journal article·2024-03-04Open access

Artificial Intelligence and Forensic Genetics: Current Applications and Future Perspectives

Francesco Sessa, Massimiliano Esposito, Giuseppe Cocimano, Sara Sablone, Michele Ahmed Antonio Karaboue, Mario Chisari, Davide Giuseppe Albano, Monica Salerno

Applied Sciences· Volume 14· Issue 5

Abstract

The term artificial intelligence (AI) was coined in the 1950s and it has successfully made its way into different fields of medicine. Forensic sciences and AI are increasingly intersecting fields that hold tremendous potential for solving complex criminal investigations. Considering the great evolution in the technologies applied to forensic genetics, this literature review aims to explore the existing body of research that investigates the application of AI in the field of forensic genetics. Scopus and Web of Science were searched: after an accurate evaluation, 12 articles were included in the present systematic review. The application of AI in the field of forensic genetics has predominantly focused on two aspects. Firstly, several studies have investigated the use of AI in haplogroup analysis to enhance and expedite the classification process of DNA samples. Secondly, other research groups have utilized AI to analyze short tandem repeat (STR) profiles, thereby minimizing the risk of misinterpretation. While AI has proven to be highly useful in forensic genetics, further improvements are needed before using these applications in real cases. The main challenge lies in the communication gap between forensic experts: as AI continues to advance, the collaboration between forensic sciences and AI presents immense potential for transforming investigative practices, enabling quicker and more precise case resolutions.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Forensic and Genetic Research · Autopsy Techniques and Outcomes · Law, AI, and Intellectual Property

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Department of Medical, Surgical and Advanced Technologies “G.F. Ingrassia”, University of Catania, 95121 Catania, Italy

Faculty of Medicine and Surgery, “Kore” University of Enna, 94100 Enna, Italy

Department of Mental and Physical Health and Preventive Medicine, University of Campania “Vanvitelli”, 80121 Napoli, Italy

Forensic Medicine Unit, University of Bari, 70100 Bari, Italy

Department of Clinical and Experimental Medicine, University of Foggia, 71122 Foggia, Italy

“Rodolico-San Marco” Hospital, Santa Sofia Street, 87, 95121 Catania, Italy

Section of Legal Medicine, Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties, University of Palermo, 90129 Palermo, Italy

References (100+)

Show source-provided references
1. Smith, C., McGuire, B., Huang, T., and Yang, G. (2006). The History of Artificial Intelligence, University of Washington.
2. From here to human-level AI10.1016/j.artint.2007.10.009 ↗
3. Management of Medico-Legal Risks in Digital Health Era: A Scoping Review10.3389/fmed.2021.821756 ↗
4. Applications of Artificial Intelligence in Forensic Sciences: Current Potential Benefits, Limitations and Perspectives10.1007/s00414-022-02928-5 ↗
5. Recent Advances in Forensic Biology and Forensic DNA Typing: INTERPOL Review 2019–2022
6. Esposito, M., Sessa, F., Cocimano, G., Zuccarello, P., Roccuzzo, S., and Salerno, M. (2023). Advances in Technologies in Crime Scene Investigation. Diagnostics, 13.10.3390/diagnostics13203169 ↗
7. Aldoseri, A., Al-Khalifa, K.N., and Hamouda, A.M. (2023). Re-Thinking Data Strategy and Integration for Artificial Intel-ligence: Concepts, Opportunities, and Challenges. Appl. Sci., 13.10.20944/preprints202305.1565.v2 ↗
8. Artificial Intelligence in Information Systems Research: A Systematic Literature Review and Research Agenda10.1016/j.ijinfomgt.2021.102383 ↗
9. Automated Age Estimation of Young Individuals Based on 3D Knee MRI Using Deep Learning
10. A Potential Method for Sex Estimation of Human Skeletons Using Deep Learning and Three-Dimensional Sur-face Scanning10.1007/s00414-021-02675-z ↗
11. Artificial Intelligence for Sex Determination of Skeletal Remains: Application of a Deep Learning Artificial Neural Network to Human Skulls10.1016/j.jflm.2019.01.004 ↗
12. Determine the Age Range Based on Machine-Learning Methods from Skeletal Angles of the Face (Glabella and Maxilla Angle and Length and Width of Piriformis) in a CT Scan10.32598/ijmtfm.v12i4.38605 ↗
13. A Fully Automated Method of Human Identification Based on Dental Panoramic Radiographs Using a Con-volutional Neural Network10.1259/dmfr.20210383 ↗
14. Diagnostic Performance of Convolutional Neural Networks for Dental Sexual Dimorphism10.1038/s41598-022-21294-1 ↗
15. Evaluating Artificial Intelligence for Comparative Radiography
16. Kondou, H., Morohashi, R., Ichioka, H., Bandou, R., Matsunari, R., Kawamoto, M., Idota, N., Ting, D., Kimura, S., and Ikegaya, H. (2023). Deep Neural Networks-Based Age Estimation of Cadavers Using CT Imaging of Vertebrae. Int. J. Environ. Res. Public Health, 20.10.3390/ijerph20064806 ↗
17. Age-Net: An Advanced Hybrid Deep Learning Model for Age Estimation Using Orthopantomograph Images10.18280/ts.400423 ↗
18. Automatic Sex Estimation Using Deep Convolutional Neural Network Based on Orthopantomogram Images10.1016/j.forsciint.2023.111704 ↗
19. Dental Biometrics: Alignment and Matching of Dental Radiographs10.1109/TPAMI.2005.157 ↗
20. Hierarchical Information Fusion for Decision Making in Craniofacial Superimposition10.1016/j.inffus.2017.03.004 ↗
21. Forensic Odontology: Automatic Identification of Persons Comparing Antemortem and Postmortem Panoramic Radiographs Using Computer Vision10.1055/a-0632-4744 ↗
22. Thurzo, A., Jančovičová, V., Hain, M., Thurzo, M., Novák, B., Kosnáčová, H., Lehotská, V., Varga, I., Kováč, P., and Moravanský, N. (2022). Human Remains Identification Using Micro-CT, Chemometric and AI Methods in Forensic Experimental Reconstruction of Dental Patterns after Concentrated Sulphuric Acid Significant Impact. Molecules, 27.10.20944/preprints202201.0049.v2 ↗
23. Stature Estimation by Semi-Automatic Measurements of 3D CT Images of the Femur10.1007/s00414-022-02921-y ↗
24. Employing Deep Learning for Sex Estimation of Adult Individuals Using 2D Images of the Humerus10.1007/s00521-022-07981-0 ↗
25. Applying Artificial Intelligence to Determination of Legal Age of Majority from Radiographic Data10.1016/j.morpho.2023.100723 ↗
26. A comparative Analysis of Human and AI Performance in Forensic Estimation of Physical Attributes10.1038/s41598-023-31821-3 ↗
27. Artificial Neural Network, Predictor Variables and Sensitivity Threshold for DNA Methylation-Based Age Prediction Using Blood Samples10.1038/s41598-021-81556-2 ↗
28. Automated Diatom Searching in the Digital Scanning Electron Microscopy Images of Drowning Cases Using the Deep Neural Networks10.1007/s00414-020-02392-z ↗
29. An Efficient Method for Building a Database of Diatom Populations for Drowning Site Inference Using a Deep Learning Algorithm10.1007/s00414-020-02497-5 ↗
30. Digital Whole-Slide Image Analysis for Automated Diatom Test in Forensic Cases of Drowning Using a Convolutional Neural Network Algorithm10.1016/j.forsciint.2019.109922 ↗
31. Identification of Diatom Taxonomy by a Combination of Region-Based Full Convolutional Network, Online Hard Example Mining, and Shape Priors of Diatoms10.1007/s00414-021-02664-2 ↗
32. Computer-Aided Craniofacial Superimposition Validation Study: The Identification of the Leaders and Participants of the Polish-Lithuanian January Uprising (1863–1864)10.1007/s00414-022-02929-4 ↗
33. Zhang, J., Vieira, D.N., Cheng, Q., Zhu, Y., Deng, K., Zhang, J., Qin, Z., Sun, Q., Zhang, T., and Ma, K. (2023). DiatomNet v1.0: A Novel Approach for Automatic Diatom Testing for Drowning Diagnosis in Forensically Biomedical Application. Comput. Methods Programs Biomed., 232.10.1016/j.cmpb.2023.107434 ↗
34. Ballistics Projectile Image Analysis for Firearm Identification10.1109/TIP.2006.877530 ↗
35. Artificial Intelligence for Human Gunshot Wound Classification10.1016/j.jpi.2023.100361 ↗
36. Haut, R.C., and Wei, F. (2017). Biomechanical Studies on Patterns of Cranial Bone Fracture Using the Immature Porcine Model. J. Biomech. Eng., 139.10.1115/1.4034430 ↗
37. Multi-Omics Integration Strategy in the Post-Mortem Interval of Forensic Science10.1016/j.talanta.2023.125249 ↗
38. The difficult role of Artificial Intelligence in Medical Liability: To Err is Not Only Human
39. Defining Medical Liability when Artificial Intelligence Is Applied on Diagnostic Algorithms: A Systematic Review10.3389/fmed.2023.1305756 ↗
40. Machine Learning Applications in Forensic DNA Profiling: A Critical Review10.1016/j.fsigen.2023.102994 ↗
41. Schlecht, J., Kaplan, M.E., Barnard, K., Karafet, T., Hammer, M.F., and Merchant, N.C. (2008). Machine-Learning Approaches for Classifying Haplogroup from Y Chromosome STR Data. PLoS Comput. Biol., 4.10.1371/journal.pcbi.1000093 ↗
42. Machine Learning Applications in Genetics and Genomics10.1038/nrg3920 ↗
43. Developments in Forensic DNA Analysis10.1042/ETLS20200304 ↗
44. Paudel, R., and Ligmann-Zielinska, A. (2023). A Largely Unsupervised Domain-Independent Qualitative Data Extraction Approach for Empirical Agent-Based Model Development. Algorithms, 16.10.3390/a16070338 ↗
45. The PRISMA 2020 Statement: An Updated Guideline for Reporting Systematic Reviews Systematic Reviews and Meta-Analyses10.1136/bmj.n71 ↗
46. PopAffiliator: Online Calculator for Individual Affiliation to a Major Population Group Based on 17 Autosomal Short Tandem Repeat Genotype Profile10.1007/s00414-010-0472-2 ↗
47. Identification of unique repeated patterns, location of mutation in DNA Finger Printing Using Artificial Intelligence Technique10.1504/IJBRA.2014.059516 ↗
48. Teaching Artificial Intelligence to Read Electropherograms10.1016/j.fsigen.2016.07.013 ↗
49. DNA Methylation-Based Age Prediction Using Massively Parallel Sequencing Data and Multiple Machine Learning Models10.1016/j.fsigen.2018.09.003 ↗
50. Automated Detection and Removal of Capillary Electrophoresis Artifacts Due to Spectral Overlap10.1002/elps.201900060 ↗
51. Artificially Intelligent Scoring and Classification Engine for Forensic Identification10.1016/j.fsigen.2019.102162 ↗
52. Validation Studies of the ParaDNA® Intelligence System with Artificial Evidence Items10.1080/20961790.2019.1665159 ↗
53. Validation of a Neural Network Approach for STR Typing to Replace Human Reading10.1016/j.fsigen.2021.102591 ↗
54. Explainable Artificial Intelligence in Forensics: Realistic Explanations for Number of Contributor Predictions of DNA Profiles10.1016/j.fsigen.2021.102632 ↗
55. Comprehensive Evaluations of Individual Discrimination, Kinship Analysis, Genetic Relationship Exploration and Biogeographic Origin Prediction in Chinese Dongxiang Group by a 60-plex DIP panel10.1186/s41065-023-00271-2 ↗
56. Kloska, A., Giełczyk, A., Grzybowski, T., Płoski, R., Kloska, S.M., Marciniak, T., Pałczyński, K., Rogalla-Ładniak, U., Malyarchuk, B.A., and Derenko, M.V. (2023). A Machine-Learning-Based Approach to Prediction of Biogeographic Ancestry within Europe. Int. J. Mol. Sci., 24.10.3390/ijms242015095 ↗
57. Fabbri, M., Alfieri, L., Mazdai, L., Frisoni, P., Gaudio, R.M., and Neri, M. (2023). Application of Forensic DNA Phenotyping for Prediction of Eye, Hair and Skin Colour in Highly Decomposed Bodies. Healthcare, 11.10.3390/healthcare11050647 ↗
58. Mapping AI-ethics’ Dilemmas in Forensic Case Work: To trust AI or Not?10.1016/j.forsciint.2023.111807 ↗
59. The Use of Facial Recognition Technology by Law Enforcement in Europe: A Non-Orwellian Draft Proposal10.1007/s10610-022-09512-y ↗
60. Ferrara, E. (2023). Fairness and Bias in Artificial Intelligence: A Brief Survey of Sources, Impacts, and Mitigation Strategies. arXiv.10.2196/preprints.48399 ↗
61. Padma, K.R., and Don, K.R. (2021). Cyber Security and Digital Forensics, Wiley Online Library.
62. Seki, T., Hsiao, Y.-Y., Ishizawa, F., Sugano, Y., and Takahashi, Y. (2023). Establishment of A Random Forest Regression Model to Estimate the Age of Bloodstains Based on Temporal Colorimetric Analysis. Leg. Med., 102343.10.1016/j.legalmed.2023.102343 ↗
63. On the Use of Machine Learning Algorithms in Forensic Anthropology10.1016/j.legalmed.2020.101771 ↗
64. A Comparative Investigation of Machine Learning Algorithms for Predicting Safety Signs Comprehension Based on Socio-Demographic Factors and Cognitive Sign Features10.1038/s41598-023-38065-1 ↗
65. Artificial Intelligence and Machine Learning Overview in Pathology & Laboratory Medicine: A general review of data preprocessing and basic supervised concepts10.1053/j.semdp.2023.02.002 ↗
66. Applications of Deep Learning in Understanding Gene Regulation10.1016/j.crmeth.2022.100384 ↗
67. Common Statistical Concepts in the Supervised Machine Learning arena10.3389/fonc.2023.1130229 ↗
68. A Review of Bioinformatic Methods for Forensic DNA analyses10.1016/j.fsigen.2017.12.005 ↗
69. DNAxs/DNAStatistX: Development and Validation of a Software Suite for the Data Management and Probabilistic Interpretation of DNA Profiles10.1016/j.fsigen.2019.06.015 ↗
70. Developmental Validation of STRmix™, Expert Software for the Interpretation of Forensic DNA Profiles10.1016/j.fsigen.2016.05.007 ↗
71. Development and validation of Kongoh ver. 3.0.1: Open-Source Software for DNA Mixture Interpretation in the GlobalFiler System Based on a Quantitative Continuous Model10.1016/j.legalmed.2021.101972 ↗
72. Internal Validation of STRmix™ for the Interpretation of Single Source and Mixed DNA Profiles10.1016/j.fsigen.2017.04.004 ↗
73. Validating TrueAllele® DNA Mixture Interpretation10.1111/j.1556-4029.2011.01859.x ↗
74. Interpreting Low Template DNA Profiles10.1016/j.fsigen.2009.03.003 ↗
75. Systematic Evaluation of STRmix™ Performance on Degraded DNA Profile Data10.1016/j.fsigen.2019.102174 ↗
76. EuroForMix: An Open Source Software Based on a Continuous model to evaluate STR DNA profiles from a mixture of contributors with artefacts10.1016/j.fsigen.2015.11.008 ↗
77. GeneMarker® HID: A Reliable Software Tool for the Analysis of Forensic STR Data10.1111/j.1556-4029.2010.01565.x ↗
78. Carratto, T.M.T., Moraes, V.M.S., Recalde, T.S.F., de Oliveira, M.L.G., and Mendes-Junior, C.T. (2022). Applications of massively parallel sequencing in forensic genetics. Genet. Mol. Biol., 45.10.1590/1678-4685-gmb-2022-0077 ↗
79. Next-Generation Sequencing and Its Application: Empowering in Public Health Beyond Reality10.1007/978-981-13-8844-6_15 ↗
80. Sequence variations, flanking region mutations, and allele frequency at 31 autosomal STRs in the central Indian population by next generation sequencing (NGS)10.1038/s41598-021-02690-5 ↗
81. Mishra, S., Yadav, S., Yadav, S., Verma, P., and Ojha, S. (2024, February 27). Artificial Intelligence: An Advanced Evolution in Forensics And Criminal Investigation. Available online: https://www.researchgate.net/profile/Sudhanshu-Mishra/publication/362815618_Artificial_Intelligence_An_Advanced_Evolution_In_Forensics_And_Criminal_Investigation/links/632c57ca873eca0c00a8f803/Artificial-Intelligence-An-Advanced-Evolution-In-Forensics-And-Criminal-Investigation.pdf.
82. Applications of Contemporary Artificial In-telligence Technology in Forensic Odontology as Primary Forensic Identifier: A Scoping Review10.3389/frai.2022.1049584 ↗
83. Migrants’ Human Rights and Health Protection during the COVID-19 Pandemic in the Mediterra-nean Sea: What We Have Learnt from Direct Inspections in Two Italian Hotspots10.3389/fpubh.2023.1129267 ↗
84. An association between Fingerprint Patterns with Blood Group and Lifestyle Based Diseases: A Review10.1007/s10462-020-09891-w ↗
85. From Fingers to Faces: Visual Semiotics and Digital Forensics10.1007/s11196-020-09766-x ↗
86. Fingerprints analysis using AI Algorithm
87. Prognosis of sexual dimorphism with unfused hyoid bone: Artificial intelligence informed decision making with discriminant analysis10.1016/j.scijus.2021.10.002 ↗
88. Recent advances in Forensic DNA Phenotyping of appearance, ancestry and age10.1016/j.fsigen.2023.102870 ↗
89. Tozzo, P., Mazzobel, E., Marcante, B., Delicati, A., and Caenazzo, L. (2022). Touch DNA Sampling Methods: Efficacy Evaluation and Systematic Review. Int. J. Mol. Sci., 23.10.3390/ijms232415541 ↗
90. Tozzo, P., D’angiolella, G., Brun, P., Castagliuolo, I., Gino, S., and Caenazzo, L. (2020). Skin Microbiome Analysis for Forensic Human Identification: What Do We Know So Far?. Microorganisms, 8.10.3390/microorganisms8060873 ↗
91. Dash, H.R., Shrivastava, P., and Lorente, J.A. (2022). Handbook of DNA Profiling, Springer.10.1007/978-981-16-4318-7 ↗
92. Touch DNA: Impact of Handling Time on Touch Deposit and Evaluation of Different Recovery Techniques: An Experimental Study10.1038/s41598-019-46051-9 ↗
93. The Origin of Unknown Source DNA from Touched Objects10.1016/j.fsigen.2016.07.015 ↗
94. Persistence of Touch DNA on Burglary-Related Tools10.1007/s00414-017-1551-4 ↗
95. DNA Profiling from Fired Cartridge Cases: A Literature Review
96. DNA Commission of the International Society for Forensic Genetics: Recommendations on the Validation of Software Programs Performing Biostatistical Calculations for Forensic Genetics Applications10.1016/j.fsigen.2016.09.002 ↗
97. “I’ve Never Been at the Crime Scene!”—Gloves as Carriers for Secondary DNA Transfer10.1007/s00414-021-02597-w ↗
98. On DNA Transfer: The Lack and Difficulty of Systematic Research and How to do it Better10.1016/j.fsigen.2019.01.012 ↗
99. Stabbing Simulations and DNA Transfer10.1016/j.fsigen.2016.02.001 ↗
100. Sessa, F., Pomara, C., Esposito, M., Grassi, P., Cocimano, G., and Salerno, M. (2023). Indirect DNA Transfer and Forensic Implications: A Literature Review. Genes, 14.10.3390/genes14122153 ↗