moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Prospective Observational Study for Breast Microcalcifications' Classification With Artificial Intelligence Techniques
Trial ID: NCT05767424
Status: ACTIVE_NOT_RECRUITING
Study type: OBSERVATIONAL
Start date: 2022-07-22
Completion date: 2028-07-25
Sponsor: Istituti Clinici Scientifici Maugeri SpA
Conditions: Breast Cancer; Microcalcification
Locations: Istituti Clinici Scientifici Maugeri SpA
Countries: Italy
Enrollment: 1426
Age range: 18 Years - 88 Years
Sex: FEMALE

Summary:
Breast microcalcifications are a common mammographic finding. Microcalcifications are considered suspicious signs of breast cancer and a breast biopsy is required, however, cancer is diagnosed in only a few patients. Reducing unnecessary biopsies and rapid characterization of breast microcalcifications are unmet clinical needs. This study intends to implement a classification method for breast microcalcifications (as begnin or malign) with Artificial Intelligence techniques on mammographic images, evaluating the diagnostic performance (accuracy) of this approach. Another aim is the development of a diagnostic tool able to determining in-situ the biomolecular characteristics of microcalcifications. Raman spectroscopy (RS) is a highly specific method from the biomolecular point of view and it is able to explore molecular composition of a given sample through its direct irradiation (through laser light) and the simultaneous acquisition of emission signals. RS information could be combined togheter with imaging features to implement an AI model for the combined classification of breast microcalcifications

Eligibility criteria:
Inclusion Criteria:

* Female subjects;
* Age between 18 and 88 years;
* Detection of microcalcifications on clinical and screening mammography with or without indication for histological assessment by biopsy;
* Subjects who agree to participate in the study by signing and dating the Informed Consent form

Exclusion Criteria:

* Personal history of breast cancer

Outcomes:
Artificial Intellicence method for classification
TACTILE : Validation of a Teleconsultation-Based Predictive Score for the Diagnosis of Acute Appendicitis in Children Using Artificial Intelligence Methods
Trial ID: NCT07479849
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-06
Completion date: 2029-06
Sponsor: University Hospital, Angers
Conditions: Acute Appendicitis
Interventions: Score-based appendicitis risk classification in teleconsultation
Locations: Angers, University Hospital
Countries: France
Enrollment: 1500
Age range: 3 Years - 16 Years
Sex: ALL

Summary:
Acute appendicitis is the most common abdominal surgical emergency in children and a frequent cause of pediatric emergency department visits. Diagnosis may be challenging due to variable clinical presentations and overlapping symptoms with other causes of abdominal pain. The increasing use of telemedicine further limits access to direct physical examination.

This study aims to evaluate and validate a predictive clinical score designed for teleconsultation to estimate the probability of acute appendicitis in children. The score is based on symptom history and simple clinical signs assessed remotely with the assistance of a parenton a 10 item checklist. During an emergency visit for acute abdominal pain, voluntary children aged 3 to 16 years and their parents will be guided by a medical student through the checklist in conditions simulating a teleconsultation. The score will be recorded for research purposes only and will not influence clinical management. All participants will subsequently undergo standard medical evaluation and management by an emergency physician, who will independently assess the probability of acute appendicitis based on routine clinical practice. The diagnostic performance of the simulated teleconsultation will then be evaluated using advanced statistical and artificial intelligence-based methods and compared with standard in-person pediatric emergency consultation.

Eligibility criteria:
Inclusion Criteria:

* Children aged 3 to 16 years
* Presentation to the pediatric emergency department with unexplained abdominal pain
* Affiliation with a social security or health insurance system
* Written informed consent from a legal guardian and assent from the child, when applicable

Exclusion Criteria:

* Previous appendectomy
* Hemodynamic instability
* Known medical condition likely to cause chronic or recurrent abdominal pain
* Inability of the child or parents to understand French
* Referral with prior laboratory or imaging investigations
* Participation in the study within the previous 48 hours

Outcomes:
Diagnostic performance of the teleconsultation-based appendicitis score
Validation of an AI-Assisted Mediastinal EUS System for Training Improvement: a Prospective, Multi-Center, Randomized Trial
Trial ID: NCT06790108
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-01-15
Completion date: 2026-06-30
Sponsor: The Third Xiangya Hospital of Central South University
Collaborators: ZhuZhou Central Hospital; The First Affiliated Hospital of University of South China; The First People's Hospital of Chenzhou; the Affiliated Nanhua Hospital, University of South China
Conditions: Mediastinal Disease
Interventions: Artificial intelligence device
Locations: The Third Xiangya Hospital of Central South University
Countries: China
Enrollment: 200
Age range: 18 Years - 80 Years
Sex: ALL

Summary:
The goal of this clinical trial is to verify the auxiliary role of the artificial intelligence (AI) system in mediastinal endoscopic ultrasound (EUS) scans. The main questions it aims to answer are as follows:

1. A comparison of the image recognition accuracy between the AI system and EUS endoscopists.
2. Whether the AI system can improve the quality of mediastinal scans for EUS endoscopists.

Participants will conduct mediastinal EUS with or without the assistance of the AI system.

Eligibility criteria:
Inclusion Criteria:

1\. Age ≥18 years old, \<80 years old 2.Patients who need endoscopic ultrasonography; 3. Agree to participate in this study and sign the informed consent form.

Exclusion Criteria:

Subjects who meet any of the following criteria cannot be selected for this trial:

First. The patient's physical condition does not meet the requirements of conventional endoscopic ultrasonography:

Poor physical condition, including hemoglobin ≤8.0g/dl, severe cardiopulmonary insufficiency, etc. Anesthesia assessment failed Pregnancy or breastfeeding In the acute stage of chemical and corrosive injury, it is very easy to cause perforation Recent acute coronary syndrome or clinically unstable ischemic heart attack Heart disease patients with right-to-left shunt, patients with severe pulmonary hypertension (pulmonary artery pressure\> 90mmHg), patients with uncontrolled systemic hypertension and patients with adult respiratory distress syndrome.

Second. Disagree to participate in this study.

Third. There are other problems that do not meet the requirements of this research or that affect the results of the research:

1. Mediastinal lesions have previously undergone surgery or radiotherapy and chemotherapy;
2. Mental illness, drug addiction, inability to express themselves or other diseases that may affect follow-up.

Outcomes:
The completeness for standard station scanning
Using Mixed-methods Approach to Explore Health Communication in Hospice Out-Patient Settings (HOUSE): From Middle-range Theory to Clinical Practice
Trial ID: NCT06008418
Status: ENROLLING_BY_INVITATION
Study type: INTERVENTIONAL
Phase: NA
Start date: 2023-08-01
Completion date: 2027-06-30
Sponsor: National Taiwan University Hospital
Collaborators: National Science and Technology Council
Conditions: Communication; Palliative Care
Interventions: The LINE Chatbot
Locations: National Taiwan University Hospital
Countries: Taiwan
Enrollment: 184
Age range: 18 Years - None
Sex: ALL

Summary:
This research project aims to investigate health communication in hospice outpatient settings and translate findings into practice by designing and testing a communication aid utilizing health information technology. The specific aims are to: (1) identify the attributes, antecedents, consequences, and implications of the concept of illness invalidation; (2) construct a theoretical framework to describe patient-healthcare provider communication; and (3) based on the theoretical framework, establish a tailored communication aid using health information technology; and investigate its effects on patient outcomes, including (a) satisfaction, (b) communication self-efficacy, (c) illness invalidation, (d) shared-decision making experience, (e) health-related quality of life, and (f) emergency room visits.

Eligibility criteria:
Inclusion Criteria:

1. terminally ill with a life expectancy of 6 months or less
2. 18-year-old or older
3. able to communicate using Mandarin or Taiwanese

Exclusion Criteria:

* Patients who are not able to use electronic devices due to physical, psychological, or cognitive issues will be excluded.

Outcomes:
Communication self-efficacy(before); Symptoms and quality of life(before); Subjects satisfaction; Communication self-efficacy(after); Decision making process; Invalidation experience; Symptoms and quality of life(after); Provider-patient communication dynamic experience
Machine Learning Model to Predict Hospital Length of Stay (HOLS) and Mortality After Discharge in Hospitalized Oncologic Patients [Plantology Database]: a Multicenter Cross-validation Study
Trial ID: NCT05534178
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2020-02-15
Completion date: 2024-03-15
Sponsor: Vall d'Hebron Institute of Oncology
Collaborators: Fundació Institut de Recerca de l'Hospital de la Santa Creu i Sant Pau; Hospital del Mar
Conditions: Solid Tumor; Nutrition Related Neoplasm/Cancer; Comorbidities and Coexisting Conditions; Mental Status Change; Artificial Intelligence; Oncology; Tumor; Quality of Life
Locations: Hospital del Mar; Hospital Universitari Vall d'Hebron; Hospital de la Santa Creu i Sant Pau
Countries: Spain; Spain; Spain
Enrollment: 2500
Age range: 18 Years - None
Sex: ALL

Summary:
The study aims to understand which are the most relevant parameters at admission which may allow to predict the hospital length of stay (HOLS) and mortality after discharge of oncologic hospitalized patients.

This is the first multicentric prospective observational study that tries to understand the complexity of the hospitalized oncologic patients. A comprehensive analysis will be performed with the help of the nutrition, nursery, internal medicine and oncology teams.

Eligibility criteria:
Inclusion Criteria:

* ≥18 years-old.
* Histological cancer confirmation.
* Hospitalization in oncology ward.

Exclusion Criteria:

* \<18 years-old.
* Not histological malignancy confirmed.
* Less than 24 hours in the hospital.

Outcomes:
Predict Mortality; Predict hospital length of stay
Development and Prospective Validation of a Multimodal Fusion Artificial Intelligence Model for Predicting the Efficacy of Neoadjuvant Treatment of Bladder Cancer
Trial ID: NCT06909643
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2022-01-01
Completion date: 2025-12-31
Sponsor: Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
Conditions: Cancer; Neoadjuvant Therapy
Interventions: Artificial intelligence (AI)-based diagnostic model
Locations: Sun Yat-sen Memorial Hospital of Sun Yat-sen University
Countries: China
Enrollment: 550
Age range: None - None
Sex: ALL

Summary:
This study is a multi-center observational study without interventions, including the construction of an AI diagnostic model and retrospective testing of a multi-center cohort. The study participants are bladder cancer patients who have undergone imaging examinations, been pathologically diagnosed, and received neoadjuvant treatment, with complete clinical and pathological data. The study plans to enroll 130 patients from our center, collecting corresponding imaging images, and gathering clinical and genomic data to build and internally validate a multimodal AI model. The model's generalization and robustness will be tested to explore the association between multimodal data and the efficacy of neoadjuvant treatment for bladder cancer. The aim is to assist clinicians in predicting and evaluating the efficacy of neoadjuvant treatment for bladder cancer, with the goal of improving patient diagnosis, treatment outcomes, and prognosis.

Eligibility criteria:
Inclusion Criteria:

* Bladder occupying lesions, with histopathological confirmation of bladder cancer after resection.
* Planned neoadjuvant therapy and radical cystectomy.

Exclusion Criteria:

* Patients who have not undergone standard bladder imaging examinations or have missing imaging or pathological data.
* Patients who have received local treatments (such as interventional embolization) or systemic treatments (such as radiotherapy, chemotherapy, immunotherapy, or targeted therapy).
* Poor quality of imaging or pathological images.

Outcomes:
sensitivity
The Evaluation of Surgical Decisions and Prognosis of the Radiomics and Watson Artificial Intelligence in Patients With Hepatocellular Carcinoma
Trial ID: NCT03917017
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2019-01-01
Completion date: 2024-12-31
Sponsor: Zhujiang Hospital
Conditions: Liver Resection
Interventions: Radiomics and Watson artificial intelligence
Locations: Zhujiang Hospital of Southern Medical University
Countries: China
Enrollment: 100
Age range: 18 Years - 80 Years
Sex: ALL

Summary:
The aim of this study was to evaluate the surgical decisions and prognosis of the radiomics and Watson artificial intelligence in patients with hepatocellular carcinoma.

Eligibility criteria:
Inclusion Criteria:

1. 18 years≤ Age ≤80 years
2. Compling with the diagnosis criteria of complex hepatic carcinoma.
3. Primary hepatic carcinoma without intrahepatic or extrahepatic extensive cancer metastasis, the metastatic hepatic carcinoma whose primary focal has been controlled
4. Preoperative liver function is Child - Pugh grade A or B.
5. The patients are volunteered for the study.

Exclusion Criteria:

1. Patients with mental illness.
2. Patients can't tolerate the operation owe to a variety of basic diseases (such as severe cardiopulmonary insufficiency, renal insufficiency, cachexia and blood system diseases, etc.)
3. The patients refused to take part in the study.
4. There are other co-existed malignant tumors.
5. Benign liver diseases.
6. Indocyanine green allergy

Outcomes:
Postoperative survival
Self-administered Remote Neurological Examination Using Mobile Application in Patients With Brain Tumors
Trial ID: NCT07236840
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2026-02-20
Completion date: 2027-11-05
Sponsor: Tata Memorial Centre
Collaborators: Bhabha Atomic Research Centre
Conditions: CNS Tumor; Artificial Intelligence (AI); Glioma; Digital Health
Interventions: Neurological assessment
Locations: Tata Memorial Centre
Countries: India
Enrollment: 600
Age range: 5 Years - None
Sex: ALL

Summary:
The goal of this observational study is to evaluate the feasibility and accuracy of a self-administered remote neurological examination using the "Iskhaa" mobile application in patients with brain tumors aged above 5 years who are able to follow app-based instructions.

The main questions it aims to answer are:

1. Development of a mobile application equipped with symptom assessment and recording videos as patients perform specific neurological tasks.
2. Development and validation of the AI model to detect functional changes and predict subsequent neurological deterioration.

Participants will:

1. Use the Iskhaa mobile application to perform guided self-neurological examinations following pre-recorded video instructions.
2. Complete EORTC QLQ-C30 and BN20 questionnaires for quality of life assessment.
3. Record and upload videos (e.g., speech, walking, limb movements) using their mobile camera for analysis.
4. In Phase 1 (onsite), 100 participants will use the app under supervision to ensure usability and accuracy.
5. In Phase 2 (offsite), 500 participants will use the app independently at home for monthly self-assessments, with reminders and follow-up support.
6. Continue routine clinic visits every 3-6 months and imaging every 6-12 months as per standard clinical care.

The study will compare app-recorded data with physician assessments to determine agreement and validity of remote neurological monitoring using artificial intelligence analysis.

Eligibility criteria:
Inclusion Criteria:

1. Diagnosis of brain tumors (histopathology/ radiology).
2. Age \> 5 years
3. Patient or caregiver have access to an android smart phone and is able to install the mobile application.
4. Expected survival more than 6 months during study accrual.
5. Signing patient consent or parent consent/ child assent form (as appropriate).

Exclusion Criteria:

1. Patient or caregiver is not reliable to follow instructions or use the mobile application.
2. Patient with severe cognitive or psychiatric issues causing difficulty in using the app or follow instructions.
3. Karnofsky Performance Status (KPS) or Lansky Performance Status LPS) \<50.
4. Terminal illness with expected life expectancy (\<6 months).

Outcomes:
Development of a mobile application equipped with symptom assessment and recording videos as patients perform specific neurological tasks.; Prediction of neurological status using mobile application
Artificial Intelligence to Improve Chest X-ray Reading in Acute Dyspnoeic Patients: A Randomized Controlled Trial
Trial ID: NCT05117320
Status: UNKNOWN
Study type: INTERVENTIONAL
Phase: NA
Start date: 2021-10-19
Completion date: 2022-07
Sponsor: Bispebjerg Hospital
Collaborators: Enlitic.com; Oxipit.ai
Conditions: Dyspnea
Interventions: AI support
Locations: University Hospital Bispebjerg and Frederiksberg
Countries: Denmark
Enrollment: 33
Age range: None - None
Sex: ALL

Summary:
Identifying the cause of breathlessness in acute patients in the emergency department is critical and challenging. The chest X-ray is central but challenging to read for non-radiologist physicians. Often the physicians read the CXR alone due to off-hours and shortage of radiology specialists. Artificial Intelligence (AI) has the potential to aid the reading of chest X-rays. The hypothesis is that AI applied to chest X-rays improves emergency physicians' diagnostic accuracy in acute breathless patients.

Eligibility criteria:
Inclusion Criteria:

* Medical Doctor (MD)
* Working experience with emergency patients

Exclusion Criteria:

* Current or former employment as a radiologist
* Unwillingness to consent

Outcomes:
Accuracy of diagnosing ADHF on acute CXR with vs without AI; Accuracy of diagnosing pneumonia on acute CXR with vs without AI
A Retrospective, Self - Controlled, Single - Center Clinical Concordance Study Between Watson for Oncology and Clinician Practice
Trial ID: NCT03400514
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2017-04-12
Completion date: 2019-01-31
Sponsor: Qingdao University
Conditions: Cancer; Artificial Intelligence
Locations: The Affiliated Hospital of Qingdao University
Countries: China
Enrollment: 350
Age range: 18 Years - 90 Years
Sex: ALL

Summary:
At present, Watson for Oncology has been applied in 14 countries worldwide, including China, the United States, Holland, Thailand, India, Korea, Poland, Slovakia and Bangladesh. In a double-blind study involving 362 patients in India, treatment recommendations from Watson for Oncology (WFO) performed a high degree of consistency with their multidisciplinary tumor board. The investigators would recruit cancer patients diagnosed as lung cancer, breast cancer, gastric cancer, colon cancer， rectal cancer，cervical cancer or ovarian cancer according to the criteria of Watson for Oncology ，using the updated version of Watson for Oncology to explore the concordance of therapeutic regimen between WFO and physicians in the Affiliated Hospital of Qingdao University.

Eligibility criteria:
Inclusion Criteria:

* a histology of lung cancer,breast cancer,gastric cancer, colon cancer， rectal cancer，cervical cancer and ovarian cancer
* had no prior systemic therapy and need neoadjuvant/adjuvant or metastatic therapy
* had prior neoadjuvant therapy, adjuvant therapy and/or surgery and are now metastatic
* have had prior surgery and now need adjuvant therapy
* seeking an additional therapy whose cancer has progressed beyond its initial metastatic therapy (second line)

Exclusion Criteria:

* not have confirmed diagnoses of invasive cancer
* under 18 years of age
* pregnant
* with multiple concurrent primary cancers or a local recurrence or a new primary at the same site of a previously treated cancer

Outcomes:
concordance
Capsule Endoscopy Delivery at sCale Through Enhanced AI anaLysis Study
Trial ID: NCT06008847
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2022-02-09
Completion date: 2024-10-16
Sponsor: University Hospitals Coventry and Warwickshire NHS Trust
Collaborators: University of Barcelona
Conditions: Bowel Cancer
Locations: University Hospitals Coventry and Warwickshire
Countries: United Kingdom
Enrollment: 720
Age range: 18 Years - None
Sex: ALL

Summary:
The CESCAIL study tests the use of Artificial Intelligence (AI) on a video taken from a minimally-invasive imaging device, to improve efficiency and accuracy of the detection of polyps, which are little outgrowths within the lining of the bowel.

Eligibility criteria:
Inclusion Criteria:

* Aged 18 or over
* Patients who have been selected by overseeing clinician meeting NHS England criteria to undergo colon capsule endoscopy as part of their standard of care

Exclusion Criteria:

* Patients unable to provide consent to take part in the study

Outcomes:
Sensitivity and specificity of polyp detection in AI enabled CCE reporting; Time taken to read video by clinicians and AI tool
The Clinical Utility of Artificial Intelligence-enabled Electrocardiograms in the Outpatient Practice - Diagnosing Aortic Stenosis and Diastolic Dysfunction
Trial ID: NCT06580158
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2024-11-08
Completion date: 2027-03
Sponsor: Mayo Clinic
Conditions: Aortic Stenosis; Diastolic Dysfunction
Interventions: AI-ECG Dashboard; Point of care ultrasound (POCUS)
Locations: Mayo Clinic
Countries: United States
Enrollment: 2000
Age range: 60 Years - None
Sex: ALL

Summary:
Two recently developed artificial intelligence-enabled electrocardiogram (AI-ECG) models have been developed to detect aortic stenosis (AS) and diastolic dysfunction (DD). AI-ECG for AS has a sensitivity of 78% and specificity of 74%, and AI-ECG for DD has a sensitivity of 83% and specificity of 80%. However, these models have never been prospectively applied to diagnose AS or DD, which may be useful for patients and providers from a diagnostic and prognostic perspective and especially in settings where access to higher- level medical care is limited. In this study, we aim to determine the clinical utility of these AI-ECG models by prospectively applying them to an outpatient cohort and then completing a focused point-of-care ultrasound to evaluate those who are AI-ECG positive for AS and DD.

Eligibility criteria:
Inclusion Criteria:

* ≥ 60 years of age must have a clinical scheduled ECG performed.

Exclusion Criteria:

* \< 59 years of age
* Is not scheduled for a clinical ECG
* Unable to provide consent.

Outcomes:
Number of patients with positive AI-ECG; Number of studies with reasonable image quality in patients with positive AI-ECG