2 years
* Current use of investigational therapy
* Self-reported pregnancy or intent to become pregnant during the study period

Outcomes:
Study Retention; Daily survey/task adherence; Oura adherence; Empatica adherence; Bodyport adherence
Multimodal Clinical Data Integration and Artificial Intelligence Modeling for Predicting Complications Following Pediatric Transcatheter Closure of Perimembranous Ventricular Septal Defect
Trial ID: NCT07375602
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2026-02-01
Completion date: 2026-06-15
Sponsor: Xinhua Hospital, Shanghai Jiao Tong University School of Medicine
Conditions: Congenital Heart Disease (CHD); Ventricular Septal Defects (VSD); Cardiac Catheterization; Postoperative Complications
Locations: Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine
Countries: China
Enrollment: 5249
Age range: None - 18 Years
Sex: ALL

Summary:
The goal of this observational study is to develop and validate a multimodal artificial intelligence prediction model for treatment-related complications in children with perimembranous ventricular septal defect (pmVSD) undergoing transcatheter device closure. The main question it aims to answer is: Can an AI model that integrates demographics, laboratory results, electronic health record text, echocardiography reports, chest radiographs, and electrocardiogram accurately predict the risk of complications at the individual patient level? Data will be retrospectively collected from routine clinical care records of pediatric patients who underwent transcatheter closure for pmVSD. Deep learning methods will be used to extract features from text and images to train and validate the prediction model.

Eligibility criteria:
Inclusion Criteria:

* Age ≤ 18 years at the time of transcatheter procedure.
* Diagnosis of perimembranous ventricular septal defect confirmed by echocardiography, and underwent transcatheter device closure at the study center.
* Medical records sufficient to ascertain the primary outcome within the pre-specified follow-up window, and availability of minimum baseline clinical information required for model development/validation.

Exclusion Criteria:

* Ventricular septal defects not classified as perimembranous on echocardiography, including muscular, outlet, or inlet VSDs, as well as multiple or complex VSDs involving more than one septal region.
* Presence of complex congenital heart disease or associated structural abnormalities requiring concomitant surgical repair (e.g., tetralogy of Fallot).
* Prior surgical VSD repair or prior transcatheter VSD closure.

Outcomes:
Composite Procedure-Related Complications After Transcatheter Closure of Perimembranous VSD
The Evaluation of a Real-time Natural Language Processing Decision Support Tool for Screening Opioid Misuse With Addiction Consult Intervention for Hospitalized Adults
Trial ID: NCT05745480
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2023-03-06
Completion date: 2023-12-31
Sponsor: University of Wisconsin, Madison
Conditions: Opioid Use Disorder; Opioid Misuse
Interventions: Opioid Misuse Screening with an Addiction Consult Service
Locations: University of Wisconsin Hospital (UW Health)
Countries: United States
Enrollment: 47502
Age range: 18 Years - None
Sex: ALL

Summary:
This is a clinical study to implement and evaluate a hospital-wide, operational intervention for a real-time natural language processing (NLP)-driven clinical decision support (CDS) tool, called Substance Misuse Algorithm for Referral to Treatment Using Artificial Intelligence (SMART-AI). The SMART-AI CDS tool will be evaluated via implementation in the UW Health electronic health record (EHR). The CDS tool is meant for screening inpatient adults for opioid misuse as part of a best practice alert to nurses and providers for addiction consult service needs.

Eligibility criteria:
Inclusion Criteria:

* Adults hospitalized at University of Wisconsin Hospital (UW Health)

Exclusion Criteria:

\-

Outcomes:
Percentage of inpatients who screened positive (or would have screened positive) based on the NLP CDS tool who received an addiction consult
Developing Trustworthy Artificial Intelligence (AI)-Driven Tools to Predict Vascular Disease Risk and Progression
Trial ID: NCT06206369
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2023-10-31
Completion date: 2029-05-01
Sponsor: Amsterdam UMC, location VUmc
Collaborators: Academisch Medisch Centrum - Universiteit van Amsterdam (AMC-UvA); Technical University of Twente; Universidade do Porto; Centre Hospitalier Universitaire de Nice; Stichting Allai; University of Belgrade; Brightfish Be; Hospital District of Helsinki and Uusimaa; University of Bergen; Asklepios Kliniken Hamburg GmbH; University of Oxford; VINČA INSTITUTE OF NUCLEAR SCIENCES Belgrado
Conditions: Aneurysm Abdominal; Peripheral Arterial Disease
Interventions: No intervention, retrospective study
Locations: Hospital District of Helsinki and Uusimaa (HUS); Asklepios kliniken hamburg; Amsterdam UMC; University Hospital Center of São João; University Clinical Centre of Serbia; Oxford University Hospitals
Countries: Finland; Germany; Netherlands; Portugal; Serbia; United Kingdom
Enrollment: 11000
Age range: 40 Years - 90 Years
Sex: ALL

Summary:
The VASCULAID-RETRO study, within the broader VASCULAID project, aims to create artificial intelligence (AI) algorithms that can predict cardiovascular events and the progression of abdominal aortic aneurysm (AAA) and peripheral arterial disease (PAD). The study plans to gather and analyze data from at least 5000 AAA and 6000 PAD patients, combining existing cohorts and retrospectively collected data. During this project, AI tools will be developed to perform automatic anatomical segmentation and analyses on multimodal imaging. AI prediction algorithms will be developed based on multisource data (imaging, medical history, -omics).

Eligibility criteria:
Inclusion Criteria:

* Males and females, 40-90 years old, with an AAA \>3cm. This includes patients with infrarenal, juxtarenal, suprarenal, iliac (defined as 1.5x its normal diameter) aneurysms, as well as mycotic aneurysms. Patients that have had interventions or ruptures will also be included
* Males and females, 40-90 years old, all PAD patients (Fontaine stages 1,2,3, and 4).

Exclusion Criteria:

* Patients with an ascending, thoracic, thoracoabdominal (type 1-3) aneurysm.

Outcomes:
Development of disease progression prediction algorithms
Factors Influencing Vaccine Hesitancy Among Parents of Children Aged 0-48 Months: A Machine Learning Prediction
Trial ID: NCT06988969
Status: ACTIVE_NOT_RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-07-02
Completion date: 2026-06-01
Sponsor: University of Yalova
Conditions: Vaccine Refusal; Vaccine Hesitancy; Machine Learning; Children
Locations: Yalova University
Countries: Turkey (Türkiye)
Enrollment: 600
Age range: 18 Years - 65 Years
Sex: ALL

Summary:
In recent years, emerging technologies such as the Internet of Things (IoT), Artificial Intelligence (AI), and Virtual Reality (VR) have rapidly become integrated into daily life. The widespread use of these applications has led to the accumulation of vast amounts of data, giving rise to what is commonly referred to as "Big Data." Due to the sheer volume, manual processing and analysis of these large datasets are not feasible. Therefore, software tools and libraries-such as Python and R libraries-have been developed to perform these analyses efficiently and to generate predictions for the future by leveraging historical data through Machine Learning (ML) algorithms.

The primary goal of machine learning algorithms is to discover patterns within existing data and use these patterns to make accurate predictions on new data. The use of machine learning in the field of healthcare has gained significant momentum in recent years. However, a review of the literature reveals that research specifically addressing childhood vaccine hesitancy remains limited.

This study aims to identify the factors contributing to vaccine hesitancy among parents of children aged 0-48 months and to develop a predictive model using machine learning techniques based on these factors. Such a model could help anticipate the likelihood of vaccine refusal among parents and thereby support the development of targeted public health strategies for at-risk populations.

Eligibility criteria:
Inclusion Criteria:

Parents who meet the following criteria will be included in the study:

* Have proficiency in understanding and reading the Turkish language,
* Are able to use technological devices such as mobile phones or computers to access social media platforms (Participants used social media platforms such as Instagram and WhatsApp to communicate) via the internet,
* Are 18 years of age or older,
* Have children aged 0-4 years,
* Consent to participate in the study,
* Have children without any absolute contraindications to vaccination.

Exclusion Criteria:

* Parents of high-risk infants (such as those with a history of systemic illness, preterm labor, anomalies, etc.),
* Parents who are unwilling to participate in the study will be excluded.

Outcomes:
Parental Vaccine Hesitancy Status
A Yolo-V5 Approaches to Evaluation of Filling and Overhanging Filling: An Artificial Intelligence Study
Trial ID: NCT06022731
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2022-01-01
Completion date: 2023-03-01
Sponsor: Eskisehir Osmangazi University
Conditions: Dentomaxillofacial Radiology
Interventions: Panoramic Radiography
Locations: Eskişehir Osmangazi University
Countries: Turkey (Türkiye)
Enrollment: 4323
Age range: None - None
Sex: ALL

Summary:
The goal of this Non-Interventional Clinical Research is to detect the prevalence and distribution of filling and overhanging filling without the need for additional bitewing radiographs using panoramic images, based on a deep CNN (Convolutional Neural Network) architecture trained through supervised learning.

In this study, retrospectively obtained radiographs were used in the development of artificial intelligence models for relevant situations. These datasets were obtained from the images of the patients who applied to ESOGU (Eskişehir Osmangazi University) Dentistry Faculty, Dentomaxillofacial Radiology clinic for various dental purposes. Eskisehir Osmangazi University Non-Interventional Clinical Research Ethics Board (decision date and decision number: 04.10.2022/22) approved the study protocol. The principles of the Helsinki Declaration were followed in the study.

Eligibility criteria:
Inclusion Criteria:

* Images of individuals in the permanent dentition period
* Artifact-free images in the examination region
* Individuals with a history of restorative dental treatment

Exclusion Criteria:

* Images of individuals in mixed dentition
* Radiographic images obtained by incorrect positioning of the patient or containing artifacts

Outcomes:
The success of artificial intelligence models for filling and overhanging filling
Artificial Intelligence System for the Medical Regulation of Emergencies
Trial ID: NCT04953845
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2021-09-01
Completion date: 2025-09-30
Sponsor: Centre Hospitalier Universitaire de Besancon
Conditions: Emergency Medical Communication Centres; Call Management
Enrollment: 500000
Age range: None - None
Sex: ALL

Summary:
Population health needs are increasing. Information and communication technologies are changing. The digital shift offers new opportunities for the exploration and analysis of mass health data. It is possible to rely on these new technologies to modernize, optimize patient management at the level of emergency medical communication centres.

Our project aims to integrate the methods and tools of artificial intelligence for emergency medical communication centres. The system aims to help regulate emergency calls at CRRA 15 in France, or Centrale 144 in Switzerland, to assess the severity of calls, identify care pathways, and improve efficiency when committing resources.

The development of such a system is aimed at securing and optimising the information system and the means of telecommunication used in the emergency medical communication centres, and provide an individualized response to the patient management.

Eligibility criteria:
Inclusion Criteria:

* all patient which call the emergency medical communication centres

Exclusion Criteria:

* patient opposed to the study

Outcomes:
Performance (sensitivity and specificity) of the artificial intelligence system in Emergency medical Communication Centres, concerning time sensitive disease
Determination of Variables Associated With the Activation Level of Diabetic Individuals and Improvement of Activation Level
Trial ID: NCT06973954
Status: ACTIVE_NOT_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-01-02
Completion date: 2025-08-25
Sponsor: Dokuz Eylul University
Conditions: Diabetes Mellitus (DM); Type 2 Diabetes Mellitus (T2DM); Patient Activation; Diabetes Self-management
Interventions: Text Message Intervention
Locations: Dokuz Eylul University Health Sciences Institute Izmir
Countries: Turkey (Türkiye)
Enrollment: 60
Age range: 18 Years - None
Sex: ALL

Summary:
This study was planned to determine the activation levels of diabetic individuals and to improve the activation level. The study, planned as a descriptive and experimental type, is an attempt to increase the activation level of diabetic individuals.

Eligibility criteria:
Inclusion Criteria:

Having diabetes for 1 year or more, Able to read and write Turkish, Having no visual, auditory, perceptual or any other physical disability, Having the physical, mental and emotional capacity to participate in self-care activities, Having no language problems, Accepting to participate voluntarily in the study.

Exclusion Criteria:

* Individuals with any mental/cognitive problems,

  * Those who cannot read or write,
  * Those with visual, hearing, perception or any physical disability,
  * Those who are immobile or bedridden,
  * Those who do not have the ability to participate in their own self-care activities,
  * Those who do not want to participate in the study were excluded from the sample.

Outcomes:
Patient Activation Measure; Patient Activation Level
Development of an Artificial Intelligence Precise Diagnosis Predictive Modle for Dense Density Breast Tissue Based on Radiomics
Trial ID: NCT04535466
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2020-09-01
Completion date: 2020-09-01
Sponsor: Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
Conditions: Breast Diseases; Artificial Intelligence; Dense Breast Density; Predictive Cancer Model
Locations: Breast tumor center of Sun Yat-sen Memorial Hospital
Countries: China
Enrollment: 1000
Age range: 18 Years - 80 Years
Sex: FEMALE

Summary:
It is a prospective, observational cohort study of patients with dense breast tissue. The study was based on the radiomics and other clinicopathological information of patients to establish the diagnostic system for breast disease by using artificial intelligence.

Eligibility criteria:
Inclusion Criteria:

* Female patients with dense breast density tissue
* Digital breast tomosynthesis was performed
* Breast ultrasound examination was performed
* Pathologic examination of breast lesions was performed

Exclusion Criteria:

* Incomplete clinicopathologic data
* Female patients with fatty glands of the breast

Outcomes:
the predictive model's area under curve(AUC)