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Enhanced Genetic Analysis of Single Human Bioparticles Recovered by Simplified Micromanipulation from Forensic ‘Touch DNA’ Evidence
Published on: March 9, 2015
Nanomaterial-based latent fingerprint detection and artificial intelligence: from laboratory innovation to forensic
Rahul Panwar1, Aastha Sharma2, Arvind Kumar1
1Department of Forensic Medicine and Toxicology, All India Institute of Medical Sciences, Jodhpur 342005, India.
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.
