ReviewForensic science international2020
Findings from machine learning in clinical medical imaging applications - Lessons for translation to the forensic setting.
Review in Forensic science international, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- Sex estimation from morphometry of lumbar vertebrae (L4-L5): A machine learning approach.International journal of legal medicine · 2026Article
- RiFNet: Automated rib fracture detection in postmortem computed tomography.Forensic science, medicine, and pathology · 2022Article
- Systematic Selection of Age-Associated mRNA Markers and the Development of Predicted Models for Forensic Age Inference by Three Machine Learning Methods.Frontiers in genetics · 2022Article
- Deep learning in forensic gunshot wound interpretation-a proof-of-concept study.International journal of legal medicine · 2021Article
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Authors and funding
5 authors.
Funding
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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.
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