Year: 2025
Type: article
Abstract: Accurately estimating the time of death (ToD) is crucial in forensic investigations, serving as a guidepost for reconstructing the events surrounding a death.Knowing the time of death helps narrow down the timeline, validate or refute alibis, and determine the sequence of events that may lead to a suspect.Several factors influence ToD estimation, including physical changes such as rigor mortis, livor mortis, algor mortis, and decomposition (Knight,2002).Each of these biological markers evolves in a predictable pattern, which can be carefully analyzed under various environmental conditions.In addition, forensic entomology, the study of insect activity, provides a biological clock that further enhances the accuracy of time assessments (Goff,2000).Environmental variables like temperature, humidity, and location significantly alter the timeline, requiring forensic professionals to consider these when analyzing a body.In recent years, the integration of artificial intelligence and computer vision into forensic science has transformed ToD estimation (Zhang, Zhou & Li,2020).Machine learning models trained on historical postmortem data can now analyses patterns and provide approximations with increasing accuracy.This paper explores the interdisciplinary techniques used to determine ToD, including biological observations, insect colonization patterns, decomposition stages, and AIdriven models.By understanding the strengths and limitations of each method, forensic investigators can apply the most appropriate tools depending on the context.Accurate estimation of time of death is not merely a scientific endeavor-it plays a vital role in upholding justice and ensuring the integrity of criminal investigations.
Authors: Rekha Mithal, Divyana Mohnani
Language: English
Subfield: Insect Science
Domain: Life Sciences
Open Access status: diamond
OpenAlex ID: W7082662965
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