Biases associated with database structure for COVID-19 detection in X-ray images | Scientific Reports – Nature.com
Wang, D. et al. Clinical characteristics of 138 hospitalized patients with 2019 novel coronavirus-infected pneumonia in Wuhan, China. JAMA 323, 1061–1069. https://doi.org/10.1001/jama.2020.1585 (2020).
Ducharme, J. World health organization declares COVID-19 a ’pandemic.’ here’s what that means. https://time.com/5791661/who-coronavirus-pandemic-declaration/ (2020).
Tahamtan, A. & Ardebili, A. Real-time rt-pcr in COVID-19 detection: Issues affecting the results. Exp. Rev. Mol. Diagn. 20, 453–454. https://doi.org/10.1080/14737159.2020.1757437 (2020).
Long, C. et al. Diagnosis of the coronavirus disease (COVID-19): rrt-pcr or ct?. Eur. J. Radiol. 126, 108961. https://doi.org/10.1016/j.ejrad.2020.108961 (2020).
Albahri, O. S. et al. Systematic review of artificial intelligence techniques in the detection and classification of COVID-19 medical images in terms of evaluation and benchmarking: Taxonomy analysis, challenges, future solutions and methodological aspects. J. Infect. Public Health 13, 1381–1396. https://doi.org/10.1016/J.JIPH.2020.06.028 (2020).
Ai, T. et al. Correlation of chest ct and rt-pcr testing for coronavirus disease 2019 (COVID-19) in china: A report of 1014 cases. Radiology 296, E32–E40. https://doi.org/10.1148/radiol.2020200642 (2020).
Balaha, H. M., El-Gendy, E. M. & Saafan, M. M. A complete framework for accurate recognition and prognosis of COVID-19 patients based on deep transfer learning and feature classification approach. Artif. Intell. Rev. 55, 5063–5108. https://doi.org/10.1007/s10462-021-10127-8 (2022).
Tartaglione, E., Barbano, C. A., Berzovini, C., Calandri, M. & Grangetto, M. Unveiling COVID-19 from chest X-ray with deep learning: A hurdles race with small data. Int. J. Environ. Res. Public Healthhttps://doi.org/10.3390/ijerph17186933 (2020).
Ghoshal, B. & Tucker, A. Estimating uncertainty and interpretability in deep learning for coronavirus (Covid-19) detection. https://doi.org/10.48550/arxiv.2003.10769 (2020).
Malhotra, A. et al. Multi-task driven explainable diagnosis of COVID-19 using chest XX-ray images. Pat. Recogn.https://doi.org/10.48550/arxiv.2008.03205 (2020).
Cruz, B. G. S., Bossa, M. N., Sölter, J. & Husch, A. D. Public COVID-19 X-ray datasets and their impact on model bias: A systematic review of a significant problem. Med. Image Anal. 74, 102225. https://doi.org/10.1016/j.media.2021.102225 (2021).
Roberts, M. et al. Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and ct scans. Nat. Mach. Intell. 3, 199–217. https://doi.org/10.1038/s42256-021-00307-0 (2021).
Gao, J. et al. Medml: Fusing medical knowledge and machine learning models for early pediatric COVID-19 hospitalization and severity prediction. iScience 25, 104970. https://doi.org/10.1016/j.isci.2022.104970 (2022).
Arias-Garzón, D. et al. COVID-19 detection in X-ray images using convolutional neural networks. Mach. Learn. Appl. 6, 100138. https://doi.org/10.1016/j.mlwa.2021.100138 (2021).
Alzate-Grisales, J. A. et al. Cov-caldas: A new COVID-19 chest X-ray dataset from state of caldas-colombia. Sci. Data 9, 757. https://doi.org/10.1038/s41597-022-01576-z (2022).
Hagendorff, T. The ethics of ai ethics: An evaluation of guidelines. Minds Mach. 30, 99–120. https://doi.org/10.1007/s11023-020-09517-8 (2020).
Floridi, L. et al. Ai4people-an ethical framework for a good ai society: Opportunities, risks, principles, and recommendations. Minds Mach. 28, 689–707. https://doi.org/10.1007/s11023-018-9482-5 (2018).
Tabares-Soto, R. et al. Analysis of ethical development for public policies in the acquisition of ai-based systems. https://doi.org/10.4018/978-1-6684-5892-1.ch010 (2022).
Saleiro, P. et al. Aequitas: A bias and fairness audit toolkit. https://doi.org/10.48550/arXiv.1811.05577 (2018).
Cohen, J. P., Morrison, P. & Dao, L. COVID-19 image data collection. arXiv https://doi.org/10.48550/arXiv.2003.11597 (2020).
Zhang, R. et al. Diagnosis of coronavirus disease 2019 pneumonia by using chest radiography: Value of artificial intelligence. Radiology 298, E88–E97. https://doi.org/10.1148/radiol.2020202944 (2021).
Afifi, A., Hafsa, N. E., Ali, M. A. S., Alhumam, A. & Alsalman, S. An ensemble of global and local-attention based convolutional neural networks for COVID-19 diagnosis on chest X-ray images. Symmetry 13, 113 (2021).
Imagawa, K. & Shiomoto, K. Performance change with the number of training data: A case study on the binary classification of COVID-19 chest X-ray by using convolutional neural networks. Comput. Biol. Med. 142, 105251. https://doi.org/10.1016/J.COMPBIOMED.2022.105251 (2022).
Bassi, P. R. A. S. & Attux, R. A deep convolutional neural network for Covid-19 detection using chest X-rays. https://doi.org/10.1007/s42600-021-00132-9 (2020).
Jain, G., Mittal, D., Thakur, D. & Mittal, M. K. A deep learning approach to detect COVID-19 coronavirus with X-ray images. Biocybern. Biomed. Eng. 40, 1391–1405. https://doi.org/10.1016/j.bbe.2020.08.008 (2020).
Kana, E. B. G., Kana, M. G. Z., Kana, A. F. D. & Kenfack, R. H. A. A web-based diagnostic tool for COVID-19 using machine learning on chest radiographs (cxr). medRxiv https://doi.org/10.1101/2020.04.21.20063263 (2020).
Zokaeinikoo, M., Kazemian, P., Mitra, P. & Kumara, S. Aidcov: An interpretable artificial intelligence model for detection of COVID-19 from chest radiography images. medRxiv https://doi.org/10.1101/2020.05.24.20111922 (2020).
Tamal, M. et al. An integrated framework with machine learning and radiomics for accurate and rapid early diagnosis of COVID-19 from chest X-ray. Exp. Syst. Appl. 180, 115152. https://doi.org/10.1016/J.ESWA.2021.115152 (2021).
Ezzat, D., Hassanien, A. E. & Ella, H. A. An optimized deep learning architecture for the diagnosis of COVID-19 disease based on gravitational search optimization. Appl. Soft Comput. 98, 106742. https://doi.org/10.1016/J.ASOC.2020.106742 (2021).
Wang, Z. et al. Automatically discriminating and localizing COVID-19 from community-acquired pneumonia on chest X-rays. Pat. Recogn. 110, 107613. https://doi.org/10.1016/J.PATCOG.2020.107613 (2021).
Khan, A. I., Shah, J. L. & Bhat, M. M. Coronet: A deep neural network for detection and diagnosis of COVID-19 from chest X-ray images. Comput. Methods Program. Biomed. 196, 105581. https://doi.org/10.1016/j.cmpb.2020.105581 (2020).
Apostolopoulos, I. D. & Mpesiana, T. A. COVID-19: Automatic detection from X-ray images utilizing transfer learning with convolutional neural networks. Phys. Eng. Sci. Med. 43, 635–640. https://doi.org/10.1007/s13246-020-00865-4 (2020).
Mangal, A. et al. Covidaid: COVID-19 detection using chest X-ray. https://doi.org/10.48550/arxiv.2004.09803 (2020).
Sayyed, A. Q. M. S., Saha, D. & Hossain, A. R. Covmunet: A multiple loss approach towards detection of COVID-19 from chest x-ray. https://doi.org/10.48550/arxiv.2007.14318 (2020).
Minaee, S., Kafieh, R., Sonka, M., Yazdani, S. & Soufi, G. J. Deep-Covid: Predicting COVID-19 from chest x-ray images using deep transfer learning. Med. Image Anal.https://doi.org/10.1016/j.media.2020.101794 (2020).
Rahaman, M. M. et al. Identification of COVID-19 samples from chest X-ray images using deep learning: A comparison of transfer learning approaches. J. X-Ray Sci. Technol. 28, 821–839. https://doi.org/10.3233/XST-200715 (2020).
Tsiknakis, N. et al. Interpretable artificial intelligence framework for COVID-19 screening on chest X-rays. Exp. Therap. Med. 20, 727–735. https://doi.org/10.3892/etm.2020.8797 (2020).
Elaziz, M. A. et al. New machine learning method for image-based diagnosis of COVID-19. PLOS ONE 15, e0235187. https://doi.org/10.1371/journal.pone.0235187 (2020).
Yamac, M. et al. Convolutional sparse support estimator based COVID-19 recognition from X-ray images. IEEE Tran. Neural Netw. Learn. Syst.https://doi.org/10.48550/arxiv.2005.04014 (2020).
Fan, Y., Liu, J., Yao, R. & Yuan, X. COVID-19 detection from X-ray images using multi-kernel-size spatial-channel attention network. Pat. Recogn. 119, 108055. https://doi.org/10.1016/J.PATCOG.2021.108055 (2021).
Farooq, M. & Hafeez, A. Covid-resnet: A deep learning framework for screening of COVID19 from radiographs. https://doi.org/10.48550/arxiv.2003.14395 (2020).
Wang, L., Lin, Z. Q. & Wong, A. Covid-net: A tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images. Sci. Rep. 10, 19549. https://doi.org/10.1038/s41598-020-76550-z (2020).
Ahmed, K. B., Goldgof, G. M., Paul, R., Goldgof, D. B. & Hall, L. O. Discovery of a generalization gap of convolutional neural networks on COVID-19 X-rays classification. IEEE Access 9, 72970–72979. https://doi.org/10.1109/access.2021.3079716 (2021).
Gil, D., Díaz-Chito, K., Sánchez, C. & Hernández-Sabaté, A. Early screening of sars-cov-2 by intelligent analysis of X-ray images. https://doi.org/10.48550/arxiv.2005.13928 (2020).
Heidari, M. et al. Improving the performance of cnn to predict the likelihood of COVID-19 using chest X-ray images with preprocessing algorithms. Int. J. Med. Inform. 144, 104284. https://doi.org/10.1016/J.IJMEDINF.2020.104284 (2020).
Qi, X., Foran, D. J., Nosher, J. L. & Hacihaliloglu, I. Multi-feature semi-supervised learning for COVID-19 diagnosis from chest X-ray images (2021).
Degerli, A., Kiranyaz, S., Chowdhury, M. E. H. & Gabbouj, M. Osegnet: Operational segmentation network for COVID-19 detection using chest X-ray images. Arxiv abs/2202.10185 (2022).
Guarrasi, V., D’Amico, N. C., Sicilia, R., Cordelli, E. & Soda, P. Pareto optimization of deep networks for COVID-19 diagnosis from chest X-rays. Pat. Recogn. 121, 108242. https://doi.org/10.1016/J.PATCOG.2021.108242 (2022).
Luz, E. et al. Towards an effective and efficient deep learning model for COVID-19 patterns detection in X-ray images. Res. Biomed. Eng.https://doi.org/10.1007/s42600-021-00151-6 (2020).
Pereira, R. M., Bertolini, D., Teixeira, L. O., Silla, C. N. & Costa, Y. M. COVID-19 identification in chest X-ray images on flat and hierarchical classification scenarios. Comput. Methods Program. Biomed. 194, 105532. https://doi.org/10.1016/J.CMPB.2020.105532 (2020).
Moura, J. D. et al. Deep convolutional approaches for the analysis of COVID-19 using chest X-ray images from portable devices. IEEE Access 8, 195594–195607. https://doi.org/10.1109/ACCESS.2020.3033762 (2020).
Kassania, S. H., Kassanib, P. H., Wesolowskic, M. J., Schneidera, K. A. & Detersa, R. Automatic detection of coronavirus disease (COVID-19) in X-ray and ct images: A machine learning based approach. Biocybern. Biomed. Eng. 41, 867–879. https://doi.org/10.1016/j.bbe.2021.05.013 (2021).
Teixeira, L. O. et al. Impact of lung segmentation on the diagnosis and explanation of COVID-19 in chest X-ray images. Sensors 21, 7116. https://doi.org/10.3390/s21217116 (2021).
Maguolo, G. & Nanni, L. A critic evaluation of methods for COVID-19 automatic detection from X-ray images. Inf. Fus. 76, 1–7. https://doi.org/10.1016/j.inffus.2021.04.008 (2021).
Singh, K. K. & Singh, A. Diagnosis of COVID-19 from chest X-ray images using wavelets-based depthwise convolution network. Big Data Min. Anal. 4, 84–93. https://doi.org/10.26599/BDMA.2020.9020012 (2021).
Li, X., Li, C. & Zhu, D. Covid-mobilexpert: On-device COVID-19 patient triage and follow-up using chest X-rays. https://doi.org/10.48550/arxiv.2004.03042 (2020).
Signoroni, A. et al. Bs-net: Learning COVID-19 pneumonia severity on a large chest X-ray dataset. Med. Image Anal. 71, 102046. https://doi.org/10.1016/J.MEDIA.2021.102046 (2021).
Bararia, A., Ghosh, A., Bose, C. & Bhar, D. Network for subclinical prognostication of COVID 19 patients from data of thoracic roentgenogram: A feasible alternative screening technology. medRxivhttps://doi.org/10.1101/2020.09.07.20189852 (2020).
Cohen, J. P. et al. Predicting COVID-19 pneumonia severity on chest X-ray with deep learning. Cureushttps://doi.org/10.48550/arxiv.2005.11856 (2020).
Irmak, E. COVID-19 disease severity assessment using cnn model. IET Image Process. 15, 1814–1824. https://doi.org/10.1049/ipr2.12153 (2021).
Tahir, A. M. et al. COVID-19 infection localization and severity grading from chest X-ray images. Comput. Biol. Med. 139, 105002. https://doi.org/10.1016/J.COMPBIOMED.2021.105002 (2021).
Park, S. et al. Multi-task vision transformer using low-level chest X-ray feature corpus for COVID-19 diagnosis and severity quantification. Med. Image Anal. 75, 102299. https://doi.org/10.1016/J.MEDIA.2021.102299 (2022).
de la Iglesia Vayá, M. et al. Bimcv covid-19+: a large annotated dataset of rx and ct images from COVID-19 patients. 1–22 (2020).
Desai, S. et al. Chest imaging representing a COVID-19 positive rural u.s. population. Sci. Data 7, 414. https://doi.org/10.1038/s41597-020-00741-6 (2020).
Winther, H. B. et al. Dataset: Covid-19 image repository, https://doi.org/10.6084/m9.figshare.12275009 (2020).
Chung, A. Actualmed-covid-chestxray-dataset: Actualmed COVID-19 chest X-ray dataset initiative (2020).
Chowdhury, M. E. H. et al. Can ai help in screening viral and COVID-19 pneumonia?. IEEE Access 8, 132665–132676. https://doi.org/10.1109/ACCESS.2020.3010287 (2020).
Covid-19 database – sirm (2020).
Yamac, M. et al. Convolutional sparse support estimator-based COVID-19 recognition from X-ray images. IEEE Trans. Neural Netw. Learn. Syst. 32, 1810–1820. https://doi.org/10.1109/TNNLS.2021.3070467 (2021).
Radiopedia.org. COVID 19 | search | radiopaedia.org (2020).
EuroRad. Euorad search results for COVID-19 (2020).
Soda, P. et al. Aiforcovid: Predicting the clinical outcomes in patients with COVID-19 applying ai to chest-X-rays. An Italian multicentre study. Med. Image Analy. 74, 102216 (2020).
Imaging, C. This is a thread of COVID-19 cxr (2020).
the British Society of Thoracic Imaging. COVID-19 british society of thoracic imaging database.
Chung, A. Figure 1 COVID-19 chest X-ray dataset initiative (2020).
Rahman, T. et al. Exploring the effect of image enhancement techniques on COVID-19 detection using chest X-ray images. Comput. Biol. Med. 132, 104319. https://doi.org/10.1016/J.COMPBIOMED.2021.104319 (2021).
Summers, R. & NIH. Cxr8 | con la tecnología de box (2020).
Irvin, J. et al. Chexpert: A large chest radiograph dataset with uncertainty labels and expert comparison. Proc. AAAI Conf. Artif. Intell. 33, 590–597. https://doi.org/10.1609/aaai.v33i01.3301590 (2019).
Bassi, P. R. & Attux, R. A deep convolutional neural network for COVID-19 detection using chest X-rays. Res. Biomed. Eng. 38, 139–148. https://doi.org/10.1007/S42600-021-00132-9/FIGURES/4 (2022).
Bustos, A., Pertusa, A., Salinas, J. M. & de la Iglesia-Vayá, M. Padchest: A large chest X-ray image dataset with multi-label annotated reports. Med. Image Anal. 66, 101797. https://doi.org/10.1016/j.media.2020.101797 (2020).
Kermany, D. S. et al. Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell 172, 1122-1131.e9. https://doi.org/10.1016/j.cell.2018.02.010 (2018).
of North America, R. S. Rsna pneumonia detection challenge | kaggle (2019).
Shiraishi, J. et al. Development of a digital image database for chest radiographs with and without a lung nodule. Am. J. Roentgenol. 174, 71–74. https://doi.org/10.2214/ajr.174.1.1740071 (2000).
Jaeger, S. et al. Two public chest X-ray datasets for computer-aided screening of pulmonary diseases. Quant. Imag. Med. Surg. 4, 475–477. https://doi.org/10.3978/j.issn.2223-4292.2014.11.20 (2014).
Kermany, D., Zhang, K. & Goldbaum, M. Large dataset of labeled optical coherence tomography (oct) and chest X-ray images. Mendel. Datahttps://doi.org/10.17632/RSCBJBR9SJ.3 (2018).
Cruz, B. G. S., Bossa, M. N., Sölter, J. & Husch, A. D. Public COVID-19 X-ray datasets and their impact on model bias: A systematic review of a significant problem. Med. Image Anal. 74, 102225. https://doi.org/10.1016/j.media.2021.102225 (2021).
The bias and fairness audit toolkit for machine learning – aequitas documentation.
Hochhegger, B. et al. O tórax e o envelhecimento: manifestações radiológicas. J. Brasil. Pneumol. 38, 656–665. https://doi.org/10.1590/S1806-37132012000500016 (2012).
Serrano, C. O. et al. Pediatric chest X-ray in COVID-19 infection. Eur. J. Radiol. 131, 109236. https://doi.org/10.1016/j.ejrad.2020.109236 (2020).
Hlabangana, L. T. et al. Inter-rater reliability in quality assurance (qa) of pediatric chest X-rays. J. Med. Imag. Radiat. Sci. 52, 427–434. https://doi.org/10.1016/j.jmir.2021.04.002 (2021).
Albrandt-Salmeron, A., Espejo-Fonseca, R. & Roldan-Valadez, E. Correlation between chest X-ray severity in COVID-19 and age in mexican-mestizo patients: An observational cross-sectional study. BioMed Res. Int. 2021, 5571144. https://doi.org/10.1155/2021/5571144 (2021).
Borghesi, A. & Maroldi, R. COVID-19 outbreak in Italy: Experimental chest X-ray scoring system for quantifying and monitoring disease progression. La Radiol. Med. 125, 509–513. https://doi.org/10.1007/s11547-020-01200-3 (2020).
Jacobi, A., Chung, M., Bernheim, A. & Eber, C. Portable chest X-ray in coronavirus disease-19 (COVID-19): A pictorial review. Clin. Imag. 64, 35–42. https://doi.org/10.1016/j.clinimag.2020.04.001 (2020).
