Purpose To develop a deep learning (DL) model that derives aligned strain values from cine (noncontrast) cardiac MRI and evaluate performance of these values to predict myocardial fibrosis in patients with Duchenne muscular dystrophy (DMD). Materials...
Purpose To develop and evaluate machine learning and deep learning-based models for automated protocoling of emergency brain MRI scans based on clinical referral text. Materials and Methods In this single-institution, retrospective study of 1953 emer...
Purpose To evaluate cancer detection and marker placement accuracy of two artificial intelligence (AI) models developed for interpretation of screening mammograms. Materials and Methods This retrospective study included data from 129 434 screening ex...
Purpose To assess the effect of scanner manufacturer and scanning protocol on the performance of deep learning models to classify aggressiveness of prostate cancer (PCa) at biparametric MRI (bpMRI). Materials and Methods In this retrospective study, ...
Fetal ventriculomegaly (VM) and its severity and associated central nervous system (CNS) abnormalities are important indicators of high risk for impaired neurodevelopmental outcomes. Recently, a novel fetal brain age prediction method using a two-dim...
Purpose To construct and evaluate the performance of a machine learning model for bone segmentation using whole-body CT images. Materials and Methods In this retrospective study, whole-body CT scans (from June 2010 to January 2018) from 90 patients (...
Purpose To evaluate the performance of eight lung cancer prediction models on patient cohorts with screening-detected, incidentally detected, and bronchoscopically biopsied pulmonary nodules. Materials and Methods This study retrospectively evaluated...
Purpose To evaluate the performance of Physics-Informed Autoencoder (PIA), a self-supervised deep learning model, in measuring tissue-based biomarkers for prostate cancer (PCa) using hybrid multidimensional MRI. Materials and Methods This retrospecti...
Purpose To develop and evaluate the performance of NNFit, a self-supervised deep learning method for quantification of high-resolution short-echo-time (TE) echo-planar spectroscopic imaging (EPSI) datasets, with the goal of addressing the computation...
Purpose To investigate whether the computational effort of three-dimensional CT-based multiorgan segmentation with TotalSegmentator can be reduced via Tucker decomposition-based network compression. Materials and Methods In this retrospective study, ...