https://doi.org/10.4081/ecj.2020.8573
Deep learning in Emergency Medicine: Recent contributions and methodological challenges
Submitted: 23 September 2019
Accepted: 20 December 2019
Published: 17 March 2020
Accepted: 20 December 2019
Abstract Views: 1314
PDF: 472
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All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
Authors
In the last few years, artificial intelligence (AI) technology has grown dramatically impacting several fields of human knowledge and medicine in particular. Among other approaches, deep learning, which is a subset of AI based on specific computational models, such as deep convolutional neural networks and recurrent neural networks, has shown exceptional performance in images and signals processing. Accordingly, emergency medicine will benefit from the adoption of this technology. However, a particular attention should be devoted to the review of these papers in order to exclude overoptimistic results from clinically transferable ones. We presented a group of studies recently published on PubMed and selected by keywords ‘deep learning emergency medicine’ and ‘artificial intelligence emergency medicine’ with the aim of highlighting their methodological strengths and weaknesses, as well as their clinical usefulness.
Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019; 25:44-56.
DOI: https://doi.org/10.1038/s41591-018-0300-7
Anwar SM, Majid M, Qayyum A, et al. Medical image analysis using convolutional neural networks: a review. J Med Syst. 2018; 42:22
DOI: https://doi.org/10.1007/s10916-018-1088-1
De Fauw J, Ledsam JR, Romera-Paredes B, et al. Clinically applicable deep learning for diagnosis and referral in retinal disease. Nat Med. 2018; 24:1342-50.
DOI: https://doi.org/10.1038/s41591-018-0107-6
Esteva A, Kuprel B, Novoa Ra, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017; 542:115-8.
DOI: https://doi.org/10.1038/nature21056
Shafaf N, Malek H. Applications of Machine Learning Approaches in Emergency Medicine; a Review Article. Arch Acad Emerg Med. 2019; 7:e34.
England JR, Cheng PM. Artifical intelligence for medical image analysis: a guide for authors and reviewers. AJR Am J Roentgenol. 2019; 212:513-9.
DOI: https://doi.org/10.2214/AJR.18.20490
Lee JG, Jun S, Cho YW, et al. Deep learning in medical imaging: general overview. Korean J Radiol. 2017; 18:570–84.
DOI: https://doi.org/10.3348/kjr.2017.18.4.570
LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015; 521:436–44.
DOI: https://doi.org/10.1038/nature14539
Raita Y, Goto T, Faridi MK, et al. Emergency department triage prediction of clinical outcomes using machine learning models. Crit Care. 2019; 23:64.
DOI: https://doi.org/10.1186/s13054-019-2351-7
Goto T, Camargo Jr CA, Faridi MK, et al. Machine learning-based prediction of clinical outcomes for children during emergency department triage. JAMA Netw Open. 2019; 2:e186937.
DOI: https://doi.org/10.1001/jamanetworkopen.2018.6937
Kim D, You S, So S, et al. A data-driven artificial intelligence model for remote triage in the prehospital environment. PLoS One. 2018; 13:e0206006.
DOI: https://doi.org/10.1371/journal.pone.0206006
Kwon JM, Lee Y, Lee Y, et al. Validation of deep-learning-based triage and acuity score using a large national dataset. PLoS One. 2018; 13:e0205836.
DOI: https://doi.org/10.1371/journal.pone.0205836
Hong WS, Haimovich AD, Taylor RA. Predicting hospital admission at emergency department triage using machine learning. PLoS One. 2018; 13:e0201016.
DOI: https://doi.org/10.1371/journal.pone.0201016
Picon A, Irusta U, Alvarez-Gila A, et al. Mixed convolutional and long short-term memory network for the detection of lethal ventricular arrhythmia. PLoS One. 2019; 14:e0216756.
DOI: https://doi.org/10.1371/journal.pone.0216756
Jang DH, Kim J, Jo YH, et al. Developing neural network models for early detection of cardiac arrest in emergency department. Am J Emerg Med. 2019; doi: 10.1016/j.ajem.2019.04.006.
DOI: https://doi.org/10.1016/j.ajem.2019.04.006
Kwon JM, Jeon KH, Kim HM, et al. Deep-learning-based out-of-hospital cardiac arrest prognostic system to predict clinical outcomes. Resuscitation. 2019; 139:84-91.
DOI: https://doi.org/10.1016/j.resuscitation.2019.04.007
Kwon JM, Lee Y, Lee Y, et al. An algorithm based on deep learning for predicting in-hospital cardiac arrest. J Am Heart Assoc. 2018; 7:e008678.
DOI: https://doi.org/10.1161/JAHA.118.008678
Smith SW, Walsh B, Grauer K, et al. A deep neural network learning algorithm outperforms a conventional algorithm for emergency department electrocardiogram interpretation. J Electrocardiol. 2019; 52:88-95.
DOI: https://doi.org/10.1016/j.jelectrocard.2018.11.013
Goto S, Kimura M, Katsumata Y, et al. Artificial intelligence to predict needs for urgent revascularization from 12-leads electrocardiography in emergency patients. PLoS One. 2019; 14:e0210103.
DOI: https://doi.org/10.1371/journal.pone.0210103
Cheng CT, Ho TY, Lee TY, et al. Application of a deep learning algorithm for detection and visualization of hip fractures on plain pelvic radiographs. Eur Radiol. 2019; 29:5469-77.
DOI: https://doi.org/10.1007/s00330-019-06167-y
Cho J, Park KS, Karki M, et al. Improving sensitivity on identification and delineation of intracranial hemorrhage lesion using cascaded deep learning models. J Digit Imaging. 2019; 32:450-61.
DOI: https://doi.org/10.1007/s10278-018-00172-1
Kwon JM, Kim KH, Jeon KH, Park J. Deep learning for predicting in-hospital mortality among heart disease patients based on echocardiography. Echocardiography. 2019; 36:213-8.
DOI: https://doi.org/10.1111/echo.14220
Chang PD, Kuoy E, Grinband J, et al. Hybrid 3D/2D convolutional neural network for hemorrhage evaluation on head CT. AJNR Am J Neuroradiol. 2018; 39:1609-16.
DOI: https://doi.org/10.3174/ajnr.A5742
How to Cite
Faita, F. (2020). Deep learning in Emergency Medicine: Recent contributions and methodological challenges. Emergency Care Journal, 16(1). https://doi.org/10.4081/ecj.2020.8573
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