ReviewDiagnostics (Basel, Switzerland)2019
Automatic Pulmonary Nodule Detection Applying Deep Learning or Machine Learning Algorithms to the LIDC-IDRI Database: A Systematic Review.
Review in Diagnostics (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers.
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Who cites it
47 citing papers in PubMed.
- Detection of Confounders and Potential Confounders in Computed Tomography Lung Datasets.Journal of imaging informatics in medicine · 2026Article
- Mapping outcomes in randomised radiology trials: Protocol for a scoping review.Acta radiologica open · 2026Article
- Construction and validation of a prediction model for malignant pulmonary nodules based on imaging, demographic, and laboratory features.Frontiers in oncology · 2026Article
- 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
- Multiple Tumor-related autoantibodies test enhances CT-based deep learning performance in diagnosing lung cancer with diameters < 70 mm: a prospective study in China.BMC pulmonary medicine · 2025Article
- A study for expert-informed active pulmonary nodule segmentation.Biomedical engineering letters · 2025Article
- 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
- A Novel Deep Learning-Based (3D U-Net Model) Automated Pulmonary Nodule Detection Tool for CT Imaging.Current oncology (Toronto, Ont.) · 2025Article
- Deep Learning-Based Detect-Then-Track Pipeline for Treatment Outcome Assessments in Immunotherapy-Treated Liver Cancer.Journal of imaging informatics in medicine · 2025Article
- A Bibliometric and Visual Analysis of Cancer Screening Based on the Web of Science Core Collection Database.International journal of health policy and management · 2025Article
- A Novel Method for 3D Lung Tumor Reconstruction Using Generative Models.Diagnostics (Basel, Switzerland) · 2024Article
- Article
- Evolving and Novel Applications of Artificial Intelligence in Thoracic Imaging.Diagnostics (Basel, Switzerland) · 2024Review
- Pulmonary nodule detection in low dose computed tomography using a medical-to-medical transfer learning approach.Journal of medical imaging (Bellingham, Wash.) · 2024Article
- Prediction of therapy response of breast cancer patients with machine learning based on clinical data and imaging data derived from breast [European journal of nuclear medicine and molecular imaging · 2024Article
- Hierarchical approach for pulmonary-nodule identification from CT images using YOLO model and a 3D neural network classifier.Radiological physics and technology · 2024Article
- ERS International Congress 2023: highlights from the Clinical Techniques, Imaging and Endoscopy Assembly.ERJ open research · 2024Article
- Application value of a computer-aided diagnosis and management system for the detection of lung nodules.Quantitative imaging in medicine and surgery · 2023Article
- Lung nodule false positive reduction using a central attention convolutional neural network on imbalanced data.Proceedings of SPIE--the International Society for Optical Engineering · 2023Article
- Assessing Barriers to Implementation of Machine Learning and Artificial Intelligence-Based Tools in Critical Care: Web-Based Survey Study.JMIR perioperative medicine · 2023Article
Corrections and comments
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Authors and funding
3 authors.
Funding
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Abstract
The aim of this study was to provide an overview of the literature available on machine learning (ML) algorithms applied to the Lung Image Database Consortium Image Collection (LIDC-IDRI) database as a tool for the optimization of detecting lung nodules in thoracic CT scans. This systematic review was compiled according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Only original research articles concerning algorithms applied to the LIDC-IDRI database were included. The initial search yielded 1972 publications after removing duplicates, and 41 of these articles were included in this study. The articles were divided into two subcategories describing their overall architecture. The majority of feature-based algorithms achieved an accuracy >90% compared to the deep learning (DL) algorithms that achieved an accuracy in the range of 82.2%⁻97.6%. In conclusion, ML and DL algorithms are able to detect lung nodules with a high level of accuracy, sensitivity, and specificity using ML, when applied to an annotated archive of CT scans of the lung. However, there is no consensus on the method applied to determine the efficiency of ML algorithms.
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