moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
STROKESTOP III - Optimized Method for Atrial Fibrillation Screening
Trial ID: NCT06275503
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-05-02
Completion date: 2026-06-30
Sponsor: Danderyd Hospital
Collaborators: Karolinska Institutet; Landstinget i Värmland
Conditions: Atrial Fibrillation; Atrial Fibrillation New Onset
Interventions: Screening invitation mode
Locations: Karolinska Institutet, Dept Med H; Region Värmland
Countries: Sweden; Sweden
Enrollment: 2200
Age range: 75 Years - 76 Years
Sex: ALL

Summary:
Atrial fibrillation is the most common sustained cardiac arrhythmia affecting more than 3% of the adult population. The symptoms of atrial fibrillation can range from asymptomatic to debilitating. It can be permanent in its nature, but also paroxysmal with only short bursts of atrial fibrillation randomly occurring and can therefore remain unnoticed. Atrial fibrillation increases the risk of stroke five fold if left untreated.

Screening for atrial fibrillation in elderly populations above age 65 years can result in detection of new atrial fibrillation cases ranging from 0,5% new AF with a single time-point ECG, up to 30% AF if an implantable loop recorder is inserted for 3 years.

Currently opportunistic screening using pulse palpation, or a single time-point ECG is recommended by the European Society of Cardiology guidelines. Systematic screening in individuals aged 75 or above, or at a high stroke risk should be considered.

Overall, participation in systematic atrial fibrillation screening trials has been shown to be relatively low with almost 50% non-participants. Participants are generally healthier, with higher socioeconomic status, hence the ones who would potentially benefit the most remain absent.

Opportunistic screening has shown promising results with higher participation rates and the possibility of better outreach. There is a lack of data from randomized trials on the difference in participation rates in systematic and opportunistic screening approaches when screening with prolonged ECG monitoring.

Eligibility criteria:
Inclusion Criteria:

* Individuals aged 75/76 in 2024 (born 1948, 1949)
* Must reside in the region of Värmland
* Must be listed at a primary care facility

Exclusion Criteria:

* Treatment with oral anti coagulation treatment (OAC)
* Contraindications for OAC treatment
* Prior atrial fibrillation
* Unable to provide consent

Outcomes:
Participation in screening
Determining the Impact of Emotive Intelligent Space on Children's Self-Regulation and Cognitive Performance
Trial ID: NCT04836286
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2021-04-01
Completion date: 2022-06-21
Sponsor: University of Idaho
Collaborators: Washington State University
Conditions: Color Perception
Interventions: Emotive Intelligent Spaces (EIS)
Locations: University of Idaho
Countries: United States
Enrollment: 40
Age range: 3 Years - 6 Years
Sex: ALL

Summary:
Many children (age 3-6) living in the Mountain West (MW) region face unique challenges that can affect their health and welfare, such as lower socioeconomic status, and limited access to healthcare and education. The proposed project aims to address those health and education gaps by improving children's self-regulation (i.e., the ability to control emotional and behavioral impulses), a critical cognitive skill that underpins future mental health and academic achievement. The project will test the effectiveness of an innovative intervention mechanism, the Emotive Intelligent Space (EIS).

The EIS consists of two adjacent 3 x 5 sq. ft. wooden wall panels with colored LED lights, creating a 90-degree semi-private space. The adaptable colored lightings are controlled by a machine learning algorithm that is developed based on a co-investigator's prior study. The EIS harnesses the power of artificial intelligence to detect children's emotions from physiological data in real-time and to translate physiological signals into environmental changes (i.e., adaptable colored lighting) that adequately respond to children's emotions, resulting in improved self-regulation, physiological stress responses, and cognitive performance.

The objective of this proposal is to determine the effect of EIS on children's (age 3-6) self-regulation, physiological, and cognitive outcomes by employing a repeated ABAB experimental design (A = no intervention, B = EIS intervention). The hypothesis is that EIS will positively impact children's self-regulation, physiological stress response, and cognitive performance. Based on a priori power analysis, 40 preschool and kindergarten children will be recruited from early childhood programs in the rural areas near Moscow, ID. During the experiment, children will be assessed under a combination of A and B conditions. A digital wristband will capture children's real-time physiological responses (i.e., Galvanic skin response, body temperature, and blood volume pulse). A machine learning algorithm will immediately translate the physiological data into three basic emotions (i.e., happy, angry/fearful, sad) represented by children's choice of colors on the EIS. A series of ANCOVA analyses will be used to determine the mean differences in self-regulation, physiological, and cognitive scores under baseline and treatment conditions.

Eligibility criteria:
Inclusion Criteria:

* Typically developing children without color blindness

Exclusion Criteria:

* Children with color blindness

Outcomes:
Self-regulation; Body temperature; Galvanic skin response; Blood volume pulse; Cognitive performance
Assessing the Prognosis of Transarterial Chemoembolization in Combination With PD-1/PD-L1 Inhibitors and Molecular Target Therapies(VEGF-TKI/Bevacizumab) for Hepatocellular Carcinoma by Using Clinical and Imaging Biomarkers
Trial ID: NCT05278195
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2022-04-01
Completion date: 2023-12-01
Sponsor: Zhongda Hospital
Conditions: Hepatocellular Carcinoma
Locations: Zhongda Hospital, Southeast University
Countries: China
Enrollment: 300
Age range: 18 Years - None
Sex: ALL

Summary:
The purpose of this study is to id

transarterial chemoembolization (TACE) in combination with immune checkpoint inhibitors (ICIs) and molecular targeted therapies in patients with hepatocellular carcinoma (HCC) .

Eligibility criteria:
Inclusion Criteria:

1. Has a diagnosis of HCC confirmed by radiology, histology, or cytology;
2. Has not received any previous systemic therapy for HCC (including chemotherapy, molecularly targeted therapy, immunotherapy);
3. Both PD-1/PD-L1 inhibitors and anti-angiogenesis drugs patients received only include marketed drugs but are not limited to HCC approval;
4. TACE was performed up to 3 months after the first PD-1/PD-L1 inhibitor/anti-angiogenic drug treatment or within 1 month before treatment;
5. Received at least 1 cycle of PD-1/PD-L1 inhibitor/anti-angiogenic drug combination therapy after TACE treatment；

7\. Has repeated measurable intrahepatic lesions according to RECIST v1.1;

Exclusion Criteria:

1. Cholangiocarcinoma, fibrolamellar, sarcomatoid hepatocellular carcinoma, and mixed hepatocellular/cholangiocarcinoma subtypes(confirmed by histology, or pathology) are not eligible;
2. Unable to meet criteria of combination timeframe described above;
3. Missing follow-up data;

Outcomes:
Overall Survival(OS)
Use of the SiDTM v2.0 Algorithm to Assist Embryologists in Sperm Selection During ICSI Procedures: Impact in Biological and Clinical Outcomes
Trial ID: NCT07163754
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-10
Completion date: 2027-10
Sponsor: Instituto Valenciano de Infertilidad, IVI VALENCIA
Collaborators: IVF 2.0 Limited
Conditions: Male Fertility; Testicular; Sperm Parameters in Fertile and Infertile Men; Sperm Selection
Interventions: Automated Sperm Selection; Conventional Sperm Selection
Enrollment: 100
Age range: 18 Years - None
Sex: ALL

Summary:
Infertility is defined as a failure of a couple to achieve a pregnancy after 12 or more months of unprotected intercourse. Males are found to be solely responsible for 20-30% of infertility cases but contribute to 50% of cases overall. The selection of sperm to microinject is completely subjective and there is high intra- and inter- observer variability. SiDTM v2.0 is an algorithm which analyses real-time seminal samples located at ICSI dishes. Particularly, it assesses morphology and several motility parameters of each sperm, and it assigns a categorical and numerical score to each one. Categorical scores are represented by colours: green colour for optimal sperm, yellow for good sperm, orange for medium-quality sperm and red for low-quality sperm. Numerical scores ranged from 0 to 100, with higher scores for those best-quality sperm. SiDTM v2.0 can reduce subjectivity of the sperm selection process to the maximum, selecting the optimal sperm in real time. In addition, it could help junior embryologists to perform this complex and tedious procedure, which is the sperm selection for ICSI. To carry out the study, we will conduct a prospective cohort study in a total of 100 couples. Therefore, the aim of this study is to validate SiDTM v2.0 as an useful Artificial Intelligence-tool for sperm selection; that means achieving , at least, same clinical results as sperm selection performed by the embryologist.

Eligibility criteria:
Inclusion Criteria:

* Patients with indication for ICSI.
* Women of advanced maternal age (\>35 years) in whom at least 4 oocytes in metaphase II stages are obtained in the follicular puncture, without excluding patients from the oocyte donation program.
* Sperm obtained by masturbation with a concentration above 1 million spermatozoa/mL after sperm capacitation.
* Normal karyotype of both partners.
* Informed Consent (IC).

Exclusion Criteria:

* Patients diagnosed with recurrent gestational loss.
* Semen extracted by testicular biopsy.
* Semen samples with globozoospermia (sperm defect due to lack of acrosome) or azoospermia (absence of spermatozoa in the ejaculate).
* Semen samples treated with pentoxifylline.
* ICSI cycles with application of calcium ionophore. o ICSI cycles with application of calcium ionophore.

Outcomes:
SID TM V2.0 VALIDATION
Changing the Future of Intestinal Failure in Intestinal Chronic Inflammation: Towards Innovative Predictive Factors and Therapeutic Targets.
Trial ID: NCT06777251
Status: ACTIVE_NOT_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-02-29
Completion date: 2025-05-18
Sponsor: Fondazione Policlinico Universitario Agostino Gemelli IRCCS
Conditions: Intestinal Failure; Inflammatory Bowel Diseases; Crohn Disease
Interventions: analysis of predictive factors of IF in patients with Crohn's disease through the characterization of "multiomics" parameters
Locations: Fondazione Policlinico Universitario A. Gemelli IRCCS
Countries: Italy
Enrollment: 254
Age range: 18 Years - 75 Years
Sex: ALL

Summary:
This is an interventional, prospective, no profit study that will be performed at CEMAD, from FONDAZIONE POLICLINICO GEMELLI IRCCS, Rome (UO1) and U.O.C. Internal Medicine and Gastroenterology from Ospedale Brotzu di Cagliari (UO4). Adult patients with Crohn Disease (CD) and chronic intestinal insufficiency (CF) and adult patients with CD at high and low risk of CF.

Eligibility criteria:
Inclusion Criteria:

* Age ≥ 18 years-old and ≤ 75 years old
* Capable of expressing informed consent;
* An established diagnosis of Crohn's disease;
* Antibiotics treatment free for at least 15 days.

Exclusion Criteria:

* Age \< 18 years-old and \> 75 years old;
* Not capable of expressing informed consent;
* Pregnant or breastfeeding patients;
* Comorbidities including: cancer pathology present or under active treatment; coagulopathies; chronic hepatopathy, heart failure, renal failure, respiratory failure.

Outcomes:
Predictive Factors for FH in Crohn's Disease Using Gut Microbiota Composition; Predictive Factors for FH in Crohn's Disease Using Metabolome Analysis; Predictive Factors for FH in Crohn's Disease Using Immunological Signature Characterization
Innovative Use of fungalAi for Antifungal Stewardship in Haematology-oncology Patients
Trial ID: NCT03793231
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2019-01-01
Completion date: 2020-12-30
Sponsor: Bayside Health
Collaborators: Monash University; Monash Health; Eastern Health; Western Sydney Local Health District; SA Health; Fremantle Hospital and Health Service; Singhealth Foundation
Conditions: Fungal Infections
Interventions: fungalAi platform technology
Locations: Alfred Health
Countries: Australia
Enrollment: 1000
Age range: None - None
Sex: ALL

Summary:
This national Australian study will validate and implement an effective approach to real-time electronic surveillance of fungal infections in patients with blood cancers using technology based on artificial intelligence. It will establish metrics for antifungal stewardship allowing benchmarking of these programs; provide decision support for radiologist interpretation of chest imaging and improve reporting, audit and feedback practices in hospitals where these infections are managed.

Eligibility criteria:
Inclusion Criteria:

* Adults and children
* Under the haematology service at participating sites
* Inpatient and ambulatory patients.

Exclusion Criteria: No exclusion criteria

Outcomes:
Accuracy of electronic surveillance using fungalAi natural language processing compared to active manual methods for detection of fungal pneumonia
DETECT-PD -- Dialysis Efficiency and Transporter Evaluation Computational Tool in Peritoneal Dialysis
Trial ID: NCT06842927
Status: ENROLLING_BY_INVITATION
Study type: OBSERVATIONAL
Start date: 2025-03-03
Completion date: 2026-03-31
Sponsor: Tuen Mun Hospital
Conditions: End-Stage Kidney Disease; End Stage Renal Disease (ESRD); End Stage Renal Disease on Dialysis (Diagnosis); End Stage Renal Failure on Dialysis; Peritoneal Dialysis; Peritoneal Dialysis Patients
Interventions: data collection; data report
Locations: Tuen Mun Hospital
Countries: Hong Kong
Enrollment: 350
Age range: 18 Years - None
Sex: ALL

Summary:
The goal of this prospective diagnostic test (correlation) study is to develop and investigate the performance of artificial intelligence in predicting peritoneum transporter status and dialysis efficiency in adult patients undergoing peritoneal dialysis (PD).

The main questions it aims to answer are:

Can artificial intelligence predict peritoneal transporter status based on simple clinical and biochemical measurements? Can artificial intelligence predict dialysis adequacy (Kt/V) using these features?

Researchers will compare the performance of the AI model with the gold standard Peritoneal Equilibration Test (PET) and Kt/V to evaluate its accuracy and reliability.

Participants will:

Provide peritoneal dialysate and spot urine samples for biochemical analysis. Undergo routine dialysis adequacy and peritoneal equilibration testing (PET). Have clinical and laboratory data collected for AI model training and validation.

The study will recruit approximately 350 peritoneal dialysis patients, with 280 participants in the training/validation arm and 70 participants in the test arm. The study duration is 12 months following enrollment.

Eligibility criteria:
Inclusion Criteria:

* Age 18 years or older
* Diagnosis of end-stage renal failure requiring peritoneal dialysis as renal replacement therapy
* Ability to give informed consent and comply with study procedures.

Exclusion Criteria:

* History of hernia or peritoneal leak, including pleuroperitoneal fistula (PPF), patent processus vaginalis (PPV) and retroperitoneal leak
* Ongoing PD peritonitis with or without antibiotic therapy
* Just finished PD peritonitis antibiotic treatment within recent 4 weeks
* Pregnancy
* Patient refusal

Outcomes:
Peritoneal Equilibration Test (PET) Parameters; Peritoneal Equilibration Test (PET) Parameters; Peritoneal Equilibration Test (PET) Parameters; Peritoneal Equilibration Test (PET) Parameters; Peritoneal Equilibration Test (PET) Parameters; Peritoneal Equilibration Test (PET) Parameters; Peritoneal Equilibration Test (PET) Parameters; Peritoneal Equilibration Test (PET) Parameters
A Prospective Cohort Study Evaluating the Utility of Artificial Intelligence-Assisted Non-Contrast Computed Tomography for Multi-Cancer Screening in Asymptomatic Individuals Undergoing Routine Health Examinations
Trial ID: NCT06632886
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-10-07
Completion date: 2027-10-07
Sponsor: Guo ShiWei
Conditions: Lung Cancers; Liver Cancer; Gastric Cancers; Colorectal, Cancer; Esophageal Cancer; Pancreatic Cancer; Breast Cancer
Interventions: AI-Assisted Non-Contrast CT for Multi-Cancer Screening
Locations: Changhai Hospital
Countries: China
Enrollment: 1000000
Age range: 18 Years - None
Sex: ALL

Summary:
Cancer poses a major public health challenge in China. Early detection can improve treatment outcomes and survival rates. In this study, we will conduct a large-scale, prospective, multi-center cohort study to evaluate the utility of AI-assisted non-contrast CT for multi-cancer screening.

The study aims to enroll 1 million asymptomatic participants undergoing routine health examinations, using an AI imaging model based on non-contrast CT to detect seven cancers such as lung, liver, gastric, colorectal, esophageal, pancreatic, and breast cancers. Positive cases will be required to be referred to Shanghai Changhai Hospital for further imaging and care based on National Comprehensive Cancer Network (NCCN) and American College of Radiology (ACR) guidelines. The goal is to assess the AI model's diagnostic performance for seven cancer types, especially for early-stage, resectable tumors.

Eligibility criteria:
Inclusion Criteria:

1. Subject is able and willing to provide informed consent and sign an informed consent form.
2. Subject has undergone an abdominal or chest non-contrast CT scan.

Exclusion Criteria:

1. Subject has been diagnosed with one of the following cancers within the last five years: lung, liver, stomach, colon, esophageal, pancreatic, or breast cancer;
2. Subject has any medical condition that contraindicates high-resolution MRI/CT/Endoscopy;
3. Subject cannot be followed up or is participating in other clinical trials.

Outcomes:
Diagnostic yield; Incidence; Resectable rate
Ability of Physicians to Distinguish Real From Artificial Colon Polyp Images
Trial ID: NCT07108569
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-11-06
Completion date: 2025-02-01
Sponsor: Wuerzburg University Hospital
Conditions: Colon Polyp; Colonic Adenoma
Interventions: Lutetia
Locations: University hospital Würzburg b
Countries: Germany
Enrollment: 53
Age range: 18 Years - None
Sex: ALL

Summary:
Training in endoscopy is essential for the early detection of precursors of colorectal cancer. Up to now, this training has been carried out with image collections of findings and in practice when working on patients.

The investigators want to use artificial intelligence (AI) to better train doctors to recognise these precursors. By using generative AI, the investigators were able to create realistic images that comply with data protection regulations and whose content can be predefined. Parts of the image can also be regenerated so that it is possible to create different precancerous stages in the same place in the image.

In this study the investigators want to identify the ability of physicians to distinguish artificial from real polyp images.

Eligibility criteria:
Inclusion Criteria:

* Physicians with or without experience in colonoscopy

Outcomes:
Ability to detect artificial images as artificial
A Comparative Clinical Study to Evaluate Crowns Designed by Artificial Intelligence Versus a Crowns Designed Traditional Design Software
Trial ID: NCT07099261
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-10-01
Completion date: 2026-09-01
Sponsor: Tishreen University
Conditions: Dental Crown
Interventions: Crowns
Enrollment: 10
Age range: 18 Years - 60 Years
Sex: ALL

Summary:
This clinical research aims to Evaluate Crowns Designed by Artificial Intelligence Versus Crowns Designed by Traditional Design Software

The research will be conducted on a sample of patients with an indication for crowning of the first lower molar. Ten dental crowns will be prepared.

After obtaining written consent from patients who meet the inclusion and exclusion criteria, 10 dental crowns will be prepared. Measurements will be taken using silicone impressions and then converted into digital format.

The digital impression will be sent to the selected dental restoration design software used in the study. The same parameters will be adjusted in all software programs for each crown.

After the design is completed, the designs will be sent to the milling unit. For each case, three crowns will be designed and milled from polymethyl methacrylate (PMMA).

The crown will be clinically examined and evaluated according to specific criteria. Marginal fit, proximal contact quality, and occlusion will be assessed.

The best crown will be milled from zirconia and delivered to the patient.

Eligibility criteria:
Inclusion Criteria:

* Patients aged 18-60 years requiring a crown for a lower first molar
* Good oral and gingival health
* adjacent teeth in good condition
* Natural opposing teeth
* Willingness to participate and sign informed consent

Exclusion Criteria:

* Abnormal occlusal habits such as bruxism
* Active periodontal diseases
* Allergy to crown materials (e.g., polymethyl methacrylate, PMMA)
* Poor oral hygiene

Outcomes:
Marginal Fit; Clinical evaluation of proximal contact points; clinical evaluation of the occlusion
Development of an Artificial Intelligence Algorithm to Enhance the Gastric Point-of-care Ultrasound. A Proof-of-concept Study.
Trial ID: NCT07580456
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2026-05-08
Completion date: 2027-12-31
Sponsor: University Health Network, Toronto
Conditions: Point-of-care Ultrasound
Locations: Toronto Western Hospital, University Health Network
Countries: Canada
Enrollment: 30
Age range: 18 Years - None
Sex: ALL

Summary:
The goal of this observational study is to train and test an AI (Artificial Intelligence)-based program to assist anesthesiologists in the interpretation of stomach ultrasound images and differentiate a "full" from an "empty" stomach.

It is a healthy-volunteer study, where the participants will undergo ultrasound examination of their stomach at three different time points to visualize the stomach contents. These are at fasting state, after taking some solid food and after taking some water. Here, the participants will be randomized to receive one of five different types solid foods and one of five different volumes of water. The stomach ultrasound images will then be used to train and test the accuracy of the model to diagnose the type of stomach content (nothing vs. clear fluid vs. solid food)

Eligibility criteria:
Inclusion Criteria:

A. Inclusion Criteria at the level of the participants

* Participants must meet all the following inclusion criteria to be eligible for the study:

  * Aged ≥18 years
  * Any sex
  * Be healthy

B. Inclusion Criteria at the level of the input data • Transverse ultrasound images (10 sec clips) of the gastric antrum in the epigastric area that contain all these structures:

* The edge of the left lobe of the liver
* The gastric antrum
* The pancreas
* The aorta

Exclusion Criteria:

A. Exclusion Criteria at the level of the participants

* Participants meeting any of the following exclusion criteria are ineligible for the study:

  * Previous gastro-esophageal surgery (e.g., gastric by-pass, sleeve gastrectomy, fundoplication, partial gastrectomy)
  * Allergy to any of the food that will be provided.

B. Exclusion Criteria at the level of the input data

• Ultrasound images (10 sec clips) where the gastric antrum cannot be positively identified.

Outcomes:
To see the overall accuracy of the AI model