17
Views
0
CrossRef citations to date
0
Altmetric
REVIEW

Artificial Intelligence in Emergency Trauma Care: A Preliminary Scoping Review

ORCID Icon, &
Pages 191-211 | Received 04 Mar 2024, Accepted 17 May 2024, Published online: 01 Jul 2024

References

  • Martinelli DD. Generative machine learning for de novo drug discovery: a systematic review. Comput Biol Med. 2022;145:105403. doi:10.1016/j.compbiomed.2022.105403
  • Paladugu PS, Ong J, Nelson N, et al. Generative adversarial networks in medicine: Important considerations for this emerging innovation in artificial intelligence. Ann Biomed Eng. 2023;51(10):2130–2142. doi:10.1007/s10439-023-03304-z
  • Ouzzani M, Hammady H, Fedorowicz Z, Elmagarmid A; Ouzzani et. al Rayyan — a web and mobile app for systematic reviews. Syst Rev. 2016;5(210). doi:10.1186/s13643-016-0384-4
  • Amir-Behghadami M, Janati A. Population, intervention, comparison, outcomes and Study (PICOS) design as a framework to formulate eligibility criteria in systematic reviews. Emerg Med J. 2020;37(6):387. doi:10.1136/emermed-2020-209567
  • Methodology Checklist 1: Systematic Reviews and Meta-Analyses. Scottish Intercollegiate Guidelines Network, Available from: https://www.sign.ac.uk/what-we-do/methodology/checklists/. Accessed May 18, 2024.
  • Moher D, Liberati A, Tetzlaff J, Altman, P DG. Group Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement.
  • Med P, Harris JD, Quatman CE, Manring MM, Siston RA, Flanigan DC. How to write a systematic review. Am J Sports Med. 2014;42(11):2761–2768. doi:10.1177/0363546513497567
  • Higgins JPT, Thomas J, Chandler J, et al. Cochrane Handbook for systematic reviews of interventions version 6.2 Cochrane; 2021.
  • Cheng CT, Lin HH, Hsu CP, et al. Deep Learning for automated detection and localization of traumatic abdominal solid organ injuries on CT scans. J Imaging Inform Med. 2024. doi:10.1007/s10278-024-01038-5
  • Michel J, Manns A, Boudersa S, et al. Clinical decision support system in emergency telephone triage: a scoping review of technical design, implementation and evaluation. Int J Med Inform. 2024;184:105347. doi:10.1016/j.ijmedinf.2024.105347
  • Russe MF, Rebmann P, Tran PH, et al. AI-based X-ray fracture analysis of the distal radius: accuracy between representative classification, detection and segmentation deep learning models for clinical practice. BMJ Open. 2024;14(1):e076954. doi:10.1136/bmjopen-2023-076954
  • Piliuk K, Tomforde S. Artificial intelligence in emergency medicine. A systematic literature review. Int J Med Inform. 2023;180:105274. doi:10.1016/j.ijmedinf.2023.105274
  • Choi J, Vendrow EB, Moor M, Spain DA. Development and validation of a model to quantify injury severity in real time. JAMA network open. 2023;6(10):e2336196. doi:10.1001/jamanetworkopen.2023.36196
  • Gao Y, Soh NYT, Liu N, et al. Application of a deep learning algorithm in the detection of Hip fractures. iScience. 2023;26(8):107350. doi:10.1016/j.isci.2023.107350
  • Sax DR, Warton EM, Sofrygin O, et al. Automated analysis of unstructured clinical assessments improves emergency department triage performance: a retrospective deep learning analysis. J Am Coll Emerg Physicians Open. 2023;4(4):e13003. doi:10.1002/emp2.13003
  • Ouyang CH, Chen CC, Tee YS, et al. The application of design thinking in developing a deep learning algorithm for hip fracture detection. Bioengineering. 2023;10(6):735. doi:10.3390/bioengineering10060735
  • Masoumian Hosseini M, Masoumian Hosseini ST, Qayumi K, Ahmady S, Koohestani HR. The aspects of running artificial intelligence in emergency care; a scoping review. Arch Acad Emerg Med. 2023;11(1):e38. doi:10.22037/aaem.v11i1.1974
  • He B, Dash D, Duanmu Y, Tan TX, Ouyang D, Zou J. Ai-Enabled Assessment Of Cardiac Function And Video Quality In Emergency Department Point-Of-Care Echocardiograms. J Emerg Med. 17:2023. doi:10.1016/j.jemermed.2023.02.005
  • Abrigo JM, Ko KL, Chen Q, et al. Artificial intelligence for detection of intracranial haemorrhage on head computed tomography scans: diagnostic accuracy in Hong Kong. Hong Kong Med J. 2023;29(2):112–120. doi:10.12809/hkmj209053
  • Sundrani S, Chen J, Jin BT, Abad ZSH, Rajpurkar P, Kim D. Predicting patient decompensation from continuous physiologic monitoring in the emergency department. NPJ Digit Med. 2023;6(1):60. doi:10.1038/s41746-023-00803-0
  • Inoue T, Maki S, Furuya T, et al. Automated fracture screening using an object detection algorithm on whole-body trauma computed tomography. Sci Rep. 2022;12(1):16549. doi:10.1038/s41598-022-20996-w
  • Rashid T, Zia MS, Najam-Ur-Rehman M, Rauf T, Kadry S HT, Kadry S. A minority class balanced approach using the DCNN-LSTM method to detect human wrist fracture. Life. 2023;13(1):133. doi:10.3390/life13010133
  • Zech JR, Santomartino SM, Yi PH. Artificial Intelligence (AI) for fracture diagnosis: an overview of current products and considerations for clinical adoption, from the ajr special series on ai applications. AJR Am J Roentgenol. 2022;219(6):869–878. doi:10.2214/AJR.22.27873
  • Wei J, Li D, Sing DC, et al. Detecting total Hip arthroplasty dislocations using deep learning: clinical and Internet validation. Emerg Radiol. 2022;29(5):801–808. doi:10.1007/s10140-022-02060-2
  • Yao LH, Leung KC, Tsai CL, Huang CH, Fu LC. A novel deep learning-based system for triage in the emergency department using electronic medical records: retrospective cohort study. J Med Internet Res. 2021;23(12):e27008. doi:10.2196/27008
  • Sánchez-Salmerón R, Gómez-Urquiza JL, Albendín-García L, et al. Machine learning methods applied to triage in emergency services: a systematic review. Int Emerg Nurs. 2022;60:101109. doi:10.1016/j.ienj.2021.101109
  • Dipnall JF, Page R, Du L, et al. Predicting fracture outcomes from clinical registry data using artificial intelligence supplemented models for evidence-informed treatment (PRAISE) study protocol. PLoS One. 2021;16(9):e0257361. doi:10.1371/journal.pone.0257361
  • Kim MW, Jung J, Park SJ, et al. Application of convolutional neural networks for distal radio-ulnar fracture detection on plain radiographs in the emergency room. Clin Exp Emerg Med. 2021;8(2):120–127. doi:10.15441/ceem.20.091
  • Joseph JW, Leventhal EL, Grossestreuer AV, et al. Deep-learning approaches to identify critically Ill patients at emergency department triage using limited information. J Am Coll Emerg Physicians Open. 2020;1(5):773–781. doi:10.1002/emp2.12218
  • Miles J, Turner J, Jacques R, Williams J, Mason S. Using machine-learning risk prediction models to triage the acuity of undifferentiated patients entering the emergency care system: a systematic review. Diagn Progn Res. 2020;4:16. doi:10.1186/s41512-020-00084-1
  • Ozkaya E, Topal FE, Bulut T, Gursoy M, Ozuysal M, Karakaya Z. Evaluation of an artificial intelligence system for diagnosing scaphoid fracture on direct radiography. Eur J Trauma Emerg Surg. 2022;48(1):585–592. doi:10.1007/s00068-020-01468-0
  • Weikert T, Noordtzij LA, Bremerich J, et al. Assessment of a deep learning algorithm for the detection of rib fractures on whole-body trauma computed tomography. Korean J Radiol. 2020;21(7):891–899. doi:10.3348/kjr.2019.0653
  • Jalal S, Parker W, Ferguson D, Nicolaou S. Exploring the role of artificial intelligence in an emergency and trauma radiology department. Can Assoc Radiol J. 2021;72(1):167–174. doi:10.1177/0846537120918338
  • Hwang EJ, Nam JG, Lim WH, et al. Deep Learning for Chest Radiograph Diagnosis in the Emergency Department. Radiology. 2019;293(3):573–580. doi:10.1148/radiol.2019191225
  • Kim J, Chae M, Chang HJ, Kim YA, Park E. Predicting CARDIAC ARREST AND RESPIRATORY FAILURE USING FEASIBLE ARTIFICIAL INTELLIGENCE WITH SIMPLE TRAJECTORIES OF PATIENT DATa. J Clin Med. 2019;8(9):1336. doi:10.3390/jcm8091336
  • Landry AP, Ting WKC, Zador Z, Sadeghian A, Cusimano MD. Using artificial neural networks to identify patients with concussion and postconcussion syndrome based on antisaccades. J Neurosurg. 2018;1–8. doi:10.3171/2018.6.JNS18607