ArticleInternational journal of legal medicine2024
Automated bone age assessment from knee joint by integrating deep learning and MRI-based radiomics.
Article in International journal of legal medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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16 citing papers in PubMed, 1 synthesis or guideline pooled it, 17 citations in OpenAlex.
- Efficacy of the methods of age determination using artificial intelligence in panoramic radiographs - a systematic review.International journal of legal medicine · 2024Pooled it
- Dual-input deep learning bone age assessment in late adolescence using hand and knee radiographs.Skeletal radiology · 2026Article
- Multifaceted Preprocessing Optimization for Post-Mortem Radiomics Analysis: A Pilot Study on Forensic Age Estimation Using Proximal Femur Radiomics.Journal of imaging informatics in medicine · 2026Article
- Radiomic analysis of postmortem lung changes: a PMCT-based approach for estimating the postmortem interval.Forensic science, medicine, and pathology · 2026Article
- Application of RNA markers in forensic body fluid analysis: from specificity and stability to polymorphism.International journal of legal medicine · 2026Review
- A novel method based on ultrasound radiomics for predicting attainment of near-adult height in adolescents.Frontiers in endocrinology · 2026Article
- Comment on "The time frame of third molar mineralization in a Northern Chinese population".Forensic science, medicine, and pathology · 2025Article
- The time frame of third molar mineralization in a northern chinese population.Forensic science, medicine, and pathology · 2025Article
- 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
- Multiplex-RNA-analysis for detection and identification of forensically relevant organ tissues.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
- Developmental validation of an mRNA-cSNP profiling panel for body fluids identification and individualization.International journal of legal medicine · 2025Article
- Bibliometric analysis of the application of artificial intelligence in orthopedic imaging.Quantitative imaging in medicine and surgery · 2025Article
- 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
- Sudden unexplained death requires not only search for variants in SUD genes. but also extended autopsy, including the brain.International journal of legal medicine · 2024Article
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Authors and funding
13 authors at 3 institutions in 1 country.
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Abstract
Bone age assessment (BAA) is a crucial task in clinical, forensic, and athletic fields. Since traditional age estimation methods are suffered from potential radiation damage, this study aimed to develop and evaluate a deep learning radiomics method based on multiparametric knee MRI for noninvasive and automatic BAA. This retrospective study enrolled 598 patients (age range,10.00-29.99 years) who underwent MR examinations of the knee joint (T1/T2*/PD-weighted imaging). Three-dimensional convolutional neural networks (3D CNNs) were trained to extract and fuse multimodal and multiscale MRI radiomic features for age estimation and compared to traditional machine learning models based on hand-crafted features. The age estimation error was greater in individuals aged 25-30 years; thus, this method may not be suitable for individuals over 25 years old. In the test set aged 10-25 years (n = 95), the 3D CNN (a fusion of T1WI, T2*WI, and PDWI) demonstrated the lowest mean absolute error of 1.32 ± 1.01 years, which is higher than that of other MRI modalities and the hand-crafted models. In the classification for 12-, 14-, 16-, and 18- year thresholds, accuracies and the areas under the ROC curves were all over 0.91 and 0.96, which is similar to the manual methods. Visualization of important features showed that 3D CNN estimated age by focusing on the epiphyseal plates. The deep learning radiomics method enables non-invasive and automated BAA from multimodal knee MR images. The use of 3D CNN and MRI-based radiomics has the potential to assist radiologists or medicolegists in age estimation.
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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.