ArticleKorean journal of radiology2022
Bone Suppression on Chest Radiographs for Pulmonary Nodule Detection: Comparison between a Generative Adversarial Network and Dual-Energy Subtraction.
Article in Korean journal of radiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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18 citing papers in PubMed, 30 citations in OpenAlex.
- GAN-based bone suppression using a combined loss function.Frontiers in artificial intelligence · 2026Article
- Facilitating Radiograph Interpretation: Refined Generative Models for Precise Bone Suppression in Chest X-rays.Journal of imaging informatics in medicine · 2025Article
- Enhancing Radiographic Diagnosis: CycleGAN-Based Methods for Reducing Cast Shadow Artifacts in Wrist Radiographs.Journal of imaging informatics in medicine · 2025Article
- Suppression of immobilisation device on wrist radiography to improve fracture visualisation.European radiology · 2025Article
- Efficient labeling for fine-tuning chest X-ray bone-suppression networks for pediatric patients.Medical physics · 2025Article
- Comparison of Various GAN-Based Bone Suppression Imaging for High-Accurate Markerless Motion Tracking of Lung Tumors in CyberKnife Treatment.Thoracic cancer · 2025Article
- A Systematic Review: The Role of Artificial Intelligence in Lung Cancer Screening in Detecting Lung Nodules on Chest X-Rays.Diagnostics (Basel, Switzerland) · 2025Review
- Conceptual review of outcome metrics and measures used in clinical evaluation of artificial intelligence in radiology.La Radiologia medica · 2024Review
- Image-Based Generative Artificial Intelligence in Radiology: Comprehensive Updates.Korean journal of radiology · 2024Review
- Survey on Value Elements Provided by Artificial Intelligence and Their Eligibility for Insurance Coverage With an Emphasis on Patient-Centered Outcomes.Korean journal of radiology · 2024Article
- Uncover This Tech Term: Generative Adversarial Networks.Korean journal of radiology · 2024Article
- Updated Primer on Generative Artificial Intelligence and Large Language Models in Medical Imaging for Medical Professionals.Korean journal of radiology · 2024Review
- Reporting Quality of Research Studies on AI Applications in Medical Images According to the CLAIM Guidelines in a Radiology Journal With a Strong Prominence in Asia.Korean journal of radiology · 2023Review
- Use of Generative Artificial Intelligence, Including Large Language Models Such as ChatGPT, in Scientific Publications: Policies ofKorean journal of radiology · 2023Article
- Effectiveness of one-shot dual-energy subtraction chest radiography with flat-panel detector in distinguishing between calcified and non-calcified nodules.Scientific reports · 2023Article
- Development and Validation of a Deep Learning-Based Synthetic Bone-Suppressed Model for Pulmonary Nodule Detection in Chest Radiographs.JAMA network open · 2023Article
- Artificial intelligence-assisted multistrategy image enhancement of chest X-rays for COVID-19 classification.Quantitative imaging in medicine and surgery · 2023Article
- A Systematic Evaluation of Ensemble Learning Methods for Fine-Grained Semantic Segmentation of Tuberculosis-Consistent Lesions in Chest Radiographs.Bioengineering (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo compare the effects of bone suppression imaging using deep learning (BSp-DL) based on a generative adversarial network (GAN) and bone subtraction imaging using a dual energy technique (BSt-DE) on radiologists' performance for pulmonary nodule detection on chest radiographs (CXRs). MATERIALS AND
methodsA total of 111 adults, including 49 patients with 83 pulmonary nodules, who underwent both CXR using the dual energy technique and chest CT, were enrolled. Using CT as a reference, two independent radiologists evaluated CXR images for the presence or absence of pulmonary nodules in three reading sessions (standard CXR, BSt-DE CXR, and BSp-DL CXR). Person-wise and nodule-wise performances were assessed using receiver-operating characteristic (ROC) and alternative free-response ROC (AFROC) curve analyses, respectively. Subgroup analyses based on nodule size, location, and the presence of overlapping bones were performed.
resultsBSt-DE with an area under the AFROC curve (AUAFROC) of 0.996 and 0.976 for readers 1 and 2, respectively, and BSp-DL with AUAFROC of 0.981 and 0.958, respectively, showed better nodule-wise performance than standard CXR (AUAFROC of 0.907 and 0.808, respectively;
conclusionBSp-DL (GAN-based bone suppression) showed comparable performance to BSt-DE and can improve radiologists' performance in detecting pulmonary nodules on CXRs. Nevertheless, for better delineation of small and peripheral nodules, further technical improvements are required.
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