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Assessing the clinicians’ pathway to embed artificial intelligence for assisted diagnostics of fracture detection.

Moreno-García, Carlos; Dang, Truong; Martin, Kyle; Patel, Manish; Thompson, Andrew; Leishman, Lesley; Wiratunga, Nirmalie

Authors

Truong Dang

Manish Patel

Andrew Thompson

Lesley Leishman



Contributors

Kerstin Bach
Editor

Razvan Bunescu
Editor

Cindy Marling
Editor

Abstract

Fracture detection has been a long-standingparadigm on the medical imaging community. Many algo-rithms and systems have been presented to accurately detectand classify images in terms of the presence and absence offractures in different parts of the body. While these solutionsare capable of obtaining results which even surpass humanscores, few efforts have been dedicated to evaluate how thesesystems can be embedded in the clinicians and radiologistsworking pipeline. Moreover, the reports that are included withthe radiography could also provide key information regardingthe nature and the severity of the fracture. In this paper, wepresent our first findings towards assessing how computer vi-sion, natural language processing and other systems could becorrectly embedded in the clinicians’ pathway to better aidon the fracture detection task. We present some initial exper-imental results using publicly available fracture datasets alongwith a handful of data provided by the National HealthcareSystem from the United Kingdom in a research initiative call.Results show that there is a high likelihood of applying trans-fer learning from different existing and pre-trained models tothe new records provided in the challenge, and that thereare various ways in which these techniques can be embeddedalong the clinicians’ pathway

Citation

MORENO-GARCÍA, C.F., DANG, T., MARTIN, K., PATEL, M., THOMPSON, A., LEISHMAN, L. and WIRATUNGA, N. 2020. Assessing the clinicians’ pathway to embed artificial intelligence for assisted diagnostics of fracture detection. In Bach, K., Bunescu, R., Marling, C. and Wiratunga, N. (eds.) Knowledge discovery in healthcare data 2020: proceedings of the 5th Knowledge discovery in healthcare data international workshop 2020 (KDH 2020), co-located with 24th European Artificial intelligence conference (ECAI 2020), 29-30 August 2020, [virtual conference]. CEUR workshop proceedings, 2675. Aachen: CEUR-WS [online], pages 63-70. Available from: http://ceur-ws.org/Vol-2675/paper10.pdf

Conference Name 5th Knowldege discovery in healthcare data international workshop conference (KDH 2020), co-located with 24th European Artificial intelligence conference (ECAI 2020)
Conference Location [virtual conference]
Start Date Aug 29, 2020
End Date Aug 30, 2020
Acceptance Date Jun 1, 2020
Online Publication Date Sep 18, 2020
Publication Date Sep 18, 2020
Deposit Date Sep 18, 2020
Publicly Available Date Sep 21, 2020
Publisher CEUR Workshop Proceedings
Pages 63-70
Series Title CEUR workshop proceedings
Series Number 2675
Series ISSN 1613-0073
Book Title Knowledge discovery in healthcare data 2020: proceedings of the 5th Knowledge discovery in healthcare data international workshop 2020 (KDH 2020), co-located with 24th European Artificial intelligence conference (ECAI 2020), 29-30 August 2020, [virtual c
Keywords Fracture detection; Natural language processing; Convolutional neural networks; Clinicians’ pathway
Public URL https://rgu-repository.worktribe.com/output/968418
Publisher URL http://ceur-ws.org/Vol-2675/

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