moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Development of a Set of Auxiliary Decision-making System for the Perioperative Period of Hepatectomy Based on Static CT and Artificial Intelligence.
Trial ID: NCT07056270
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-08
Completion date: 2028-02
Sponsor: Tongji Hospital
Collaborators: Suzhou Institute of Biomedical Engineering and Technology of the Chinese Academy of Sciences
Conditions: Liver Cirrhosis; Liver Cancer
Interventions: static CT scans
Enrollment: 500
Age range: 18 Years - 75 Years
Sex: ALL

Summary:
The study will prospectively recruit patients with chronic liver disease and liver cancer for static CT scans to establish a high-definition CT database. Combining clinical data and pathological information, artificial intelligence technology will be utilized to construct models for assessing liver function and liver cirrhosis, as well as predicting microvascular invasion (MVI).

Eligibility criteria:
Inclusion Criteria:

1. Patients who are clinically diagnosed with primary liver cancer and other space-occupying liver lesions preoperatively and are planned for surgical resection, or those with underlying liver diseases and cirrhosis;
2. Aged 18-75 years;
3. Willing to participate in this study and have signed the informed consent.

Exclusion Criteria:

1. Planned or unplanned pregnancy and pregnant women;
2. Glomerular filtration rate (GFR) ≤60 ml/min;
3. History of contrast media allergy.

Outcomes:
microvascular invasion; liver cirhosis; liver function
AN ARTIFICIAL INTELLIGENCE MODEL FOR INTENSIVE CARE LENGTH OF STAY, NEUROLOGICAL OUTCOME AND COSTS ESTIMATION AFTER CARDIOPULMONARY RESUSCITATION: A COHORT STUDY
Trial ID: NCT07210866
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-10-01
Completion date: 2025-12-01
Sponsor: Bezmialem Vakif University
Conditions: Cost Analysis; Length of ICU Stay; Neurological Outcome
Interventions: no physical or medical interventions
Enrollment: 5000
Age range: 18 Years - None
Sex: ALL

Summary:
The study aims to overview patients registered to Bezmialem Vakıf University Hospital Intensive Care Unit after successive cardiac arrest resuscitation from October 2010 to September 2025. The goal is to determine length of stay in reanimation, neurological clinical outcome and costs of these patients at discharge from the department. All these data is intended to be evaluated by artificial intelligence to evaluate a predictive model.

Eligibility criteria:
Inclusion Criteria:

* age\>18 years
* successive cardiopulmonary resuscitation
* at least 1 hour long admission to ICU after Return Of Spontaneous Circulation (ROSC)

Exclusion Criteria:

* age \< 18 years
* \>80% missing data in patient records
* patients with no ROSC

Outcomes:
Machine Learning Python programme
Prediction of Gastric Cancer in Intestinal Metaplasia and Atrophic Gastritis - Application of Artificial Intelligence in Histology and Clinical Data
Trial ID: NCT04840056
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2021-04-15
Completion date: 2025-12-31
Sponsor: Chinese University of Hong Kong
Conditions: Gastric Cancer; Intestinal Metaplasia; Atrophic Gastritis
Locations: Prince of Wales Hospital
Countries: Hong Kong
Enrollment: 1300
Age range: 18 Years - None
Sex: ALL

Summary:
The primary objectives of this study are:

* To identify clinical or histological factors associated with gastric cancer development in patients with IM and AG
* To establish a machine learning algorithm for prediction of future gastric cancer risks and individual risk stratification in patient with IM and AG

Eligibility criteria:
Inclusion Criteria:

* Adults \>= 18 years of age
* Histologically proven atrophic gastritis or intestinal metaplasia (at antrum and/or body and/or angular of stomach)

Exclusion Criteria:

\- none

Outcomes:
Gastric cancer and gastric dysplasia
Electroencephalogram Based Real-Time Sedation Level Prediction Using Artificial Intelligence
Trial ID: NCT05757622
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2023-03-01
Completion date: 2025-04-01
Sponsor: University Medical Center Groningen
Conditions: Procedural Sedation
Interventions: Propofol; Remifentanil
Enrollment: 1000
Age range: 18 Years - None
Sex: ALL

Summary:
This is an observational pilot study. Patients undergoing elective surgery or Cath lab interventions under general anesthesia or procedural sedation at the department of anesthesiology at the UMCG will be enrolled.

1. Recruitment procedure:

   Patients identified as eligible for the study by research/clinical staff will be enrolled after written informed consent. Clinical staff involved in this study will approach the patient for obtaining informed consent prior to the scheduled procedure, according to the UMCG´s daily published OR/Cath lab schedule. Patients enrolled in this study will receive standard clinical care, as defined in the standard operating procedures of the department of Anesthesiology.
2. Monitor and recording set up:

   1. A vital sign monitor will continuously record routine physiological data, including ECG, noninvasive and/or invasive blood pressure (blood pressure cuff and/or arterial line), and pulse oximetry.
   2. The noninvasive EEG-monitor A disposable sensor will be attached to the forehead of the patient and passive recording of EEG and physiological data will begin prior to induction of anesthesia at the OR or Cath lab.
   3. Vital sign monitor and EEG-monitor used for this study are mounted permanently on the anesthesia machine and sedation workstation which is used routinely at the department of Anesthesiology and data obtained are directly streamed to the UMCG patient data record system , and stored monitor for case-based wave analysis.
3. Sedation/Pain assessments:

As per routine clinical practice, the investigators will use Modified Observer's Assessment of Alertness/Sedation Scale (MOAA/S) scoring system for sedation assessment system for pain assessment. During each assessment MOAA/S scores will be determined prior to induction of anesthesia or sedation, during the procedure, and after the procedure until the patient leaves the operating room/cath lab.

Eligibility criteria:
Inclusion Criteria:

* Patients older than 18 years, undergoing elective procedures under general anesthesia or procedural sedation

Exclusion Criteria:

* Deemed unsuitable for selection by the research team or service care providers due to any medical, legal, social, or interpersonal issues that would either compromise the study or the routine care of patients.

Outcomes:
EEG; Vital signs; MOAAS score
Clinicopathology-based Machine Learning Model for Prediction of Pathologic Complete Response to Neoadjuvant Chemotherapy in Breast Cancer
Trial ID: NCT07426653
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2010-01-01
Completion date: 2025-12-31
Sponsor: Florence Nightingale Hospital, Istanbul
Conditions: Breast Cancer
Enrollment: 298
Age range: 18 Years - 90 Years
Sex: FEMALE

Summary:
This retrospective observational study aims to develop and validate a clinicopathology-based machine learning model to predict pathological complete response (pCR) following neoadjuvant chemotherapy in patients with breast cancer. Clinical and pathological data collected between 2010 and 2025 were used to train and evaluate multiple machine learning algorithms using cross-validation and independent holdout testing. The primary outcome was pathological complete response after neoadjuvant chemotherapy. Model performance was assessed using discrimination and classification metrics, including ROC-AUC, precision-recall AUC, F1-score, and Matthews correlation coefficient. The resulting model is intended to support clinical decision-making by providing individualized probability estimates of treatment response.

Eligibility criteria:
Inclusion Criteria:

* Histologically confirmed breast cancer
* Receipt of neoadjuvant chemotherapy
* Available clinicopathological data required for model development
* Surgical treatment performed following neoadjuvant chemotherapy
* Pathological response assessment available
* Recorded pathological details

Exclusion Criteria:

* Missing pathological response information
* Incomplete clinicopathological data required for model analysis
* Patients not treated with neoadjuvant chemotherapy
* Non-invasive breast cancer without indication for neoadjuvant treatment

Outcomes:
Pathological Complete Response (pCR)
A Prospective, Multi-center Study to Characterize Intestinal Fibrosis in Patients With Crohn's Disease (CD) Using MR Enterography (MRE)-Based Artificial Intelligence
Trial ID: NCT06858553
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-06-03
Completion date: 2027-02-28
Sponsor: Minhu Chen
Collaborators: Sixth Affiliated Hospital, Sun Yat-sen University; Sir Run Run Shaw Hospital; Jinling Hospital, China; MSD R&D (China) Co., Ltd.; Ruijin Hospital
Conditions: Crohn Disease (CD)
Locations: The First Affiliated Hospital,Sun Yat-sen University; Sixth Affiliated Hospital of Sun Yat-sen University; Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University; Ruijin Hospital, Shanghai Jiaotong University School of Medicine; Sir Run Run Shaw Hospital, Zhejiang University School of Medicine
Countries: China; China; China; China; China
Enrollment: 234
Age range: 18 Years - 75 Years
Sex: ALL

Summary:
Intestinal fibrotic strictures represent a severe complication of Crohn's disease (CD), affecting over half of the patients. Despite the continuous emergence of novel medications, effective treatment options remain scarce. Endoscopy fails to identify the full-thickness fibrosis of the bowel wall, and standardized assessment for cross-sectional imaging has yet to be established. Previous studies have demonstrated that radiomics models based on computed tomography and deep learning models exhibit commendable diagnostic capability. Thus, this project seeks to conduct a prospective multicenter study, with plans to recruit 234 CD patients requiring bowel resection from five medical centers. The aim is to develop and validate a deep learning model based on magnetic resonance enterography (MRE) to accurately characterize intestinal fibrosis.

Eligibility criteria:
Inclusion Criteria:

1. Patients Over 18 years old with a confirmed diagnosis of CD based on the criteria of ECCO guideline.
2. Planning to receive a bowel resection due to stricture in ileum or colon, and availability of histological specimens of resected intestinal walls matched with MRE are expected to be available.
3. Clear boundaries of the target bowel tract enable accurate semi-automatic or fully automatic intestinal segmentation

Exclusion Criteria:

1. Cannot undergo MRI examination
2. Difficult to obtain suitable tissue after surgery
3. MRE imaging is of poor quality or contains artifacts
4. The target bowel is located at the anastomosis (ie, anastomotic stricture)
5. Intestinal lesions concurrent with other diseases

Outcomes:
histologic inflammation score; histologic fibrosis score; Magnetization Transfer Ratio; Apparent Diffusion Coefficient; Percentage of Enhancement Gain
ANEURYSM@RISK: Automatic Intracranial Aneurysm Quantification and Feature Learning Modelling to Optimize Intracranial Aneurysm Rupture Prediction
Trial ID: NCT07111975
Status: ACTIVE_NOT_RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-01-01
Completion date: 2028-12
Sponsor: UMC Utrecht
Collaborators: Bichat Hospital; Universitätsklinikum Hamburg-Eppendorf; Philips Medical Systems
Conditions: Intracranial Aneurysms; Unruptured Intracranial Aneurysm; Subarachnoid Hemorrhage (SAH) From Ruptured Aneurysm
Locations: University Medical Center (UMC) Utrecht
Countries: Netherlands
Enrollment: 3800
Age range: 18 Years - None
Sex: ALL

Summary:
ANEURYSM@RISK is an observational study aiming to develop and validate an artificial intelligence (AI)-based prediction model for the growth and rupture of intracranial aneurysms (IAs). By applying automated 3D segmentation and morphological quantification of IAs from MR angiography (MRA) scans, the model is intended to provide clinicians with objective and reproducible risk estimates of aneurysm instability.

The study utilizes retrospective imaging data from multiple European centers, including UMC Utrecht, AP-HP Paris, and University Medical Center Hamburg-Eppendorf (UKE). A clinical vignette study will evaluate the model's clinical utility and user experience among interventional radiologists.

This study is exempt from medical ethics review (non-WMO in the Netherlands), as it involves only existing, anonymized data and imposes no additional burden on patients.

Eligibility criteria:
Inclusion Criteria:

* Available MR angiography (MRA) scans of the Circle of Willis
* Presence of at least one intracranial aneurysm (IA)
* Availability of follow-up imaging or clinical records indicating stability, growth, or rupture

Exclusion Criteria:

* Imaging of insufficient quality for segmentation or analysis
* Lack of follow-up data

Outcomes:
Discriminative performance of AI model for aneurysm instability prediction
Validation of a Medical-grade Wearable Device with Interpretable Artificial Intelligence System for the Obstructive Sleep Apnea-hypopnea Syndrome (OSAHS) Evaluation
Trial ID: NCT06474390
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2024-06-01
Completion date: 2025-05-31
Sponsor: PranaQ Pte. Ltd.
Conditions: Obstructive Sleep Apnea
Locations: Taipei Veterans General Hospital
Countries: Taiwan
Enrollment: 125
Age range: 20 Years - None
Sex: ALL

Summary:
A validation study has been designed to assess the performance of a home sleep test device, TipTraQ, for screening sleep apnea. The study involves participants wearing the TipTraQ device on their fingertip during a traditional sleep test, known as polysomnography (PSG), conducted in a sleep center. The performance of the device will then be evaluated by comparing the results from the TipTraQ system with those from the traditional sleep test.

Eligibility criteria:
Inclusion Criteria:

Subjects from the age of 20 and older that have an indication for an in-lab PSG study in the Duke University Hospital. The subject should be able to understand the study and sign the informed consent before being enrolled in the study. During the study period, the subject can withdraw from the study anytime.

Exclusion Criteria:

Subjects with the following medical conditions will be excluded from this study:

1. Heart transplant
2. Heart failure, New Youk Heart Association (NYHA) classification III or IV
3. Chronic Obstructive Lung Disease, Global Initiative for Chronic Obstructive Lung Disease (GOLD) categorization 3 or 4
4. Chronic opioid medication user Devastating Severe strokes, with the modified Rankin score (mRS)≥4
5. Tracheostomy
6. Incapable of comprehending and signing the informed consent and questionnaires, including but not limited to subjects with severe Alzheimers, unconscious by head trauma, or of someone with limited mental capacity Cannot correctly follow the order to use the TipTraQ device

Outcomes:
Oxygen Desaturation Index(ODI); Apnea Hypopnea Index(AHI); Total Sleep Time(TST)
Prostate Specific Membrane Antigen (PSMA) Scan and Multiparametric MRI for Prostate Biopsy Detection of Clinically Significant Prostate Cancer
Trial ID: NCT05820724
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: PHASE2
Start date: 2023-05-01
Completion date: 2026-05-01
Sponsor: VA Greater Los Angeles Healthcare System
Collaborators: Lantheus Medical Imaging
Conditions: Prostate Cancer Diagnosis
Interventions: PSMA PET scan; No PSMA PET; 18F- DCFPyl Injection
Locations: VA Greater Los Angeles Healthcare System
Countries: United States
Enrollment: 100
Age range: 18 Years - None
Sex: MALE

Summary:
Prospective, randomized, phase 2 clinical trial to determine if PSMA PET imaging plus mpMRI improves detection of clinically significant prostate cancer as compared to mpMRI alone.

Eligibility criteria:
Inclusion Criteria:

* Patients with PI-RADS 4-5 lesion(s) on mpMRI and have elected to undergo a prostate biopsy
* Prostate mpMRI completed within 9 months prior to enrollment
* Patient capable of providing written informed consent
* Patient willing to be randomized to prostate mpMRI only vs prostate mpMRI + DCFPyL PET/CT

Exclusion Criteria:

* Less than 18 years-old at the time of radiotracer administration
* Medical condition, serious concurrent illness, or other extenuating circumstance that, in the opinion of the Investigator, may significantly interfere with study procedures or compliance.
* Creatinine clearance exceeding institutional requirements for prostate mpMRI

Outcomes:
Detection of Clinically Significant Prostate Cancer
Effect of Mobile Application- and Artificial Intelligence-Supported Video-Based Patient Education on Patient Outcomes in Teaching Clean Intermittent Catheterization
Trial ID: NCT07489261
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-04-01
Completion date: 2027-04-01
Sponsor: Mersin University
Conditions: Clean Intermittent Catheterization
Interventions: Mobile application; Artifial intellgience
Locations: Mersin Unıversıty
Countries: Turkey (Türkiye)
Enrollment: 99
Age range: 18 Years - None
Sex: ALL

Summary:
The study aimed to determine the effects of a mobile application and an artificial intelligence-supported educational video, developed for patients who will perform clean intermittent catheterization (CIC), on patients' adherence, levels of difficulty, self-confidence, infection parameters (urinalysis, urine culture, infection incidence), and recurrent hospital admissions.The number of CIC patients who applied to the Urology Outpatient Clinic of Mersin University within the last year was 110. Since no similar study was found, the sample size was calculated using an a priori power analysis with G\*Power 3.1.9.7 software. For the one-way analysis of variance (ANOVA), the parameters used were α error probability = 0.05, power (1-β error probability) = 0.80, number of groups = 3, and effect size f = 0.40 (large effect size). As a result of the power analysis, the total sample size was calculated as 90, with 30 participants in each group. Considering a possible data loss (dropout) rate of approximately 10% (Hernández-Rodríguez et al., 2022), it was planned to include 33 participants in each group (a total of 99 participants) at the beginning of the study.Data will be collected using the "Descriptive Information Form," "Intermittent Catheterization Adherence Scale," "Self-Confidence Scale in Clean Intermittent Self-Catheterization," "Intermittent Catheterization Difficulty Questionnaire," and the "Patient Follow-up Form."Patients who will perform clean intermittent catheterization will receive training through a mobile application (intervention group 1) and an artificial intelligence-supported educational video (intervention group 2). Patients in the control group will be trained using the routine brochure provided in the outpatient clinic.

Eligibility criteria:
Inclusion Criteria:

* Patients who require intermittent catheterization (TAK),
* Aged 18 years or older,
* Willing to participate in the study,
* Able to understand Turkish,
* Literate,
* Without visual or hearing impairments,
* Owning a smartphone, tablet, or computer,
* Without difficulties using technology.

Exclusion Criteria:

* Patients who do not require intermittent catheterization (TAK),
* Under 18 years of age,
* Not willing to participate in the study,
* Do not understand Turkish,
* Illiterate,
* With visual or hearing impairments,
* Do not own a smartphone, tablet, or computer,
* Having difficulties using technology.

Outcomes:
Adherence; Self-Confidence
Precision Detection and Prediction of Atrial Arrhythmias Using Artificial Intelligence and Consumer Wearable Devices (REMOTE-AF2)
Trial ID: NCT07291570
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-12
Completion date: 2026-08
Sponsor: Royal Brompton & Harefield NHS Foundation Trust
Conditions: Atrial Fibrillation (AF)
Locations: Royal Brompton and Harefield Hospitals, Guy's and St Thomas' NHS Foundation Trust
Countries: United Kingdom
Enrollment: 40
Age range: 18 Years - None
Sex: ALL

Summary:
Atrial fibrillation (AF) is the most prevalent cardiac arrhythmia affecting over one million people in the UK. It is associated with increased cardiovascular morbidity and mortality and costs the NHS between £1.4 billion and 2.5 billion annually. Current methods to detect AF include opportunistic pulse palpation, single time point 12-lead electrocardiograms (ECGs), ambulatory Holter monitoring, and implantable loop recorders (ILRs). The more widely used intermittent monitoring methods, such as ECGs and Holter monitoring, are limited in terms of duration and have lower detection yields of atrial arrhythmias. At the other end of the spectrum, the ILR can give continuous and accurate arrhythmia detection but is invasive and requires specialist expertise to implant, monitor, and analyse.

In recent years, the use of wearable mobile health (mHealth) devices has emerged as a direct-to-consumer option for monitoring parameters such as heart rate and activity levels. From a clinical perspective they potentially offer a less invasive and cost-effective investigative approach, with remote monitoring solutions to possibly predict and detect AF. This technology has significant potential in terms of passive, non-invasive and continuous monitoring to aid the early diagnosis and management of AF.

The original REMOTE-AF study (NCT05037136) developed novel methodology to detect AF using PPG-dervived data from a wearable. This study will further enhance this foundational work by recruiting patients to develop a AI-enabled, multi-parametric algorithm using PPG-derived data to detect AF.

Eligibility criteria:
Inclusion Criteria:

1. Adults aged 18 and above with a confirmed diagnosis of paroxysmal AF or those who have undergone treatment for paroxysmal, or persistent AF and had sinus rhythm restored.
2. Capability to provide informed consent, coupled with self-reported sufficiency of digital literacy.
3. Regular access to a Wi-Fi connection (at least weekly).
4. Own a smartphone (released after 2017).

Exclusion Criteria:

1. Individuals with permanent or persistent AF that remains uncontrolled despite receiving treatment.
2. Conditions or disabilities that preclude adherence to study instructions or proper use of the devices.
3. A known severe allergy to any of the materials in the wearable or ECG device poses a risk to participant safety.

Outcomes:
To evaluate the accuracy of an AI algorithm based on PPG-derived metrics in predicting and detecting AF against intermittent rhythm monitoring.
Improving Charge Nurse Conflict Resolution Communication Using Artificial Intelligence
Trial ID: NCT05594394
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2022-09-19
Completion date: 2023-08-29
Sponsor: Methodist Health System
Conditions: Conflict Resolution
Interventions: Orai: Master Public Speaking
Locations: Methodist Mansfield Medical Center
Countries: United States
Enrollment: 35
Age range: 18 Years - 75 Years
Sex: ALL

Summary:
Nurses are being challenged to provide increasingly complex care with more inexperienced nurses due to higher rates of retirement and registered nurse (RN) turnover in general.

High-performing charge nurses with excellent interpersonal, conflict management, communication, and leadership competencies are vital to achieving optimal outcomes. Implementing a charge nurse leadership development program focused on improving communication competencies in conflict management situations can support a healthy work environment.

Eligibility criteria:
Inclusion Criteria:

* RN Clinical Team Leader or Charge Nurse employed at MMMC

Exclusion Criteria:

* Non-employees of MMMC
* MMMC staff not in the role of Clinical Team Leader or Charge Nurse

Outcomes:
Charge nurses that have completed the program