ArticleBMC medical informatics and decision making2022
A novel deep learning-based method for COVID-19 pneumonia detection from CT images.
Article in BMC medical informatics and decision making, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
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Who cites it
3 citing papers in PubMed, 1 synthesis or guideline pooled it, 14 citations in OpenAlex.
- Combating COVID-19 Crisis using Artificial Intelligence (AI) Based Approach: Systematic Review.Current topics in medicinal chemistry · 2024Pooled it
- A Systematic Review on Deep Structured Learning for COVID-19 Screening Using Chest CT from 2020 to 2022.Healthcare (Basel, Switzerland) · 2023Review
- Machine learning-based prediction of in-ICU mortality in pneumonia patients.Scientific reports · 2023Article
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe sensitivity of RT-PCR in diagnosing COVID-19 is only 60-70%, and chest CT plays an indispensable role in the auxiliary diagnosis of COVID-19 pneumonia, but the results of CT imaging are highly dependent on professional radiologists.
aimsThis study aimed to develop a deep learning model to assist radiologists in detecting COVID-19 pneumonia.
methodsThe total study population was 437. The training dataset contained 26,477, 2468, and 8104 CT images of normal, CAP, and COVID-19, respectively. The validation dataset contained 14,076, 1028, and 3376 CT images of normal, CAP, and COVID-19 patients, respectively. The test set included 51 normal cases, 28 CAP patients, and 51 COVID-19 patients. We designed and trained a deep learning model to recognize normal, CAP, and COVID-19 patients based on U-Net and ResNet-50. Moreover, the diagnoses of the deep learning model were compared with different levels of radiologists.
resultsIn the test set, the sensitivity of the deep learning model in diagnosing normal cases, CAP, and COVID-19 patients was 98.03%, 89.28%, and 92.15%, respectively. The diagnostic accuracy of the deep learning model was 93.84%. In the validation set, the accuracy was 92.86%, which was better than that of two novice doctors (86.73% and 87.75%) and almost equal to that of two experts (94.90% and 93.88%). The AI model performed significantly better than all four radiologists in terms of time consumption (35 min vs. 75 min, 93 min, 79 min, and 82 min).
conclusionThe AI model we obtained had strong decision-making ability, which could potentially assist doctors in detecting COVID-19 pneumonia.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.