ArticleImaging science in dentistry2021
A pilot study of an automated personal identification process: Applying machine learning to panoramic radiographs.
Article in Imaging science in dentistry, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Automatic Personal Identification Using a Single MRI Slice.Bioengineering (Basel, Switzerland) · 2026Article
- Automatic and robust estimation of sex and chronological age from panoramic radiographs using a multi-task deep learning network: a study on a South Korean population.International journal of legal medicine · 2024Article
- Artificial intelligence in forensic medicine and forensic dentistry.The Journal of forensic odonto-stomatology · 2023Review
- Automatic identification of individuals using deep learning method on panoramic radiographs.Journal of dental sciences · 2023Article
- Deep learning for preliminary profiling of panoramic images.Oral radiology · 2023Article
- Automatic detection of teeth and dental treatment patterns on dental panoramic radiographs using deep neural networks.Forensic sciences research · 2022Article
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Authors and funding
5 authors.
Funding
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Abstract
purposeThis study aimed to assess the usefulness of machine learning and automation techniques to match pairs of panoramic radiographs for personal identification. MATERIALS AND
methodsTwo hundred panoramic radiographs from 100 patients (50 males and 50 females) were randomly selected from a private radiological service database. Initially, 14 linear and angular measurements of the radiographs were made by an expert. Eight ratio indices derived from the original measurements were applied to a statistical algorithm to match radiographs from the same patients, simulating a semi-automated personal identification process. Subsequently, measurements were automatically generated using a deep neural network for image recognition, simulating a fully automated personal identification process.
resultsApproximately 85% of the radiographs were correctly matched by the automated personal identification process. In a limited number of cases, the image recognition algorithm identified 2 potential matches for the same individual. No statistically significant differences were found between measurements performed by the expert on panoramic radiographs from the same patients.
conclusionPersonal identification might be performed with the aid of image recognition algorithms and machine learning techniques. This approach will likely facilitate the complex task of personal identification by performing an initial screening of radiographs and matching ante-mortem and post-mortem images from the same individuals.
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