ArticleBMC medicine2021
Automated detection of lung nodules and coronary artery calcium using artificial intelligence on low-dose CT scans for lung cancer screening: accuracy and prognostic value.
Article in BMC medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 73 papers, 5 of them syntheses that pooled it.
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Who cites it
73 citing papers in PubMed, 5 syntheses or guidelines pooled it, 135 citations in OpenAlex.
- Software with artificial intelligence-derived algorithms for detecting and analysing lung nodules in CT scans: systematic review and economic evaluation.Health technology assessment (Winchester, England) · 2025Pooled it
- Application of artificial intelligence in laryngeal lesions: a systematic review and meta-analysis.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2025Pooled it
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- Pooled it
- The value of artificial intelligence in the diagnosis of lung cancer: A systematic review and meta-analysis.PloS one · 2023Pooled it
- Potential to improve early detection of lung cancer through expansion of focused and opportunistic screening approaches: a scoping review focusing on potential expansion of screening risk factors and adoption of artificial intelligence models.Lung cancer management · 2026Review
- Scientific evidence of commercial artificial intelligence products for pulmonary nodule assessment on CT scans: a systematic review.European radiology · 2026Article
- Recent advances in artificial intelligence across interventional pulmonology: a narrative review.Journal of thoracic disease · 2026Review
- Developing a calibration method to utilize low-dose chest CT for assessment of coronary artery calcification score.Journal of applied clinical medical physics · 2026Article
- Current trends and future directions of artificial intelligence in lung cancer diagnosis.Chinese journal of cancer research = Chung-kuo yen cheng yen chiu · 2026Article
- Review
- Review
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- Human vs. artificial intelligence: Physicians outperform ChatGPT in real-world pharmacotherapy counselling.British journal of clinical pharmacology · 2026Article
- Artificial Intelligence in Lung Cancer: A Narrative Review of Recent Advances in Diagnosis, Biomarker Discovery, and Drug Development.Pharmaceutics · 2026Review
- Article
- Factors influencing diagnostic yield and complication risk in computed tomography fluoroscopy-guided lung biopsies: a 10-year single-center study.Diagnostic and interventional radiology (Ankara, Turkey) · 2026Article
- Advancements in artificial intelligence for cancer diagnosis and prognosis prediction: current applications and emerging opportunities.Frontiers in cell and developmental biology · 2026Review
- Acupoint temperature as a biomarker: infrared thermography in the diagnosis of adolescents with major depressive disorder.Frontiers in psychiatry · 2026Article
- Incidental Pulmonary Nodules: What Should We Do in 2026?Respiration; international review of thoracic diseases · 2026Review
13 more citing papers are in PubMed but not listed here.
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Authors and funding
17 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundArtificial intelligence (AI) in diagnostic radiology is undergoing rapid development. Its potential utility to improve diagnostic performance for cardiopulmonary events is widely recognized, but the accuracy and precision have yet to be demonstrated in the context of current screening modalities. Here, we present findings on the performance of an AI convolutional neural network (CNN) prototype (AI-RAD Companion, Siemens Healthineers) that automatically detects pulmonary nodules and quantifies coronary artery calcium volume (CACV) on low-dose chest CT (LDCT), and compare results to expert radiologists. We also correlate AI findings with adverse cardiopulmonary outcomes in a retrospective cohort of 117 patients who underwent LDCT.
methodsA total of 117 patients were enrolled in this study. Two CNNs were used to identify lung nodules and CACV on LDCT scans. All subjects were used for lung nodule analysis, and 96 subjects met the criteria for coronary artery calcium volume analysis. Interobserver concordance was measured using ICC and Cohen's kappa. Multivariate logistic regression and partial least squares regression were used for outcomes analysis.
resultsAgreement of the AI findings with experts was excellent (CACV ICC = 0.904, lung nodules Cohen's kappa = 0.846) with high sensitivity and specificity (CACV: sensitivity = .929, specificity = .960; lung nodules: sensitivity = 1, specificity = 0.708). The AI findings improved the prediction of major cardiopulmonary outcomes at 1-year follow-up including major adverse cardiac events and lung cancer (AUC
conclusionWe conclude the AI prototype rapidly and accurately identifies significant risk factors for cardiopulmonary disease on standard screening low-dose chest CT. This information can be used to improve diagnostic ability, facilitate intervention, improve morbidity and mortality, and decrease healthcare costs. There is also potential application in countries with limited numbers of cardiothoracic radiologists.
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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.