References
- Laupland KB. Incidence of bloodstream infection: a review of populationbased studies. Clin Microbiol Infect. 2013; 19:492-500. DOI: 10.1111/1469-0691.12144
- Wisplinghoff H, Bischoff T, Tallent SM, Seifert H, Wenzel RP, Edmond MB. Nosocomial bloodstream infections in US hospitals: analysis of 24,179 cases from a prospective nationwide surveillance study. Clin Infect Dis. 2004 Aug; 1;39(3):309-17. DOI: 10.1086/421946
- Schwaber MJ, Carmeli Y. Mortality and delay in effective therapy associated with extended-spectrum beta-lactamase production in Enterobacteriaceae bacteraemia: a systematic review and meta-analysis. J Antimicrob Chemother. 2007; 60:913-20. DOI: 10.1093/jac/dkm318
- Kang CI, Kim SH, Kim HB, Park SW, Choe YJ, Oh MD, et al. Pseudomonas aeruginosa bacteremia: risk factors for mortality and influence of delayed receipt of effective antimicrobial therapy on clinical outcome. Clin Infect Dis. 2003; 37:745-51. DOI: 10.1086/377200
- Barenfanger J, Graham DR, Kolluri L, Sangwan G, Lawhorn J, Drake CA, et al. Decreased mortality associated with prompt Gram staining of blood cultures. Am J Clin Pathol. 2008; 130:870-6. DOI: 10.1309/AJCPVMDQU2ZJDPBL
- Garcia E, Ali AM, Soles RM, Lewis DG. The American Society for Clinical Pathology’s 2014 vacancy survey of medical laboratories in the United States. Am J Clin Pathol. 2015; 144:432-43. DOI: 10.1309/AJCPN7G0MXMSTXCD
- Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convolutional neural networks. Communications of the ACM. 2017; 60: 84-90. DOI: 10.1145/3065386
- Rhoads DD. Computer vision and artificial intelligence are emerging diagnostic tools for the clinical microbiologist. J Clin Microbiol. 2020; 58:e00511-20. DOI: 10.1128/JCM.00511-20
- Meyer J, Pare G. Telepathology impacts and implementation challenges: a scoping review. Arch Pathol Lab Med. 2015; 139:1550-7. DOI: 10.5858/arpa.2014-0606-RA
- Martinez RM, Wolk DM. Bloodstream infections. Microbiol Spectrum. 2016; 4(4):DMIH2-0031-2016. DOI: 10.1128/microbiolspec.DMIH2-0031-2016
- He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition. 2016; pp. 770-8. DOI: 10.1109/CVPR.2016.90
- Jou B, Chang SF. Deep cross residual learning for multitask visual recognition. In Proceedings of the 24th ACM international conference on Multimedia. 2016; pp. 998-1007. DOI: 10.1145/2964284.2964309
- He T, Zhang Z, Zhang H, Zhang Z, Xie J, Li M. Bag of Tricks for Image Classification with Convolutional Neural Networks, IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 2019; pp. 558-67. DOI: 10.1109/CVPR.2019.00065
- Xie S, Girshick R, Dollár P, Tu Z, He K. Aggregated residual transformations for deep neural networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, 2017; pp. 1492-500. DOI: 10.1109/CVPR.2017.634
- Howard A, Sandler M, Chu G, Chen LC, Chen B, Tan M, Adam H. Searching for mobilenetv3. In Proceedings of the IEEE/CVF international conference on computer vision. 2019; pp. 1314-24. DOI: 10.1109/ICCV.2019.00140
- Howard J, Gugger S. Fastai: A Layered API for Deep Learning. Information. 2020; 11:108. DOI: 10.3390/info11020108
- Goodfellow I, Bengio Y, Courville A. Deep learning. 2016; The MIT press.
- Russell, S. Artificial Intelligence: A Modern Approach, eBook, Global Edition; Pearson Education, Limited.
- LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015; 521:436-44. DOI: 10.1038/nature14539
- Yu KH, Beam AL, Kohane IS. Artificial intelligence in healthcare. Nat Biomed Eng. 2018; 2(10):719-31. DOI: 10.1038/s41551-018-0305-z
- Egli A, Schrenzel J, Greub G. Digital microbiology. Clin Microbiol Infect. 2020; 26(10):1324-31. DOI: 10.1016/j.cmi.2020.06.023
- Smith KP, Kirby JE. Image analysis and artificial intelligence in infectious disease diagnostics. Clin Microbiol Infect. 2020; 26:1318-23. DOI: 10.1016/j.cmi.2020.03.012
- Smith KP, Kang AD, Kirby JE. Automated Interpretation of blood culture Gram stains by use of a deep convolutional neural network. J Clin Microbiol. 2018 Feb 22;56(3):e01521-17. DOI: 10.1128/JCM.01521-17
- Linares M. Collaborative intelligence and gamification for online malaria species differentiation. Malar J. 2019; 18:21. DOI: 10.1186/s12936-019-2662-9
- Perkel JM. Pocket laboratories. Nature. 2017; 545:119-21. DOI: 10.1038/545119a
- Song Y, He L, Zhou F, Chen S, Ni D, Lei B, et al. Segmentation, splitting, and classification of overlapping bacteria in microscope images for automatic bacterial vaginosis diagnosis IEEE J Biomed Health Inform, 21, 2017; pp. 1095-104. DOI: 10.1109/JBHI.2016.2594239
- Poostchi M, Silamut K, Maude RJ, Jaeger S, Thoma G. Image analysis and machine learning for detecting malaria Transl Res, 194, 2018; pp. 36-55. DOI: 10.1016/j.trsl.2017.12.004
- Panicker R, Kalmady K, Rajan J, Sabu M. Automatic detection of tuberculosis bacilli from microscopic sputum smear images using deep learning methods. Biocybern Biomed Eng. 2018; 38(3):691-9. DOI: 10.1016/j.bbe.2018.05.007
- Fischer A, Azam N, Rasga L, Barras V, Tangomo M, Renzi G, et al. Performances of automated digital imaging of Gramstained slides with on-screen reading against manual microscopy. Eur J Clin Microbiol Infect Dis. 2021; 40(10):2171-6. DOI: 10.1007/s10096-021-04233-2
- Shaik F, Kumar Sharma A, Musthak Ahmed S, Kumar Gunjan V, Naik C. An improved model for analysis of diabetic Retinopathy related imagery, Indian Journal of Science and Technology. 2016; 9: 44, 1-6. DOI: 10.17485/ijst/2016/v9i44/105298
- Jameela T, Athota K, Singh N, Gunjan VK, Kahali S. Deep learning and transfer learning for malaria detection. Comput. Intell. Neurosci. 2022; 2022:1-14. DOI: 10.1155/2022/2221728
- Alnussairi MHD, İbrahim AA. Malaria parasite detection using deep learning algorithms based on (CNNs) technique, Comput. Elect. Eng. 2022; 103: 108316. DOI: 10.1016/j. compeleceng.2022.108316