COVLIAS 1.0Lesion vs. MedSeg An Artificial Intelligence Framework for Automated Lesion Segmentation in COVID-19 Lung Computed Tomography Scans /
COVID-19 is a disease with multiple variants, and is quickly spreading throughout the world. It is crucial to identify patients who are suspected of having COVID-19 early, because the vaccine is not readily available in certain parts of the world.Lung computed tomography (CT) imaging can be used to...
Elmentve itt :
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| Dokumentumtípus: | Cikk |
| Megjelent: |
2022
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| Sorozat: | DIAGNOSTICS
12 No. 5 |
| Tárgyszavak: | |
| doi: | 10.3390/diagnostics12051283 |
| mtmt: | 32913495 |
| Online Access: | http://publicatio.bibl.u-szeged.hu/24631 |
| Tartalmi kivonat: | COVID-19 is a disease with multiple variants, and is quickly spreading throughout the world. It is crucial to identify patients who are suspected of having COVID-19 early, because the vaccine is not readily available in certain parts of the world.Lung computed tomography (CT) imaging can be used to diagnose COVID-19 as an alternative to the RT-PCR test in some cases. The occurrence of ground-glass opacities in the lung region is a characteristic of COVID-19 in chest CT scans, and these are daunting to locate and segment manually. The proposed study consists of a combination of solo deep learning (DL) and hybrid DL (HDL) models to tackle the lesion location and segmentation more quickly. One DL and four HDL models-namely, PSPNet, VGG-SegNet, ResNet-SegNet, VGG-UNet, and ResNet-UNet-were trained by an expert radiologist. The training scheme adopted a fivefold cross-validation strategy on a cohort of 3000 images selected from a set of 40 COVID-19-positive individuals.The proposed variability study uses tracings from two trained radiologists as part of the validation. Five artificial intelligence (AI) models were benchmarked against MedSeg. The best AI model, ResNet-UNet, was superior to MedSeg by 9% and 15% for Dice and Jaccard, respectively, when compared against MD 1, and by 4% and 8%, respectively, when compared against MD 2. Statistical tests-namely, the Mann-Whitney test, paired t-test, and Wilcoxon test-demonstrated its stability and reliability, with p < 0.0001. The online system for each slice was <1 s.The AI models reliably located and segmented COVID-19 lesions in CT scans. The COVLIAS 1.0Lesion lesion locator passed the intervariability test. |
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| Terjedelem/Fizikai jellemzők: | Terjedelem: 34 p.-Azonosító: 1283 |
| ISSN: | 2075-4418 |