moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Prediction of Peritoneal Metastasis for Gastric Cancer Based on Radiomics: a Multi-center Prospective Study
Trial ID: NCT05722275
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2023-01-01
Completion date: 2028-12-31
Sponsor: Chinese Academy of Sciences
Collaborators: Peking University Cancer Hospital & Institute; Zhenjiang First People's Hospital; The First Affiliated Hospital of Zhengzhou University; Nanfang Hospital, Southern Medical University; Guizhou Provincial People's Hospital; Henan Cancer Hospital; Yunnan Cancer Hospital; Guangdong Provincial People's Hospital; Guangzhou Medical University; Fujian Medical University Union Hospital; Shanxi Province Cancer Hospital; Sun Yat-sen University; Beihang University; Scientific Institute San Raffaele
Conditions: Gastric Cancer; Peritoneal Metastases
Interventions: Peritoneal metastasis status ascertainment
Locations: Peking University Cancer Hospital & Institute; Fujian Medical University Union Hospital; Affiliated Cancer Hospital and Institute of Guangzhou Medical University; Guangdong Provincial People's Hospital; Nanfang Hospital of Southern Medical University; Sun Yat-Sen University Cancer Hospital; Guizhou Provincial People's Hospital; Yunnan Cancer Hospital; Shanxi Province Cancer Hospital; Henan Cancer Hospital; The First Affiliated Hospital of Zhengzhou University; Zhenjiang First People's Hospital; Scientific Institute San Raffaele
Countries: China; China; China; China; China; China; China; China; China; China; China; China; Italy
Enrollment: 400
Age range: 18 Years - None
Sex: ALL

Summary:
Peritoneal metastasis of gastric cancer is difficult to be detected in time, thus delaying treatment. Based on the conventional CT images of gastric cancer, this study plans to develop, improve and validate an intelligent analysis system based on radiomics. By extracting and combining the radiomics features related to peritoneal metastasis of gastric cancer, the intelligent analysis system could predict the risk of peritoneal metastasis, and provide personalized decision suggestions for the treatment of gastric cancer.

Eligibility criteria:
Inclusion Criteria:

* (1) diagnosed advanced gastric cancer (≥cT3) by endoscopy-biopsy pathology, combined with CT and/or endoscopic ultrasound;
* (2) with both enhanced CT and laparoscopy;
* (3) without typical peritoneal metastasis indications in CT (diffuse omental nodules or omental cake, large amount of ascites, obvious irregular thickening with high peritoneal enhancement);
* (4) without other evidence of distant metastasis, and no stage IV features on CT.

Exclusion Criteria:

* (1) previous abdominal surgery;
* (2) previous abdominal malignancies or inflammatory diseases;
* (3) time intervals between CT and laparoscopy longer than 2 weeks;
* (4) CT image artifacts that undermine peritoneal lesion assessment.

Outcomes:
The aera under the receiver operating characteristic curve (AUC) of intelligent analysis system
A Randomized Controlled Trial Comparing Performance of Primary Care Clinicians in the Interpretation of SPIROmetry With or Without Artificial Intelligence Decision Support Software
Trial ID: NCT05933694
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2023-06-27
Completion date: 2024-09-30
Sponsor: Royal Brompton & Harefield NHS Foundation Trust
Collaborators: National Institute for Health Research, United Kingdom
Conditions: Lung Disease
Interventions: Artificial Intelligence-powered Spirometry Interpretation Report
Locations: Royal Brompton & Harefield Hospitals
Countries: United Kingdom
Enrollment: 228
Age range: 18 Years - 99 Years
Sex: ALL

Summary:
To evaluate whether an artificial intelligence decision support software (ArtiQ.Spiro) improves the diagnostic accuracy of spirometry interpreted by primary care clinicians, as measured by Clinician Diagnostic Accuracy (vs Reference Standard).

Eligibility criteria:
Inclusion Criteria:

1. Clinicians working in primary care (for at least 50% of their job plan) in the UK, who refer for or perform spirometry (typically GP, practice nurse)
2. Able to access spirometry traces on study platform
3. Provide written informed consent via study platform

Exclusion Criteria:

1\. Clinicians who have completed specialist training in respiratory medicine and recognised by the General Medical Council with a right to practise as a NHS consultant in respiratory medicine

Outcomes:
Preferred Diagnostic Performance
AI-Enabled ECG Stratification and Timing of Potassium-Lowering Therapy in Patients With Hyperkalemia: A Target Trial Emulation Study
Trial ID: NCT07307729
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-01-01
Completion date: 2026-03-31
Sponsor: National Defense Medical Center, Taiwan
Conditions: Hyperkalaemia
Interventions: Timely treatment; Delayed treatment
Enrollment: 5000
Age range: 18 Years - None
Sex: ALL

Summary:
Hyperkalemia is a common and potentially life-threatening electrolyte disorder, yet there is limited evidence guiding the optimal timing of potassium-lowering therapy in routine clinical practice. Although electrocardiographic (ECG) abnormalities are recommended to inform treatment decisions, such findings are often subtle and difficult to recognize consistently by clinicians.

This study aims to emulate a target trial to evaluate the association between the timing of potassium-lowering therapy (timely versus delayed initiation) and short-term mortality among patients with laboratory-confirmed hyperkalemia presenting to the emergency department. In addition, the study examines whether artificial intelligence-enabled ECG (AI-ECG) stratification identifies patient subgroups that may differentially benefit from earlier treatment.

Using observational electronic health record data from multiple healthcare systems, including publicly available critical care databases and institutionally governed hospital datasets, treatment strategies are compared using causal inference methods designed to approximate randomized assignment. The primary outcome is 90-day all-cause mortality.

The results of this study are intended to inform clinical decision-making regarding treatment timing in hyperkalemia and to evaluate the potential role of AI-ECG as a risk stratification tool in real-world settings.

Eligibility criteria:
Inclusion Criteria:

1. patients visit to emergency department
2. serum potassium ≥5.5 mEq/L
3. had a standard 12-lead ECG performed within 1 hour of the qualifying potassium measurement

Exclusion Criteria:

1. potassium-lowering therapy administered before the index blood draw
2. no potassium-lowering therapy initiated within 4 hours after the index draw
3. duplicate encounters
4. missing key covariates

Outcomes:
All-cause mortality
Rapid Research in Diagnostics Development for Tuberculosis Network (R2D2 Kids) and Assessing Diagnostics At Point-of-care for Tuberculosis in Children (ADAPT for Kids)
Trial ID: NCT05989802
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-01-26
Completion date: 2026-09-01
Sponsor: University of California, San Francisco
Collaborators: Instituto Nacional de Saúde, Mozambique; National Referral Hospital; University of Cape Town; Johns Hopkins University; Elizabeth Glaser Pediatric AIDS Foundation; National Institute of Allergy and Infectious Diseases (NIAID); University of California, Irvine
Conditions: Tuberculosis; Diagnostics; Global Health
Interventions: Oral swab molecular testing; Automated Cough Sound Analysis; Automated Lung Sound Analysis; Chest X Ray Computer Aided Detection
Locations: Instituto Nacional de Saúde; Dora Nginza Hospital; Mulago National Referral Hospital
Countries: Mozambique; South Africa; Uganda
Enrollment: 2100
Age range: None - 65 Years
Sex: ALL

Summary:
Every year there are an estimated 230,000 childhood deaths from TB. There is an urgent need for novel tests for TB diagnosis in children under 15 years. The Rapid Research in Diagnostics Development for TB Network (R2D2 Kids) and the Assessing Diagnostics at Point-of-care for Tuberculosis in children (ADAPT for Kids) studies seek to reduce the burden of TB worldwide by evaluating faster, simpler, and less expensive TB triage and diagnostic tests for use in children.

Eligibility criteria:
Participant eligibility criteria:

Participants will include children (age \<15 years) who present to care with:

A. 2 or more of the following:

* Unexplained cough for any duration
* TB contact or tuberculin skin test or interferon gamma release assay positive
* Abnormal chest X-ray (any abnormality) OR

B. Any one of criteria A AND any one of the following:

* Unexplained weight loss OR unexplained failure to thrive OR Severe Acute Malnutrition
* Unexplained fever ≥2 weeks
* Unexplained lethargy or reduced playfulness ≥2 weeks

The study will exclude participants who:

1. Completed preventive or active TB treatment within the past 12 months (to increase TB prevalence and reduce false-positive results, respectively);
2. Have taken any medication with anti-mycobacterial activity for any reason for greater than 3 days at the time of enrollment (to reduce false-negatives);
3. Are unable to return for follow-up visits; or
4. Whose parents/guardians are unwilling to provide informed consent or who are unwilling to provide assent if applicable (age determined by local IRB)

Assessment of the usability of novel TB tests:

The study will also include health workers at each clinical site who are 1) aged ≥18 years and 2) involved in routine TB testing (collecting specimens for or performing TB tests). Personnel who are unwilling to provide informed consent will be excluded.

Outcomes:
Proportion with positive index test result among participants with tuberculosis (TB); Proportion with negative index test result among participants without tuberculosis (TB)
Non-invasive Evaluation of Lymphoma Patients Based on Artificial Intelligence and PET/MRI
Trial ID: NCT04154228
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2019-12-10
Completion date: 2021-12-10
Sponsor: Tel-Aviv Sourasky Medical Center
Conditions: Lymphoma; Non Hodgkin Lymphoma; Follicular Lymphoma
Interventions: PET/MR scan
Enrollment: 100
Age range: 18 Years - 120 Years
Sex: ALL

Summary:
18F-FDG PET/MR imaging protocol integrating advanced MR vascular imaging sequences, along with computerized quantitative methods for data analysis, is expected to serve as an objective tool for assessment of lymphoma patients. The aim of this prospective study is to develop an automatic artificial intelligence-based tool for the assessment of early response to treatment and evaluation of residual masses in patients with lymphoma. Specific objectives are:

1. To evaluate the added value of 18F-FDG PET/MRI compared with PET/CT in imaging lymphoma.
2. To optimize PET/MR imaging protocol for lymphoma assessment.
3. To develop an automated tool for staging patients with lymphoma.
4. To develop an automated method for early prediction of response to therapy and prognosis in patients with lymphoma.
5. To develop an automated non-invasive tool for discriminating benign from active residual masses at end of treatment in patients with lymphoma.

Eligibility criteria:
Inclusion Criteria:

1. patients with newly diagnosed hodgkin's, aggressive non-hodgkin's and follicular lymphoma (for whom the PET/CT is the imaging modality of choice)/
2. Patients aged 18 years or older of both sexes.
3. Patients treated at Tel-Aviv Sourasky Medical center.

Exclusion Criteria:

1. pregnancy,
2. contraindication to MRI or to intravenous gadolinium injection.

Outcomes:
Patients that preform 18F-FDG PET/MRI and the routinely PET/CT
Adherence to Inhaled Therapy - a Globally Overlooked Problem in Patients With Chronic Obstructive Pulmonary Disease and Bronchial Asthma
Trial ID: NCT07183657
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-06-03
Completion date: 2028-12-31
Sponsor: Charles University, Czech Republic
Collaborators: Ministry of Health, Czech Republic; University Hospital Hradec Kralove; University of Hradec Hralove
Conditions: Chronic Obstructive Pulmonary Disease (COPD); Bronchial Asthma
Interventions: Education in inhalation technique
Locations: University Hospital Hradec Králové
Countries: Czechia
Enrollment: 1000
Age range: 18 Years - None
Sex: ALL

Summary:
Comprehensive assessment and interventions to promote adherence to inhaled therapy in patients with bronchial asthma and chronic obstructive pulmonary disease involving health care professionals caring for these patients in different settings and using the Five Steps Assessment tool and artificial intelligence methods and tools.

Eligibility criteria:
PATIENTS

Inclusion Criteria:

1. signed informed consent,
2. age ≥ 18 years,
3. previously diagnosed BA/COPD meeting GOLD 2024 and GINA 2024 diagnostic criteria,
4. ability to cooperate,
5. at least 8 weeks without exacerbation,
6. use of a particular inhaler for at least 3 months.

Exclusion Criteria:

1. pre-terminal/terminal stage of any disease,
2. uncooperative patient,
3. significant limitation of mobility or cognitive functions.

HEALTHCARE PROFESSIONALS

Inclusion Criteria:

1. signed informed consent,
2. completed education in medicine, pharmacy, or nursing,
3. more than one year of working experience,
4. care for patients taking inhaled therapy for BA/COPD.

Outcomes:
Adherence to inhaled treatment measured by Five Steps Assessment (FSA).; Adherence to inhaled treatment measured by the Medication Adherence Report Scale (MARS).; Critical knowledge measured by our own questionnaire in healthcare professionals caring for patients with COPD and bronchial asthma.; Critical skills related to inhalation techniques measured by FSA in healthcare professionals caring for patients with COPD and bronchial asthma.
Efficacy of an Artificial Intelligence System for Lesion Detection and Characterization (CADe and CADx) During Colorectal Cancer Screening Colonoscopies: A Randomized Clinical Trial
Trial ID: NCT07125300
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2023-10-01
Completion date: 2025-02-28
Sponsor: Javier Santos Fernández
Conditions: Colorectal Neoplasms; Colorectal Cancer; Adenoma Colon Polyp
Interventions: Artificial Intelligence-Assisted Colonoscopy
Locations: University Care Complex of Palencia
Countries: Spain
Enrollment: 368
Age range: 50 Years - 74 Years
Sex: ALL

Summary:
The goal of this clinical trial is to determine whether using artificial intelligence (AI) can improve the detection and characterization of abnormal growths (polyps) during colonoscopy in adults aged 50 to 74 years who are undergoing colorectal cancer screening after a positive stool test.

The main questions it aims to answer are:

* Does AI assistance increase the detection of adenomas or advanced colorectal neoplasia?
* Does AI provide more accurate optical diagnosis of polyps compared to standard assessment by endoscopists?

Researchers will compare colonoscopies performed with AI assistance (using the CAD EYE™ system) to standard colonoscopies without AI to see if AI improves detection rates or diagnostic accuracy.

Participants will:

* Undergo a screening colonoscopy after a positive fecal immunochemical test (FIT)
* Be randomly assigned to either an AI-assisted or standard colonoscopy group
* Have any detected polyps removed and analyzed
* Receive either AI-based or physician-based optical diagnosis of polyps during the procedure

This study helps evaluate whether AI can make colonoscopies more effective and reduce unnecessary polyp removals.

Eligibility criteria:
Inclusion Criteria:

* Adults aged 50 to 74 years
* Positive fecal immunochemical test (FIT) result (≥100 ng/mL)
* Scheduled for screening colonoscopy within a population-based colorectal cancer screening program
* Able and willing to provide written informed consent

Exclusion Criteria:

* Incomplete colonoscopy (e.g., failure to reach the cecum)
* Inadequate bowel preparation
* History of colorectal surgery
* Inability to provide informed consent

Outcomes:
To compare the adenoma detection rate (ADR) and advanced colorectal neoplasia detection rate between conventional colonoscopy and AI-assisted colonoscopy.
A Study to Evaluate the Performance and Potential Benefits of an Assistive Artificial Intelligence Device ScanNav Anatomy Peripheral Nerve Block - US V1.0 for Ultrasound-guided Regional Anesthesia
Trial ID: NCT04918693
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2021-05-16
Completion date: 2021-11-30
Sponsor: IntelligentUltrasound Limited
Conditions: Ultrasound Imaging of Anatomical Structures
Interventions: Ultrasound scanning
Locations: Oregon Health & Science University
Countries: United States
Enrollment: 34
Age range: 18 Years - 70 Years
Sex: ALL

Summary:
This is a single-center, prospective study to be undertaken at University of Oregon, Portland, USA. The aim is to provide data on the clinical performance of ScanNav Anatomy PNB for identification of anatomical structures during UGRA scanning, in particular:

1. Assess the benefits of the device to intended users when supervising a trainee who is performing UGRA scanning.
2. Assess the benefits of the device when intended users perform UGRA scanning.
3. Assess risk mitigation by the intended users when performing UGRA scanning.

Eligibility criteria:
Participant Inclusion criteria

1. Completed advanced training (e.g., fellowship) in or hold a qualification related to UGRA
2. Regularly delivers direct clinical care using UGRA, including for 'awake' surgery where indicated
3. Member of a relevant professional society (e.g., ASA, ASRA, ESRA, RA-UK)
4. Regularly teach UGRA in the course of their clinical work, including advanced techniques where indicated

Participant exclusion criteria

1. Unwilling or unable to provide informed consent.
2. Involved in development of the ScanNav Anatomy PNB device.

Trainee Inclusion criteria

1. U.S. board-eligible/board-certified health practitioner who holds a qualification licensing to perform UGRA
2. NOT capable or confident of independent UGRA practice

Trainee exclusion criteria

1. Unwilling or unable to provide informed consent.
2. Involved in development of the ScanNav Anatomy PNB device.

Models Inclusion Criteria

1. Male or female, at least 18 years of age;
2. Able to comprehend and sign the Informed Consent prior to enrolment in the study.
3. To fit either BMI category: BMI \<30 OR BMI \>=30

Model Exclusion Criteria

1. Aged \<18 years of age or over 60 years or age
2. Unwilling or unable to provide informed consent.
3. BMI\> 39 kg/m2
4. Known pathology of the area to be scanned.

Outcomes:
Obtaining the correct ultrasound view
Exploring the Efficacy of Assistive Artificial Intelligence for Ultrasound Guided Regional Anesthesia in Residency Training
Trial ID: NCT06667401
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2025-03-10
Completion date: 2026-01-01
Sponsor: Medical College of Wisconsin
Conditions: Regional Anesthesia
Interventions: The ScanNav, a novel artificial intelligence device designed to assist in Ultrasound guided regional anesthesia
Locations: Medical College of Wisconsin
Countries: United States
Enrollment: 20
Age range: None - None
Sex: ALL

Summary:
The purpose of this study is to investigate the efficacy of a novel artificial intelligence (AI) device designed to assist in Ultrasound guided regional anesthesia (ScanNav Anatomy Peripheral Nerve Block; ScanNav), in the teaching and training of anesthesiology residents in the subspecialty of regional anesthesia.

Eligibility criteria:
Inclusion Criteria:

* Inclusion criteria include any anesthesia resident with no prior experience with UGRA.

Exclusion Criteria:

* Exclusion criteria include any anesthesia resident who has undergone the regional elective service prior to the inception of the study.

Outcomes:
Qualtric questionaire of participants
EyeMatics - Eye Diseases "Treated" With Interoperable Medical Informatics
Trial ID: NCT06810011
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2024-03-01
Completion date: 2028-02-29
Sponsor: University Hospital Muenster
Collaborators: University Medicine Greifswald; University Medical Center Mainz; University Hospital, Aachen; Klinikum Chemnitz gGmbH; Technische Universität Dresden; University of Leipzig; University Hospital Tuebingen; Deutsche Ophthalmologische Gesellschaft e.V.; Pro Retina Deutschland e.V.
Conditions: Intravitreal Injections
Locations: Klinikum Chemnitz gGmbH; Universitätsklinikum Aachen, AöR; Universitätsmedizin Greifswald; Universität Leipzig; Universität Münster; Universitätsklinikum Tübingen
Countries: Germany; Germany; Germany; Germany; Germany; Germany
Enrollment: 2000
Age range: 18 Years - None
Sex: ALL

Summary:
The overarching goal of EyeMatics is to promote an interoperable, sustainable infrastructure for the integration and sharing of data from clinical research to improve personalized medicine in ophthalmology. To achieve this, EyeMatics bundles the expertise and preliminary work of the four consortia of the Medical Informatics Initiative (MII) and the ophthalmological centers of excellence in Germany. The perspective and concerns of patients will be strongly incorporated into the project work through close cooperation with patient representatives and the inclusion of patient feedback on quality of life and treatment success.

EyeMatics aims to improve the understanding of the therapeutic success of eye diseases that are usually treated with intravitreal surgical medication (IVOM). To this end, an exemplary analysis platform for "real" clinical data will be provided and a dashboard for data visualization and analysis will be developed as a web application. The dashboard is to be introduced at all participating hospital sites for research purposes.

Eligibility criteria:
Inclusion Criteria:

* OPS code: "5-156.9 Injection of drugs into the posterior segment of the eye"

Outcomes:
Medical success
Developing an Artificial Intelligence (AI)-Powered Exposure Therapy Chatbot for Adolescents and Young Adults With Eating Disorders
Trial ID: NCT07237607
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-03-23
Completion date: 2027-06-30
Sponsor: Milton S. Hershey Medical Center
Conditions: Eating Disorders; Anorexia Nervosa; Bulimia Nervosa; Binge-Eating Disorder
Interventions: "Exp-ED" Digital Exposure-Therapy Chatbot app
Locations: Penn State College of Medicine
Countries: United States
Enrollment: 20
Age range: 12 Years - 26 Years
Sex: ALL

Summary:
The goal of this study is to develop and refine an artificial intelligence (AI)-powered Exposure Therapy for Eating Disorders chatbot web-app that effectively engages adolescents and young adults and decreases anxiety about weight gain. Participants will use the web-app as a supplemental therapeutic tool during their first six weeks of treatment in a Partial Hospitalization Program. Participants will complete assessments every two weeks, including: (1) semi-structured interviews about their experience with the chatbot web-app; (2) implementation outcome measures (e.g., feasibility, usability, acceptability); and (3) anxiety about weight gain.

Eligibility criteria:
Inclusion Criteria:

* Age is between or equal to 12-26 years
* Admitted to the Penn State Health Eating Disorder Partial Hospital Program (PHP) or Intensive Outpatient Program (IOP)
* Have a personal smartphone
* Have a diagnosis of anorexia nervosa (AN), bulimia nervosa (BN), binge-eating disorder (BED), or other specified feeding or eating disorder (OSFED)
* Be English speaking

Exclusion Criteria:

* Diagnosis of avoidant/restrictive food intake disorder (ARFID)
* Have acute suicidality
* Diagnosis of an intellectual disability

Outcomes:
Number of times participants use web-app.; Number of minutes participants engage with the web-app.; Percent of eligible Adolescents and Young Adults Consented; System Usability Scale Survey (SUS); Weight Gain Anxiety Questionnaire