ArticleBMC medical imaging2021
Cardiothoracic ratio measurement using artificial intelligence: observer and method validation studies.
Article in BMC medical imaging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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11 citing papers in PubMed, 27 citations in OpenAlex.
- AI-based measurement of cardiothoracic ratio in chest X-rays and prediction of echocardiographic congestive heart failure.International journal of cardiology. Heart & vasculature · 2025Article
- Measurement of Cardiothoracic Ratio on Chest X-rays Using Artificial Intelligence-A Systematic Review and Meta-Analysis.Journal of clinical medicine · 2024Review
- Deep Learning in Cardiothoracic Ratio Calculation and Cardiomegaly Detection.Journal of clinical medicine · 2024Article
- Automatic cardiothoracic ratio calculation based on lung fields abstracted from chest X-ray images without heart segmentation.Frontiers in physiology · 2024Article
- Deep learning-based cardiothoracic ratio measurement on chest radiograph: accuracy improvement without self-annotation.Quantitative imaging in medicine and surgery · 2023Article
- The Performance of a Deep Learning-Based Automatic Measurement Model for Measuring the Cardiothoracic Ratio on Chest Radiographs.Bioengineering (Basel, Switzerland) · 2023Article
- Validation of an Automated Cardiothoracic Ratio Calculation for Hemodialysis Patients.Diagnostics (Basel, Switzerland) · 2023Article
- Observer performance evaluation of the feasibility of a deep learning model to detect cardiomegaly on chest radiographs.Acta radiologica open · 2022Article
- A clinical evaluation study of cardiothoracic ratio measurement using artificial intelligence.BMC medical imaging · 2022Article
- U-Net-Based Medical Image Segmentation.Journal of healthcare engineering · 2022Review
- Evaluation of the feasibility of explainable computer-aided detection of cardiomegaly on chest radiographs using deep learning.Scientific reports · 2021Article
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Authors and funding
8 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundArtificial Intelligence (AI) is a promising tool for cardiothoracic ratio (CTR) measurement that has been technically validated but not clinically evaluated on a large dataset. We observed and validated AI and manual methods for CTR measurement using a large dataset and investigated the clinical utility of the AI method.
methodsFive thousand normal chest x-rays and 2,517 images with cardiomegaly and CTR values, were analyzed using manual, AI-assisted, and AI-only methods. AI-only methods obtained CTR values from a VGG-16 U-Net model. An in-house software was used to aid the manual and AI-assisted measurements and to record operating time. Intra and inter-observer experiments were performed on manual and AI-assisted methods and the averages were used in a method variation study. AI outcomes were graded in the AI-assisted method as excellent (accepted by both users independently), good (required adjustment), and poor (failed outcome). Bland-Altman plot with coefficient of variation (CV), and coefficient of determination (R-squared) were used to evaluate agreement and correlation between measurements. Finally, the performance of a cardiomegaly classification test was evaluated using a CTR cutoff at the standard (0.5), optimum, and maximum sensitivity.
resultsManual CTR measurements on cardiomegaly data were comparable to previous radiologist reports (CV of 2.13% vs 2.04%). The observer and method variations from the AI-only method were about three times higher than from the manual method (CV of 5.78% vs 2.13%). AI assistance resulted in 40% excellent, 56% good, and 4% poor grading. AI assistance significantly improved agreement on inter-observer measurement compared to manual methods (CV; bias: 1.72%; - 0.61% vs 2.13%; - 1.62%) and was faster to perform (2.2 ± 2.4 secs vs 10.6 ± 1.5 secs). The R-squared and classification-test were not reliable indicators to verify that the AI-only method could replace manual operation.
conclusionsAI alone is not yet suitable to replace manual operations due to its high variation, but it is useful to assist the radiologist because it can reduce observer variation and operation time. Agreement of measurement should be used to compare AI and manual methods, rather than R-square or classification performance tests.
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