Evidence map›Paper›PMID 37721660›Full record

ArticleForensic science, medicine, and pathology2024

Discrimination of human and animal bloodstains using hyperspectral imaging.

Gary Sean Cooney, Hannes Köhler, Claire Chalopin, Carsten Babian

Abstract read
In one paragraph

Article in Forensic science, medicine, and pathology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Gary Sean CooneyInnovation Center Computer Assisted Surgery (ICCAS), Leipzig University, Leipzig, Germany.
Hannes KöhlerInnovation Center Computer Assisted Surgery (ICCAS), Leipzig University, Leipzig, Germany.
Claire ChalopinInnovation Center Computer Assisted Surgery (ICCAS), Leipzig University, Leipzig, Germany.
Carsten BabianInstitute for Legal Medicine, Leipzig University, Leipzig, Germany. carsten.babian@medizin.uni-leipzig.de.ORCID 0000-0002-6621-9705

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Blood is the most encountered type of biological evidence in violent crimes and contains pertinent information to a forensic investigation. The false presumption that blood encountered at a crime scene is human may not be realised until after costly and sample-consuming tests are performed. To address the question of blood origin, the novel application of visible-near infrared hyperspectral imaging (HSI) is used for the detection and discrimination of human and animal bloodstains. The HSI system used is a portable, non-contact, non-destructive method for the determination of blood origin. A support vector machine (SVM) binary classifier was trained for the discrimination of bloodstains of human (n = 20) and five animal species: pig (n = 20), mouse (n = 16), rat (n = 5), rabbit (n = 5), and cow (n = 20). On an independent test set, the SVM model achieved accuracy, precision, sensitivity, and specificity values of 96, 97, 95, and 96%, respectively. Segmented images of bloodstains aged over a period of two months were produced, allowing for the clear visualisation of the discrimination of human and animal bloodstains. The inclusion of such a system in a forensic investigation workflow not only removes ambiguity surrounding blood origin, but can potentially be used in tandem with HSI bloodstain age determination methods for rapid on-scene forensic analysis.

Indexed as

Blood StainsHyperspectral ImagingSupport Vector MachineAnimalsCattleForensic MedicineHumansMiceRabbitsRatsSwineAnimal bloodForensicsHyperspectral imaging (HSI)Neighbourhood component feature selection (NCFS)Support vector machine (SVM)

Identifiers

PMID37721660
PMCPMC11297111

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.