ArticleScientific reports2020
Classification of Interstitial Lung Abnormality Patterns with an Ensemble of Deep Convolutional Neural Networks.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
37 citing papers in PubMed, 1 synthesis or guideline pooled it, 98 citations in OpenAlex.
- Idiopathic Pulmonary Fibrosis (an Update) and Progressive Pulmonary Fibrosis in Adults: An Official ATS/ERS/JRS/ALAT Clinical Practice Guideline.American journal of respiratory and critical care medicine · 2022Guideline
- Interstitial lung abnormalities in a large clinical lung cancer screening cohort: association with mortality and ILD diagnosis.Respiratory research · 2023Trial
- From ideal respiratory health to chronic lung disease: indicators of impaired respiratory health.Expert review of respiratory medicine · 2026Review
- Automated interstitial lung abnormalities detection at CT: external validation and potential recognition of traction bronchiectasis/bronchiolectasis.Japanese journal of radiology · 2026Article
- Deep learning-derived quantitative interstitial abnormalities in early rheumatoid arthritis and healthy controls: A multicenter, prospective cross-sectional study.medRxiv : the preprint server for health sciences · 2026Article
- Clinical Insights and Therapeutic Strategies for the Treatment of Interstitial Lung Disease in Patients with Anti-Neutrophil Cytoplasmic Antibody-Associated Vasculitis: Current Trends and Future Directions.Journal of clinical medicine · 2025Review
- A KNN-based model for non-invasive prediction of hemorrhagic shock severity in prehospital settings: integrating MAP, PBiomedical engineering online · 2025Article
- Interstitial Lung Abnormalities: Unraveling the Journey from Incidental Discovery to Clinical Significance.Diagnostics (Basel, Switzerland) · 2025Review
- Development of a machine learning model in prediction of the rapid progression of interstitial lung disease in patients with idiopathic inflammatory myopathy.Quantitative imaging in medicine and surgery · 2024Article
- Automated Interstitial Lung Abnormality Probability Prediction at CT: A Stepwise Machine Learning Approach in the Boston Lung Cancer Study.Radiology · 2024Article
- Pattern classification of interstitial lung diseases from computed tomography images using a ResNet-based network with a split-transform-merge strategy and split attention.Physical and engineering sciences in medicine · 2024Article
- Utility of peripheral protein biomarkers for the prediction of incident interstitial features: a multicentre retrospective cohort study.BMJ open respiratory research · 2024Article
- The influence of image reconstruction methods on the diagnosis of pulmonary emphysema with convolutional neural network.Radiological physics and technology · 2023Article
- Deep Learning Assessment of Progression of Emphysema and Fibrotic Interstitial Lung Abnormality.American journal of respiratory and critical care medicine · 2023Article
- Computer-Aided Pulmonary Fibrosis Detection Leveraging an Advanced Artificial Intelligence Triage and Notification Software.Journal of clinical medicine research · 2023Article
- Lung Imaging in COPD Part 2: Emerging Concepts.Chest · 2023Review
- Semi-Supervised Segmentation of Interstitial Lung Disease Patterns from CT Images via Self-Training with Selective Re-Training.Bioengineering (Basel, Switzerland) · 2023Article
- Beyond Visual Interpretation: Quantitative Analysis and Artificial Intelligence in Interstitial Lung Disease Diagnosis "Expanding Horizons in Radiology".Diagnostics (Basel, Switzerland) · 2023Review
- Recent Advances of Artificial Intelligence Applications in Interstitial Lung Diseases.Diagnostics (Basel, Switzerland) · 2023Review
- Interstitial lung disease diagnosis and prognosis using an AI system integrating longitudinal data.Nature communications · 2023Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 2 countries.
Funding
Abstract
Subtle interstitial changes in the lung parenchyma of smokers, known as Interstitial Lung Abnormalities (ILA), have been associated with clinical outcomes, including mortality, even in the absence of Interstitial Lung Disease (ILD). Although several methods have been proposed for the automatic identification of more advanced Interstitial Lung Disease (ILD) patterns, few have tackled ILA, which likely precedes the development ILD in some cases. In this context, we propose a novel methodology for automated identification and classification of ILA patterns in computed tomography (CT) images. The proposed method is an ensemble of deep convolutional neural networks (CNNs) that detect more discriminative features by incorporating two, two-and-a-half and three- dimensional architectures, thereby enabling more accurate classification. This technique is implemented by first training each individual CNN, and then combining its output responses to form the overall ensemble output. To train and test the system we used 37424 radiographic tissue samples corresponding to eight different parenchymal feature classes from 208 CT scans. The resulting ensemble performance including an average sensitivity of 91,41% and average specificity of 98,18% suggests it is potentially a viable method to identify radiographic patterns that precede the development of ILD.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.