Forensic science is a multidisciplinary field that allows the analysis of different biological data when solving a crime. The complexity of the data, such as DNA, RNA, protein, and epigenetic markers, requires advanced processing supported by computers.1
The use of CRISPR-Cas technology, which has revolutionized the field of biotechnology through its precise therapeutic application, has shown potential for forensic DNA analysis.2
Additionally, emerging technologies, including artificial intelligence, machine learning, computer vision, microfluidic chip technology, and advanced spectroscopy, have enabled advancements in forensic science in how data is analyzed and interpreted.1,3,4
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Exploring CRISPR in Forensics
Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) sequences were discovered in several bacteria species including E.coli. Interestingly, CRISPR relies on the Cas9 enzyme, which can target any DNA sequence with the right programming and its genome editing applications hold significant implications for many diseases.2
A CRISPR-dependent technique was found to be more beneficial compared with Next Generation Sequencing (NGS) techniques for assay design and sequencing efficiency. Due to this, it can be useful in forensic science as many samples at crime scenes are found to be degraded, carrying fragmented DNA, which can be resolved with CRISPR-Cas9 technology using DNA repair and replication.2
Additionally, CRISPR-Cas9-dependent PCR-independent techniques can be used when analyzing complex DNA mixtures that consist of multiple DNA profiles, which are usually a significant challenge for forensic scientists.2
CRISPR technology can be used to link each sequence to a specific DNA molecule to identify various DNA profiles and find the true suspect.2
Another benefit of using CRISPR technology over traditional PCR technology is the lack of PCR artefacts, which is usually a concern and can impact the results.2
Unleashing the Power of AI
AI and machine learning have innovated many fields and have the potential to transform forensic science in the modern era, as the use of digital evidence in criminal investigations has risen, as well as cybercrime.5
Advanced technology is required to collect and analyze digital evidence during investigations, and AI and machine learning may enable the processing of significant amounts of data quickly and efficiently to detect critical evidence.5
AI can be used in forensic science to improve the performance of experts and overcome subject bias limitations of traditional approaches.6
It can also be used to collect new information, recognizing patterns from large datasets, reduce human errors and subjectivity. AI can provide tools that reinforce the scientific method, providing an alternative opinion on a crime case, leading to faster crime scene analysis and more efficient workflows.6
Training AI forensic tools to recognize specific patterns and anomalies that may go undetected by the human eye can be beneficial for finding concealed evidence, enabling more accurate and reliable results.5
Convolution Neural Network (CNN) is an example of a deep learning algorithm that is used in pattern recognition and image processing, with the main benefit of detecting critical features without requiring any human supervision. This algorithm can be used in forensic science for many applications, including determining age or gender.7
Additionally, implementing three-dimensional CNN may improve the morphology of facial soft tissues when attempting facial reconstruction from a skull, a goal currently limited by available technology in forensic science.7
Exploring Other Emerging Technologies
Other emerging technologies such as microfluidic devices, may also provide benefits for forensic DNA analysis, including decreasing the risk of contamination, reducing time on analysis, and offering more direct application at the crime scene.3
Microfluidic chip technology is revolutionary for medical applications, such as for point-of-care use. Interestingly, it can also be used for forensic science, with this innovative technology being used to analyze trace evidence of biological samples that may contain human DNA at the crime scene.3
Using microfluidic chip technology for this purpose may have significant impact in forensic science, as the DNA analysis process in forensic laboratories can take days, and the outcome may not be as relevant as first thought. Waiting for results may hinder the progress of a crime investigation, giving the perpetrator time to destroy relevant evidence or commit another crime.3
Additionally, advanced spectroscopy is also a significant technology used in forensic science. Raman spectroscopy, for example, is a versatile analytical technique that can be used to analyze various bodily fluids, fibers, explosives and even gunshot residue. The advantage of this technique is that it is non-destructive and does not need sample preparation, enabling the samples to be preserved for repeated analyses, if required.4
Having a biological trace evidence analyzer in the field enables the case to progress quickly and eliminate innocent suspects that might have been mistakenly taken into custody.3
The first hours of investigation are known as the “golden hours” and having relevant information as quickly as possible is strongly needed during this time to provide direction and progression for investigators on the case.3
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Challenges and the Road Ahead
While new technologies are being explored for various fields, including forensic science, some challenges may limit their progression.2,3
The evolution of CRISPR technology is progressing rapidly; however, some obstacles from a technology and application perspective includes, off-target cutting, non-specific DNA binding, and the need to standardize a gold-standard technique for delivering CRISPR components into cells.2
Additionally, the challenges of applying AI in forensic science include feasibility, as in-depth processes cannot currently explain the approaches used by computational methods to create inputs in order to gain reliable results.
Computational models also require training by large datasets to provide effective and efficient performance. While some promising studies on future applications exist, they may not be fully ready for current implementation.6
The challenge facing microfluidic technology for forensic science is that most microfluidic devices use a biological sample in a solution or pure DNA as the input material, but commercially available machines require swabs as the input. Integrating trace sampling through swabs on a chip device can be difficult and has not yet been overcome at the research level.3
Conclusion
With various emerging technologies finding success in different fields, collaboration with other professionals, such as scientists, forensic investigators and legal professionals, may lead to more effective advancements in forensic science.2,6
The use of advanced technologies can have significant implications for the future of forensic science and may enable a faster and more effective approach to solving crime with innovative strategies.2,3,4,6
References
- Sessa F, Esposito M, Cocimano G, et al. Artificial Intelligence and Forensic Genetics: Current applications and future perspectives. Applied Sciences. 2024;14(5):2113. doi:10.3390/app14052113
- Dash HR, Arora M. CRISPR-CASB technology in forensic DNA analysis: Challenges and solutions. Applied Microbiology and Biotechnology. 2022;106(12):4367-4374. doi:10.1007/s00253-022-12016-8
- Bruijns B, Van Asten A, Tiggelaar R, Gardeniers H. Microfluidic devices for forensic DNA analysis: A Review. Biosensors. 2016;6(3):41. doi:10.3390/bios6030041
- Doty KC, Lednev IK. Raman spectroscopy for forensic purposes: Recent applications for serology and gunshot residue analysis. TrAC Trends in Analytical Chemistry. 2018;103:215-222. doi:https://doi.org/10.1016/j.trac.2017.12.003
- Dunsin D, Ghanem MC, Ouazzane K, Vassilev V. A comprehensive analysis of the role of artificial intelligence and machine learning in modern digital forensics and incident response. Forensic Science International: Digital Investigation. 2024;48:301675. doi:https://doi.org/10.1016/j.fsidi.2023.301675
- Galante N, Cotroneo R, Furci D, Lodetti G, Casali MB. Applications of artificial intelligence in forensic sciences: Current potential benefits, limitations and perspectives. International Journal of Legal Medicine. 2022;137(2):445-458. doi:10.1007/s00414-022-02928-5
- Thurzo A, Kosnáčová HS, Kurilová V, et al. Use of Advanced Artificial Intelligence in Forensic Medicine, Forensic Anthropology and Clinical Anatomy. Healthcare. 2021;9(11):1545. doi:https://doi.org/10.3390/healthcare9111545
Further Reading