ArticleScientific reports2020
Development and clinical application of deep learning model for lung nodules screening on CT images.
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 44 papers, 1 of them a synthesis that pooled it.
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44 citing papers in PubMed, 1 synthesis or guideline pooled it, 97 citations in OpenAlex.
- Accuracy of Low-Dose Chest CT-Based Artificial Intelligence Models in Osteoporosis Detection: A Systematic Review and Meta-analysis.Calcified tissue international · 2025Pooled it
- Lung nodule detection and potential impact on guideline-based management: a retrospective post-market evaluation of three commercial software systems.European radiology · 2026Article
- Diagnostic performance of commercial AI systems versus participating radiologists for pulmonary nodule detection in routine clinical practice.Japanese journal of radiology · 2026Article
- Retrospective Evaluation of an AI-Based Computer-Aided Detection Algorithm for Lung Cancer Detection on Cardiac CT: A Multicenter Study.Journal of Korean medical science · 2026Article
- Pediatric lung ground glass nodules: a real-world, large-scale CT cohort analysis.BMC medical imaging · 2026Article
- Advancing the early detection of lung cancer: Outcomes of a surgeon-led lung nodule clinic in a distressed patient population.JTCVS open · 2026Article
- Artificial Intelligence and Machine Learning in Lung Cancer: Advances in Imaging, Detection, and Prognosis.Cancers · 2025Review
- Review
- Staging CT chest for cT1a renal masses: Does it change management?BJUI compass · 2025Article
- Narrative review of the application of artificial intelligence-related technologies in the diagnosis of pulmonary nodules with recommendations for clinical practice and future research.Journal of thoracic disease · 2025Review
- Deep learning reconstruction improves computer-aided pulmonary nodule detection and measurement accuracy for ultra-low-dose chest CT.BMC medical imaging · 2025Article
- Review
- Reducing the workload of medical diagnosis through artificial intelligence: A narrative review.Medicine · 2025Review
- Artificial Intelligence and Early Detection of Breast, Lung, and Colon Cancer: A Narrative Review.Cureus · 2025Review
- An artificial intelligence algorithm for the detection of pulmonary ground-glass nodules on spectral detector CT: performance on virtual monochromatic images.BMC medical imaging · 2024Article
- Estimation of pathological subtypes in subsolid lung nodules using artificial intelligence.Heliyon · 2024Article
- Effect of emphysema on AI software and human reader performance in lung nodule detection from low-dose chest CT.European radiology experimental · 2024Article
- Artificial intelligence in lung cancer screening: Detection, classification, prediction, and prognosis.Cancer medicine · 2024Review
- AI-Driven Models for Diagnosing and Predicting Outcomes in Lung Cancer: A Systematic Review and Meta-Analysis.Cancers · 2024Review
- Early Detection of Lung Nodules Using a Revolutionized Deep Learning Model.Diagnostics (Basel, Switzerland) · 2023Article
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Authors and funding
9 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Lung cancer screening based on low-dose CT (LDCT) has now been widely applied because of its effectiveness and ease of performance. Radiologists who evaluate a large LDCT screening images face enormous challenges, including mechanical repetition and boring work, the easy omission of small nodules, lack of consistent criteria, etc. It requires an efficient method for helping radiologists improve nodule detection accuracy with efficiency and cost-effectiveness. Many novel deep neural network-based systems have demonstrated the potential for use in the proposed technique to detect lung nodules. However, the effectiveness of clinical practice has not been fully recognized or proven. Therefore, the aim of this study to develop and assess a deep learning (DL) algorithm in identifying pulmonary nodules (PNs) on LDCT and investigate the prevalence of the PNs in China. Radiologists and algorithm performance were assessed using the FROC score, ROC-AUC, and average time consumption. Agreement between the reference standard and the DL algorithm in detecting positive nodules was assessed per-study by Bland-Altman analysis. The Lung Nodule Analysis (LUNA) public database was used as the external test. The prevalence of NCPNs was investigated as well as other detailed information regarding the number of pulmonary nodules, their location, and characteristics, as interpreted by two radiologists.
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