moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Dual-track Residential Exercise With AI and Monitoring for Sleep in Older Adults With Chronic Insomnia (DREAMS Study)
Trial ID: NCT07513584
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-08-01
Completion date: 2027-07-31
Sponsor: National Sun Yat-sen University
Conditions: Chronic Insomnia Characterized; Difficulty Falling Asleep; Difficulty Maintaining Sleep; Healthy Aging and Independent Living
Interventions: Fit Mirror-Guided Home-Based Exercise Program
Enrollment: 60
Age range: 60 Years - None
Sex: ALL

Summary:
As societies rapidly transition toward aging demographics, sleep issues among community-dwelling older adults have emerged as a critical concern affecting healthy aging and independent living. Current single-track exercise intervention models are often difficult to implement due to suboptimal adherence. Therefore, this study aims to utilize artificial intelligence technology combined with a dual-track residential exercise mode to improve sleep quality, thereby enhancing the self-care and independent living abilities of the elderly

Eligibility criteria:
Inclusion Criteria:

* Age ≥ 60 years
* Capable of independent mobility (without the use of assistive devices).4
* Meeting one of the following sleep disturbance criteria:

Core symptoms of DSM-5 chronic insomnia (self-reported) for ≥ 3 months. Insomnia Severity Index (ISI) ≥ 15 (moderate-to-severe insomnia). Pittsburgh Sleep Quality Index (PSQI) \> 5 (poor sleep quality).

* Basic ability to use a tablet or smartphone (caregivers may assist with login, but exercise must be performed by the participant).
* Mini-Cog score ≥ 3.
* Consent to wear wearable devices and participate in data collection.

Exclusion Criteria:

* Major cardiovascular events within the past 3 months (e.g., acute myocardial infarction, unstable angina), severe heart failure, or uncontrolled hypertension (e.g., SBP ≥ 180 or DBP ≥ 110 mmHg).
* Severe osteoarticular or neuromuscular diseases that prevent the safe completion of exercise (e.g., recent hip fracture, severe Parkinsonian imbalance).
* Severe psychiatric disorders or substance use disorders that may affect adherence.
* Untreated moderate-to-severe obstructive sleep apnea (OSA) with extreme daytime sleepiness (the study will use objective measurements for preliminary screening).
* Currently receiving structured psychotherapy for insomnia (e.g., CBT-I or BBTi) and not yet stabilized.
* Severe visual or hearing impairment that prevents following voice or visual instructions.

Outcomes:
Multi-dimensional Sleep Health Assessment via Objective Actigraphy and Subjective Diaries; Multi-component Functional Physical Fitness Performance
Prediction Of Serious Adverse Event in Emergency Department With dysKAlemia, Retrospective Study
Trial ID: NCT05788523
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2023-01-02
Completion date: 2023-12-31
Sponsor: Centre Hospitalier Régional Metz-Thionville
Collaborators: Georgia Institute of Technology
Conditions: Emergencies
Locations: CHR Metz-Thionville/Hopital de Mercy
Countries: France
Enrollment: 500
Age range: 18 Years - None
Sex: ALL

Summary:
Dyskalemia, hypo- and hyperkalemia are common in the emergency department and are associated with increased morbidity and mortality.

The potential seriousness of dyskalemia results from the potential alteration of intracardiac conduction and the increase in cardiac rhythm disorders, all associated with a lethal risk. Given the aspecific symptoms of dyskalemia and the complex causal links to be acquired, it is difficult to judge the severity of the disorders in front of an initial clinical presentation of a patient in the emergency room, as the patient may present a serious condition during his stay in the emergency room, related to the dyskalemia and not prejudged at first. The ECG is recommended to judge the severity of the disorder, in association with the level of kalemia, but the electrical changes of its pattern in the context of dyskalemia are sometimes so fine that even the eye of the practitioner is not able to detect them. To date and to our knowledge, there is no tool for predicting the risk of RTA or death in dyskalemia. The existing studies on the subject, including ECGs, seek to detect dyskalemia and not its complications, and the proposed tools only take into account the ECG and not the clinical context of the patient.

Eligibility criteria:
Inclusion Criteria:

* Major patient consulting in an emergency department participating in the study
* who received at least a digital ECG AND an ionogram in the emergency department during the study period

Exclusion Criteria:

* Patient who has expressed opposition to the reuse of their data for the study

Outcomes:
Number of participants with Cardiac arrest and/or rhythm disorder
Using Artificial Intelligence (AI)-Assisted Pulse Diagnosis Analysis on Precision Critical Medicine.
Trial ID: NCT04675424
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2020-11-15
Completion date: 2022-10-06
Sponsor: Chang Gung Memorial Hospital
Conditions: ICU Patients; Shock
Interventions: ANSwatch
Locations: Chang Gung Memorial Hospital, Linkou branch
Countries: Taiwan
Enrollment: 45
Age range: 20 Years - 80 Years
Sex: ALL

Summary:
Precision/personalized medicine becomes an important part of modern medical system in the recent years. In the past, the treatments for patients have been decided by doctors according patients' symptoms and/or regular biochemical profiles. However, it is not uncommon that patients' condition varies tremendously even they have same diagnosis, and under such condition, treatment efficacy may be limited due to the heterogeneity among patients. Therefore, lack of therapeutic efficacy may be not really ineffective, and the main reason may be inadequate patient classification. For this reason, the "omics"-based personal/precision medicine emerges recently and becomes more and more important. However, in contrast to feasible and common "personalized" medicine, the approach of precision medicine to the molecular medicine level is still difficult, especially among patients in intensive critical units (ICUs). In contrast to cancer, which has remarkable advances in the past decades, the precision/personal medicine is more difficult in critical and emergent medicine. One reason is the amount of omics data is quite huge and thus dealing with omics data is time consuming. Therefore, it is not effective in daily clinical practice in ICUs care. For this condition, the investigators propose that the combination of clinical data, including pulse diagnosis by traditional Chinese medicine (TCM) doctor or ANSwatch wrist sphygmomanometer, fluid responsiveness by "Masimo" Radical-7 Pulse CO-Oximeter, and the specific database from monitors in ICUs may be a feasible way to predict outcome among ICU patients. There are two main goals for this study: (1) After establishing clinical traditional Chinese medicine (TCM) pulse diagnosis and ICU clinical parameters databases, acquiring and features of pulse diagnosis by applying AI and (2) analyzing the correlations between the features of pulse diagnosis and important clinical parameters.

Eligibility criteria:
Inclusion Criteria:

* 1.The patient in ICU is willing to participate in the research project or patient's family member is willing patient to participate in the research project.
* 2.Acute circulatory failure

Exclusion Criteria:

* 1\. The patient do not want to participate in the research project or patient's family member do not want the patient to participate in the research project.
* 2\. Pregnant woman.
* 3\. Goals of treatment is palliative care.
* 4\. Patient is heritable immunodeficiency or acquired immunodeficiency.
* 5\. Known existing cardiac arrhythmia, valvular heart disease, right ventricular dysfunction, intra-cardiac shunt, air leaking from chest drains, abdominal compartment syndrome.

Outcomes:
28-days mortality; Pulse diagnosis
The Effects of the Course on Technology Use in Nursing on Students' Self-directed Learning Readiness and Attitudes Toward Technology: A Quasi-experimental Study
Trial ID: NCT06791759
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-03-01
Completion date: 2024-05-30
Sponsor: Akdeniz University
Conditions: Nursing Student; Technology; Self Directed Learning
Interventions: The course on technology use in nursing
Locations: Akdeniz University
Countries: Turkey (Türkiye)
Enrollment: 109
Age range: None - None
Sex: ALL

Summary:
This study aimed to examine the effect of the course on technology use in nursing on students' readiness for self-directed learning (SDLR) and attitudes toward technology. Research hypotheses are as follows:

* H1-1: The course on technology use in nursing affects nursing students' SDLR.
* H1-2: The course on technology use in nursing affects nursing students' attitudes toward technology.

Eligibility criteria:
Inclusion Criteria:

* Voluntary participation in the study
* Enrollment in one of the elective courses: 'Technology use in nursing' or 'health assessment'
* Taking elective courses for the first time

Exclusion Criteria:

* Refusal to participate in the study at any stage of the research process
* Incomplete completion of the data collection forms

Outcomes:
Self-directed Learning Readiness; Attitudes toward technology
Novel Neuroradiological Workflow for the Assisted DIAgnosis and Management of DEMentia with Artificial Intelligence
Trial ID: NCT06877182
Status: ACTIVE_NOT_RECRUITING
Study type: OBSERVATIONAL
Start date: 2024-08-30
Completion date: 2027-08-31
Sponsor: IRCCS SYNLAB SDN
Collaborators: UNINA; UNITO; Ospedale Fate bene Fratelli di Brescia
Conditions: Alzheimer Disease; Dementia
Interventions: Magnetic Resonance Imaging with Contrast
Locations: Irccs Synlab Sdn
Countries: Italy
Enrollment: 80000
Age range: None - None
Sex: ALL

Summary:
Identifying, screening and monitoring individuals at risk of Alzheimer's disease (AD) and dementia is a formidable challenge. Neuroimaging, and in particular magnetic resonance imaging (MRI), is crucial to detect structural neurodegeneration. However, current quantification tools are mainly limited to research contexts and produce non-standardised results. DIADEMA will build a systematic and standardised workflow to support neuro(radio)logical diagnosis. By combining artificial intelligence (AI) and machine learning (ML) the investigators will significantly enhance the clinical diagnosis of AD in neuroradiology. The investigator's main hypothesis is that an efficient workflow and associated higher diagnostic accuracy will substantially reduce healthcare costs, support clinical decision-making, provide second-opinion tools and improve patient care. This dual advance will have a profound impact on the healthcare system, marking an important step in the fight against Alzheimer's disease and dementia.

Eligibility criteria:
Inclusion Criteria:

* patients who perform brain magnetic resonance during the last 20 years

Exclusion Criteria:

\-

Outcomes:
Improvement of the neuroradiological workflow performance
Supporting Care Partners' Well-Being With AI Chatbots: A Feasibility and Acceptability Study
Trial ID: NCT07145723
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-09-22
Completion date: 2026-02-04
Sponsor: University of Michigan
Conditions: Caregivers
Interventions: Earkick app
Locations: University of Michigan
Countries: United States
Enrollment: 26
Age range: 18 Years - None
Sex: ALL

Summary:
This study is being completed to test an AI therapy chatbot application called Earkick to see whether it is feasible and acceptable in supporting care partners who support individuals living with dementia.

Eligibility criteria:
Inclusion Criteria:

* Participants need to be a care partner (informal caregiver) of an individual living with dementia
* Participant provides emotional, physical, and/or financial support/assistance to the individual with dementia (per protocol)
* Participants indicate high levels of caregiver burden, confirmed using the 12-item Zarit Burden questionnaire (per protocol)
* Provide informed consent and read, speak, and understand English
* Have an Apple smartphone, tablet, or laptop, have access to the internet and text messages, and be willing to use this device as well as internet and text message access for the study, including downloading the AI chatbot app
* Be willing to complete all study assessments

Exclusion Criteria:

* Professional, paid caregivers
* Anything that would preclude safe or meaningful participation in the study

Outcomes:
Feasibility and Acceptability as measured by the Feasibility and Acceptability Questionnaire
Shanxi Key Laboratory of Artificial Intelligence Assisted Diagnosis and Treatment for Mental Disorder
Trial ID: NCT06322524
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2021-05-01
Completion date: 2022-12-30
Sponsor: The First Affiliated Hospital of Shanxi Medical University
Collaborators: Shanxi Medical University
Conditions: Sleep Quality; Sleep Duration; Sleep Onset Latency
Interventions: Gamma sensory flicker
Locations: Yong Xu
Countries: China
Enrollment: 37
Age range: 18 Years - 70 Years
Sex: ALL

Summary:
This prospective observational study enrolled adult volunteers for a sleep quality research study through a questionnaire survey distributed via a WeChat two-dimensional code from May 2021 to April 2022. Participants were exposed to flicker stimulation through a light and sound device for a duration of 8 weeks with daily monitoring.

Eligibility criteria:
Inclusion Criteria:

Clinical diagnosis of insomnia according to DSM-5 Cumulative score of ≥7 on the seven-factor components of the Pittsburgh Sleep Quality Index. Sleep disturbances manifest at least three times per week.

Exclusion Criteria:

Secondary insomnia related to other mental disorders Substance-induced sleep disorders Environmental sleep disorders Sleep apnea syndrome. Stroke patients Insomnia caused by physical diseases, Severe ophthalmic diseases Brain tumors Suspected or confirmed history of alcohol or drug abuse Psychoactive drugs Pregnancy Previous participation in clinical trials within the past three months Epilepsy or family history of epilepsy.

Outcomes:
Treatment adherence rate; Patient treatment adherence rate; Incidence rate of adverse reaction; Differences in sleep duration between the first and eighth weeks; Differences in sleep onset latencies between the first and eighth weeks; Differences in awakening times between the first and eighth weeks
Construction and Validation of a Computer-aided (CADx）System in Real-time Characterization of Colorectal Ulcerative Diseases: A Multicenter, Retrospective Study
Trial ID: NCT06208982
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2023-06-01
Completion date: 2024-06
Sponsor: Nanfang Hospital, Southern Medical University
Collaborators: Prince of Wales Hospital, Shatin, Hong Kong; ZhuHai Hospital; Wuxi People's Hospital; The University of Hong Kong-Shenzhen Hospital
Conditions: Colorectal Ulcers; Computer-aided System
Interventions: Colonoscopy images and videos
Locations: Nanfang Hospital, Southern Medical University
Countries: China
Enrollment: 1000
Age range: 18 Years - 100 Years
Sex: ALL

Summary:
The goal of this observational study is to construct and validate a Computer-aided (CADx）System in Real-time Characterization of Colorectal Ulcrerative Diseases. The main question it aims to answer are to demonstrate whether the newly developed CADx system has a high-level diagnostic accuracy in predicting characterization of colorectal ulcerative diseases.

It is a multi-center, retrospective study. The study retrospectively collected colonoscopy images and videos of colorectal ulcers (including colorectal cancer, Crohn's disease, Ulcerative colitis, Intestinal tuberculosis and ischemic enteritis). A training cohort will be developed from majority of the included cases, followed by a validation cohort with the remaining cases. A CADx system in real-time characterization of colonic ulcer diseases was constructed using artificial intelligence to extract endoscopic features from the training set. Subsequently, the performance of the CADx system was preliminarily tested through the validation set.

Eligibility criteria:
Inclusion Criteria:

1. They underwent endoscopic examination and are found to have ulcerative lesions in the large intestine;
2. They have endoscopic images and videos captured and stored during colonoscopy which are available to be retrieved;
3. They have the complete medical records and clear diagnosis.

Exclusion Criteria:

1)Poor quality endoscopic images and videos defined as:

1. Incomplete visualization of the colorectal ulcer due to technical reasons (e.g. out-of-focus, motion-blurred or insufficient illumination);
2. Artifacts due to mucus, air bubbles, stool, or blood. 2)Obscured view due to poor bowel preparation; 3)Incomplete medical record; 4)Prior history of intestinal resection, fistula, or anastomosis.

Outcomes:
The diagnostic accuracy
Comparison of a CNN-Based Artificial Intelligence Model With Dermatologists and Non-Dermatologist Physicians in the Diagnosis of Solitary Skin Lesions
Trial ID: NCT07415291
Status: ACTIVE_NOT_RECRUITING
Study type: OBSERVATIONAL
Start date: 2026-01-15
Completion date: 2026-05-31
Sponsor: Istanbul Training and Research Hospital
Conditions: Solitary Skin Lesions; Skin Neoplasms; Skin Neoplasm Malignant
Locations: S.B.Ü. İstanbul Eğitim ve Araştırma Hastanesi
Countries: Turkey (Türkiye)
Enrollment: 17625
Age range: None - None
Sex: ALL

Summary:
The goal of this observational study is to evaluate the diagnostic accuracy of a CNN-based artificial intelligence model in patients with solitary skin lesions. The main questions it aims to answer are:

* What is the diagnostic performance (sensitivity and specificity) of the CNN-based model in identifying solitary skin lesions using macroscopic clinical images?
* How does the diagnostic accuracy of the CNN-based model compare with the evaluations performed by dermatologists and non-dermatologist physicians?

Researchers will compare the AI model's diagnostic outputs to the independent evaluations of dermatologists and non-dermatologist physicians to see if the AI model can achieve a diagnostic performance comparable to or better than human clinicians.

Participants (physicians acting as clinical readers) will:

* Independently review a predefined set of anonymized macroscopic clinical images sourced from a retrospective patient archive.
* Provide a primary diagnosis for each lesion based solely on the images, without access to patient history or histopathological results.
* Submit their assessments to be compared against the gold standard (histopathological diagnosis) and the AI model's results.

Eligibility criteria:
Inclusion Criteria:

* Patients who have provided informed consent for the use of their clinical images in scientific research.
* Clinical images with a resolution exceeding 224x224 pixels, ensuring compatibility with the artificial intelligence architecture.
* Retrospective records of solitary skin lesions with confirmed diagnoses.

Exclusion Criteria:

* Patients who have not consented to the use of their clinical photographs for research purposes.
* Images containing potentially identifiable personal information or visual features that compromise patient anonymity.
* Images with a resolution lower than 224x224 pixels or poor diagnostic quality (e.g., blurring, significant occlusion).
* Duplicate images or entries for the same lesion.

Outcomes:
Diagnostic accuracy of the CNN-based artificial intelligence model
Does AI Make Clinicians More Appropriately Confident? A Randomized Study in Preterm Birth Prediction
Trial ID: NCT07402668
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-02-03
Completion date: 2026-06
Sponsor: Rigshospitalet, Denmark
Collaborators: Technical University of Denmark; The Foundation of 17.12.1981; Department of Computer Science, University of Copenhagen, Denmark; Copenhagen Academy for Medical Education and Simulation
Conditions: Preterm Birth; Artificial Intelligence (AI) in Diagnosis
Interventions: AI prediction (binary); AI risk estimate (%)
Locations: Copenhagen University Hospital, Rigshospitalet; Herlev Hospital; Copenhagen University Hospital, North Zealand; Holbæk Hospital; Hvidovre Hospital; Zealand University Hospital, Roskilde; Slagelse Hospital
Countries: Denmark; Denmark; Denmark; Denmark; Denmark; Denmark; Denmark
Enrollment: 125
Age range: None - None
Sex: ALL

Summary:
The goal of this randomized questionnaire-based study is to evaluate how different presentations of artificial intelligence (AI) decision support influence clinical judgment among medical doctors working in obstetrics and gynecology when assessing the risk of spontaneous preterm birth using clinical case vignettes with cervical ultrasound images. The study specifically compares two AI presentation formats: a binary classification (preterm vs term birth) and an individualized risk estimate of preterm birth.

The main questions it aims to answer are:

* Which AI presentation format leads to better alignment between clinicians' confidence and decision accuracy (diagnostic calibration)?
* Do different AI presentation formats lead to helpful or harmful changes in clinical decisions?

Participants will complete an online questionnaire in which they review clinical cases, make diagnostic and management decisions, rate their diagnostic confidence before and after seeing the AI output, and report their trust in the AI.

Eligibility criteria:
Inclusion Criteria:

* Medical doctors currently working in or training within the field of obstetrics and gynecology.
* Experience performing transvaginal cervical ultrasound examinations.

Exclusion Criteria:

\- No prior experience performing transvaginal cervical ultrasound examinations.

Outcomes:
Clinician diagnostic calibration (accuracy-confidence alignment) after AI exposure.
Rapid Throughput Screening for Human COVID-19 Infection: A ECG Study
Trial ID: NCT04725097
Status: TERMINATED
Study type: OBSERVATIONAL
Start date: 2020-12-10
Completion date: 2021-06-03
Sponsor: Mayo Clinic
Conditions: Covid19
Locations: Mayo Clinic; Mayo Clinic; LSUHSC Shreveport; Henry Ford Health System; Mayo Clinic; Hackensack Meridian Health; Sri Jayadeva; King Edward Memorial Hospital; Gregorio Marañon Hospital; Imperial College
Countries: United States; United States; United States; United States; United States; United States; India; India; Spain; United Kingdom
Enrollment: 2821
Age range: 18 Years - 89 Years
Sex: ALL

Summary:
The purpose of this study is to assess the ability of an artificial intelligence smartphone-enabled point of care ECG to detect COVID-19.

Eligibility criteria:
Inclusion Criteria:

* Adult males and females age 18 or greater.
* Undergoing COVID-19 testing by PCR.
* Willing and able to provide informed consent.
* Pregnant females (minimal risk study, no risk from ECG).

Exclusion Criteria:

\- Unwilling or unable to provide informed consent.

Outcomes:
COVID 19 Detection