ReviewEuropean radiology2020
Deep learning: definition and perspectives for thoracic imaging.
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 33 papers, 1 of them a synthesis that pooled it.
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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.
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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
33 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence for glaucoma.The Cochrane database of systematic reviews · 2025Pooled it
- Diagnostic performance of deep learning models in differentiating benign and malignant pulmonary nodules: a systematic review and meta-analysis.Quantitative imaging in medicine and surgery · 2026Article
- Imaging and characterization of pulmonary nodules in children with solid tumors: what makes a pulmonary nodule a metastasis?Pediatric radiology · 2026Review
- Prevalence of Interstitial Lung Abnormalities (ILAs) in Italian Lung Cancer Screening Programs: A Monocentric Study.Journal of clinical medicine · 2026Article
- Machine learning models for clinical and structural knee osteoarthritis prediction: Recent advancements and future directions.Osteoarthritis and cartilage open · 2025Article
- Advanced imaging techniques and artificial intelligence in pleural diseases: a narrative review.European respiratory review : an official journal of the European Respiratory Society · 2025Review
- Artificial Intelligence and Early Detection of Breast, Lung, and Colon Cancer: A Narrative Review.Cureus · 2025Review
- The Role of Artificial Intelligence in Urogynecology: Current Applications and Future Prospects.Diagnostics (Basel, Switzerland) · 2025Review
- Deep Learning Models of Multi-Scale Lesion Perception Attention Networks for Diagnosis and Staging of Pneumoconiosis: A Comparative Study with Radiologists.Journal of imaging informatics in medicine · 2024Article
- Differential diagnosis between low-risk and high-risk thymoma: Comparison of diagnostic performance of radiologists with and without deep learning model.Acta radiologica open · 2024Article
- Artificial intelligence in COPD CT images: identification, staging, and quantitation.Respiratory research · 2024Review
- A Proposal for a Process from as Low as Reasonably Achievable to an Ultra-Low-Level Goal in Chest Computed Tomography.Journal of clinical medicine · 2024Article
- Revolutionizing Pathology with Artificial Intelligence: Innovations in Immunohistochemistry.Journal of personalized medicine · 2024Review
- Deep Learning Model for Real‑time Semantic Segmentation During Intraoperative Robotic Prostatectomy.European urology open science · 2024Article
- Review
- Advancing Patient Care: How Artificial Intelligence Is Transforming Healthcare.Journal of personalized medicine · 2023Review
- Implementation of artificial intelligence in thoracic imaging-a what, how, and why guide from the European Society of Thoracic Imaging (ESTI).European radiology · 2023Review
- A machine learning approach for preoperatively assessing pulmonary function with computed tomography in patients with lung cancer.Quantitative imaging in medicine and surgery · 2023Article
- Artificial intelligence in lung cancer: current applications and perspectives.Japanese journal of radiology · 2023Review
- Predicting the Severity of COVID-19 from Lung CT Images Using Novel Deep Learning.Journal of medical and biological engineering · 2023Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Relevance and penetration of machine learning in clinical practice is a recent phenomenon with multiple applications being currently under development. Deep learning-and especially convolutional neural networks (CNNs)-is a subset of machine learning, which has recently entered the field of thoracic imaging. The structure of neural networks, organized in multiple layers, allows them to address complex tasks. For several clinical situations, CNNs have demonstrated superior performance as compared with classical machine learning algorithms and in some cases achieved comparable or better performance than clinical experts. Chest radiography, a high-volume procedure, is a natural application domain because of the large amount of stored images and reports facilitating the training of deep learning algorithms. Several algorithms for automated reporting have been developed. The training of deep learning algorithm CT images is more complex due to the dimension, variability, and complexity of the 3D signal. The role of these methods is likely to increase in clinical practice as a complement of the radiologist's expertise. The objective of this review is to provide definitions for understanding the methods and their potential applications for thoracic imaging. KEY POINTS: • Deep learning outperforms other machine learning techniques for number of tasks in radiology. • Convolutional neural network is the most popular deep learning architecture in medical imaging. • Numerous deep learning algorithms are being currently developed; some of them may become part of clinical routine in the near future.
Indexed as
Identifiers
31811431What 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.