moncefem/clinical-trials-ai-2000-2025 huggingface dataset:
Passive Evaluation in Operational Environment of the AI Clinician Decision Support System for Sepsis Treatment
Trial ID: NCT05287477
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2022-03-24
Completion date: 2024-08-31
Sponsor: Imperial College London
Collaborators: National Institute for Health Research, United Kingdom
Conditions: Sepsis
Locations: Univeristy College London Hospitals NHS Foundation Trust; Imperial College Hospitals NHS Trust
Countries: United Kingdom; United Kingdom
Enrollment: 15
Age range: 18 Years - None
Sex: ALL

Summary:
Sepsis, or systemic infection, is a common reason for ICU admission and death throughout the world. Despite advances in the way we treat this condition, it remains a significant economic and healthcare burden. A key part of the treatment of sepsis is the administration of IV fluids and blood pressure medication. However, there is huge uncertainty around dosing of these drugs in an individual patient. A tool to personalise these medications could improve patient survival. The study team has developed a new method to automatically and continuously review and recommend the correct medication doses to doctors, which was created using artificial intelligence (AI) techniques applied to large medical databases. The researchers' previous work has shown it has the potential to improve patient survival rates. The tool will be capable of processing patient data within the electronic patient record of NHS hospitals in real-time to suggest a course of action. This tool will be evaluated and refined in simulation studies and then be tested in two NHS Trusts in "shadow mode" (results not provided to duty clinicians). This will allow comparison of actual decisions made and recommended decisions from the AI system. The second stage of this clinical evaluation will display the recommendations to clinicians to assess the acceptability of the tool and confirm technical feasibility to inform future clinical trials. The long-term expected benefits of this project are numerous: improved patient survival, reduced use of precious intensive care resources and reduction in healthcare costs.

Eligibility criteria:
Inclusion Criteria:

For patients:

* Adult patient \> 18 years old
* Admitted to an intensive care unit
* Likely or confirmed diagnosis of sepsis as per sepsis-3 definition (as defined in the glossary)
* ICU length of stay \> 24h

For Evaluators:

\- ICU doctors at the senior registrar, ICU fellow or consultant level

Exclusion Criteria:

For patients:

* Not for full active care, e.g. not for vasopressors
* Not expected to survive more than 24h
* Elective surgical admission (these patients are regularly on antibiotics but given as a prophylaxis, with no sepsis)
* Opted-out for use of their data for research (NHS and NHS-X website)

For both patients and evaluators:

Declined participation No patient consent is required

Outcomes:
Data Availability; Anonymised patients' data; Rate of intravenous fluids administered to patients; Evaluators' data (the doctors assessing the AI in the background); System Availability
Performance of the Diagnostic Value of Bone Age Assessment Software Based on Deep Learning in Chinese Children
Trial ID: NCT05137301
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2022-02-21
Completion date: 2024-12-31
Sponsor: Tongji Hospital
Collaborators: Changchun GeneScience Pharmaceutical Co., Ltd.
Conditions: Endocrine Diseases
Interventions: X-ray bone age instrument
Locations: Tongji Hospital Tongji Medical College of Hust
Countries: China
Enrollment: 1000
Age range: 1 Year - 18 Years
Sex: ALL

Summary:
High accuracy and precision bone age assessment is very important for the diagnosis and treatment monitoring of various pediatric diseases. The commonly used bone age assessment methods include GP atlas, TW3 score and Zhonghua 05. GP method is to compare wrist X-ray films with atlas reference X-ray films. Its main disadvantages are strong subjectivity and long atlas standard interval. Different from GP method, TW3 method is to grade and score each bone, add each epiphyseal score to calculate the total score of bone maturity, and obtain the corresponding final bone age value. Although TW3 scoring method is relatively accurate, it is complex and time-consuming, and there is great variability among evaluators. In order to evaluate bone age more efficiently and accurately, a method based on computer image automatic recognition technology can help to overcome these problems.

In this study, 1000 children aged 1-18 in 5 hospitals are selected as the research objects. After taking bone age films with bone age instrument, the film reading results and evaluation time of AI Group, artificial group and standard group are recorded. One month later, the artificial group re-analyzes 1000 films with the assistance of AI system, and the evaluation time is recorded. Finally, the accuracy and time difference of artificial group, AI Group, artificial combined AI Group and standard group are compared.

The purpose of this study is to use the most advanced artificial intelligence deep learning bone age evaluation software to explore the value of bone age instrument to improve the accuracy and diagnostic efficiency of bone age evaluation by pediatricians.

Eligibility criteria:
Inclusion Criteria:

1. Children aged 1 to 18 years.
2. Clinical diagnosis of requiring bone age examination.

Exclusion Criteria:

1. There are obstructions or implants in the wrist bone, such as metal.
2. Poor resolution, which affects the observation of bone characteristics.
3. Poor shooting position of the wrist due to various reasons.

Outcomes:
bone age
The Volume Effect of Hydrodissection for Injection Therapies in Patients With Carpal Tunnel Syndrome - Evaluation Model by Shear Wave Ultrasound Elastography and Artificial Intelligence Imaging Analysis
Trial ID: NCT03598322
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2018-06-30
Completion date: 2019-07-30
Sponsor: National Taiwan University Hospital
Conditions: Carpal Tunnel Syndrome
Interventions: Dextrose 1mL injection; Dextrose 2mL injection; Dextrose 4mL injection
Locations: Department of Physical Medicine & Rehabilitation , National Taiwan University Hospital
Countries: Taiwan
Enrollment: 63
Age range: 20 Years - 80 Years
Sex: ALL

Summary:
The investigators investigated the volume effect of hydrodissection for Injection therapies in patients with Carpal Tunnel Syndrome. The investigators evaluated participants by Shear Wave Ultrasound Elastography and Artificial Intelligence Imaging Analysis

Eligibility criteria:
Inclusion Criteria:

* Age 20-80
* Electrophysiological diagnosis of carpal tunnel syndrome
* At least one of the following two:

  1. Tenderness or numbness over median nerve innervated dermatome over 2 months
  2. Phalen test or Tinel sign or flick sign positive

Exclusion Criteria:

* History of wrist surgery
* Wrist trauma in recent two years
* Received wrist injection in recent three months
* History of brachial plexopathy, cervical radiculopathy or thoracic outlet syndrome
* History of DM, thyroid disorder or autoimmune disease
* Unable to cooperate with interview, examination or injection

Outcomes:
Change of Visual analog scale (VAS)
Development and Validation of an Artificial Intelligence Prediction Model and a Survival Risk Stratification for Lung Metastasis in Colorectal Cancer From Highly Imbalanced Data
Trial ID: NCT05816902
Status: COMPLETED
Study type: OBSERVATIONAL
Start date: 2016-01-01
Completion date: 2020-12-31
Sponsor: Peking Union Medical College
Collaborators: The Second Affiliated Hospital of Harbin Medical University
Conditions: Colorectal Cancer; Lung Metastases
Interventions: The location of the patient's treatment
Enrollment: 2779
Age range: None - None
Sex: ALL

Summary:
Background:

To assist clinicians with diagnosis and optimal treatment decision-making, we attempted to develop and validate an artificial intelligence prediction model for lung metastasis (LM) in colorectal cancer (CRC) patients.

Method:

The clinicopathological characteristics of 46037 CRC patients from the Surveillance, Epidemiology, and End Results (SEER) database and 2779 CRC patients from a multi-center external validation set were collected retrospectively. After feature selection by univariate and multivariate analyses, six machine learning (ML) models, including logistic regression, K-nearest neighbor, support vector machine, decision tree, random forest, and balanced random forest (BRF), were developed and validated for the LM prediction. The optimization model with best performance was compared to the clinical predictor. In addition, stratified LM patients by risk score were utilized for survival analysis.

Eligibility criteria:
Inclusion Criteria:

* patients with pathologic confirmation of a primary CRC diagnosis

Exclusion Criteria:

* (1) patients with multiple primary cancers or other malignancies; (2) patients identified via autopsy or death certificate; and (3) patients with uncertain clinical data values

Outcomes:
lung metastasis
The Yield of Artificial Intelligence (GI Genius) in Lynch Syndrome - A Randomized Tandem-colonoscopy Trial
Trial ID: NCT06654128
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2024-11-03
Completion date: 2025-11-01
Sponsor: Sheba Medical Center
Conditions: Lynch Syndrome
Interventions: artificial intelligence
Locations: Sheba Medical Center
Countries: Israel
Enrollment: 100
Age range: 18 Years - None
Sex: ALL

Summary:
We aim to investigate the incremental detection rate of polyps using AI- assisted colonoscopy compared with high-definition white-light endoscopy (HD-WLE).

We will conduct a single-center randomized tandem colonoscopy trial of patients with Lynch syndrome with and without AI. Patients will undergo tandem colonoscopy, the second colonoscopy with either HD-WLE or an AI- assisted device.

Eligibility criteria:
Inclusion Criteria:

* patients with a known pathogenic variant in one of the mismatch repair genes, who are undergoing endoscopic screening and surveillance in our institute

Exclusion Criteria:

* patients \< 18 years old
* lack of informed consent
* concomitant inflammatory bowel disease
* previous total colectomy
* inadequate bowel cleansing (Boston Bowel Preparation Scale (BBPS) ≤5 or \< 2 in one of the 3 segments)

Outcomes:
adenoma incremental detection rate
A.I and Machine Learning Based Risk Prediction Model to Improve the Clinical Management of Endometrial Cancer: a Composite Approach Integrating the MultiOMics IMmune-IConographic Pattern (MOMIMIC Score) Towards Precision Oncology and Surgery.
Trial ID: NCT06841653
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2024-06-20
Completion date: 2026-06-20
Sponsor: Regina Elena Cancer Institute
Collaborators: University of Rome Tor Vergata; Casa Sollievo della Sofferenza-IRCCS, San Giovanni Rotondo; Universita degli Studi di Palermo
Conditions: Endometrium Cancer
Locations: IRCCS National Cancer Institute "Regina Elena"
Countries: Italy
Enrollment: 40
Age range: 18 Years - None
Sex: FEMALE

Summary:
Prediction of preoperative endometrial biopsy: the evolution from hyperplasia to cancer, the prognosis and the risk of recurrence. Intelligence methods artificial risk will be used to redefine the current risk classes including our profile immuno-mutational to provide a more precise characterization and closer to the real prognosis of the patient.

Eligibility criteria:
Inclusion Criteria:

* Age \> 18 years;
* Histological diagnosis of endometrial hyperplasia, endometrioid adenocarcinoma of the endometrium, healthy endometrium in patients undergoing total hysterectomy for benign extra-endometrial disease;
* Written informed consent (to the study and data processing), for the party's patients only prospective and/or in follow-up) For the retrospective cohort: availability of samples adequately stored at the biobank of the Institute and availability of data relating to follow-up (at least 2 years)

Exclusion Criteria:

All exclusion criteria adopted in the surgical protocols will be applied to the study. In particular:

* Comorbidities not controlled with adequate medical therapy;
* Infections of the endometrial cavity (pyometra);
* Synchronous cancer;
* Neoadjuvant treatments;
* Previous radiotherapy treatments of the pelvic region;
* Hormone therapies.

Outcomes:
OS (overall survival); DSF (disease-free survival)
The Development of the Simulation Curriculum and Artificial Intelligence-incorporated Assessment Tools for Transesophageal Echocardiography in Patients With Cardiac Arrest
Trial ID: NCT06567912
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2023-08-01
Completion date: 2026-12-31
Sponsor: National Taiwan University Hospital
Conditions: Out-Of-Hospital Cardiac Arrest
Interventions: TEE simulation curriculum
Locations: Lien
Countries: Taiwan
Enrollment: 100
Age range: 18 Years - 99 Years
Sex: ALL

Summary:
This study aims to establish a novel transesophageal echocardiography (TEE) simulation curriculum based on previous research results and suggestions from the American College of Emergency Physicians. To better evaluate the efficacy of the curriculum, an evaluation model utilizing artificial intelligence (AI) would be introduced. The study population is at least 60 junior physicians in the emergency department or other departments interested in TEE. After the curriculum, junior physicians could utilize TEE on cardiac arrest patients and improve diagnostic accuracy and patient outcomes.

Eligibility criteria:
Inclusion Criteria:

Emergency physicians interested in TEE learning.

Exclusion Criteria:

Emergency physicians are not interested in TEE learning.

Outcomes:
learning outcomes:immediate assessment; learning outcomes: 3-month evaluation; the efficiency of AI model
Clinical Validation of AI-powered Smart Vehicle Assisted Gait Training in Neurodegenerative Disorders
Trial ID: NCT07230366
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-01-02
Completion date: 2028-12-31
Sponsor: Taipei Medical University Shuang Ho Hospital
Conditions: Parkinson's Disease (PD); Dementia
Interventions: BestShape Go Intelligent Generation Transformable Electric Vehicle
Locations: Taipei Medical University Shuang Ho Hospital
Countries: Taiwan
Enrollment: 120
Age range: 50 Years - 85 Years
Sex: ALL

Summary:
This study aims to verify the safety and preliminary clinical benefits of long-term gait training using AI-powered smart electric vehicles for patients with neurodegenerative diseases such as Parkinson's disease and dementia.

Eligibility criteria:
Inclusion Criteria:

1. Patients with neurodegenerative diseases (such as Parkinson's disease or dementia).
2. Age between 50-85 years.
3. Capable of walking for at least 10 meters, but may have unsteady gait or history of falls.
4. Able to understand the trial procedures and give informed consent.

Exclusion Criteria:

1. Severe cardiopulmonary disease or recent major surgery.
2. Unable to walk or requiring comprehensive physical support.
3. Severe cognitive impairment preventing understanding of the trial procedures or giving informed consent.
4. Visual or auditory impairments severe enough to prevent following trial instructions.

Outcomes:
Gait speed
Multimodal Hyperspectral Imaging and Raman Spectroscopy for Intraoperative Assessment of Breast Tumor Resection Margins
Trial ID: NCT07111728
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-10-06
Completion date: 2028-12
Sponsor: Istituti Clinici Scientifici Maugeri SpA
Conditions: Breast Cancer
Interventions: Spectra-BREAST system in Invasive Breast Cancer; Spectra-BREAST system in DCIS Breast Cancer
Locations: Istituti Clinici Scientifici Maugeri IRCCS
Countries: Italy
Enrollment: 104
Age range: 18 Years - 85 Years
Sex: FEMALE

Summary:
Breast-conserving surgery (lumpectomy) aims to remove cancer while preserving healthy tissue, but up to 20% of patients require a second operation because cancer cells remain at the edge (margin) of the removed tissue. The Spectra-BREAST study evaluates a new optical device that combines hyperspectral imaging (HIS) and Raman spectroscopy (RS) with artificial-intelligence analysis to quickly assess the entire surface of excised breast specimens during surgery. By flagging areas at risk of positive margins in real time, the device may help surgeons remove any remaining cancer in a single procedure.

In this prospective, single-arm diagnostic study, surgeons will use the Spectra-BREAST system on freshly resected breast tissue from up to 99 women undergoing lumpectomy for invasive carcinoma or ductal carcinoma in situ. First, the device's cancer-detection algorithms will be trained on 74 specimens with known pathology. Then, in a separate group of patients, the fully integrated device will be tested on all six faces of each lumpectomy specimen and its predictions will be compared against the gold-standard histopathology margin assessment. Key measures include the sensitivity and specificity of the device's margin predictions, the time needed to generate results, and the device's usability in a clinical setting.

Eligibility criteria:
Inclusion Criteria:

Histologically confirmed diagnosis of ductal carcinoma in situ (B5a) or invasive breast carcinoma (B5b) on pre-operative core needle biopsy

Scheduled to undergo breast-conserving surgery (lumpectomy)

Able and willing to provide written informed consent

Exclusion Criteria:

Pre-operative histologic or cytologic diagnosis of a benign breast lesion (B2/C2)

Initial diagnosis of advanced (stage III/IV) or metastatic breast carcinoma, or indication for neoadjuvant chemotherapy

Neurocognitive disorders that would impair comprehension of the study procedures or consent process

Concurrent pregnancy or breastfeeding

Outcomes:
Sensitivity and specificity of Spectra-BREAST system margin status prediction
Microbiota-Inflammation-Brain Axis in Heart Failure: New Food, biomarkerS and Artificial Intelligence Approach for the Prevention of undeRnutrition in Older
Trial ID: NCT07076329
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2022-02-07
Completion date: 2025-09-30
Sponsor: Azienda Ospedaliero-Universitaria Careggi
Collaborators: University of Florence; Northumbria University; Synbiotec Srl; University College Dublin; Fundació d'investigació Sanitària de les Illes Balears
Conditions: Atrial Fibrillation (AF); Heart Failure
Interventions: Ambrosia bar
Locations: Azienda Ospedaliera Universitaria Careggi
Countries: Italy
Enrollment: 60
Age range: 70 Years - None
Sex: ALL

Summary:
The goal of this clinical trial is to learn if the AMBROSIA Bar helps prevent undernutrition and muscle loss in older adults with heart failure or atrial fibrillation.

The main questions it aims to answer are:

Does the AMBROSIA Bar help improve muscle mass and physical performance in older adults with heart conditions? What effects does the AMBROSIA Bar have on body composition, quality of life, inflammation, nutrition status, and gut microbiota?

Researchers will compare people who assume the AMBROSIA Bar in addition to nutritional counseling to those who only receive nutritional counseling.

Participants will:

* Receive nutritional counseling and consume one AMBROSIA Bar per day for 6 months (intervention group) or receive nutritional counseling only (control group).
* Receive clinical assessment at the moment of enrolment, after 3 months, and after 6 months
* Give samples (blood, saliva, urine, and stool) and complete questionnaires to check their nutrition, cognitive, and physical health

Eligibility criteria:
Inclusion Criteria:

* Age \>70 years;
* Caucasian ethnicity;
* Diagnosis of atrial fibrillation (AF) and/or heart failure (HF);
* Normal nutritional status or at risk of malnutrition;
* Adherence to Mediterranean diet

Exclusion Criteria:

* Obesity (BMI \>30 Kg/m2);
* Underweight (BMI \<18.5 Kg/m2);
* Use of antibiotics, corticosteroids and immune-suppressors in the last 6 months;
* Immunodeficiency;
* Trip to exotic areas in the last 12 months;
* Recent (\<3 months) major surgical procedure;
* Uncontrolled diabetes mellitus;
* Active cancer;
* Chronic significant hepatic diseases;
* Chronic renal failure (KDIGO stages 4-5);
* Dementia and severe mental diseases;
* Other conditions interfering with autonomous drugs assumption;
* Chronic use of NSAIDs;
* Relevant history of allergy or infectious diseases

Outcomes:
Change from Baseline in the Skeletal Muscle Mass at 6 months
Automated AI Voice Calls for Postoperative Orthopedic Follow-up: A Randomized Trial of SMS Pre-notification and Blinded Validation of PROM Capture.
Trial ID: NCT04972331
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-01-27
Completion date: 2026-02-13
Sponsor: Institut de Chirurgie Reparatrice Locomoteur et Sports
Conditions: Shoulder Instability
Interventions: SMS Pre-notification; No Pre-notification
Locations: ICR Clinique Kantys Centre
Countries: France
Enrollment: 310
Age range: 18 Years - None
Sex: ALL

Summary:
This prospective, single-center randomized clinical trial evaluates the technical efficacy, accuracy, and operational performance of an automated conversational artificial intelligence (AI) voice agent for postoperative follow-up after arthroscopic shoulder instability surgery. The study examines whether SMS pre-notification improves survey completion during the first automated outbound call and assesses the accuracy of AI-captured patient-reported outcomes compared with blinded human review.

Eligibility criteria:
Inclusion:

* Age ≥18 years
* History of arthroscopic shoulder instability surgery
* Minimum postoperative follow-up of 12 months
* Valid telephone contact information

Exclusion:

* Previous ipsilateral surgery
* Non-consent

Outcomes:
Survey completion during the first automated outbound call
The Benefits of Artificial Intelligence Algorithms (CNNs) for Discriminating Between COVID-19 and Influenza Pneumonitis in an Emergency Department Using Chest X-Ray Examinations
Trial ID: NCT04313946
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2020-03-18
Completion date: 2020-08-18
Sponsor: Professor Adrian Covic
Collaborators: Falcon Trading Iasi; Romanian Academy of Medical Sciences
Conditions: COVID-19; Pneumonia, Viral; Influenza With Pneumonia; Flu Symptom; Flu Like Illness; Pneumonia, Interstitial; Pneumonia, Ventilator-Associated; Pneumonia Atypical
Interventions: Scanning Chest X-rays and performing AI algorithms on images
Locations: U.O. Multidisciplinare di Patologia Mammaria e Ricerca Traslazionale; Dipartimento Universitario Clinico di Scienze Mediche, Chirurgiche e della Salute Università degli Studi di Trieste; University of Medicine and Pharmacy Gr T Popa; Department of Cardiology at Chelsea and Westminster NHS hospital
Countries: Italy; Romania; United Kingdom
Enrollment: 200
Age range: None - None
Sex: ALL

Summary:
This project aims to use artificial intelligence (image discrimination) algorithms, specifically convolutional neural networks (CNNs) for scanning chest radiographs in the emergency department (triage) in patients with suspected respiratory symptoms (fever, cough, myalgia) of coronavirus infection COVID 19. The objective is to create and validate a software solution that discriminates on the basis of the chest x-ray between Covid-19 pneumonitis and influenza

Eligibility criteria:
Inclusion Criteria:

* flu-like symptoms: myalgia, cough, fever, sputum
* Chest X-Rays
* COVID-19 biological tests

Exclusion Criteria:

* patient refusal
* uncertain radiographs
* uncertain tests results

Outcomes:
COVID-19 positive X-Rays; COVID-19 negative X-Rays