moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Artificial Intelligence-Generated Written Communication for Families of Intensive Care Unit Patients - A Pilot Study
Trial ID: NCT06969196
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-07-07
Completion date: 2025-10-20
Sponsor: Rush University Medical Center
Conditions: Communication; ICU
Interventions: Written Communicatoin
Locations: Rush University Medical Center
Countries: United States
Enrollment: 27
Age range: 18 Years - None
Sex: ALL

Summary:
Our research group has developed an approach for providing families of ICU patients with daily written summaries of care as a supplement to traditional verbal communication. Written summaries describe the patient's main ICU problems and management plan and are delivered to families each day. Despite the benefits of written communication to both the family and clinician experience, the main barrier to implementing this communication approach is the time required for clinicians to create a written summary. For the proposed pilot study, the investgators will ask ICU clinicians to identify patients and respective families for whom there has been a challenge with communication. The investigators will ask ICU clinicians to edit AI-generated written summaries for content and clarity before they are delivered to families. The investigators hypothesize that this process will acceptable and feasible for ICU clinicians and families.

Eligibility criteria:
Surrogate Inclusion

* One self-identified adult decision maker for an adult medical ICU patient cared for on the 10W ICU
* Member of the primary ICU team believes the respective patient will require at least three additional days of ICU care
* Member of the primary ICU team has identified at least one communication challenge with the patient's surrogate(s) including but not limited to:

  1. Complex medical issues requiring frequent updates to families
  2. Complex family dynamics (i.e., multiple involved family members)
  3. Increased anxiety/stress of surrogate(s)
  4. Difficult to provide updates to family members because they do not visit ICU regularly or are hard to reach by phone

Surrogate Exclusion

* Surrogate cannot read or understand English
* Surrogate is unwilling or unable to participate in study procedures
* Patient is decisional and refuses to allow surrogate participation

Clinician Inclusion • Advanced practice provider (APP) on the medical ICU team on the 10W ICU

Clinician Exclusion

• None

Outcomes:
Communication Quality (Clinicians); Communication Quality (Families/Surrogates); Acceptability of intervention; Feasibility of intervention
Artificial Intelligence-based Tumor Microenvironment Analysis for Prognosis Prediction in Gallbladder Cancer
Trial ID: NCT07092397
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2022-04-05
Completion date: 2024-09-30
Sponsor: Samsung Medical Center
Conditions: Gallbladder Cancer
Locations: Samsung Medical Center
Countries: South Korea
Enrollment: 266
Age range: None - None
Sex: ALL

Summary:
This retrospective study aims to investigate the prognostic significance of artificial intelligence (AI)-powered analysis of tumor-infiltrating lymphocytes (TILs) and the tumor microenvironment (TME) in patients with resected gallbladder cancer. Using hematoxylin and eosin (H\&E)-stained slides, the study quantifies spatial TME features, including TIL density, tertiary lymphoid structures, and fibroblast density, to identify potential TME-related risk factors associated with survival.

Eligibility criteria:
Inclusion Criteria:

* Patients who underwent curative surgery for gallbladder cancer.

Exclusion Criteria:

* Patients who did not achieve R0 resection.
* Patients with metastatic disease.

Outcomes:
Overall survival
EXoPERT EMERALD: Early Multi-cancer Study of EV's Ramen-AL Linked Diagnosis Clinical Study Protocol
Trial ID: NCT07186296
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-05-09
Completion date: 2026-06-30
Sponsor: EXoPERT
Collaborators: MCRA
Conditions: Breast Cancer; Colon Cancer; Pancreatic Cancer; Ovarian Cancer; Lung Cancer
Interventions: EXoPred
Locations: Tibor Rubin VA Medical Center; City of Hope Duarte Cancer Center; University of Hawaii Cancer Center; Lexington VA Healthcare System; KCTL; John Hopkins University; Albany Stratton VA Medical Center; Bronx Veterans Medical Research Foundation; VA Hudson Valley Healthcare System
Countries: United States; United States; United States; United States; United States; United States; United States; United States; United States
Enrollment: 1400
Age range: 45 Years - None
Sex: ALL

Summary:
Invitro diagnostic test for multiple cancer diagnosis for patients with early-stage cancers by analyzing surface-enhanced Ramen spectroscopy (SERS) profiles of extracellular vesicles (EV) using artificial intelligence.

Eligibility criteria:
Inclusion Criteria:

* Subject aged 45 years or older with a biopsy-proven or clinically suspected primary lung, breast, colorectal, pancreatic, or ovarian cancer, based on objective findings such as radiological, serological, endoscopic, or cytological findings, whose blood was collected prior to any systemic or definitive therapy for the cancer.
* Subjects who are willing and able to provide written informed consent.
* Subjects who are willing and able to comply with the study requirements.

Exclusion Criteria:

* Any history of cancer diagnosed and treated within 5 years prior to the date of consent.
* Subjects with a history of previous cancer treatment via surgical resection, hormonal cancer treatment, chemotherapy, radiotherapy within the past 6 months for recent cancer diagnosis.
* Subjects who have any history of an allogeneic bone marrow, stem cell transplant, or solid organ transplant.
* Subjects who are pregnant or breastfeeding women.
* Subjects who have consented and have undergone treatment in any other cancer related clinical trials withinthe past 6 months.
* Subjects who are currently in active treatment for drug abuse.
* Subjects who have received any treatment related to lung, breast, colorectal, pancreatic, or ovarian nodules, such as hormones prior to entering the study.
* Unsuitable sample for testing due to contamination, hemolysis, etc.

Outcomes:
Test Sufficient Samples; Evaluate Performance
Development of Artificial Intelligence Tools for the Detection of Stress Markers and Consideration of Stress States in the Monitoring of Subjects With Type 1 Diabetes
Trial ID: NCT06985862
Status: SUSPENDED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-09-03
Completion date: 2026-12
Sponsor: Centre d'Etudes et de Recherche pour l'Intensification du Traitement du Diabète
Conditions: Diabetes Type 1
Interventions: Voice recording 4 times a day
Locations: Rennes University Hospital; Strasbourg University Hospital; Angers University Hospital; CERITD (Centre d'Etudes et de Recherches pour l'Intensification du Traitement du Diabète); Grenoble University Hospital
Countries: France; France; France; France; France
Enrollment: 35
Age range: 18 Years - None
Sex: ALL

Summary:
Stress refers to all the reactions of an organism subjected to exogenous or endogenous stress. In the context of diabetes, stress plays a critical role. There are two forms of stress: acute and chronic, both of which can have a significant impact on patients' glycaemic control. Acute stress, if repeated, can cause rapid increases in blood glucose levels, while chronic stress can lead to insulin resistance. It is therefore essential to develop tools for recognising and quantifying stress states specific to patients with diabetes. These tools would provide a better understanding of the role of stress in diabetes management, paving the way for more targeted therapeutic interventions and improving patients' quality of life.

We are currently training algorithms using advanced machine learning and artificial intelligence techniques to recognise and quantify stress states using existing databases, including voice and physiological data. These technological advances will make it possible to identify moments of stress more accurately and provide appropriate responses, thereby contributing to better diabetes management.

The SMART-T1D study is an ancillary study of the EVASTRESS study.

Eligibility criteria:
Inclusion Criteria:

* Patient who has signed the SMART-T1D free and informed consent form
* Patient able to speak and read French

Exclusion Criteria:

* Mute patient.
* Patient with severe speech problems that may prevent voice recordings from being made.

Outcomes:
Voice recording of participants
Assessing the Burden of Tuberculosis Among Pregnant and Postpartum Women: Implications for Integrating Tuberculosis Screening Into Routine Antenatal Care in Guinea-Bissau
Trial ID: NCT07419568
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-02
Completion date: 2027-11
Sponsor: Aarhus University Hospital
Collaborators: Bandim Health Project
Conditions: Tuberculosis (TB); Tuberculosis Infection, Latent; Pregnancy; Tuberculosis Diagnosis; Maternal Health
Interventions: Enhanced protocol
Locations: Projecto Saude de Bandim
Countries: Guinea-Bissau
Enrollment: 720
Age range: 15 Years - None
Sex: FEMALE

Summary:
This study aims to address critical diagnostic and data gaps in tuberculosis (TB) care among pregnant and postpartum women in Guinea-Bissau, a high-burden, resource-limited setting. Recognising that current TB screening during antenatal care (ANC) relies largely on unstructured symptom questions, the study will integrate more systematic and innovative approaches into routine maternal health services.

The project will implement the Bandim TBscore II as a structured triage tool to classify symptom severity and guide referrals. To strengthen diagnostic capacity, two novel tools will be evaluated: stool-based GeneXpert testing (a method recommended in children and explored here as a feasible alternative for pregnant women) and artificial intelligence-powered chest X-ray interpretation software designed to enhance TB detection where radiological expertise is lacking.

The study will also generate comprehensive, population-based data on TB and TB infection (TBI) among pregnant, postpartum women and women of reproductive age in Guinea-Bissau. The results are intended to inform health policy, both locally and in high-income countries, by providing evidence to improve TB screening protocols and care for this vulnerable group. Ultimately, the study seeks to develop scalable strategies that can be replicated across low- and middle-income countries to advance maternal and child health and support global TB eradication efforts.

Eligibility criteria:
Inclusion Criteria:

* Pregnant women ≥ 15 years of age
* Residing in the study
* Attending ANC consultations at health centre in Bandim, Belem or Cuntum
* Must provide informed consent

Exclusion Criteria:

* Pregnant women ≤ 15 years of age
* Inability to provide informed consent
* Residing outside of the study area
* Receives TB treatment at the time of, or a year prior to, enrolment

Outcomes:
Number of active TB cases
Peri-luminal COROnary CTa AI-driven radiOMICS to Identify Vulnerable Patients
Trial ID: NCT06029777
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2023-07-31
Completion date: 2024-09-24
Sponsor: IRCCS San Raffaele
Collaborators: Ministry of Health, Italy
Conditions: Coronary Artery Disease
Locations: IRCCS San Raffaele
Countries: Italy
Enrollment: 2190
Age range: 18 Years - None
Sex: ALL

Summary:
CAD is a leading cause of mortality in Europe. cCTA is recommended to rule out obstructive CAD, but, in most patients, it shows non-obstructive CAD. The management of these patients is unclear due to lack of reproducible quantitative measurement, beyond stenosis severity, capable to assess the risk of disease progression towards developing MACEs. To improve identification and phenotypization of patients at high risk of disease progression, the investigators propose the application of artificial intelligence algorithms to cCTA images to automatically extract periluminal radiomics features to characterize the atherosclerotic process. By leveraging machine-learning empowered radiomics the investigators aim to improve patients' risk stratification in a robust, quantitative and reproducible fashion. By developing a novel quantitative AI based cCTA measure, the investigators expect to provide a risk score capable to identify patients who can benefit of a more aggressive medical treatment and management, thus improving outcome

Eligibility criteria:
Inclusion Criteria:

1. Patients with CT performed for CAD assessment between 2017 and 2019.
2. Follow-up duration of at least 4 years.

Exclusion Criteria:

1. Refusal to participate in the study
2. Age \<18 years old
3. History of previous coronary revascularization
4. Presence of other cardiovascular comorbidities (e.g. inflammatory cardiomyopathy, valvular cardiomyopathy, idiopathic dilated cardiomyopathy, infiltrative cardiomyopathy)

Outcomes:
Composite outcome
Performance Evaluation of Artificial Intelligence Assisted Diabetic Retinopathy Grading in the Leuven University Hospital: Can Technology Improve the Resident?
Trial ID: NCT05260281
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2022-03-01
Completion date: 2022-11-01
Sponsor: Universitaire Ziekenhuizen KU Leuven
Conditions: To Evaluate the Added Value of the Use of Artificial Intelligence in the Diagnosis of Referable Diabetic Retinopathy in a Teaching Hospital Setting
Interventions: MONA algorithm
Enrollment: 139
Age range: 18 Years - None
Sex: ALL

Summary:
To evaluate the added value of the use of artificial intelligence in the diagnosis of referable diabetic retinopathy in a teaching hospital setting

Eligibility criteria:
Inclusion Criteria:

* \- Diagnosis of diabetes mellitus
* Age \> 18 years old
* Patient is capable of giving informed consent
* Fluent in written and oral Dutch, or interpreter present

Exclusion Criteria:

* \- History of treatment for diabetic retinopathy or diabetic macular edema (laser or intravitreal injections)
* Participant is contraindicated for imaging by fundus imaging systems used in the study

Outcomes:
does AI augment diagnostic performance of resident
Analyzing the Link Between Tongue Images and Gastric Cancer Cascade Response Using Artificial Intelligence Techniques
Trial ID: NCT05368636
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2022-06-30
Completion date: 2025-06-30
Sponsor: Shandong University
Conditions: Artificial Intelligence; Tongue; Gastric Cancer
Locations: Qilu hosipital
Countries: China
Enrollment: 4000
Age range: 40 Years - 80 Years
Sex: ALL

Summary:
This study combines artificial intelligence with tongue images, by collating and collecting tongue images and diagnostic and pathological results of gastroscopic diseases, mining and analysing the correlation between tongue images and OLGA, OLGIM stages, Correa sequences and constructing prediction models, to deeply investigate the relationship between tongue images and precancerous diseases, precancerous lesions and gastric cancer.

Eligibility criteria:
Inclusion Criteria:

* Patients between 40 and 80 years of age who are scheduled for gastroscopy.
* Patients all gave their informed consent and signed the informed consent form.

Exclusion Criteria:

* Persons with severe cardiac, cerebral, pulmonary or renal dysfunction or psychiatric disorders who are unable to participate in gastroscopy.
* Patients with previous surgical procedures on the gastrointestinal tract.
* Patients taking bismuth or other staining drugs.

Outcomes:
Sensitivity; Specificity; Positive predictive values(PPV); Negative predictive values(NPV); AUC (95% CI); Accuracy
Randomized Trial of Reinforcement Learning for the Dosing of Warfarin
Trial ID: NCT03962400
Status: WITHDRAWN
Study type: INTERVENTIONAL
Phase: PHASE4
Start date: 2022-01-01
Completion date: 2025-12-31
Sponsor: University of Louisville
Conditions: Clotting Disorder
Interventions: Reinforcement Learning; Heath Care Provider
Enrollment: 0
Age range: 18 Years - 99 Years
Sex: ALL

Summary:
This is a clinical study designed to test the hypothesis that a computer model for dosing warfarin is superior to current clinical practice. Subjects will be randomized to two groups based on how warfarin dose is determined and followed for 6 months. The primary outcome is the percent of the time that the INR is maintained in the effective range.

Eligibility criteria:
Inclusion Criteria:

* Receiving anticoagulation treatment with warfarin.

Exclusion Criteria:

* Pregnancy.

  * History of hemorrhagic cerebrovascular incident.
  * Acquired or inherited hemophilia.
  * Thrombocytopenia (\<100,000 platelets per mm3) on 2 occasions separated by 2 days.
  * Anemia with hemoglobin concentration \< 10 g/dL.
  * Active cancer excluding non-melanoma skin cancers.
  * Active liver disease as documented by prolonged baseline INR ≥ 1.6.
  * Uncontrolled hypertension with 2 readings \>180/110.
  * Recent (\< 2 weeks) neurosurgical procedure.
  * Enrollment in hospice program for any diagnosis.

Outcomes:
Percent in Range
Demonstration Clinical Study of Dry Eye Disease Advantage
Trial ID: NCT06605495
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-09-25
Completion date: 2025-10-01
Sponsor: Xuejing Lu
Conditions: Dry Eye Disease (DED)
Interventions: fumigation with Chinese medicine; Oral herbal medicine; Modern Western Medicine
Enrollment: 4000
Age range: 18 Years - 75 Years
Sex: ALL

Summary:
In this study, dry eye disease (leukoaraiosis) is used as a model disease, and the methodological system for evaluating the clinical efficacy of Chinese medicine for dry eye disease is constructed with the core of "subgrouping dynamic and static parallel group design", so as to carry out a real-world demonstration study of dry eye disease as a dominant disease in Chinese medicine treatment. The purpose of this study is to demonstrate the selection of individualized clinical interventions and the superiority of dynamic diagnosis and treatment using dry eye as a disease model. The study provides a clinical example of a subgroup-based dynamic and static parallel group design for TCM clinical research on "disease-based integrated treatment". Based on the conclusions of the efficacy evaluation study, investigators will provide real-world clinical data of dry eye syndrome for the artificial intelligence prediction of the integrated platform of individualized clinical evaluation and evidence support of Chinese medicine with multi-source data integration.

Eligibility criteria:
Inclusion Criteria:

* Be 18-75 years of age and of any gender
* Meets Western diagnostic criteria for dry eye
* Formed consent and signed the Informed Consent Form

Exclusion Criteria:

* Those who are unable to perform a dry eye examination due to various factors
* Patients with severe diseases of the liver or renal system, or infectious eye diseases
* Those who are allergic to the medications used in this study
* Women who are pregnant or breastfeeding Mental illness or intellectual disability; Participants in clinical trials of other drugs within the last 3 months.

Outcomes:
Chinese Medical Evidence Scale; Ocular Surface Disease Index; Fluorescein Breakup Time
A Prospective Randomized Controlled Trial of AI-box , a Real-time Quality Control System of Magnetic-controlled Capsule Gastroscopy.
Trial ID: NCT04954677
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2021-08-27
Completion date: 2022-08-04
Sponsor: Shandong University
Collaborators: Ancon Technologies Ltd; Shandong Provincial Hospital; Binzhou Medical University
Conditions: Magnet Controlled Capsule Endoscopy
Interventions: AI-box
Locations: Qilu Hospital of Shandong University
Countries: China
Enrollment: 194
Age range: 18 Years - 75 Years
Sex: ALL

Summary:
With the rapid development of artificial intelligence technology, more and more deep learning technology has been applied to medicine. Our research is to develop a set of quality control system for magnetic capsule gastroscope using deep learning technology, and conduct a randomized controlled trial to verify its practical efficiency.

Eligibility criteria:
Inclusion Criteria:

* Consecutive patients aged 18-75 years old who underwent MCCG examination in Qilu Hospital of Shandong University, Shandong Provincial Hospital affilated to Shandong First Medican University, and the Binzhou Medical University Hospital.

Exclusion Criteria:

* People who are allergic to the ingredients prepared in the stomach before examination;
* Patients with astrointestinal bleeding or ulcers, or prior gastrointestinal bleeding or ulcers within the last 24 months;
* Patients with esophageal and gastric tumor diseases that are undergoing active surgical treatment;
* Contraindications to the MCE test, including suspected or known gastrointestinal obstruction, stenosis, fistula, diverticula, etc; presence of gastrointestinal obstruction symptoms such as pain or dysphagia;
* Prior gastrointestinal tract or abdominal surgery other than simple procedures which would not change the gastrointestinal tract anatomy, such as polyp removal, cholecystectomy or appendectomy;
* Inoperative conditions or refusal to undergo abdominal surgery if required (ie, if the capsule will not pass and cannot be removed by endoscopy);
* A cardiac pacemaker is installed in the body, except when the pacemaker is a new MRE-compatible product;
* Electronic devices such as cochlear implant, magnetic metal drug infusion pump, nerve stimulator and magnetic metal foreign body are implanted in vivo;
* Planned MRI examination before capsule endoscopy discharge;
* Pregnant women;
* Patients with deglutition disorders or gastric emptizing disorders.

Outcomes:
Mean inspection completeness