Peña-Solórzano, CA, Albrecht, DW, Bassed, RB, Burke, MD and Dimmock, MR ORCID: https://orcid.org/0000-0002-2737-7946 (2020) Findings from machine learning in clinical medical imaging applications - Lessons for translation to the forensic setting. Forensic Science International, 316 (110538). -.

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Abstract

Machine learning (ML) techniques are increasingly being used in clinical medical imaging to automate distinct processing tasks. In post-mortem forensic radiology, the use of these algorithms presents significant challenges due to variability in organ position, structural changes from decomposition, inconsistent body placement in the scanner, and the presence of foreign bodies. Existing ML approaches in clinical imaging can likely be transferred to the forensic setting with careful consideration to account for the increased variability and temporal factors that affect the data used to train these algorithms. Additional steps are required to deal with these issues, by incorporating the possible variability into the training data through data augmentation, or by using atlases as a pre-processing step to account for death-related factors. A key application of ML would be then to highlight anatomical and gross pathological features of interest, or present information to help optimally determine the cause of death. In this review, we highlight results and limitations of applications in clinical medical imaging that use ML to determine key implications for their application in the forensic setting.

Item Type: Article
Additional Information: The final version of this article and all relevant information related to it, including copyrights, can be found on the publisher website.
Subjects: R Medicine > R Medicine (General)
T Technology > T Technology (General)
Divisions: Faculty of Medicine and Health Sciences > School of Allied Health Professions
Related URLs:
Depositing User: Symplectic
Date Deposited: 26 Jul 2022 09:57
Last Modified: 26 Jul 2022 09:57
URI: https://eprints.keele.ac.uk/id/eprint/11126

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