ArticlePloS one2025
Personal identification via matching of curved multiplanar computed tomography reconstructions and panoramic radiographs.
Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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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.
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Who cites it
1 citing paper in PubMed.
- Automatic Personal Identification Using a Single MRI Slice.Bioengineering (Basel, Switzerland) · 2026Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Computer vision (CV)-based personal identification enables automated matching of recent radiological images with clinical databases to identify unknown individuals. This study aimed to assess whether a panoramic radiograph (PR)-like image reconstructed from computed tomography (CT) data using curved multiplanar reconstruction could enable CV-based personal identification using a PR database. A method was developed to automatically generate PR-like images with adjustable parameters, based on 50 CT examinations including the jaw region (38.64 ± 16.72 years; 17 females, 33 males), allowing for variations such as tooth rotations. Systematic modification of parameters enabled the generation of different representations to determine optimal settings for a large number of individuals. Multiple PR-like images per identity were tested against a PR database containing 82,036 PRs from 43,379 individuals. Utilizing the most effective individual parameter settings, 72% (36/50) of individuals were correctly identified at rank 1, 82% (41/50) at rank 10, and 96% (48/50) at rank 100 - out of 43,379 possible individuals. The rank describes the position of the matched image in a list sorted after a descending similarity score. When the optimal parameters were applied to a larger number of individuals, the identification rates were 50% (25/50) at rank 1, 64% (32/50) at rank 10, and 78% (39/50) at rank 100. In conclusion, CV demonstrates potential for personal identification by comparing automatically generated PR-like images with a large PR database.
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