ArticleBMC medical imaging2022
Development and performance evaluation of a deep learning lung nodule detection system.
Article in BMC medical imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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14 citing papers in PubMed, 22 citations in OpenAlex.
- Artificial intelligence-based lung nodule detection for pulmonary arteriovenous fistulas on chest computed tomography.World journal of radiology · 2026Article
- Identifying a Critical Blind Spot: How Commercial AI (CAD) Systems Fail to Detect Faint Ground-Glass Opacities at -730 HU on Low-Dose CT.Diagnostics (Basel, Switzerland) · 2026Article
- LungGANDetectAI: a GAN-augmented and attention-guided deep learning framework for accurate and explainable lung cancer detection.Scientific reports · 2026Article
- A Custom Annotated Dataset for Segmentation of Pulmonary Veins, Arteries, and Airways.Scientific data · 2025Article
- Performance of a deep-learning-based lung nodule detection system using 0.25-mm thick ultra-high-resolution CT images.Japanese journal of radiology · 2025Article
- Artificial intelligence for the detection of airway nodules in chest CT scans.European radiology · 2025Article
- A Systematic Review of AI Performance in Lung Cancer Detection on CT Thorax.Healthcare (Basel, Switzerland) · 2025Review
- External validation of the performance of commercially available deep-learning-based lung nodule detection on low-dose CT images for lung cancer screening in Japan.Japanese journal of radiology · 2025Article
- Development and validation of computer-aided detection for colorectal neoplasms using deep learning incorporated with computed tomography colonography.BMC gastroenterology · 2025Article
- A Thorough Review of the Clinical Applications of Artificial Intelligence in Lung Cancer.Cancers · 2025Review
- Deep Learning in Thoracic Oncology: Meta-Analytical Insights into Lung Nodule Early-Detection Technologies.Cancers · 2025Review
- Optimized deep learning approach for lung cancer detection using flying fox optimization and bidirectional generative adversarial networks.PeerJ. Computer science · 2025Article
- A Comprehensive Review of Performance Metrics for Computer-Aided Detection Systems.Bioengineering (Basel, Switzerland) · 2024Review
- New trend in artificial intelligence-based assistive technology for thoracic imaging.La Radiologia medica · 2023Review
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Authors and funding
16 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundLung cancer is the leading cause of cancer-related deaths throughout the world. Chest computed tomography (CT) is now widely used in the screening and diagnosis of lung cancer due to its effectiveness. Radiologists must identify each small nodule shadow from 3D volume images, which is very burdensome and often results in missed nodules. To address these challenges, we developed a computer-aided detection (CAD) system that automatically detects lung nodules in CT images.
methodsA total of 1997 chest CT scans were collected for algorithm development. The algorithm was designed using deep learning technology. In addition to evaluating detection performance on various public datasets, its robustness to changes in radiation dose was assessed by a phantom study. To investigate the clinical usefulness of the CAD system, a reader study was conducted with 10 doctors, including inexperienced and expert readers. This study investigated whether the use of the CAD as a second reader could prevent nodular lesions in lungs that require follow-up examinations from being overlooked. Analysis was performed using the Jackknife Free-Response Receiver-Operating Characteristic (JAFROC).
resultsThe CAD system achieved sensitivity of 0.98/0.96 at 3.1/7.25 false positives per case on two public datasets. Sensitivity did not change within the range of practical doses for a study using a phantom. A second reader study showed that the use of this system significantly improved the detection ability of nodules that could be picked up clinically (p = 0.026).
conclusionsWe developed a deep learning-based CAD system that is robust to imaging conditions. Using this system as a second reader increased detection performance.
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