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Nanomaterial-based latent fingerprint detection and artificial intelligence: from laboratory innovation to forensic practice.

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Τίτλος: Nanomaterial-based latent fingerprint detection and artificial intelligence: from laboratory innovation to forensic practice.
Συγγραφείς: Panwar R; Department of Forensic Medicine and Toxicology, All India Institute of Medical Sciences, Jodhpur 342005, India., Sharma A; National Forensic Sciences University Gandhinagar, India. Electronic address: Jod.aastha2005@gmail.com., Kumar A; Department of Forensic Medicine and Toxicology, All India Institute of Medical Sciences, Jodhpur 342005, India., Agrawal M; Department of Forensic Medicine and Toxicology, All India Institute of Medical Sciences, Jodhpur 342005, India.
Πηγή: Legal medicine (Tokyo, Japan) [Leg Med (Tokyo)] 2026 Sep; Vol. 85, pp. 102952. Date of Electronic Publication: 2026 Aug 10.
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 , Dermatoglyphics* , Artificial Intelligence* , Nanostructures*, Humans
Περίληψη: Background: Latent fingerprint (LFP) visualization and automated recognition are fundamental to forensic identification, yet conventional physicochemical methods remain constrained by substrate specificity, chemical toxicity, limited analytical sensitivity, and incompatibility with modern digital analysis pipelines. The convergence of fluorescent nanomaterials and artificial intelligence (AI) offers a promising but operationally premature alternative.
Objective: This critical review appraises advances in fluorescent nanomaterial-based LFP development and AI-powered image analysis (2019-2026), with emphasis on evidence quality, methodological limitations, forensic admissibility, and ethical considerations.
Methods: A structured narrative review was conducted using PubMed/MEDLINE, Scopus, Web of Science Core Collection and Google Scholar. Approximately 3200 records were identified after deduplication; 350 underwent full-text screening, and 78 peer-reviewed studies met the inclusion criteria. Additional studies were identified through citation tracking.
Results: Six major nanomaterial classes demonstrated distinct performance advantages, but none achieved ISO/IEC 17025-compliant forensic validation. NH₂-functionalized polymer dots remain the only nanomaterial reported with NFIQ-2 scores (49-58), Level 1-3 ridge feature visualization, and AI-integrated minutiae extraction, although evidence is limited to a single laboratory. GAN-based enhancement raises unresolved evidence integrity concerns, while vision transformers and diffusion models show promise but lack forensic validation. Explainable AI (XAI) is essential for courtroom acceptance. No fully integrated end-to-end workflow has been demonstrated. Comparative analyses of nanomaterial classes and AI modalities are presented.
Conclusion: Routine forensic implementation is limited by the lack of standardized validation, harmonized reporting, regulatory oversight, and courtroom-compatible explainability. Addressing these gaps is essential for translating laboratory advances into forensic practice.
(Copyright © 2026 Elsevier B.V. All rights reserved.)
Contributed Indexing: Keywords: Aggregation-induced emission; Artificial intelligence; Carbon quantum dots; Daubert standard; Fluorescent nanomaterials; Latent fingerprint
Entry Date(s): Date Created: 20260813 Date Completed: 20260822 Latest Revision: 20260822
Update Code: 20260823
DOI: 10.1016/j.legalmed.2026.102952
PMID: 42594526
Βάση Δεδομένων: MEDLINE
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  Data: Nanomaterial-based latent fingerprint detection and artificial intelligence: from laboratory innovation to forensic practice.
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  Data: Background: Latent fingerprint (LFP) visualization and automated recognition are fundamental to forensic identification, yet conventional physicochemical methods remain constrained by substrate specificity, chemical toxicity, limited analytical sensitivity, and incompatibility with modern digital analysis pipelines. The convergence of fluorescent nanomaterials and artificial intelligence (AI) offers a promising but operationally premature alternative.&lt;br /&gt;Objective: This critical review appraises advances in fluorescent nanomaterial-based LFP development and AI-powered image analysis (2019-2026), with emphasis on evidence quality, methodological limitations, forensic admissibility, and ethical considerations.&lt;br /&gt;Methods: A structured narrative review was conducted using PubMed/MEDLINE, Scopus, Web of Science Core Collection and Google Scholar. Approximately 3200 records were identified after deduplication; 350 underwent full-text screening, and 78 peer-reviewed studies met the inclusion criteria. Additional studies were identified through citation tracking.&lt;br /&gt;Results: Six major nanomaterial classes demonstrated distinct performance advantages, but none achieved ISO/IEC 17025-compliant forensic validation. NH₂-functionalized polymer dots remain the only nanomaterial reported with NFIQ-2 scores (49-58), Level 1-3 ridge feature visualization, and AI-integrated minutiae extraction, although evidence is limited to a single laboratory. GAN-based enhancement raises unresolved evidence integrity concerns, while vision transformers and diffusion models show promise but lack forensic validation. Explainable AI (XAI) is essential for courtroom acceptance. No fully integrated end-to-end workflow has been demonstrated. Comparative analyses of nanomaterial classes and AI modalities are presented.&lt;br /&gt;Conclusion: Routine forensic implementation is limited by the lack of standardized validation, harmonized reporting, regulatory oversight, and courtroom-compatible explainability. Addressing these gaps is essential for translating laboratory advances into forensic practice.&lt;br /&gt; (Copyright &#169; 2026 Elsevier B.V. All rights reserved.)
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  Data: &lt;i&gt;Keywords: &lt;/i&gt;Aggregation-induced emission; Artificial intelligence; Carbon quantum dots; Daubert standard; Fluorescent nanomaterials; Latent fingerprint
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