In BMC medical informatics and decision making ; h5-index 38.0
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.
Luo Ju, Sun Yuhao, Chi Jingshu, Liao Xin, Xu Canxia
2022-Nov-02
Artificial intelligence, COVID-19, CT image, Community acquired pneumonia, Deep learning
