Clinical trials: Machine Learning
Major Depressive Disorder
Optimize and Predict Antidepressant Efficacy for Patient With MDD Using Multi-omics Analysis and AI-predictive Tool
OPADE is a non-profit, observational, multicenter, open-label study aimed at defining personalized treatment for Major Depressive Disorder (MDD).
- Trial name
- OPADE (NCT06550037)
- Where
- Italy
- Volunteers
- 350 people
- Sponsor
- Alessio Fasano
Out-Of-Hospital Cardiac Arrest
Machine Learning Assisted Recognition of Out-of-Hospital Cardiac Arrest During Emergency Calls.
Emergency medical Services Copenhagen has developed a machine learning model that analyzes the calls to 1-1-2 (9-1-1) in real time. The model are able to recognize calls where a cardiac arrest is suspected.
- Trial name
- NCT04219306
- Testing
- Alert on dispatchers screen 'Suspect cardiac arrest'
- Where
- Denmark
- Volunteers
- 5,242 people
- Sponsor
- Emergency Medical Services, Capital Region, Denmark
Renal Clear Cell Carcinoma, HIF-2α
Predicting HIF-2α Levels in Clear Cell Kidney Cancer Using Machine Learning
This project aims to conduct a multicenter retrospective study to collect clinical, CT imaging, and pathological data from patients.
- Conditions
- Renal Clear Cell Carcinoma, HIF-2α, Radiomics, Nomogram
- Trial name
- NCT07332923
- Where
- China
- Volunteers
- 500 people
- Sponsor
- First Affiliated Hospital of Fujian Medical University
Renal Insufficiency, Chronic, Arteriovenous Fistula
Artificial Intelligence-based Prediction of Radio-cephalic Arteriovenous Fistula Maturation Using Preoperative Duplex Examination
The goal of this observational study is to assess the efficacy of AI-driven models in analyzing comprehensive ultrasonographic variables across multiple forearm locations to predict successful AVF maturation.
- Conditions
- Renal Insufficiency, Chronic, Arteriovenous Fistula, Artificial Intelligence (AI), Machine Learning
- Trial name
- NCT06600750
- Testing
- Arteriovenous fistula
- Where
- South Korea
- Volunteers
- 494 people
- Sponsor
- Seoul National University Hospital
Prostatic Neoplasms
Machine-learning Optimization for Prostate Brachytherapy Planning
The proposed, mono-institutional, randomized-controlled trial aims to determine whether the dosimetric outcomes following prostate Low-Dose-Rate (LDR) brachytherapy, planned using a novel machine learning (ML-LDR) algorithm, are…
- Trial name
- MOPP (NCT02943824)
- Testing
- Machine Learning Planning, Radiation Therapist Planning
- Where
- Canada
- Volunteers
- 42 people
- Sponsor
- Sunnybrook Health Sciences Centre
Glioma
MR Based Survival Prediction of Glioma Patients Using Artificial Intelligence
This registry aims to collect clinical, molecular and radiologic data including detailed survival data, clinical parameters, molecular pathology (1p/19q codeletion, MGMT methylation, IDH and TERTp mutations, etc) and…
- Trial name
- NCT04215211
- Testing
- Survival prediction for glioma patients
- Where
- China
- Volunteers
- 2,500 people
- Sponsor
- The First Affiliated Hospital of Zhengzhou University
Perioperative/Postoperative Complications
Perioperative Risk Calculator
The aim of this project is to develop a machine-learning model for calculating the risk of postoperative complications. In addition to the data collected during the premedication, the model will include all intraoperative values…
- Trial name
- PROTECT (NCT04092933)
- Volunteers
- 175,559 people
- Sponsor
- Technical University of Munich
Cardiotoxicity, DPP3
Cardiovascular Complications in Patients Undergoing Allogeneic Hematopoietic Stem Cell Transplantation.
Allogeneic hematopoietic stem cell transplantation (HSCT) represents a major therapeutic strategy for malignant hematologic diseases, with the number of procedures steadily increasing in France each year.
- Conditions
- Cardiotoxicity, DPP3, HSCT, Allogeneic Hematopoietic Stem Cell Transplantation
- Trial name
- ALLOCARDIOTOX (NCT07157670)
- Where
- France
- Volunteers
- 400 people
- Sponsor
- Assistance Publique - Hôpitaux de Paris
Locally Advanced Rectal Cancer (LARC)
AI-Based Prediction of Pathological Response in Rectal Cancer Patients Receiving Total Neoadjuvant Therapy
This study aims to better understand how body composition, inflammation, and nutrition affect how rectal cancer responds to treatment.
- Trial name
- CINR-AI (NCT07049627)
- Testing
- Total Neoadjuvant Therapy (TNT)
- Where
- 2 sites in Turkey (Türkiye)
- Volunteers
- 93 people
- Sponsor
- Ankara Etlik City Hospital
Pediatric ALL, Sepsis
Evaluation of Pediatric eCART Implementation
This is a study comparing 3 years of retrospective data (pre-implementation) to 2 years of prospective data after the implementation of a pediatric version of Electronic Cardiac Arrest Risk Triage (pediatric eCART), a clinical…
- Trial name
- NCT06771830
- Testing
- Pediatric eCART
- Where
- United States
- Volunteers
- 30,000 people
- Sponsor
- University of Wisconsin, Madison
Acute Kidney Injury
Prospective Validation of AKI Prediction
The purpose of this prospective observational study is to implement, deploy, and quantify accuracy of an existing Pediatric Early AKI Risk Score algorithm.
- Trial name
- NCT06804200
- Where
- United States
- Volunteers
- 800 people
- Sponsor
- Adam C Dziorny
Spinal Muscular Atrophy
MAP THE SMA: a Machine-learning Based Algorithm to Predict THErapeutic Response in Spinal Muscular Atrophy
Spinal Muscular Atrophy (SMA) is caused by the homozygous loss of the Survival Motor Neuron (SMN) 1 gene, which leads to degeneration of spinal alpha-motor neurons and muscle atrophy.
- Trial name
- MAP_THE_SMA-01 (NCT05769465)
- Testing
- disease modifying treatments
- Where
- Italy
- Volunteers
- 247 people
- Sponsor
- Fondazione Policlinico Universitario Agostino Gemelli IRCCS
Pleural Diseases, Lung Cancer
Developing a Machine Learning Model to Predict Pleural Adhesion Preoperatively Using Pleural Ultrasound
This study aims to investigate the accuracy of using pleural ultrasound (USP) to identify pleural adhesions in patients who plan to receive video-assisted thoracoscopic surgery.
- Conditions
- Pleural Diseases, Lung Cancer, Machine Learning
- Trial name
- NCT06423066
- Testing
- Pleural ultrasound
- Where
- China
- Volunteers
- 200 people
- Sponsor
- Peking Union Medical College Hospital
Attention Deficit-hyperactivity Disorder
Assesment of TDApp1 an eHelath Tool to Make Therapeutic Recommendations for Patients With ADHD
This is a 3-week open label, one group study to determine the agreement between the pharmacological treatment recommended by TDApp1 and the interventions recommended by relevant clinical practice guidelines.
- Trial name
- NCT04228094
- Testing
- TDApp1: an eHealth intervention
- Where
- Spain
- Volunteers
- 33 people
- Sponsor
- Universitat de Girona
Papillary Thyroid Microcarcinoma
PMRA and Shapley-Based Machine Learning for Predicting Lymph Node Metastasis in Central Subregions of Clinically Node-Negative Papillary Thyroid Microcarcinoma: a Prospective Multicenter Validation and Development of a Web Calculator
Background:Management of clinically node-negative(cN0) papillary thyroid microcarcinoma (PTMC) is complicated by high occult lymph node metastasis (LNM) rates.
- Trial name
- PMRA (NCT06871956)
- Testing
- PMRA and Shapley-Based Machine Learning for Predicting Lymph Node Metastasis in Central Subregions of Clinically Node-Negative Papillary Thyroid Microcarcinoma
- Where
- China
- Volunteers
- 4,882 people
- Sponsor
- First Affiliated Hospital of Chongqing Medical University
Severe Malaria
Clinical Prediction Models for Pediatric In-Hospital Death Risk in Congolese Severe Malaria Children Using Machine Learning Based-Algorithms
The goal of this observational study is to optimize the management of severe childhood malaria, based on understanding and controlling the severity factors of the disease in Congolese children aged 2 to 9 years (the age group at…
- Trial name
- NCT07355426
- Testing
- Puncture of the radial artery for instant arterial blood gaz as well as for venous biochemistry
- Where
- Democratic Republic of the Congo
- Volunteers
- 100 people
- Sponsor
- University of Kinshasa
Cerebral Palsy
European Newborn Study: Early Markers for a Better LifE
The goal of this observational study is to create a computer model to help doctors predict any problems occurring during development of babies after being diagnosed with brain injury.
- Trial name
- ENSEMBLE (NCT07189494)
- Where
- 8 sites in France, Germany and 3 more
- Volunteers
- 1,000 people
- Sponsor
- dr. M.J.N.L. Benders
Arrhythmias, Cardiac, Cardiac Arrest
Development of a Novel Convolution Neural Network for Arrhythmia Classification
Identifying the correct arrhythmia at the time of a clinic event including cardiac arrest is of high priority to patients, healthcare organizations, and to public health.
- Conditions
- Arrhythmias, Cardiac, Cardiac Arrest, Cardiac Arrythmias
- Trial name
- AI-ECG (NCT03662802)
- Testing
- Neural Network Classifier
- Where
- United States
- Volunteers
- 25,458 people, healthy volunteers welcome
- Sponsor
- Scripps Clinic
Dental Caries
AI-Based Detection of Dental Caries in Children
This study aims to evaluate the effects of an artificial intelligence (AI)-based caries detection system on the diagnosis and categorization of dental caries in pediatric patients.
- Trial name
- NCT06984029
- Where
- Egypt
- Volunteers
- 100 people, healthy volunteers welcome
- Sponsor
- Noura Nageh Ramadan Ahmed
Autism Spectrum Disorder, Depression - Major Depressive Disorder
Mental Health, Intellectual and Neurodevelopmental Disorder Detection With Artificial Intelligence Models
This study investigates whether AI-driven analysis of speech can accurately predict clinical diagnoses and assess risk for various mental or behavioral health conditions, including attention-deficit/hyperactivity disorder (ADHD)…
- Conditions
- Autism Spectrum Disorder, Depression - Major Depressive Disorder, Anxiety, Generalized, Bipolar Disorder (BD)
- Trial name
- MINDAIM (NCT06792175)
- Testing
- Solicue Machine Learning Models, Mercuria Machine Learning Models
- Where
- 2 sites in United States
- Volunteers
- 500 people
- Sponsor
- Psyrin Inc.