ArticleScientific reports2024
Accelerating computer vision-based human identification through the integration of deep learning-based age estimation from 2 to 89 years.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Hybrid approach with convolutional neural network and regression models for age estimation in panoramic radiographs.International journal of legal medicine · 2026Observational
- AI-based organ weight estimation from postmortem computed tomography.Scientific reports · 2026Article
- Age and sex estimation using post-mortem CT scout view.International journal of legal medicine · 2026Article
- Automatic Personal Identification Using a Single MRI Slice.Bioengineering (Basel, Switzerland) · 2026Article
- Accuracy and challenges in age estimation in adults: a scoping review of anthropological, dental, biochemical, and molecular methods.Forensic science, medicine, and pathology · 2025Article
- Automated body weight measurement using computed tomography.Scientific reports · 2025Article
- Computer vision-based personal identification using 2D maximum intensity projection CT images.European radiology · 2025Article
- Automated identification of unknown decedents: matching postmortem CT images with clinical databases.International journal of legal medicine · 2025Article
- Deep learning for forensic age estimation using orthopantomograms in children, adolescents, and young adults.European radiology · 2025Article
- Automatic personal identification using a single CT image.European radiology · 2025Article
- Reply to Letter to the Editor: "Can single CT slices revolutionize personal identification in emergency settings?"European radiology · 2025Article
- Personal identification via matching of curved multiplanar computed tomography reconstructions and panoramic radiographs.PloS one · 2025Article
- Impact of Deep Learning-Based Image Reconstruction on Tumor Visibility and Diagnostic Confidence in Computed Tomography.Bioengineering (Basel, Switzerland) · 2024Article
- Reliability of artificial intelligence algorithms in automated age estimation using orthopantomograms: A scoping review.Digital healthReview
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1 author.
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Abstract
Computer Vision (CV)-based human identification using orthopantomograms (OPGs) has the potential to identify unknown deceased individuals by comparing postmortem OPGs with a comprehensive antemortem CV database. However, the growing size of the CV database leads to longer processing times. This study aims to develop a standardized and reliable Convolutional Neural Network (CNN) for age estimation using OPGs and integrate it into the CV-based human identification process. The CNN was trained on 50,000 OPGs, each labeled with ages ranging from 2 to 89 years. Testing included three postmortem OPGs, 10,779 antemortem OPGs, and an additional set of 70 OPGs within the context of CV-based human identification. Integrating the CNN for age estimation into CV-based human identification process resulted in a substantial reduction of up to 96% in processing time for a CV database containing 105,251 entries. Age estimation accuracy varied between postmortem and antemortem OPGs, with a mean absolute error (MAE) of 2.76 ± 2.67 years and 3.26 ± 3.06 years across all ages, as well as 3.69 ± 3.14 years for an additional 70 OPGs. In conclusion, the incorporation of a CNN for age estimation in the CV-based human identification process significantly reduces processing time while delivering reliable results.
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