AIMC Topic: Retrospective Studies

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Diagnostic performance of an AI algorithm for the detection of appendicular bone fractures in pediatric patients.

European journal of radiology
PURPOSE: To evaluate the diagnostic performance of an Artificial Intelligence (AI) algorithm, previously trained using both adult and pediatric patients, for the detection of acute appendicular fractures in the pediatric population on conventional X-...

Artificial intelligence for predicting shockable rhythm during cardiopulmonary resuscitation: In-hospital setting.

Resuscitation
AIM OF THE STUDY: This study aimed to develop an artificial intelligence (AI) model capable of predicting shockable rhythms from electrocardiograms (ECGs) with compression artifacts using real-world data from emergency department (ED) settings. Addit...

Accelerated cardiac magnetic resonance imaging using deep learning for volumetric assessment in children.

Pediatric radiology
BACKGROUND: Ventricular volumetry using a short-axis stack of two-dimensional (D) cine balanced steady-state free precession (bSSFP) sequences is crucial in any cardiac magnetic resonance imaging (MRI) examination. This task becomes particularly chal...

Predicting Clostridioides difficile infection outcomes with explainable machine learning.

EBioMedicine
BACKGROUND: Clostridioides difficile infection results in life-threatening short-term outcomes and the potential for subsequent recurrent infection. Predicting these outcomes at diagnosis, when important clinical decisions need to be made, has proven...

Machine Learning for Early Discrimination Between Lung Cancer and Benign Nodules Using Routine Clinical and Laboratory Data.

Annals of surgical oncology
BACKGROUND: Lung cancer poses a global health threat necessitating early detection and precise staging for improved patient outcomes. This study focuses on developing and validating a machine learning-based risk model for early lung cancer screening ...

A machine learning algorithm for stratification of risk of cardiovascular disease in metabolic dysfunction-associated steatotic liver disease.

European journal of internal medicine
BACKGROUND: Steatotic liver disease (SLD) is associated with adverse cardiac events. Metabolic dysfunction-associated steatotic liver disease (MASLD) has emerged as a condition characterized by the abnormal accumulation of hepatic lipids that is clos...

Machine learning models predict triage levels, massive transfusion protocol activation, and mortality in trauma utilizing patients hemodynamics on admission.

Computers in biology and medicine
BACKGROUND: The effective management of trauma patients necessitates efficient triaging, timely activation of Massive Blood Transfusion Protocols (MTP), and accurate prediction of in-hospital outcomes. Machine learning (ML) algorithms have emerged as...

Prediction of hematoma expansion in spontaneous intracerebral hemorrhage using a multimodal neural network.

Scientific reports
Hematoma expansion occasionally occurs in patients with intracerebral hemorrhage (ICH), associating with poor outcome. Multimodal neural networks incorporating convolutional neural network (CNN) analysis of images and neural network analysis of tabul...

Deep Learning Assisted Classification of T1ρ-MR Based Intervertebral Disc Degeneration Phases.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: According to the T1ρ value of nucleus pulposus, our previous study has found that intervertebral disc degeneration (IDD) can be divided into three phases based on T1ρ-MR, which is helpful for the selection of biomaterial treatment timing....

Diagnostic utility of transfer learning by using convolutional neural network for cytological diagnosis of malignant effusions.

Diagnostic cytopathology
INTRODUCTION: Cytological analysis of effusion specimens provides critical information regarding the diagnosis and staging of malignancies, thus guiding their treatment and subsequent monitoring. Keeping in view the challenges encountered in the morp...