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Evaluating the utility of time-lapse imaging in the estimation of post-mortem interval: An Australian case study
Estimating post-mortem interval is an important aspect in forensic investigations. The aim of this study was to investigate if time-lapse imaging can be used to improve estimates of post-mortem interval using Megyesi et al.‘s [1] method for a human donor decomposing in an Australian environment. To...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7219161/ https://www.ncbi.nlm.nih.gov/pubmed/32411972 http://dx.doi.org/10.1016/j.fsisyn.2019.08.003 |
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author | Wilson, Alyson Serafin, Stanley Seckiner, Dilan Berry, Rachel Mallett, Xanthé |
author_facet | Wilson, Alyson Serafin, Stanley Seckiner, Dilan Berry, Rachel Mallett, Xanthé |
author_sort | Wilson, Alyson |
collection | PubMed |
description | Estimating post-mortem interval is an important aspect in forensic investigations. The aim of this study was to investigate if time-lapse imaging can be used to improve estimates of post-mortem interval using Megyesi et al.‘s [1] method for a human donor decomposing in an Australian environment. To achieve this, time-lapse images were taken every 30 min over a 6-month period. The Megyesi et al. [1] total body score (TBS) system was used to quantify the level of decomposition of the donor for each image. Linear regression was performed to determine if observing decomposition more than once a day leads to increased accuracy in predicting PMI (post-mortem interval). Decomposition initially progressed quickly and then plateaued at 1004 hours PMI, with a TBS of 24. Individual timestamps were created from the time-lapse images taken each day at 08:00 hrs, 11:00 hrs, 14:00 hrs, 15:00 hrs, and 17:00 hrs. All timestamps produced R(2) values > 0.80, indicating that the Megyesi et al. [1] method accurately predicts PMI for this donor. The 08:00 hrs timestamp had the highest value R(2) = 0.886, whilst the combined timestamp (which included the scores from all five images for each 24-hour period) R(2) = 0.823 was the lowest. This study supports the validity of Megyesi et al.‘s [1] TBS model to estimate PMI. Two other interesting findings were that the results suggest that scoring TBS multiple times per day does not improve estimates of PMI, however scoring TBS at daybreak produces more accurate results than scoring TBS later in the day. This may be an important consideration in forensic scenarios. |
format | Online Article Text |
id | pubmed-7219161 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-72191612020-05-14 Evaluating the utility of time-lapse imaging in the estimation of post-mortem interval: An Australian case study Wilson, Alyson Serafin, Stanley Seckiner, Dilan Berry, Rachel Mallett, Xanthé Forensic Sci Int Synerg Interdisciplinary Forensics Estimating post-mortem interval is an important aspect in forensic investigations. The aim of this study was to investigate if time-lapse imaging can be used to improve estimates of post-mortem interval using Megyesi et al.‘s [1] method for a human donor decomposing in an Australian environment. To achieve this, time-lapse images were taken every 30 min over a 6-month period. The Megyesi et al. [1] total body score (TBS) system was used to quantify the level of decomposition of the donor for each image. Linear regression was performed to determine if observing decomposition more than once a day leads to increased accuracy in predicting PMI (post-mortem interval). Decomposition initially progressed quickly and then plateaued at 1004 hours PMI, with a TBS of 24. Individual timestamps were created from the time-lapse images taken each day at 08:00 hrs, 11:00 hrs, 14:00 hrs, 15:00 hrs, and 17:00 hrs. All timestamps produced R(2) values > 0.80, indicating that the Megyesi et al. [1] method accurately predicts PMI for this donor. The 08:00 hrs timestamp had the highest value R(2) = 0.886, whilst the combined timestamp (which included the scores from all five images for each 24-hour period) R(2) = 0.823 was the lowest. This study supports the validity of Megyesi et al.‘s [1] TBS model to estimate PMI. Two other interesting findings were that the results suggest that scoring TBS multiple times per day does not improve estimates of PMI, however scoring TBS at daybreak produces more accurate results than scoring TBS later in the day. This may be an important consideration in forensic scenarios. Elsevier 2019-08-17 /pmc/articles/PMC7219161/ /pubmed/32411972 http://dx.doi.org/10.1016/j.fsisyn.2019.08.003 Text en © 2019 The Authors https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Interdisciplinary Forensics Wilson, Alyson Serafin, Stanley Seckiner, Dilan Berry, Rachel Mallett, Xanthé Evaluating the utility of time-lapse imaging in the estimation of post-mortem interval: An Australian case study |
title | Evaluating the utility of time-lapse imaging in the estimation of post-mortem interval: An Australian case study |
title_full | Evaluating the utility of time-lapse imaging in the estimation of post-mortem interval: An Australian case study |
title_fullStr | Evaluating the utility of time-lapse imaging in the estimation of post-mortem interval: An Australian case study |
title_full_unstemmed | Evaluating the utility of time-lapse imaging in the estimation of post-mortem interval: An Australian case study |
title_short | Evaluating the utility of time-lapse imaging in the estimation of post-mortem interval: An Australian case study |
title_sort | evaluating the utility of time-lapse imaging in the estimation of post-mortem interval: an australian case study |
topic | Interdisciplinary Forensics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7219161/ https://www.ncbi.nlm.nih.gov/pubmed/32411972 http://dx.doi.org/10.1016/j.fsisyn.2019.08.003 |
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