On the analyses of medical images using traditional machine learning techniques and convolutional neural networks
Convolutional neural network (CNN) has shown dissuasive accomplishment on different
areas especially Object Detection, Segmentation, Reconstruction (2D and 3D), Information …
areas especially Object Detection, Segmentation, Reconstruction (2D and 3D), Information …
Artificial intelligence in cardiovascular CT and MR imaging
LRM Lanzafame, GM Bucolo, G Muscogiuri, S Sironi… - Life, 2023 - mdpi.com
The technological development of Artificial Intelligence (AI) has grown rapidly in recent
years. The applications of AI to cardiovascular imaging are various and could improve the …
years. The applications of AI to cardiovascular imaging are various and could improve the …
The role of artificial intelligence in cardiovascular magnetic resonance imaging
AA Aromiwura, JL Cavalcante, RY Kwong… - Progress in …, 2024 - Elsevier
Cardiovascular magnetic resonance (CMR) imaging is the gold standard test for myocardial
tissue characterization and chamber volumetric and functional evaluation. However, manual …
tissue characterization and chamber volumetric and functional evaluation. However, manual …
Machine learning-based diagnosis and risk classification of coronary artery disease using myocardial perfusion imaging SPECT: A radiomics study
This study aimed to investigate the diagnostic performance of machine learning-based
radiomics analysis to diagnose coronary artery disease status and risk from rest/stress …
radiomics analysis to diagnose coronary artery disease status and risk from rest/stress …
High-dimensional multinomial multiclass severity scoring of COVID-19 pneumonia using CT radiomics features and machine learning algorithms
We aimed to construct a prediction model based on computed tomography (CT) radiomics
features to classify COVID-19 patients into severe-, moderate-, mild-, and non-pneumonic. A …
features to classify COVID-19 patients into severe-, moderate-, mild-, and non-pneumonic. A …
Myocardial perfusion SPECT imaging radiomic features and machine learning algorithms for cardiac contractile pattern recognition
A U-shaped contraction pattern was shown to be associated with a better Cardiac
resynchronization therapy (CRT) response. The main goal of this study is to automatically …
resynchronization therapy (CRT) response. The main goal of this study is to automatically …
Interpretable radiomic signature for breast microcalcification detection and classification
Breast microcalcifications are observed in 80% of mammograms, and a notable proportion
can lead to invasive tumors. However, diagnosing microcalcifications is a highly complicated …
can lead to invasive tumors. However, diagnosing microcalcifications is a highly complicated …
Machine learning‐based prediction of 1‐year mortality in hypertensive patients undergoing coronary revascularization surgery
Background Machine learning (ML) has shown promising results in all fields of medicine,
including preventive cardiology. Hypertensive patients are at higher risk of mortality after …
including preventive cardiology. Hypertensive patients are at higher risk of mortality after …
Post-revascularization ejection fraction prediction for patients undergoing percutaneous coronary intervention based on myocardial perfusion SPECT imaging …
In this study, the ability of radiomics features extracted from myocardial perfusion imaging
with SPECT (MPI-SPECT) was investigated for the prediction of ejection fraction (EF) post …
with SPECT (MPI-SPECT) was investigated for the prediction of ejection fraction (EF) post …
Dual-centre harmonised multimodal positron emission tomography/computed tomography image radiomic features and machine learning algorithms for non-small cell …
Aims We aimed to build radiomic models for classifying non-small cell lung cancer (NSCLC)
histopathological subtypes through a dual-centre dataset and comprehensively evaluate the …
histopathological subtypes through a dual-centre dataset and comprehensively evaluate the …