Application of artificial intelligence and machine learning technology for the prediction of postmortem interval: A systematic review of preclinical and clinical studies

Sharma, Rishi; Diksha, .; Bhute, Ashish Ramesh; Bastia, Binaya Kumar

doi:10.1016/j.forsciint.2022.111473

Cited by 6 publications

(4 citation statements)

References 57 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Learning from experience is a crucial aspect of human intelligence, and machine learning (ML) is a subset of AI that addresses this ability, enabling machines and software to learn from experience. While AI and ML are closely related, ML is a distinct subset of AI [91]. Through classification techniques, ML can enhance outcome prediction by sorting data into predetermined categories.…”

Section: Artificial Intelligencementioning

confidence: 99%

Raman spectroscopy for postmortem interval estimation of human skeletal remains: A scoping review

Woess

Huck

Badzoka

et al. 2023

Journal of Biophotonics

View full text Add to dashboard Cite

Estimating postmortem intervals (PMI) is crucial in forensic investigations, providing insights into criminal cases and determining the time of death. PMI estimation relies on expert experience and a combination of thanatological data and environmental factors but is prone to errors. The lack of reliable methods for assessing PMI in bones and soft tissues necessitates a better understanding of bone decomposition. Several research groups have shown promise in PMI estimation in skeletal remains but lack valid data for forensic cases. Current methods are costly, time‐consuming, and unreliable for PMIs over 5 years. Raman spectroscopy (RS) can potentially estimate PMI by studying chemical modifications in bones and teeth correlated with burial time. This review summarizes RS applications, highlighting its potential as an innovative, nondestructive, and fast technique for PMI estimation in forensic medicine.

show abstract

Section: Artificial Intelligencementioning

confidence: 99%

Raman spectroscopy for postmortem interval estimation of human skeletal remains: A scoping review

Woess

Huck

Badzoka

et al. 2023

Journal of Biophotonics

View full text Add to dashboard Cite

show abstract

“…Indeed, AI models may be designed from any type of numerical data, such as continuous or discrete variables, photos, videos, or texts, and are able to combine those data types to improve model performance and reliability [25]. For instance, the methods used in PMI estimation are based on taphonomic processes; biomolecular, environmental, and entomological analyses; and postmortem imagery [26]. All those fields of expertise may be leveraged using AI to improve PMI estimation.…”

Section: Ai Beyond the Mere Improvement Of Pre-existing Techniques: T...mentioning

confidence: 99%

Artificial Intelligence and Diagnostics in Medicine and Forensic Science

Lefèvre,

Tournois

2023

Diagnostics

View full text Add to dashboard Cite

Diagnoses in forensic science cover many disciplinary and technical fields, including thanatology and clinical forensic medicine, as well as all the disciplines mobilized by these two major poles: criminalistics, ballistics, anthropology, entomology, genetics, etc. A diagnosis covers three major interrelated concepts: a categorization of pathologies (the diagnosis); a space of signs or symptoms; and the operation that makes it possible to match a set of signs to a category (the diagnostic approach). The generalization of digitization in all sectors of activity—including forensic science, the acculturation of our societies to data and digital devices, and the development of computing, storage, and data analysis capacities—constitutes a favorable context for the increasing adoption of artificial intelligence (AI). AI can intervene in the three terms of diagnosis: in the space of pathological categories, in the space of signs, and finally in the operation of matching between the two spaces. Its intervention can take several forms: it can improve the performance (accuracy, reliability, robustness, speed, etc.) of the diagnostic approach, better define or separate known diagnostic categories, or better associate known signs. But it can also bring new elements, beyond the mere improvement of performance: AI takes advantage of any data (data here extending the concept of symptoms and classic signs, coming either from the five senses of the human observer, amplified or not by technical means, or from complementary examination tools, such as imaging). Through its ability to associate varied and large-volume data sources, but also its ability to uncover unsuspected associations, AI may redefine diagnostic categories, use new signs, and implement new diagnostic approaches. We present in this article how AI is already mobilized in forensic science, according to an approach that focuses primarily on improving current techniques. We also look at the issues related to its generalization, the obstacles to its development and adoption, and the risks related to the use of AI in forensic diagnostics.

show abstract

“…Finally, different features of mobile forensics are compared to the cost, MD5 hashing algorithm, general ease of use, and platform support survey. The technique for mobile forensic tools is laid up in a series of phases, including data gathering, sleuthing, processing, and storage [18], [22]. The tools may be recovered from smartphones and produce reports relying on excellent forensic procedures.…”

Section: Introductionmentioning

confidence: 99%

Navigating the cyber forensics landscape a review of recent innovations

Panigrahi,

Barpanda,

Sethy

2024

IJ-ICT

View full text Add to dashboard Cite

<p><span>The extensive relevance of digital forensics in today's data-driven environment has been emphasized in this article. The free software and the commercial software community are debatable, despite users and developers often differing views on important topics like software safety and usability. This article primarily uses pre-defined criteria and a platform-oriented approach to examine promising freeware (Magnet Forensics and Sleuth Kit) vs. profitable (ProDiscover and Oxygen Forensic Suite) mobile forensics tools. Under diverse settings, the tools' capacity to develop and analyze forensically sound digital forensic media sources is validated. After erasing data, each media type was tested again after formatting. The study concludes with a comparison matrix that may aid in determining the best-fit option for the investigation's requirements among the tools. The findings indicate the potential for freeware to supplant numerous proprietary applications, as users can opt for freeware instead of incurring costs associated with proprietary software. Furthermore, this perception can be put into practice.</span></p>

show abstract

Application of artificial intelligence and machine learning technology for the prediction of postmortem interval: A systematic review of preclinical and clinical studies

Cited by 6 publications

References 57 publications

Raman spectroscopy for postmortem interval estimation of human skeletal remains: A scoping review

Raman spectroscopy for postmortem interval estimation of human skeletal remains: A scoping review

Artificial Intelligence and Diagnostics in Medicine and Forensic Science

Navigating the cyber forensics landscape a review of recent innovations

Contact Info

Product

Resources

About