Next-generation bloodstain pattern analysis: methodological gaps, digital advances, and forensic admissibility.
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 advanc...
| Publicado en: | Legal Medicine Vol. 85 |
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| Autores principales: | , |
| Formato: | review Journal Article |
| Publicado: |
Elsevier B.V.
Sep2026
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=196393141&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 196393141 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 13446223 KIS jtl: Legal Medicine issn: 13446223 maglogo: N pubinfo: dt: Sep2026 vid: 85 pid: 467 pub: Elsevier B.V. place: New York, New York artinfo: ui: 196393141 196393141 196393141 10.1016/j.legalmed.2026.102937 196393141 ppct: 1 formats: tig: atl: Next-generation bloodstain pattern analysis: methodological gaps, digital advances, and forensic admissibility. aug: au: Ince, Mehmet Alp Ince, Esra affil: Department of Morgue Specialization, Hatay Regional Directorate, Council of Forensic Medicine, Hatay, Türkiye sug: subj: Bloodstain Analysis Evaluation Microscopy Lasers Digital Technology Artificial Intelligence Forensic Medicine Software Deep Learning Clinical Reasoning Algorithms Evidence, Legal Courts Reliability Judgment Decision Making ab: 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. • Critical evaluation of BPA's paradigm shift from expert intuition to quantitative fluid dynamics. • Comparative framework contrasting classical, AI-based, and hybrid BPA digital methodologies. • Identification of critical gaps in substrate heterogeneity modeling and error propagation. • Analysis of computational and machine learning limitations under Daubert admissibility criteria. • A roadmap advocating for Likelihood Ratio (LR) frameworks and global OSAC/ASB standardization. pubtype: Academic Journal doctype: review Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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