A clinical evaluation study of cardiothoracic ratio measurement using artificial intelligence
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Author list: Saiviroonporn, Pairash; Wonglaksanapimon, Suwimon; Chaisangmongkon, Warasinee; Chamveha, Isarun; Yodprom, Pakorn; Butnian, Krittachat; Siriapisith, Thanogchai; Tongdee, Trongtum;
Publisher: BioMed Central
Publication year: 2022
Volume number: 22
Issue number: 1
ISSN: 1471-2342
eISSN: 1471-2342
Languages: English-Great Britain (EN-GB)
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Abstract
Background: Artificial intelligence, particularly the deep learning (DL) model, can provide reliable results for automated cardiothoracic ratio (CTR) measurement on chest X-ray (CXR) images. In everyday clinical use, however, this technology is usually implemented in a non-automated (AI-assisted) capacity because it still requires approval from radiologists. We investigated the performance and efficiency of our recently proposed models for the AI-assisted method intended for clinical practice. Methods: We validated four proposed DL models (AlbuNet, SegNet, VGG-11, and VGG-16) to find the best model for clinical implementation using a dataset of 7517 CXR images from manual operations. These models were investigated in single-model and combined-model modes to find the model with the highest percentage of results where the user could accept the results without further interaction (excellent grade), and with measurement variation within ± 1.8% of the human-operating range. The best model from the validation study was then tested on an evaluation dataset of 9386 CXR images using the AI-assisted method with two radiologists to measure the yield of excellent grade results, observer variation, and operating time. A Bland–Altman plot with coefficient of variation (CV) was employed to evaluate agreement between measurements. Results: The VGG-16 gave the highest excellent grade result (68.9%) of any single-model mode with a CV comparable to manual operation (2.12% vs 2.13%). No DL model produced a failure-grade result. The combined-model mode of AlbuNet + VGG-11 model yielded excellent grades in 82.7% of images and a CV of 1.36%. Using the evaluation dataset, the AlbuNet + VGG-11 model produced excellent grade results in 77.8% of images, a CV of 1.55%, and reduced CTR measurement time by almost ten-fold (1.07 ± 2.62 s vs 10.6 ± 1.5 s) compared with manual operation. Conclusion: Due to its excellent accuracy and speed, the AlbuNet + VGG-11 model could be clinically implemented to assist radiologists with CTR measurement. © 2022, The Author(s).
Keywords
Clinical evaluation, CXR
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