Development and Validation of a Deep Learning–Based Synthetic Bone-Suppressed Model for Pulmonary Nodule Detection in Chest Radiographs
Sign Up to like & getrecommendations! Published in 2023 at "JAMA Network Open"
DOI: 10.1001/jamanetworkopen.2022.53820
Abstract: Key Points Question Can a deep learning–based synthetic bone-suppressed (DLBS) model additionally improve the detection of pulmonary nodules on chest radiographs? Findings In this decision analytical modeling study of 1449 patients, the DLBS model was… read more here.
Keywords: detection; chest radiographs; deep learning; model ... See more keywords
Multi‐institution consensus paper for acquisition of portable chest radiographs through glass barriers
Sign Up to like & getrecommendations! Published in 2021 at "Journal of Applied Clinical Medical Physics"
DOI: 10.1002/acm2.13330
Abstract: Abstract Background To conserve personal protective equipment (PPE) and reduce exposure to potentially infected COVID‐19 patients, several Californian facilities independently implemented a method of acquiring portable chest radiographs through glass barriers that was originally developed… read more here.
Keywords: glass; chest radiographs; glass barriers; radiographs glass ... See more keywords
Opportunistic Osteoporosis Screening Using Chest Radiographs With Deep Learning: Development and External Validation With a Cohort Dataset
Sign Up to like & getrecommendations! Published in 2021 at "Journal of Bone and Mineral Research"
DOI: 10.1002/jbmr.4477
Abstract: Osteoporosis is a common, but silent disease until it is complicated by fractures that are associated with morbidity and mortality. Over the past few years, although deep learning‐based disease diagnosis on chest radiographs has yielded… read more here.
Keywords: deep learning; chest radiographs; radiographs; model ... See more keywords
Interpretation of pediatric chest radiographs by non-radiologist clinicians in Botswana using World Health Organization criteria for endpoint pneumonia
Sign Up to like & getrecommendations! Published in 2020 at "Pediatric Radiology"
DOI: 10.1007/s00247-020-04625-0
Abstract: Background In low- and middle-income countries, chest radiographs are most frequently interpreted by non-radiologist clinicians. Objective We examined the reliability of chest radiograph interpretations performed by non-radiologist clinicians in Botswana and conducted an educational intervention… read more here.
Keywords: endpoint pneumonia; radiologist; chest radiographs; non radiologist ... See more keywords
COVID-19 in pediatric patients: a case series from the Bronx, NY
Sign Up to like & getrecommendations! Published in 2020 at "Pediatric Radiology"
DOI: 10.1007/s00247-020-04782-2
Abstract: Coronavirus disease 2019 (COVID-19) primarily affects adults, with a lower incidence in children. To report our experience with critically ill children with COVID-19. We reviewed the medical records of children with COVID-19 who were admitted… read more here.
Keywords: chest radiographs; disease; patients case; covid pediatric ... See more keywords
Organ doses in preterm and full-term neonates and infants — a retrospective study on 1,064 chest radiographs
Sign Up to like & getrecommendations! Published in 2022 at "Pediatric Radiology"
DOI: 10.1007/s00247-022-05324-8
Abstract: Chest radiography is the most frequent X-ray examination performed in the neonatal period. However, commonly used dosimetric entities do not describe the radiation risk sufficiently. The aim of this study was to investigate selected organ… read more here.
Keywords: neonates infants; organ doses; chest radiographs; full term ... See more keywords
Computer-Aided Reporting of Chest Radiographs: Efficient and Effective Screening in the Value-Based Imaging Era
Sign Up to like & getrecommendations! Published in 2017 at "Journal of Digital Imaging"
DOI: 10.1007/s10278-017-9952-y
Abstract: In the post-PACS era, mammography is unique in adopting specialized ergonomic interfaces to improve efficiency in a high volume setting. Chest radiography is also a high volume area of radiology. The authors hypothesize that applying… read more here.
Keywords: era; productivity; chest radiographs; radiology ... See more keywords
Fully automated lung segmentation from chest radiographs using SLICO superpixels
Sign Up to like & getrecommendations! Published in 2018 at "Analog Integrated Circuits and Signal Processing"
DOI: 10.1007/s10470-018-1153-1
Abstract: This project aims to create a computer-aided diagnosis (CAD) system that can be used to identify tuberculosis (TB) from chest radiographs (CXRs) and, in particular, to observe the progress of the disease where patients have… read more here.
Keywords: automated lung; fully automated; chest radiographs; lung segmentation ... See more keywords
Classification of cardioembolic stroke based on a deep neural network using chest radiographs
Sign Up to like & getrecommendations! Published in 2021 at "EBioMedicine"
DOI: 10.1016/j.ebiom.2021.103466
Abstract: Background: Although chest radiographs have not been utilised well for classifying stroke subtypes, they could provide a plethora of information on cardioembolic stroke. This study aimed to develop a deep convolutional neural network that could… read more here.
Keywords: network; cardioembolic stroke; chest radiographs; stroke based ... See more keywords
Low order adaptive region growing for lung segmentation on plain chest radiographs
Sign Up to like & getrecommendations! Published in 2018 at "Neurocomputing"
DOI: 10.1016/j.neucom.2017.09.053
Abstract: Abstract This study proposes a computer-aided region segmentation for the plain chest radiographs. It incorporates an avant-garde contrast enhancement that increases the opacity of the lung regions. The region of interest (ROI) is localized preliminarily… read more here.
Keywords: region growing; segmentation; chest radiographs; plain chest ... See more keywords
Radiologists can visually predict mortality risk based on the gestalt of chest radiographs comparable to a deep learning network
Sign Up to like & getrecommendations! Published in 2021 at "Scientific Reports"
DOI: 10.1038/s41598-021-99107-0
Abstract: Deep learning convolutional neural network (CNN) can predict mortality from chest radiographs, yet, it is unknown whether radiologists can perform the same task. Here, we investigate whether radiologists can visually assess image gestalt (defined as… read more here.
Keywords: risk; gestalt; deep learning; chest radiographs ... See more keywords