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Training used 5,178 radiographs from a single institution, while paired emergency department and ICU studies set change thresholds and enabled temporal validation. Performance was measured with receiver operating characteristic AUC and compared with a thoracic radiologist across settings, showing generally comparable accuracy.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":35,"@type":76,"position":81},"https://docshare.wps.com/document/healthcare/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/automated-evaluation-of-pulmonary-lesion-changes-on-chest-radiograph-during-follow-up-using-semantic-segmentation/434679/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/automated-evaluation-of-pulmonary-lesion-changes-on-chest-radiograph-during-follow-up-using-semantic-segmentation/434679.png","ImageObject",300,407,{"name":92,"@type":93},"Aditya","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-06","2026-09-29",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",12,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What is the study’s main goal for follow-up chest radiographs?","Question",{"text":113,"@type":114},"The goal is to develop and validate a deep learning model that uses lesion-specific segmentation to classify consolidation and pleural effusion as changed or unchanged on paired chest radiographs during follow-up.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"How was the model trained and validated?",{"text":118,"@type":114},"The model was trained on lesion segmentation using 5,178 chest radiographs from a single institution. Paired radiographs from the emergency department and ICU were used to determine change thresholds and perform temporal validation.",{"name":120,"@type":111,"acceptedAnswer":121},"How did the model perform compared with a thoracic radiologist?",{"text":122,"@type":114},"In both emergency and ICU settings, the model achieved high AUCs for consolidation and pleural effusion. Accuracy results were generally comparable to the radiologist, with no significant differences for consolidation in the ICU or both targets in the ED, except for pleural effusion in the ICU.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},434679,1790785597,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":34,"category_name":35,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":34,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":145,"preview_page_count":4},962085564549,"https://ap-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45","Copyright @ 2026 Author(s) -[Available online at dirjournal.org.](Available online at dirjournal.org. ORIGINAL)[ ORIGINAL](Available online at dirjournal.org. ORIGINAL) ARTICLE  \nContent of this journal is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.  \nAutomated evaluation of pulmonary lesion changes on chest radiograph during follow-up using semantic segmentation  \n Youngjae Kim 1,2* Yura Ahn3*  \n Sang Min Lee3  \n Han Na Noh4  \n Jongjun Won2  \n Chaewon Kim2  Hyunna Lee5  \n1University of Ulsan Faculty of Medicine, Department of Biomedical Engineering, AMIST, Asan Medical Center, Seoul, Republic of Korea  \n2University of Ulsan Faculty of Medicine, Department of Convergence Medicine, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, Seoul, Republic of Korea  \n3University of Ulsan Faculty of Medicine, Department of Radiology and Research Institute of Radiology, Asan Medical Center, Seoul, Republic of Korea  \n4University of Ulsan Faculty of Medicine, Health Screening and Promotion Center, Asan Medical Center, Seoul, Republic of Korea  \n5Bigdata Research Center, Asan Institute for Life Science, Asan Medical Center, Seoul, Republic of Korea  \n*Joint first authors  \nCorresponding author: Sang Min Lee  \nE-mail: [sangmin.lee.md@gmail.com](sangmin.lee.md@gmail.com)  \nReceived 22 July 2025; revision requested 24 August 2025; accepted 23 September 2025.  \nPublication date: 02.01.2026  \nDOI: 10.4274/dir.2025.253567  \nPURPOSE  \nTo develop and validate a deep learning-based model utilizing lesion-specific segmentation to determine the changed/unchanged status of consolidation and pleural effusion in paired chest radiographs (CRs) .  \nMETHODS  \nThe model was trained using 5.178 CRs from a single institution for lesion segmentation. Paired CRs from the emergency department (ED) and intensive care unit (ICU) were used to determine the thresholds for change and temporal validation. Model performance was evaluated through the area under the receiver operating characteristic curve (AUC), and its accuracy was compared with that of a thoracic radiologist.  \nRESULTS  \nIn the ED, the model achieved AUCs of 0.988 and 0.883 for consolidation and pleural effusion, respectively, with accuracies of 0.900 (36/40) and 0.825 (33/40). The radiologist showed accuracies of 0.975 (39/40) and 0.950 (38/40), respectively. In the ICU, model AUCs were 0.970 (consolidation) and 0.955 (pleural effusion), with accuracies of 0.875 (35/40) and 0.800 (32/40), respectively. Radiologist performance was 0.975 (39/40) for consolidation and 1.000 (40/40) for pleural effusion. No significant accuracy differences were observed between the model and radiologist for consolidation in the ICU or both targets in the ED (all P > 0.05), except for pleural effusion in the ICU (P = 0.01) .  \nCONCLUSION  \nThe lesion-specific deep learning model was feasible for identifying interval changes in consolidation and pleural effusion on follow-up CRs.  \nCLINICAL SIGNIFICANCE  \nIt could potentially be utilized for prioritizing interpretation, generating alerts, and extracting time-series data from multiple follow-up CRs.  \nKEYWORDS  \nRadiography, thoracic, follow-up studies, diagnosis, computer-assisted, artificial intelligence, segmentation  \nChest radiography is a widely used medical imaging modality due to its cost-effective  \nness and low radiation exposure. Chest radiographs (CRs) detect thoracic abnormalities  \nand track changes during follow-ups. Monitoring abnormalities such as pleural effusion or consolidation is crucial for evaluating disease progression and treatment response.1-4 However, frequent follow-up CRs increase workload. For example, in intensive care units (ICUs), CRis often performed daily for patients who are critically ill or after device adjustments, generating millions of ICU CRs annually in the United States.5,6 Consequently, the timely and accurate interpretation of follow-up CRs is becoming more","cbCaiu70L5Ro32wx","https://ap.wps.com/l/cbCaiu70L5Ro32wx","pdf",2625560,"English","# PURPOSE\n# METHODS\n# RESULTS\n# CONCLUSION\n# CLINICAL SIGNIFICANCE\n# KEYWORDS","[{\"question\":\"What is the study’s main goal for follow-up chest radiographs?\",\"answer\":\"The goal is to develop and validate a deep learning model that uses lesion-specific segmentation to classify consolidation and pleural effusion as changed or unchanged on paired chest radiographs during follow-up.\"},{\"question\":\"How was the model trained and validated?\",\"answer\":\"The model was trained on lesion segmentation using 5,178 chest radiographs from a single institution. Paired radiographs from the emergency department and ICU were used to determine change thresholds and perform temporal validation.\"},{\"question\":\"How did the model perform compared with a thoracic radiologist?\",\"answer\":\"In both emergency and ICU settings, the model achieved high AUCs for consolidation and pleural effusion. Accuracy results were generally comparable to the radiologist, with no significant differences for consolidation in the ICU or both targets in the ED, except for pleural effusion in the ICU.\"}]","Automated evaluation of pulmonary lesion changes on chest radiograph during follow-up using semantic segmentation | PDF",1790670226,18]