ArticleForensic science, medicine, and pathology2026
Forensic 3D avatars - AI-assisted injury detection and interactive digital-twin visualization.
Article in Forensic science, medicine, and pathology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed.
- Narrative Review of Digital Twins in the Health Domain: Development, Application, and Evidence Consolidation.Medical sciences (Basel, Switzerland) · 2026Review
- Advancing forensic medicine through AI-based injury detection: innovation as the catalyst for forensic 3D avatars and translation as the pathway toward courtroom-grade digital twins.Forensic science, medicine, and pathology · 2026Article
- From 3D Surface Documentation to Visualization in Forensic Practice: A Review of Current Methods and Emerging Technologies.APMIS : acta pathologica, microbiologica, et immunologica Scandinavica · 2026Review
- From forensic 3D avatars to courtroom-grade digital twins: the next translational step for AI-assisted injury documentation.Forensic science, medicine, and pathology · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This feasibility study explores the application of a dermatology-based 3D full-body scanning system (VECTRA WB360) combined with artificial intelligence (AI) software for forensic documentation. The system generates high-resolution, color-accurate 3D avatars within minutes, enabling automated detection, localization, and measurement of simulated injuries. AI-assisted analysis reliably identified cutaneous findings, including in anatomically challenging regions, and displayed them in an interactive forensic dashboard. Advantages include speed, standardization, and anatomically precise localization. Limitations concern dermatology-focused training data, motion artifacts, and costs. With forensic-specific AI modules and validation, this approach may significantly improve efficiency, objectivity, and admissibility in forensic medicine.
Identifiers
41766042What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.