A Systematic Review on the Use of Artificial Intelligence Techniques in the Diagnosis of COVID-19 from Chest X-Ray Images

Abstract

Background: In this study, the artificial intelligence (AI) techniques used for the detection of coronavirus disease 2019 (COVID-19) from the chest x-ray were reviewed. Methods: PubMed, arXiv, and Google Scholar were used to search for AI studies. Results: A total of 20 papers were extracted from Google Scholar, 14 from arXiv, and 5 from PubMed. In 17 papers, publicly available datasets and in 3 papers, independent datasets were used. 10 papers disclosed source codes. Nine papers were about creating a novel AI software, 8 papers reported the modification of the existing AI models, and 3 compared the performance of the existing AI software programs. All papers have used deep learning as AI technique. Most papers reported accuracy, specificity, and sensitivity of the models, and also the area under the curve (AUC) for investigation of the model performance for the prediction of COVID-19. Nine papers reported accuracy, sensitivity, and specificity. The number of datasets used in the studies ranged from 50 to 94323. The accuracy, sensitivity, and specificity of the models ranged from 0.88 to 0.98, 0.80 to 1.00, and 0.70 to 1.00, respectively. Conclusion: The studies revealed that AI can help human in fighting the new Coronavirus.

Deep learning in ovarian cancer diagnosis: a comprehensive review of various imaging modalities

“…DL techniques, particularly convolutional neural networks (CNNs), have exhibited remarkable potential in various medical imaging applications. By leveraging the power of artificial intelligence and machine learning algorithms, DL algorithms can automatically extract meaningful features from complex medical images, aiding in the identification and classification of potential pathologies [ 4 , 5 ].…”