Development of deep learning-based classification models for opacity differentiation in pediatric chest radiography
Opacities of non-interstitial origin in a pediatric patient's chest radiograph may indicate either consolidations and/or atelectasis, based on the appropriate clinical context. However, the overlapping and complex symptomatology of respiratory tract diseases in pediatric patients can make it di...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-01-01
|
| Series: | Informatics in Medicine Unlocked |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S235291482400162X |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850175220629897216 |
|---|---|
| author | Germán Enrique Galvis Ruiz Johana Benavides-Cruz Daniela Muñoz Corredor Esteban Morales-Mendoza Héctor Daniel Alejandro Cotrino Palma Andrés Cely-Jiménez |
| author_facet | Germán Enrique Galvis Ruiz Johana Benavides-Cruz Daniela Muñoz Corredor Esteban Morales-Mendoza Héctor Daniel Alejandro Cotrino Palma Andrés Cely-Jiménez |
| author_sort | Germán Enrique Galvis Ruiz |
| collection | DOAJ |
| description | Opacities of non-interstitial origin in a pediatric patient's chest radiograph may indicate either consolidations and/or atelectasis, based on the appropriate clinical context. However, the overlapping and complex symptomatology of respiratory tract diseases in pediatric patients can make it difficult for physicians to interpret opacities. Artificial intelligence models are frequently employed by physicians for diagnostic support in healthcare, especially to evaluate aspects of radiographs that are not visible with the naked eye. In this study, a prediction model based on deep learning was used to differentiate between atelectasis and consolidations in pediatric chest radiographs from a clinical perspective. The radiologist can assist pediatricians in diagnosing respiratory pathologies based on the type of opacities using the machine learning model. We used 1297 chest X-ray images of pediatric patients with opacities including consolidations (n=500), atelectasis (n=499); and images without opacities (n=298). The images were preprocessed, and various deep learning models were applied to determine the model with the best metrics. The InceptionV3 model demonstrated a significant improvement over its initial results. |
| format | Article |
| id | doaj-art-7eff5f5c020b4cbfbe0bd4ac487c066b |
| institution | OA Journals |
| issn | 2352-9148 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Informatics in Medicine Unlocked |
| spelling | doaj-art-7eff5f5c020b4cbfbe0bd4ac487c066b2025-08-20T02:19:30ZengElsevierInformatics in Medicine Unlocked2352-91482025-01-015210160510.1016/j.imu.2024.101605Development of deep learning-based classification models for opacity differentiation in pediatric chest radiographyGermán Enrique Galvis Ruiz0Johana Benavides-Cruz1Daniela Muñoz Corredor2Esteban Morales-Mendoza3Héctor Daniel Alejandro Cotrino Palma4Andrés Cely-Jiménez5Radiology and Diagnostic Imaging Program, Fundación Universitaria Sanitas, Street 170 #8-41, 110151, Bogotá D.C., ColombiaResearch Unit, Fundación Universitaria Sanitas, Street 170 #8-41, 110151, Bogotá D.C., Colombia; Corresponding author. Street 170 8-41, 110151, Bogotá D.C, Colombia.Healthcare Management Institute, Fundación Universitaria Sanitas, Street 170 #8-41, 110151, Bogotá D.C., ColombiaHealthcare Management Institute, Fundación Universitaria Sanitas, Street 170 #8-41, 110151, Bogotá D.C., ColombiaRadiology and Diagnostic Imaging Program, Fundación Universitaria Sanitas, Street 170 #8-41, 110151, Bogotá D.C., ColombiaDepartment of Data Management, Keralty, Street 100 #11b – 67, 110221, Bogotá D.C., ColombiaOpacities of non-interstitial origin in a pediatric patient's chest radiograph may indicate either consolidations and/or atelectasis, based on the appropriate clinical context. However, the overlapping and complex symptomatology of respiratory tract diseases in pediatric patients can make it difficult for physicians to interpret opacities. Artificial intelligence models are frequently employed by physicians for diagnostic support in healthcare, especially to evaluate aspects of radiographs that are not visible with the naked eye. In this study, a prediction model based on deep learning was used to differentiate between atelectasis and consolidations in pediatric chest radiographs from a clinical perspective. The radiologist can assist pediatricians in diagnosing respiratory pathologies based on the type of opacities using the machine learning model. We used 1297 chest X-ray images of pediatric patients with opacities including consolidations (n=500), atelectasis (n=499); and images without opacities (n=298). The images were preprocessed, and various deep learning models were applied to determine the model with the best metrics. The InceptionV3 model demonstrated a significant improvement over its initial results.http://www.sciencedirect.com/science/article/pii/S235291482400162XArtificial intelligenceDeep learningRadiographyPulmonary atelectasisChildrenLung diseases |
| spellingShingle | Germán Enrique Galvis Ruiz Johana Benavides-Cruz Daniela Muñoz Corredor Esteban Morales-Mendoza Héctor Daniel Alejandro Cotrino Palma Andrés Cely-Jiménez Development of deep learning-based classification models for opacity differentiation in pediatric chest radiography Informatics in Medicine Unlocked Artificial intelligence Deep learning Radiography Pulmonary atelectasis Children Lung diseases |
| title | Development of deep learning-based classification models for opacity differentiation in pediatric chest radiography |
| title_full | Development of deep learning-based classification models for opacity differentiation in pediatric chest radiography |
| title_fullStr | Development of deep learning-based classification models for opacity differentiation in pediatric chest radiography |
| title_full_unstemmed | Development of deep learning-based classification models for opacity differentiation in pediatric chest radiography |
| title_short | Development of deep learning-based classification models for opacity differentiation in pediatric chest radiography |
| title_sort | development of deep learning based classification models for opacity differentiation in pediatric chest radiography |
| topic | Artificial intelligence Deep learning Radiography Pulmonary atelectasis Children Lung diseases |
| url | http://www.sciencedirect.com/science/article/pii/S235291482400162X |
| work_keys_str_mv | AT germanenriquegalvisruiz developmentofdeeplearningbasedclassificationmodelsforopacitydifferentiationinpediatricchestradiography AT johanabenavidescruz developmentofdeeplearningbasedclassificationmodelsforopacitydifferentiationinpediatricchestradiography AT danielamunozcorredor developmentofdeeplearningbasedclassificationmodelsforopacitydifferentiationinpediatricchestradiography AT estebanmoralesmendoza developmentofdeeplearningbasedclassificationmodelsforopacitydifferentiationinpediatricchestradiography AT hectordanielalejandrocotrinopalma developmentofdeeplearningbasedclassificationmodelsforopacitydifferentiationinpediatricchestradiography AT andrescelyjimenez developmentofdeeplearningbasedclassificationmodelsforopacitydifferentiationinpediatricchestradiography |