ArticleDiagnostics (Basel, Switzerland)2019
Predicting Pulmonary Function Testing from Quantified Computed Tomography Using Machine Learning Algorithms in Patients with COPD.
Article 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 13 papers.
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Who cites it
13 citing papers in PubMed, 30 citations in OpenAlex.
- Development of a machine learning model in prediction of the rapid progression of interstitial lung disease in patients with idiopathic inflammatory myopathy.Quantitative imaging in medicine and surgery · 2024Article
- Artificial intelligence in COPD CT images: identification, staging, and quantitation.Respiratory research · 2024Review
- Computed tomography (CT) quantitative assessment of single lobe emphysema correlates with chronic obstructive pulmonary disease (COPD) severity: a cross-sectional study with retrospective data collection.Quantitative imaging in medicine and surgery · 2024Article
- Evaluation of retrieval accuracy and visual similarity in content-based image retrieval of chest CT for obstructive lung disease.Scientific reports · 2024Article
- The utility of quantitative computed tomography in cohort studies of chronic obstructive pulmonary disease: a narrative review.Journal of thoracic disease · 2023Review
- A machine learning approach for preoperatively assessing pulmonary function with computed tomography in patients with lung cancer.Quantitative imaging in medicine and surgery · 2023Article
- Measuring pulmonary function in COPD using quantitative chest computed tomography analysis.European respiratory review : an official journal of the European Respiratory Society · 2021Review
- Artificial Intelligence and Machine Learning in Chronic Airway Diseases: Focus on Asthma and Chronic Obstructive Pulmonary Disease.International journal of medical sciences · 2021Review
- Machine learning-based data analytic approaches for evaluating post-natal mouse respiratory physiological evolution.Respiratory physiology & neurobiology · 2021Article
- Applications of Machine Learning Predictive Models in the Chronic Disease Diagnosis.Journal of personalized medicine · 2020Review
- What's New on Quantitative CT Analysis as a Tool to Predict Growth in Persistent Pulmonary Subsolid Nodules? A Literature Review.Diagnostics (Basel, Switzerland) · 2020Review
- From infancy to adulthood-Developmental changes in pulmonary quantitative computed tomography parameters.PloS one · 2020Article
- Solid Indeterminate Pulmonary Nodules of Less Than 300 mmDiagnostics (Basel, Switzerland) · 2019Article
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Authors and funding
9 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionQuantitative computed tomography (qCT) is an emergent technique for diagnostics and research in patients with chronic obstructive pulmonary disease (COPD). qCT parameters demonstrate a correlation with pulmonary function tests and symptoms. However, qCT only provides anatomical, not functional, information. We evaluated five distinct, partial-machine learning-based mathematical models to predict lung function parameters from qCT values in comparison with pulmonary function tests.
methods75 patients with diagnosed COPD underwent body plethysmography and a dose-optimized qCT examination on a third-generation, dual-source CT with inspiration and expiration. Delta values (inspiration-expiration) were calculated afterwards. Four parameters were quantified: mean lung density, lung volume low-attenuated volume, and full width at half maximum. Five models were evaluated for best prediction: average prediction, median prediction,
resultsThe lowest mean relative error (MRE) was calculated for the kNN model with 16%. Similar low MREs were found for polynomial regression as well as gradient boosting-based prediction. Other models led to higher MREs and thereby worse predictive performance. Beyond the sole MRE, distinct differences in prediction performance, dependent on the initial dataset (expiration, inspiration, delta), were found.
conclusionDifferent, partially machine learning-based models allow the prediction of lung function values from static qCT parameters within a reasonable margin of error. Therefore, qCT parameters may contain more information than we currently utilize and can potentially augment standard functional lung testing.
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