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Automated quantification of SARS-CoV-2 pneumonia with large vision model knowledge adaptation.
- Source :
-
New microbes and new infections [New Microbes New Infect] 2024 Aug 15; Vol. 62, pp. 101457. Date of Electronic Publication: 2024 Aug 15 (Print Publication: 2024). - Publication Year :
- 2024
-
Abstract
- Background: Large vision models (LVM) pretrained by large datasets have demonstrated their enormous capacity to understand visual patterns and capture semantic information from images. We proposed a novel method of knowledge domain adaptation with pretrained LVM for a low-cost artificial intelligence (AI) model to quantify the severity of SARS-CoV-2 pneumonia based on frontal chest X-ray (CXR) images.<br />Methods: Our method used the pretrained LVMs as the primary feature extractor and self-supervised contrastive learning for domain adaptation. An encoder with a 2048-dimensional feature vector output was first trained by self-supervised learning for knowledge domain adaptation. Then a multi-layer perceptron (MLP) was trained for the final severity prediction. A dataset with 2599 CXR images was used for model training and evaluation.<br />Results: The model based on the pretrained vision transformer (ViT) and self-supervised learning achieved the best performance in cross validation, with mean squared error (MSE) of 23.83 (95 % CI 22.67-25.00) and mean absolute error (MAE) of 3.64 (95 % CI 3.54-3.73). Its prediction correlation has the R 2 of 0.81 (95 % CI 0.79-0.82) and Spearman ρ of 0.80 (95 % CI 0.77-0.81), which are comparable to the current state-of-the-art (SOTA) methods trained by much larger CXR datasets.<br />Conclusion: The proposed new method has achieved the SOTA performance to quantify the severity of SARS-CoV-2 pneumonia at a significantly lower cost. The method can be extended to other infectious disease detection or quantification to expedite the application of AI in medical research.<br />Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.<br /> (Published by Elsevier Ltd.)
Details
- Language :
- English
- ISSN :
- 2052-2975
- Volume :
- 62
- Database :
- MEDLINE
- Journal :
- New microbes and new infections
- Publication Type :
- Academic Journal
- Accession number :
- 39253407
- Full Text :
- https://doi.org/10.1016/j.nmni.2024.101457