ArticleInternational journal of legal medicine2021
Automated age estimation of young individuals based on 3D knee MRI using deep learning.
Article in International journal of legal medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 1 of them a synthesis that pooled it.
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Who cites it
29 citing papers in PubMed, 1 synthesis or guideline pooled it, 59 citations in OpenAlex.
- Magnetic resonance imaging of the knee for chronological age estimation-a systematic review.European radiology · 2023Pooled it
- Dual-input deep learning bone age assessment in late adolescence using hand and knee radiographs.Skeletal radiology · 2026Article
- Age estimation from pubic symphysis based on cinematic volume rendering: comparison between Suchey-Brooks staging and deep learning.International journal of legal medicine · 2026Article
- Imaging-derived biological age across multiple organs links to mortality and aging-related health outcomes.npj aging · 2026Article
- Radiographic legal age estimation based on third molar development using Machine-learning algorithms.PloS one · 2026Article
- Forensic age estimation using Dedouit classification in adolescents of the Southwestern Chinese Han population based on the knee MRI.International journal of legal medicine · 2025Article
- Skull CT metadata for automatic bone age assessment by using three-dimensional deep learning framework.International journal of legal medicine · 2025Article
- The optimization of youth football training using deep learning and artificial intelligence.Scientific reports · 2025Article
- A review of methods of age estimation based on postmortem computed tomography.Forensic sciences research · 2025Review
- Bone Age Assessment Using Various Medical Imaging Techniques Enhanced by Artificial Intelligence.Diagnostics (Basel, Switzerland) · 2025Review
- A new method for early diagnosis and treatment of meniscus injury of knee joint in student physical fitness tests based on deep learning method.BioImpacts : BI · 2025Article
- Forensic bone age assessment of hand and wrist joint MRI images in Chinese han male adolescents based on deep convolutional neural networks.International journal of legal medicine · 2024Article
- Radiological age assessment based on clavicle ossification in CT: enhanced accuracy through deep learning.International journal of legal medicine · 2024Article
- Artificial intelligence in the practice of forensic medicine: a scoping review.International journal of legal medicine · 2024Article
- Automated bone age assessment from knee joint by integrating deep learning and MRI-based radiomics.International journal of legal medicine · 2024Article
- Brain structure ages-A new biomarker for multi-disease classification.Human brain mapping · 2024Article
- Article
- Affecting Factors of Prostate Volume in Forensic Autopsied Decedents.Healthcare (Basel, Switzerland) · 2023Article
- Preparing computed tomography images for machine learning in forensic and virtual anthropology.Forensic science international. Synergy · 2023Article
- Current and emerging artificial intelligence applications for pediatric musculoskeletal radiology.Pediatric radiology · 2022Review
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
Abstract
Age estimation is a crucial element of forensic medicine to assess the chronological age of living individuals without or lacking valid legal documentation. Methods used in practice are labor-intensive, subjective, and frequently comprise radiation exposure. Recently, also non-invasive methods using magnetic resonance imaging (MRI) have evaluated and confirmed a correlation between growth plate ossification in long bones and the chronological age of young subjects. However, automated and user-independent approaches are required to perform reliable assessments on large datasets. The aim of this study was to develop a fully automated and computer-based method for age estimation based on 3D knee MRIs using machine learning. The proposed solution is based on three parts: image-preprocessing, bone segmentation, and age estimation. A total of 185 coronal and 404 sagittal MR volumes from Caucasian male subjects in the age range of 13 and 21 years were available. The best result of the fivefold cross-validation was a mean absolute error of 0.67 ± 0.49 years in age regression and an accuracy of 90.9%, a sensitivity of 88.6%, and a specificity of 94.2% in classification (18-year age limit) using a combination of convolutional neural networks and tree-based machine learning algorithms. The potential of deep learning for age estimation is reflected in the results and can be further improved if it is trained on even larger and more diverse datasets.
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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.