ReviewBiomedical engineering online2018
Computer-aided detection in chest radiography based on artificial intelligence: a survey.
Review in Biomedical engineering online, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 104 papers, 4 of them syntheses that pooled it.
What it found
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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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Who cites it
104 citing papers in PubMed, 4 syntheses or guidelines pooled it, 379 citations in OpenAlex.
- Advances in Deep Learning for Tuberculosis Screening using Chest X-rays: The Last 5 Years Review.Journal of medical systems · 2022Pooled it
- Pediatric chest radiograph interpretation: how far has artificial intelligence come? A systematic literature review.Pediatric radiology · 2022Pooled it
- Accuracy of computer-aided image analysis in the diagnosis of odontogenic cysts: A systematic review.Medicina oral, patologia oral y cirugia bucal · 2021Pooled it
- Accuracy of computer-assisted image analysis in the diagnosis of maxillofacial radiolucent lesions: A systematic review and meta-analysis.Dento maxillo facial radiology · 2020Pooled it
- Diagnostic performance of an open-source artificial intelligence model for detecting chest infections on after-hours emergency department chest radiographs in Singapore.BMJ health & care informatics · 2026Article
- Advancements in echocardiography for congenital heart defects (CHDs): a literature review.Annals of medicine and surgery (2012) · 2026Review
- Generalist foundation models from a multimodal dataset for 3D computed tomography.Nature biomedical engineering · 2026Article
- A 19-layer convolutional neural network for accurate COVID-19 detection in chest X-ray images: comparative analysis with pretrained networks.Scientific reports · 2026Article
- An optimized EfficientNetB0 framework with CLAHE-based preprocessing for accurate multi-class chest X-ray classification.Scientific reports · 2026Article
- Non-contact lung disease classification via orthogonal frequency division multiplexing-based passive 6G integrated sensing and communication.Communications medicine · 2026Article
- A Deep Network Incorporating Depthwise Separable Convolutions for Pathological Diagnosis of Chest X-Ray Images: A Model Development and Validation Study.Health science reports · 2026Article
- Classification of Chest X-Ray Images Using Modern Machine Learning Techniques by Orang3 Data Mining Tool.International journal of biomedical imaging · 2026Article
- Evaluation of Machine Learning Methods Developed for Prediction and Diagnosis of Pneumonia: A Systematic Review.Health science reports · 2025Review
- Review
- Article
- Threshold optimization in AI chest radiography analysis: integrating real-world data and clinical subgroups.European radiology experimental · 2025Article
- Standardisation and Optimisation of Chest and Pelvis X-Ray Imaging Protocols Across Multiple Radiography Systems in a Radiology Department.Diagnostics (Basel, Switzerland) · 2025Article
- An evaluation of a checklist in musculoskeletal radiographic image interpretation when using artificial intelligence.Journal of medical radiation sciences · 2025Article
- Semantic Consistency-Based Uncertainty Quantification for Factuality in Radiology Report Generation.Findings of ACL. NAACL · 2025Article
- A New Pes Planus Automatic Diagnosis Method: ViT-OELM Hybrid Modeling.Diagnostics (Basel, Switzerland) · 2025Article
44 more citing papers are in PubMed but not listed here.
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
As the most common examination tool in medical practice, chest radiography has important clinical value in the diagnosis of disease. Thus, the automatic detection of chest disease based on chest radiography has become one of the hot topics in medical imaging research. Based on the clinical applications, the study conducts a comprehensive survey on computer-aided detection (CAD) systems, and especially focuses on the artificial intelligence technology applied in chest radiography. The paper presents several common chest X-ray datasets and briefly introduces general image preprocessing procedures, such as contrast enhancement and segmentation, and bone suppression techniques that are applied to chest radiography. Then, the CAD system in the detection of specific disease (pulmonary nodules, tuberculosis, and interstitial lung diseases) and multiple diseases is described, focusing on the basic principles of the algorithm, the data used in the study, the evaluation measures, and the results. Finally, the paper summarizes the CAD system in chest radiography based on artificial intelligence and discusses the existing problems and trends.
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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.