Academic Journal
Next-generation bloodstain pattern analysis: methodological gaps, digital advances, and forensic admissibility.
| Τίτλος: | Next-generation bloodstain pattern analysis: methodological gaps, digital advances, and forensic admissibility. |
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| Συγγραφείς: | Ince MA; Department of Morgue Specialization, Hatay Regional Directorate, Council of Forensic Medicine, Hatay, Türkiye. Electronic address: dralpince@gmail.com., Ince E; Dörtyol Forensic Medicine Branch Office, Council of Forensic Medicine, Hatay, Turkey. |
| Πηγή: | Legal medicine (Tokyo, Japan) [Leg Med (Tokyo)] 2026 Sep; Vol. 85, pp. 102937. Date of Electronic Publication: 2026 Jul 22. |
| Τύπος έκδοσης: | Journal Article; Review |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: Elsevier Science Ireland Ltd Country of Publication: Ireland NLM ID: 100889186 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1873-4162 (Electronic) Linking ISSN: 13446223 NLM ISO Abbreviation: Leg Med (Tokyo) Subsets: MEDLINE |
| Imprint Name(s): | Publication: <2003->: East Park, Co. Clare, Ireland : Elsevier Science Ireland Ltd. Original Publication: Tokyo : Japanese Society of Legal Medicine, c1999- |
| Ιατρικοί όροι (MeSH): | Forensic Sciences*/methods , Forensic Medicine*/methods , Blood Stains*, Humans ; Artificial Intelligence ; Bayes Theorem ; Algorithms ; Software ; Reproducibility of Results |
| Περίληψη: | Bloodstain Pattern Analysis (BPA) is transitioning from an experiential, expert-dependent craft to a quantitative, multidisciplinary forensic science. This review critically evaluates the evolutionary trajectory of BPA, contrasting traditional macroscopic examination with modern computational advancements, including 3D laser scanning, terrestrial LiDAR, and artificial intelligence (AI)-driven computer vision models. While automated software platforms (e.g., HemoSpat, BackTrack) and deep learning networks significantly improve area-of-origin reproducibility and pattern classification speed, their operational integration remains constrained by severe methodological fragmentation, dataset bias, and a historical lack of multi-variable substrate validation. Furthermore, a critical translational gap persists regarding legal admissibility under Daubert-style frameworks due to insufficient error propagation modeling. To overcome these epistemological boundaries, this paper proposes a structured transition toward a hybrid forensic paradigm that synthesizes human expert reasoning with physicodynamically constrained algorithms. Future directions must prioritize the construction of validated ground-truth datasets, the implementation of Likelihood Ratio (LR) Bayesian inference models for objective uncertainty quantification, and universal compliance with international standardization initiatives (OSAC/ASB). Ultimately, establishing these quantitative frameworks is essential for safeguarding the evidentiary value and courtroom reliability of BPA in modern judicial decision-making. (Copyright © 2026 Elsevier B.V. All rights reserved.) |
| Competing Interests: | Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. |
| Contributed Indexing: | Keywords: Area of origin; Artificial intelligence; Bayesian inference; Bloodstain pattern analysis; Crime scene reconstruction; Forensic admissibility |
| Entry Date(s): | Date Created: 20260729 Date Completed: 20260822 Latest Revision: 20260822 |
| Update Code: | 20260823 |
| DOI: | 10.1016/j.legalmed.2026.102937 |
| PMID: | 42526128 |
| Βάση Δεδομένων: | MEDLINE |
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