tetano
Editor, Senior Moderator
BMC Med Inform Decis Mak
. 2022 Nov 2;22(1):284.
doi: 10.1186/s12911-022-02022-1.
A novel deep learning-based method for COVID-19 pneumonia detection from CT images
Ju Luo[SUP] 1 [/SUP], Yuhao Sun[SUP] 2 [/SUP], Jingshu Chi[SUP] 1 [/SUP], Xin Liao[SUP] 2 [/SUP], Canxia Xu[SUP] 3 [/SUP]
Affiliations
Abstract
Background: The 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.
Aims: This study aimed to develop a deep learning model to assist radiologists in detecting COVID-19 pneumonia.
Methods: The 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.
Results: In 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).
Conclusion: The AI model we obtained had strong decision-making ability, which could potentially assist doctors in detecting COVID-19 pneumonia.
Keywords: Artificial intelligence; COVID-19; CT image; Community acquired pneumonia; Deep learning.
. 2022 Nov 2;22(1):284.
doi: 10.1186/s12911-022-02022-1.
A novel deep learning-based method for COVID-19 pneumonia detection from CT images
Ju Luo[SUP] 1 [/SUP], Yuhao Sun[SUP] 2 [/SUP], Jingshu Chi[SUP] 1 [/SUP], Xin Liao[SUP] 2 [/SUP], Canxia Xu[SUP] 3 [/SUP]
Affiliations
- PMID: 36324135
- DOI: 10.1186/s12911-022-02022-1
Abstract
Background: The 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.
Aims: This study aimed to develop a deep learning model to assist radiologists in detecting COVID-19 pneumonia.
Methods: The 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.
Results: In 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).
Conclusion: The AI model we obtained had strong decision-making ability, which could potentially assist doctors in detecting COVID-19 pneumonia.
Keywords: Artificial intelligence; COVID-19; CT image; Community acquired pneumonia; Deep learning.