moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Evaluation of Axillary Lymph Node Metastasis Status of Breast Cancer Based on Pathological Images and Virtual Staining
Trial ID: NCT06486155
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2024-08
Completion date: 2025-12
Sponsor: Yunnan Cancer Hospital
Collaborators: Affiliated Cancer Hospital of Shantou University Medical College
Conditions: Virtual Staining; Lymph Node Metastasis; Breast Cancer; Digital Pathology
Enrollment: 2200
Age range: 18 Years - 75 Years
Sex: ALL

Summary:
The goal of this observational study is to develop an artificial intelligence model to transform unstained lymph node tissue slice images directly into stained images. The main questions it aims to answer are:

Can the virtual staining model generate hematoxylin and eosin (H\&E) and immunohistochemistry (IHC) images suitable for clinical diagnosis from unstained paraffin-embedded lymph node slice images, including those from breast axillary lymph nodes and other tumor lymph nodes?

Can the virtual staining model generate H\&E and IHC images suitable for clinical diagnosis from unstained frozen sentinel lymph node slice images from breast cancer patients?

Researchers will retrospectively collect paraffin-embedded lymph node slices from tumor patients and prospectively collect frozen sentinel lymph node slices from breast cancer patients.

Eligibility criteria:
Inclusion Criteria:

Part 1:

Female patients aged 18-75 with breast cancer; Undergoing surgical excision of breast cancer and sentinel lymph node biopsy/axillary lymph node dissection; Lymph nodes with clear postoperative paraffin pathological results.

Part 2:

Patients aged 18-75 with one of the following cancers: thyroid, lung, esophagus, stomach, colorectal, prostate, bladder, or cervix; Undergoing surgical resection of lymph nodes; Lymph nodes with clear postoperative paraffin pathological results.

Part 3:

Female patients aged 18-75 with breast cancer; Undergoing surgical excision of breast cancer and sentinel lymph node biopsy; Sentinel lymph nodes with clear postoperative paraffin pathological results.

Exclusion Criteria:

Part 1 / Part 2:

Lymph node diagnosis is missing; Absence of lymph node component in the slice.

Part 3:

Sentinel lymph node diagnosis is missing; Absence of lymph node component in the frozen slice.

Outcomes:
Lymph node metastasis status; Accuracy, Sensitivity, Specificity，Area under the curve，; Positive predictive value，Negative predictive value
Effectiveness and Implementation of a Community-based Health Coach-led Artificial Intelligence (AI)-Powered Digital Self-Regulation Program for Individuals With Metabolic Syndrome (MetS)
Trial ID: NCT07410325
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-01-01
Completion date: 2027-01
Sponsor: National University Health System, Singapore
Conditions: Metabolic Syndrome (MetS); Metabolic Syndrome
Interventions: LIGHTER-MetS
Locations: National University of Singapore
Countries: Singapore
Enrollment: 316
Age range: 21 Years - 65 Years
Sex: ALL

Summary:
This study aims to evaluate the effectiveness of the Lifestyle Intervention for Gentle, Healthy Transformation and Enhanced Weight Reduction-metabolic syndrome (LIGHTER-MetS) program on dietary self-regulation and cardiovascular risk among individuals with metabolic syndrome (MetS). The LIGHTER-MetS program, a community-based, grassroots-led initiative, integrates health coaching with the eTRIP© app to promote sustainable lifestyle changes focusing on diet, exercise, and emotion regulation.

Eligibility criteria:
Inclusion Criteria:

1. are between 21 to 65 years of age;
2. have a waist circumference ≥90 cm in men and ≥80 cm in women;
3. have 2 or more of the following to be classified as having metabolic syndrome:

   * triglyceride level of ≥1.7 mmol/L or on specific treatment for this lipid abnormality;
   * high density lipoprotein (HDL) cholesterol \<1.03 mmol/L in men, and \<1.29 mmol/L in women, or on specific treatment for this lipid abnormality;
   * blood pressure of ≥130/85 mmHg or on treatment of previously diagnosed hypertension;
   * fasting blood glucose level of ≥ 5.6 mmol/L or previously diagnosed type 2 diabetes;
4. are not receiving other structured lifestyle modification programs;
5. consent to audio-recording;
6. English speaking and literate.

Exclusion Criteria:

1. underwent or are scheduled for metabolic surgery within 1 year;
2. are pregnant or planning for pregnancy at the time of recruitment;
3. are unable to speak English;
4. do not own a smartphone; and
5. are unable to provide informed consent.

Outcomes:
Self regulation of Eating Behaviour Questionnaire (SREBQ); Cardiovascular Risk
Development and Validation of Growth Prediction Model for Ovarian Cancer Organoids Based on Bright Field Image and Deep Learning
Trial ID: NCT06317610
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2022-01-01
Completion date: 2024-05-30
Sponsor: Chongqing University Cancer Hospital
Conditions: Ovarian Cancer
Interventions: growth prediction of ovarian cancer organoids in the frame of bright field image by leveraging AI
Locations: Chongqing Cancer Hospital
Countries: China
Enrollment: 100
Age range: None - None
Sex: FEMALE

Summary:
The present study aims to collect early bright field image of patient-derived organoids with ovarian cancer. By leveraging artificial intelligence, this study will seek to construct and refine algorithms that able to predict growth of ovarian cancer organoids.

Eligibility criteria:
Inclusion Criteria:

* Patients must have histologically confirmed diagnosis of epithelial ovarian cancer
* Patients received biopsy or puncture to obtain tumor tissues or malignant effusion
* Patients voluntarily participated in the study and signed informed consent.

Exclusion Criteria:

* Non-epithelial ovarian cancer
* No sufficient amount of tumor tissues or malignant effusion for organoids establishment.

Outcomes:
AUC of growth prediction performance using deep learning model; Accuracy of growth prediction using deep learning model
Development of an Artificial Intelligence Model in Lung Cancer Screening for the Diagnosis of Lung Nodules and Risk Stratification in Subjects With Occupational and/or Smoking Exposure
Trial ID: NCT06444373
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2022-12-02
Completion date: 2024-05-30
Sponsor: Scientific Institute San Raffaele
Conditions: Lung Cancer Screening; Asbestos
Locations: IRCCS San Raffaele Scientific Institute
Countries: Italy
Enrollment: 728
Age range: 50 Years - None
Sex: ALL

Summary:
Single-center, non-profit, observational, retrospective study of collection of clinical and amnestic data and images to create, implement and develop a pilot model of an integrated virtual platform.

Eligibility criteria:
Inclusion Criteria:

* Age \&gt; 50 years;
* smokers for at least 20 pack-years (20 cigarettes a day for 20 years) or former heavy smokers if they quit less than 15 years ago;
* and/or previous professional exposure to asbestos;
* absence of lung cancer symptoms;
* who performed lung cancer screening after the year 2000 upon approval of the study by the relevant Etical Committee

Exclusion Criteria:

* Age \&lt; 50 years
* never smokers
* lung cancer symptoms

Outcomes:
AIM 1 Pilot deep learning model
Pivotal Trial of Automated AI-based System for Early Diagnosis of Diabetic Retinopathy Using Retinal Color Imaging
Trial ID: NCT07151001
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-01-01
Completion date: 2027-07-31
Sponsor: iHealthScreen Inc
Collaborators: National Institutes of Health (NIH)
Conditions: Diabetes; Diabetic Retinopathy
Interventions: No intervention
Locations: iHealthScreen Inc.
Countries: United States
Enrollment: 922
Age range: 22 Years - None
Sex: ALL

Summary:
In this pivotal trial, we aim to perform a prospective study to find the efficacy of iPredict-DR, an artificial intelligence (AI) based software tool on early diagnosis of Diabetic Retinopathy (DR) in the primary care and endocrinology clinics. DR is one of the leading causes of blindness in the United States and other developed countries. Every individual with diabetes is at risk of DR. It does not show any symptoms until the disease is progressed to advanced stages. If the disease is caught at an early stage, it can be prevented, managed, or treated effectively. Currently, screening for DR is done by the Ophthalmologists, which is limited to areas with limited availability. This is also time-consuming and expensive. All of these can be complemented by automated screening and set up the screening in the primary care clinics.

Eligibility criteria:
Inclusion Criteria:

* Age of Subjects: Patients ≥ 22 years of age.
* Gender of Subjects: Both males and females will be invited to participate.
* Subjects with diabetes (A1C level ≥ 6.5).
* Subjects must be willing and are able to comply with clinic visit, understand the study-related procedures/provisions, and provide signed informed consent.

Exclusion Criteria:

* Unable to understand the study, Our unable to or unwilling to sign the informed consent
* Previously diagnosed with macular edema, any form of diabetic retinopathy, radiation retinopathy, or retinal vein occlusion
* participants who are experiencing persistent vision loss, blurred vision, or other vision problems that should be evaluated by an eye care provider
* subjects whose retinal images were used in training, validating, or developing the device
* Currently participating in another investigational eye study or actively receiving investigational product for DR or DME.
* A condition that, in the opinion of the investigator, would preclude participation in the study;
* Contraindicated for imaging by fundus imaging systems used in the study because of hypersensitivity to light, recently underwent photodynamic therapy, or was taking medication that causes photosensitivity.

Outcomes:
mtmDR detected (Referable DR) OR mtmDR not detected (non-referable DR); The accuracy of the iPredict-DR software developed by iHealthScreen system in early diagnosis of DR using color retinal photos vs. that of human expert graders
Validity, Reliability and Feasibility of an Automated Photographic Measurement/Assessment of Food Intake in the Hospitalized Elderly
Trial ID: NCT03650686
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2018-05-03
Completion date: 2019-12
Sponsor: Centre Hospitalier Universitaire Dijon
Conditions: Elderly People; Food Intake Measurement
Interventions: Food weighing (gold standard); Automated photographic method (method to be evaluated); Semi-quantitative method (routine method)
Locations: Chu Dijon Bourogne
Countries: France
Enrollment: 70
Age range: 18 Years - None
Sex: ALL

Summary:
Malnutrition affects 50% to 70% of hospitalized elderly people, and is all the more worrying in the elderly because of its clinical impact. A measurement of food consumption is essential to recognize needs, monitor the nutritional status of the elderly in hospital and implement specific therapeutic action such as supplements or an increase in energy-protein to combat malnutrition or the risk of malnourishment. Unfortunately, this measure is rarely done effectively in practice, keeping the patient in nutritional deficit, contributing to a risk of increased morbidity and mortality.

Although weighing food intake is the reference method, it is a routine burden for healthcare teams. To overcome these constraints in hospital environments, intake is estimated by food readings over three consecutive days using a semi-quantitative method. It should be noted that this method remains complex, imprecise and reserved only for the most malnourished patients. In recent years, the development of photographic methods has become an interesting alternative to the measurement by weight. Based on photographs taken before and after the meal in order to deduce what is actually ingested, these methods obtain results comparable to the weighing method, though there is still a number of limitations (need for human intervention, constraint to have standardized menus in weight and lack of nutritional management adapted to patients). To overcome these limitations, an automated photographic method based on modern techniques for automatic processing of 2D and 3D images coupled with techniques derived from artificial intelligence has recently been developed in the investigator's unit, but has not yet been validated.

The originality and innovation of this project lies in the automated analysis of the photos taken and the conversion into percentage of remaining food thanks to the design of algorithms for image preprocessing and neural classification by a 2D and 3D software (patent pending).

Eligibility criteria:
Inclusion Criteria:

* patient who has given oral consent to participate
* adult patient
* inpatient geriatric rehabilitation follow-up care (SSRG) and acute geriatric units
* patient eating alone or with help

Exclusion Criteria:

* patient with enteral or parenteral nutrition
* patient not affiliated to a social security scheme
* end-of-life patient or palliative care

Outcomes:
Average percentage quantification of ingesta
A Liquid Biopsy to Screen for Barrett's Esophagus, Dysplasia, and Esophageal Adenocarcinoma: the EMERALD Multi-center Study.
Trial ID: NCT06381583
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2023-04-15
Completion date: 2024-04-24
Sponsor: City of Hope Medical Center
Conditions: Esophagus Cancer; Esophageal Cancer Stage; Esophageal Cancer; Esophageal Neoplasms; Esophagus Adenocarcinoma; Esophagus, Barrett; Barrett Esophagus; Barrett Adenocarcinoma; Barrett Epithelium; Barretts Esophagus With Dysplasia; Barrett's Esophagus Without Dysplasia; Barretts Esophagus With High Grade Dysplasia; Barretts Esophagus With Low Grade Dysplasia; Barrett Esophagus, Long-Segment; Barrett's Esophagus With Dysplasia, Unspecified; Barrett's Esophagus With Esophagitis; Gastroesophageal Reflux; Reflux Disease; Esophageal Adenocarcinoma; Esophageal Dysplasia; Esophageal Neoplasms Malignant
Interventions: EMERALD (Esophageal MicroRNAs for BaRRett's esophagus, Adenocarcinoma, and Dysplasia)
Locations: City of Hope Medical Center
Countries: United States
Enrollment: 658
Age range: 18 Years - None
Sex: ALL

Summary:
This study aims to develop a highly sensitive, specific, and cost-effective blood assay for the early detection of esophageal adenocarcinoma and its precursor lesions, using advanced machine learning and state-of-the-art biological analyses.

Eligibility criteria:
Inclusion Criteria:

* All individuals included in the study need to have had an endoscopic evaluation at the time of blood sampling.
* Received standard diagnostic and staging (as necessary) procedures as per local guidelines, and at least one sample was drawn before receiving any curative-intent treatment.
* Received standard pathological and endoscopic diagnosis and assessment for cohort assignment.

Exclusion Criteria:

* Lack of written informed consent.
* Short segment Barrett's esophagus with no evidence of dysplasia
* Ultra-short segment Barrett's esophagus with no evidence of dysplasia

Outcomes:
Sensitivity
The Combined Usage of Clinical Physiological Finding in Management of COVID 19 Patients
Trial ID: NCT07504341
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2026-04
Completion date: 2027-03-31
Sponsor: St. Martin De Porress Hospital
Conditions: COVID-19 (Coronavirus Disease 2019)
Interventions: physiological parameters
Locations: St. Martin De Porres Hospital; St. Martin De Porres Hospital
Countries: Taiwan; Taiwan
Enrollment: 15000
Age range: None - None
Sex: ALL

Summary:
This study utilizes physiological indicators of COVID-19 patients as tools for assessing, tracking, worsening, and improving the clinical symptoms of COVID-19 patients. This study employs simple and easy-to-use assessment methods, which can be further aided by artificial intelligence technology to facilitate early response and medication.

Eligibility criteria:
Inclusion Criteria:

* Reviewing the patient's medical history, the primary focus was on hospital records and existing physiological parameters such as blood pressure, heart rate, respiratory rate, and level of consciousness. The patient received treatment at the hospital between May 2022 and March 2027

Exclusion Criteria:

* Excluding patients with incomplete clinical data or missing information in their medical records.

Outcomes:
Shock index(SI) in the management of COVID -19; Mordified shock index(MSI) in the management of COVID -19; Age shock index(Age SI) in the management of COVID -19; Respiratory adjusted shock index (RASI) in the management of COVID-19
Expanded Studies of Risk Factors for Pulmonary-, Liver-, and Cardiovascular Disease in the SCAPIS 2 Stockholm Reexamination
Trial ID: NCT07476703
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2024-04-08
Completion date: 2026-08-30
Sponsor: Danderyd Hospital
Collaborators: Region Stockholm; Karolinska Institutet; Göteborg University
Conditions: Coronary Artery Disease; Arterial Stiffness, Blood Pressure; AORTIC VALVE DISEASES; Mitral Valve Stenosis and/or Insufficiency; Heart Failure; Pulmonary Disease, Chronic Obstructive (COPD); Steatohepatitis; Inflammation Biomarkers; Lipid Profile; Oral Microbiota; MASLD - Metabolic Dysfunction-Associated Steatotic Liver Disease; Metabolic Associated-dysfunction Steatohepatitis (MASH)
Interventions: Coronary computer tomography (CCTA); Echocardiograhy and aortic ultrasound; Liver elastography; Arterial stiffness
Locations: Cardiovascular Research Lab, Dept Cardiology, Danderyd University Hospital
Countries: Sweden
Enrollment: 1400
Age range: 56 Years - 74 Years
Sex: ALL

Summary:
The Swedish CArdioPulmonary bioImage Study (SCAPIS) is a unique, large-scale national research initiative involving 30,000 randomly selected individuals aged 50-64, recruited between 2014 and 2018. The study is a collaborative effort among six university hospitals across Sweden. A follow-up study, SCAPIS 2, is conducted for half of the original participants. In Stockholm, 2,500 individuals will be re-examined at Danderyd University Hospital and Karolinska Institutet.

SCAPIS 2 includes a core set of examinations involving blood sampling, questionnaires, and imaging. In addition to these, complementary local investigations are conducted to enable more detailed research questions. This protocol describes the additional studies conducted in the Stockholm cohort. All complementary assessments aim to identify risk factors for current and future lung, liver, and cardiovascular diseases.:

EXTENDED SAMPLING: Saliva and Blood Samples with Blood Cell Isolation. EXTENDED QUESTIONNAIRES: Dyspnea, Sleep, Respiratory Infections, and Dental Health.

EXTENDED IMAGING AND PHYSIOLOGICAL MEASUREMENTS Cardiac Ultrasound and Abdominal Aortic Measurements. Liver Elastography. Vascular Stiffness by cuff-based pulse wave analysis and Photoplethysmography (PPG). Valvular and Vascular Calcification by CT imaging.

Eligibility criteria:
Inclusion Criteria: Subjects already included in the main / general SCAPIS reexamination study in Stockholm.

Exclusion Criteria: Inability to provide consent.

Outcomes:
Diagnostic Performance of Machine Learning Analysis of Finger Photoplethysmography for Detection of Coronary Artery Disease (CAD-RADS ≥2); Long-Term Incident Major Adverse Renal and Cardiovascular Events (MARCE); Immune Activation Biomarkers and Prevalent Atherosclerotic Cardiovascular Disease; Lipid Mediator Profiles and Prevalent Atherosclerotic Cardiovascular Disease; Correlation Between Oral Microbiota Composition and Prevalent Coronary Artery Disease; Prevalence of Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD); Prevalence of Abdominal Aortic Aneurysm
The Effect of Artificial Intelligence-assisted Visual Case Analysis on Nursing Students' Knowledge and Attitudes Towards Pressure Injury Prevention: A Single-blind Randomized Controlled Trial
Trial ID: NCT06978283
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-05-09
Completion date: 2025-07-10
Sponsor: Çankırı Karatekin University
Conditions: Artificial Intelligence (AI); Visual Case Analysis; Nursing Students; Pressure Injury Prevention; A Single-blind Randomized Controlled Trial
Interventions: AI-assisted case analysis; Traditional case analysis
Locations: Cankiri Karatekin University. Faculty of Health Sciences.
Countries: Turkey (Türkiye)
Enrollment: 84
Age range: 18 Years - 24 Years
Sex: ALL

Summary:
The goal of this clinical trial is to examine the effect of artificial intelligence-assisted visual case analysis on nursing students' knowledge and attitudes towards pressure injury prevention. The main hypotheses it aims to answer are:

* AI-assisted visual case analysis affects nursing students' knowledge about pressure injury prevention.
* AI-assisted visual case analysis affects nursing students' attitudes toward pressure injury prevention.

Researchers will compare AI-assisted case analysis to conventional case analysis to see if it affected nursing students' knowledge and attitudes about pressure injury prevention.

Participants,

* The case group will shown an AI-supported presentation explaining the pressure injury prevention case. The control group will given a classic case analysis.
* At the end of the case, the PUPKAI and APuP scales will administered to the students in written form in the classroom.

Eligibility criteria:
Inclusion Criteria:

* Taking the Fundamentals of Nursing II course for the first time
* Not working as a nurse in a health institution.
* Volunteering to participate in the study.

Exclusion Criteria:

* Having previously received patient pressure injury prevention training;
* Having repeated the Fundamentals of Nursing II course;
* Having difficulty speaking and understanding Turkish;
* Exceeding the course attendance time
* Not attending pressure injury prevention training and evaluation
* Filling in incomplete data
* Graduating from a health-related high school or university.

Outcomes:
The Pressure Ulcer Prevention Knowledge Assessment Instrument (PUPKAI)