{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["6(1)"],"submitter":["Kim H"],"pubmed_abstract":["<h4>Importance</h4>Dual-energy chest radiography exhibits better sensitivity than single-energy chest radiography, partly due to its ability to remove overlying anatomical structures.<h4>Objectives</h4>To develop and validate a deep learning-based synthetic bone-suppressed (DLBS) nodule-detection algorithm for pulmonary nodule detection on chest radiographs.<h4>Design, setting, and participants</h4>This decision analytical modeling study used data from 3 centers between November 2015 and July 2019 from 1449 patients. The DLBS nodule-detection algorithm was trained using single-center data (institute 1) of 998 chest radiographs. The DLBS algorithm was validated using 2 external data sets (institute 2, 246 patients; and institute 3, 205 patients). Statistical analysis was performed from Marc"],"journal":["JAMA network open"],"pagination":["e2253820"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9890286"],"repository":["biostudies-literature"],"pubmed_title":["Development and Validation of a Deep Learning-Based Synthetic Bone-Suppressed Model for Pulmonary Nodule Detection in Chest Radiographs."],"pmcid":["PMC9890286"],"pubmed_authors":["Hur J","Choi BW","Han K","Im DJ","Kim H","Kim JY","Hong YJ","Lee KH","Lee JW"],"additional_accession":[]},"is_claimable":false,"name":"Development and Validation of a Deep Learning-Based Synthetic Bone-Suppressed Model for Pulmonary Nodule Detection in Chest Radiographs.","description":"<h4>Importance</h4>Dual-energy chest radiography exhibits better sensitivity than single-energy chest radiography, partly due to its ability to remove overlying anatomical structures.<h4>Objectives</h4>To develop and validate a deep learning-based synthetic bone-suppressed (DLBS) nodule-detection algorithm for pulmonary nodule detection on chest radiographs.<h4>Design, setting, and participants</h4>This decision analytical modeling study used data from 3 centers between November 2015 and July 2019 from 1449 patients. The DLBS nodule-detection algorithm was trained using single-center data (institute 1) of 998 chest radiographs. The DLBS algorithm was validated using 2 external data sets (institute 2, 246 patients; and institute 3, 205 patients). Statistical analysis was performed from Marc","dates":{"release":"2023-01-01T00:00:00Z","publication":"2023 Jan","modification":"2025-04-21T18:16:09.682Z","creation":"2025-04-05T17:07:52.435Z"},"accession":"S-EPMC9890286","cross_references":{"pubmed":["36719681"],"doi":["10.1001/jamanetworkopen.2022.53820"]}}