ReviewEuropean radiology2020
Deep learning in interstitial lung disease-how long until daily practice.
Review in European radiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
19 citing papers in PubMed, 54 citations in OpenAlex.
- Semi-Automated Lung Segmentation Based on Region-Growing Methods in Interstitial Lung Disease.Journal of clinical medicine · 2026Article
- Explainable pulmonary fibrosis detection using edge-strengthened dilated holistic edge detection-based lung segmentation and ResNet-V2 classification.Frontiers in artificial intelligence · 2026Article
- Radiomics and Artificial Intelligence in Pulmonary Fibrosis.Journal of imaging informatics in medicine · 2025Review
- Diagnosis of early idiopathic pulmonary fibrosis: current status and future perspective.Respiratory research · 2025Review
- Artificial intelligence in fracture detection on radiographs: a literature review.Japanese journal of radiology · 2025Review
- Pulmonary Diseases in Older Patients: Understanding and Addressing the Challenges.Geriatrics (Basel, Switzerland) · 2024Article
- Treatable Traits in Systemic Sclerosis.Clinical reviews in allergy & immunology · 2023Review
- Semi-Supervised Segmentation of Interstitial Lung Disease Patterns from CT Images via Self-Training with Selective Re-Training.Bioengineering (Basel, Switzerland) · 2023Article
- Artificial intelligence-based diagnosis of asbestosis: analysis of a database with applicants for asbestosis state aid.European radiology · 2023Article
- Interstitial lung disease diagnosis and prognosis using an AI system integrating longitudinal data.Nature communications · 2023Article
- A review on lung disease recognition by acoustic signal analysis with deep learning networks.Journal of big data · 2023Article
- Quantitative Assessment and Comparative Analysis of Longitudinal Lung CT Scans of Chest-Irradiated Nonhuman Primates.Radiation research · 2023Article
- Quantitative CT and machine learning classification of fibrotic interstitial lung diseases.European radiology · 2022Article
- Enhancing Imagistic Interstitial Lung Disease Diagnosis by Using Complex Networks.Medicina (Kaunas, Lithuania) · 2022Article
- A Novel Method for Lung Image Processing Using Complex Networks.Tomography (Ann Arbor, Mich.) · 2022Article
- Post-COVID-19 interstitial lung disease: Insights from a machine learning radiographic model.Frontiers in medicine · 2022Article
- Evaluation of a Novel Content-Based Image Retrieval System for the Differentiation of Interstitial Lung Diseases in CT Examinations.Diagnostics (Basel, Switzerland) · 2021Article
- [Radiological features of interstitial lung diseases].Der Pathologe · 2021Review
- A Survey on Artificial Intelligence in Pulmonary Imaging.Wiley interdisciplinary reviews. Data mining and knowledge discoveryArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Interstitial lung diseases are a diverse group of disorders that involve inflammation and fibrosis of interstitium, with clinical, radiological, and pathological overlapping features. These are an important cause of morbidity and mortality among lung diseases. This review describes computer-aided diagnosis systems centered on deep learning approaches that improve the diagnostic of interstitial lung diseases. We highlighted the challenges and the implementation of important daily practice, especially in the early diagnosis of idiopathic pulmonary fibrosis (IPF). Developing a convolutional neuronal network (CNN) that could be deployed on any computer station and be accessible to non-academic centers is the next frontier that needs to be crossed. In the future, early diagnosis of IPF should be possible. CNN might not only spare the human resources but also will reduce the costs spent on all the social and healthcare aspects of this deadly disease.Key Points• Deep learning algorithms are used in pattern recognition of different interstitial lung diseases.• High-resolution computed tomography plays a central role in the diagnosis and in the management of all interstitial lung diseases, especially fibrotic lung disease.• Developing an accessible algorithm that could be deployed on any computer station and be used in non-academic centers is the next frontier in the early diagnosis of idiopathic pulmonary fibrosis.
Indexed as
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What OpenQuestion holds
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.