Clinical trials: Predictive Learning Models
Kidney Transplantation, Delirium - Postoperative
A Predictive Model for Postoperative Delirium in Kidney Transplant Patients
This study aims to develop and prospectively validate a machine learning-based prediction model for postoperative delirium in kidney transplant recipients, using perioperative clinical data.
- Conditions
- Kidney Transplantation, Delirium - Postoperative, Cognitive
- Trial name
- NCT07078578
- Volunteers
- 4,800 people
- Sponsor
- Hua Zheng
Difficult Airway Intubation
Research on a Machine Learning-Based Predictive Model for Difficult Intubation Using Specific Vocal Characteristics
1. Study Purpose This research aims to develop a novel, non-invasive, and simple method to predict difficult intubation before surgery.
- Trial name
- NCT07278232
- Testing
- No Intervention: Observational Cohort
- Where
- China
- Volunteers
- 300 people
- Sponsor
- Shanghai Jiao Tong University Affiliated Sixth People's Hospital
Renal Transplanted Recipients, Kidney Function
Renal Function Prediction in Living Donor Transplant Using Baseline Data
This study aims to predict early post-transplant kidney function in living donor kidney transplant recipients using baseline characteristics of donors and recipients.
- Trial name
- NCT06728995
- Where
- South Korea
- Volunteers
- 3,500 people
- Sponsor
- Seoul National University Hospital
Unruptured Intracranial Aneurysm, Intracranial Aneurysm Rupture
Prospective Observational Cohort Study for Predicting Unruptured Intracranial Aneurysm Rupture Risk Using Blood Pressure Variability and 3D Structural Evolution
This is a single-center, prospective, longitudinal observational cohort study in adults with unruptured intracranial aneurysms (UIAs) who are managed by conservative observation rather than immediate surgery or endovascular…
- Trial name
- NCT07893574
- Where
- China
- Volunteers
- 2,000 people
- Sponsor
- Beijing Tiantan Hospital
Sepsis
A Machine Learning Predictive Model for Sepsis
Timely and accurately predicting the occurrence of sepsis and actively intervening in treatment may effectively improve the survival and cure rate of patients with sepsis.
- Trial name
- NCT04771429
- Where
- China
- Volunteers
- 4,500 people, healthy volunteers welcome
- Sponsor
- Xinhua Hospital, Shanghai Jiao Tong University School of Medicine
Intracerebral Hemorrhage, Sepsis
Machine Learning Predictive Models for Sepsis Risk in ICU Patients With Intracerebral Hemorrhage
Patients with intracerebral hemorrhage (ICH) in the intensive care unit (ICU) are at heightened risk of developing sepsis, significantly increasing mortality and healthcare burden.
- Trial name
- NCT06326385
- Testing
- no intervention
- Where
- China
- Volunteers
- 1,800 people
- Sponsor
- Xiangya Hospital of Central South University
Spine Disease, Spinal Fusion
Validity and Accuracy of a Machine Learning Predictive Model in the Exploitation of Patients Related Outcomes in Spine Surgery
The objective of the study is the establishment, optimization and prospective evaluation of a digital predictive platform capable of providing for each lumbar spine operated patient a clinical predictive status: Patient green…
- Conditions
- Spine Disease, Spinal Fusion, Surgery, Spine Degeneration
- Trial name
- DeepSurgery (NCT05166018)
- Testing
- SuMO Patient
- Where
- 2 sites in France
- Volunteers
- 174 people
- Sponsor
- Cortexx Medical Intelligence
Sleep-Disordered Breathing, Sleep Architecture
Validation of Belun Ring Gen3 Deep Learning Algorithms With Subxiphoid Body Sensor
Hypothesis: BR's Gen3 DL algorithms, combined with its subxiphoid body sensor, can accurately diagnose OSA, categorize its severity, identify REM OSA and supine OSA, and detect central sleep apnea (CSA).
- Conditions
- Sleep-Disordered Breathing, Sleep Architecture, Arrhythmia
- Trial name
- NCT06255613
- Testing
- Belun Ring, Belun Cor
- Where
- 2 sites in United States
- Volunteers
- 79 people
- Sponsor
- Belun Technology Company Limited
Acute Hypoxemic Respiratory Failure, COVID 19
Predictive Model for High-Flow Nasal Cannula Failure in COVID-19-related Acute Hypoxemic Respiratory Failure Based on Metabolomics and Clinical Data
The aim of this prospective observational cohort study is to develop a predictive model for high-flow nasal cannula (HFNC) failure in patients with acute hypoxemic respiratory failure (AHRF) caused by SARS-CoV-2.
- Trial name
- COVIDOMICS (NCT06695442)
- Where
- Spain
- Volunteers
- 111 people
- Sponsor
- Hospital del Mar Research Institute (IMIM)
Pancreatic Cancer
A Study on a Predictive Model for Efficacy and Prognosis of Pancreatic Carcinoma Based on Multimodal Data
The goal of this observational study is to learn if combining information from CT scans, blood tests, and pathology reports can better predict how pancreatic cancer will progress.
- Trial name
- NCT07561814
- Where
- China
- Volunteers
- 1,030 people
- Sponsor
- Shanghai Zhongshan Hospital
PHYSIOLOGICAL PHENOMENA, Homeostasis
Truway Whole-Body Digital Twin and Physiologic Resilience Study (TRU-OMNIBODY)
TRU-OMNIBODY is a prospective observational study designed to develop and independently validate a research-only whole-body digital twin.
- Conditions
- PHYSIOLOGICAL PHENOMENA, Homeostasis, Physical Functional Performance, Cardiorespiratory Fitness
- Trial name
- TRU-OMNIBODY (NCT07805044)
- Testing
- Standardized Physiologic Resilience Challenge, Multimodal Whole-Body Physiologic Monitoring, Research-Only Whole-Body Digital Twin Analysis
- Where
- United States
- Volunteers
- 450 people, healthy volunteers welcome
- Sponsor
- Truway Health, Inc.
Colorectal Cancer Liver Metastasis
Prospective Validation of an AI Model for Predicting Liver Metastasis in Colorectal Cancer
This is a prospective, multicenter, observational study designed to validate the predictive accuracy of a pre-developed multimodal deep learning model.
- Trial name
- NCT07392567
- Testing
- Multimodal Deep Learning Prediction Model
- Where
- China
- Volunteers
- 160 people
- Sponsor
- Tongji Hospital
Funcional Status, Return to Work
Machine Learning Model for Predicting Recovery After Critical Illness
This study aims to develop and test an artificial intelligence (AI) model to predict long-term functional status and return to work after critical illness.
- Conditions
- Funcional Status, Return to Work, Predictive Learning Models
- Trial name
- NCT07616388
- Testing
- No interventions were performed.
- Where
- Brazil
- Volunteers
- 2,016 people
- Sponsor
- Hospital de Clinicas de Porto Alegre
Gastric (Stomach) Cancer
Development of a Predictive Model for Gastric Cancer Peritoneal Metastasis and Cachexia Using BUB1 and Radiopathomics Data With Deep Learning
This clinical trial aims to develop a predictive model for gastric cancer (GC) peritoneal metastasis and cachexia by integrating BUB1 gene data with radiological and pathological data using advanced deep learning techniques.
- Trial name
- BUDDLE (NCT06858644)
- Testing
- BUB1-Integrated Deep Learning Model for Gastric Cancer Metastasis and Cachexia Prediction
- Volunteers
- 500 people
- Sponsor
- Qun Zhao
Persistent Spinal Pain Syndrome Type 2 (PSPS-T) Lower Spine, Chronic Low Back Pain
SPINE-RISK VE: Multimodal Predictive Model for Failed Back Surgery Syndrome in Venezuelan Surgical Patients
SPINE-RISK VE is a prospective multicenter cohort study designed to develop and internally validate a multimodal preoperative predictive model for Failed Back Surgery Syndrome (FBSS), now classified as Persistent Spinal Pain…
- Conditions
- Persistent Spinal Pain Syndrome Type 2 (PSPS-T) Lower Spine, Chronic Low Back Pain, Postoperative Pain, Lumbar Spine Surgery
- Trial name
- SPINE-RISK VE (NCT07693517)
- Testing
- SPINE-RISK VE multimodal preoperative assessment
- Where
- Venezuela
- Volunteers
- 150 people
- Sponsor
- juan jose valero quintero,MD
Artificial Intelligence (AI), Machine Learning
Development and Pre-validation of a Machine Learning-based Prediction Algorithm for Early Functional Recovery in Patients Undergoing Hip and Knee Replacement Surgery
The goal of this observational study is to develop and pre-validate a machine learning algorithm to predict early recovery of mobility in patients undergoing hip or knee joint replacement surgery.
- Conditions
- Artificial Intelligence (AI), Machine Learning, Joint Replacement, Predictive Model
- Trial name
- FISIO_IA (NCT07333560)
- Testing
- Predictive Model for Early Mobility Recovery and Length of Stay
- Where
- 2 sites in Italy
- Volunteers
- 943 people
- Sponsor
- Istituto Ortopedico Rizzoli
Myopia, Small-incision Lenticule Extraction (SMILE) Surgery
Potential Risk Factors and Predictive Model Construction of OBL During SMILE
To explore the prediction of OBL by deep learning model in SMILE surgery
- Trial name
- NCT06577012
- Testing
- Small incision lenticule extraction
- Where
- China
- Volunteers
- 4,678 people, healthy volunteers welcome
- Sponsor
- Second Affiliated Hospital of Nanchang University
COVID-19 (Coronavirus Disease 2019), COVID-19 Pneumonia
Convolutional Neural Network Model to Detect Coronavirus Disease 2019 (COVID-19) Pneumonia in Chest Radiographs
This study aims to design a Convolutional Neural Network (CNN) and apply an attention model to help differentiate pneumonia due to Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), pneumonia due to other…
- Trial name
- RedNeumon (NCT05722665)
- Testing
- Categorization of chest xrays images
- Where
- Colombia
- Volunteers
- 3,599 people, healthy volunteers welcome
- Sponsor
- Fundacion Clinica Valle del Lili
Gangrenous Cholecystitis
Construction of a Predictive Model of Gangrenous Cholecystitis Based on Machine Learning
Gangrenous cholecystitis is the most common complication of acute cholecystitis. There is no research using machine learning models to construct predictive diagnostic models for gangrenous cholecystitis.
- Trial name
- NCT06399081
- Testing
- Observational
- Where
- China
- Volunteers
- 1,006 people
- Sponsor
- Dalian Medical University
ICL, Vault
Predictive Performance of a Generative Model for Corneal Tomography After ICL Implantation
To evaluate the efficacy of a corneal tomography Imaging model in predicting postoperative vault based on preoperative corneal topography in Implantable Collamer Lens (ICL) surgery.
- Conditions
- ICL, Vault, Deep Learning, AI (Artificial Intelligence)
- Trial name
- NCT07146737
- Testing
- Corneal tomography generation model after ICL surgery
- Where
- China
- Volunteers
- 818 people
- Sponsor
- Second Affiliated Hospital of Nanchang University