Clinical trials: Machine Learning
Critical Illness, Enteral Nutrition Intolerance
Predictive Value of Gastrointestinal Blood Flow for Enteral Nutrition Intolerance in Critically Ill Patients
This study aims to explore the correlation between gastrointestinal blood flow and the incidence of enteral nutrition intolerance (ENI) and its symptoms in critically ill patients, construct and compare predictive models…
- Conditions
- Critical Illness, Enteral Nutrition Intolerance, Enteral Nutrition Feeding, Prediction Models
- Trial name
- NCT07521111
- Where
- China
- Volunteers
- 500 people
- Sponsor
- Ruijin Hospital
Rectal Cancers, Pathological Complete Response
Predicting Pathological Complete Response in Rectal Cancer Using Machine Learning
This study aims to develop and validate a robust machine learning-based prediction model utilizing baseline clinical data and magnetic resonance imaging (MRI) features.
- Conditions
- Rectal Cancers, Pathological Complete Response, Neoadjuvant Chemoradiotherapy, Machine Learning
- Trial name
- NCT07509632
- Testing
- No interventions
- Where
- China
- Volunteers
- 320 people
- Sponsor
- Peking University People's Hospital
Asthma Childhood, Asthma Attack
Remote Monitoring of Asthma in Children and Young People
The objective of this study is to determine whether healthcare data and remotely collected patient data can accurately predict asthma attacks in children and young people aged 5-17 years.
- Conditions
- Asthma Childhood, Asthma Attack, Remote Monitoring, Risk Assessment
- Trial name
- NCT07129616
- Where
- United Kingdom
- Volunteers
- 900 people
- Sponsor
- University of Edinburgh
Paraspinal Muscles, Low Back Pain
Multimodal Exercise Therapy for Non-Surgical Intervention of Nonspecific Low Back Pain.
This multicenter, assessor-blinded, two-arm parallel randomized controlled trial (N = 314) will compare the efficacy and safety of a 6-week multidimensional exercise program plus usual pharmacological care (experimental arm)…
- Conditions
- Paraspinal Muscles, Low Back Pain, Machine Learning, Radiomics
- Trial name
- METNLBP (NCT07353372)
- Testing
- Multidimensional exercise intervention, Oral NSAIDs
- Where
- China
- Volunteers
- 314 people
- Sponsor
- Xuanwu Hospital, Beijing
Delirium, Prediction Models
Research on the Development and Validation of an Early Prediction Model for Delirium
Delirium has a high incidence rate and significantly affects patient prognosis. Diagnosis often relies on manual assessment, which is subject to strong subjectivity, high rates of missed diagnosis, and poor stability.
- Conditions
- Delirium, Prediction Models, Machine Learning
- Trial name
- NCT07337356
- Volunteers
- 795 people
- Sponsor
- Ruijin Hospital
Chemotherapy Side Effects, Machine Learning
Prospective Validation of Machine Learning Model to Predict Platinum Induced Nephrotoxicity in Cancer Patients
This study aims to investigate the utility of predictive models for chemotherapy-induced nephrotoxicity in the Taiwanese cancer population.
- Conditions
- Chemotherapy Side Effects, Machine Learning, Acute Kidney Injury, Acute Kidney Disease
- Trial name
- NCT07114276
- Testing
- Machine learning models predictions of acute kidney injury and acute kidney disease
- Where
- Taiwan
- Volunteers
- 77 people, healthy volunteers welcome
- Sponsor
- Taipei Medical University
Machine Learning
MUSCLE-ML: Multimodal Integration of Muscle Strength, Structure by Machine Learning for Precision Rehabilitation After ACL Injury
The goal of this clinical trial is to use machine learning (ML) to predict functional recovery by integrating muscle-related factors and other relevant parameters for identification of non-responders to conventional…
- Trial name
- NCT07284771
- Testing
- No Intervention: Observational Cohort
- Volunteers
- 182 people, healthy volunteers welcome
- Sponsor
- Chinese University of Hong Kong
Accidental Falls, Risk Assessment
AI-Powered Fall Risk Prediction in Nursing Care
The goal of this study is to develop a nursing clinical decision support system for fall risk prediction using machine learning and computer vision techniques.
- Conditions
- Accidental Falls, Risk Assessment, Nursing Assessment, Machine Learning
- Trial name
- NCT07000981
- Where
- Turkey (Türkiye)
- Volunteers
- 177 people
- Sponsor
- Inonu University
Blood Volume Analysis, Ultrasound
A Deep Learning Model for Blood Volume Estimation From Multi-modal Ultrasound
1. Background \& Rationale: Accurate assessment of a patient's blood volume (BV) status before surgery is critical for preventing perioperative complications.
- Conditions
- Blood Volume Analysis, Ultrasound, Machine Learning
- Trial name
- NCT06957587
- Where
- 2 sites in China
- Volunteers
- 800 people
- Sponsor
- Shanghai 6th People's Hospital
Chronic Pancreatitis, Pancreatic Neoplasm
Prediction Model of Pancreatic Neoplasms in CP Patients With Focal Pancreatic Lesions
This study aims to develop XGBoost machine learning model to predict pancreatic neoplasms in CP patients with focal pancreatic lesions.
- Conditions
- Chronic Pancreatitis, Pancreatic Neoplasm, Machine Learning
- Trial name
- NCT07045181
- Testing
- XGBoost machine learning
- Where
- China
- Volunteers
- 113 people
- Sponsor
- Changhai Hospital
Keratoconus, Refractive Surgery
AI-Orchestrated Workflow Versus Consultant Ophthalmologist for Refractive Surgery and Keratoconus Diagnosis (AEYE Trial)
Background and Rationale: Laser vision correction procedures, such as LASIK (Laser-Assisted In Situ Keratomileusis), PRK (Photorefractive Keratectomy), and SMILE (Small Incision Lenticule Extraction), are highly effective but…
- Conditions
- Keratoconus, Refractive Surgery, Machine Learning, Artifical Intelligence
- Trial name
- AEYE (NCT07096232)
- Testing
- Multi-Agent AI Diagnostic Workflow (AEYE)
- Where
- Egypt
- Volunteers
- 50 people
- Sponsor
- Hazem Yassin Clinics
Optic Neuropathy, Optic Neuropathy, Ischemic
Building Research With Artificial Intelligence in Neuro-Ophthalmology
The research team, recognized as a world leader in Artificial Intelligence for neuro-ophthalmology, has shown that it is possible to diagnose certain neuro-ophthalmologic or neurologic disorders from a single retinal fundus image…
- Conditions
- Optic Neuropathy, Optic Neuropathy, Ischemic, Optic Neuritis, Optic Nerve Diseases
- Trial name
- BRAIN (NCT06390579)
- Testing
- Deep learning algorithm applied on retrospectively collected color fundus photographs
- Where
- France
- Volunteers
- 693 people
- Sponsor
- Fondation Ophtalmologique Adolphe de Rothschild
Phenotyping, Machine Learning
Derivation and Validation of Hemodynamic Phenotypes of Cardiac Surgery
Background \& Objective: Cardiac surgery patients differ significantly in their health conditions and how they react during operations.
- Conditions
- Phenotyping, Machine Learning, Cardiac Surgery, Hemodynamic Parameters
- Trial name
- NCT07085208
- Testing
- Unsupervised Machine Learning for Clinical Phenotyping
- Volunteers
- 10,847 people
- Sponsor
- Nanjing First Hospital, Nanjing Medical University
Takotsubo Cardiomyopathy, Machine Learning
Prognotic Role of CMR in Takotsubo Syndrome
The primary objective of this observational registry is to develop a comprehensive clinical and imaging score (incorporating echocardiography and cardiac magnetic resonance data) that enhances risk stratification for patients…
- Conditions
- Takotsubo Cardiomyopathy, Machine Learning, Magnetic Resonance Imaging
- Trial name
- EVOLUTION (NCT06277297)
- Where
- Italy
- Volunteers
- 350 people
- Sponsor
- University of Cagliari
Uterine Cervical Neoplasms, Locally Advanced Cervical Cancer
US Radiomics in Advanced Cervical Cancer
This is a retrospective, multicenter observational study aimed at evaluating the role of ultrasound-based radiomics in patients with locally advanced cervical cancer (LACC).
- Conditions
- Uterine Cervical Neoplasms, Locally Advanced Cervical Cancer, Radiomics, Ultrasound Imaging
- Trial name
- RU-LACC (NCT06984289)
- Testing
- Radiomic Analysis of Ultrasound Images
- Where
- Italy
- Volunteers
- 220 people
- Sponsor
- Fondazione Policlinico Universitario Agostino Gemelli IRCCS
Machine Learning
Prediction of Risk Factors for Adverse Events After Head and Neck Vascular Recanalization Surgery Based on Machine Learning Models
Prediction of risk factors for adverse events after head and neck vascular recanalization surgery based on machine learning models
- Trial name
- NCT06120478
- Testing
- observe
- Where
- China
- Volunteers
- 1,300 people
- Sponsor
- Tang-Du Hospital
Degenerative Lumbar Diseases, Cage
Development and Validation of Interpretable Machine Learning Models Incorporating Paraspinal Muscle Quality for to Predict Cage Subsidence Risk Followingposterior Lumbar Interbody Fusion
The study focuses on identifying risk factors for cage subsidence after posterior lumbar interbody fusion (PLIF) and developing an interpretable machine learning model to predict these risks.
- Conditions
- Degenerative Lumbar Diseases, Cage, Machine Learning
- Trial name
- NCT06888739
- Testing
- MR4
- Where
- China
- Volunteers
- 720 people, healthy volunteers welcome
- Sponsor
- Hao Liu
Keratoconus, Machine Learning
Detection of Keratoconus Progression Using Machine Learning
Keratoconus (KC) is a bilateral ocular disease characterized by progressive thinning and steepening of the cornea, usually in its inferotemporal region.
- Trial name
- NCT06873399
- Testing
- MS-39
- Where
- Austria
- Volunteers
- 200 people
- Sponsor
- Vienna Institute for Research in Ocular Surgery
Voiding Dysfunction, Voiding Disorders
Artificial Intelligence-Based Analysis of Uroflowmetry Patterns in Children: a Machine Learning Perspective
Uroflowmetry is the one of the most commonly used non-invasive test for evaluating children with lower urinary tract symptoms (LUTS).
- Conditions
- Voiding Dysfunction, Voiding Disorders, Machine Learning
- Trial name
- NCT06814847
- Where
- Turkey (Türkiye)
- Volunteers
- 500 people, healthy volunteers welcome
- Sponsor
- Marmara University
Mild Cognitive Impairment, Sleep
Home Sleep Therapy for Older Adults With MCI
The goal of this clinical trial is to learn about the ability of non-invasive brain stimulation during sleep to enhance people's deep sleep and its potential benefit on memory in people with mild cognitive impairment via home use…
- Conditions
- Mild Cognitive Impairment, Sleep, Transcranial Electrical Stimulation, Machine Learning
- Trial name
- NCT05771844
- Testing
- Transcranial Electrical Stimulation
- Where
- 2 sites in United States
- Volunteers
- 60 people, healthy volunteers welcome
- Sponsor
- Brain Electrophysiology Laboratory Company