ArticleInternational journal of legal medicine2018
The role of multislice computed tomography of the costal cartilage in adult age estimation.
Article in International journal of legal medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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9 citing papers in PubMed, 21 citations in OpenAlex.
- Forensic age estimation and legal age thresholds classification based on the elbow MRI and data mining in a Chinese population.International journal of legal medicine · 2026Article
- Skull CT metadata for automatic bone age assessment by using three-dimensional deep learning framework.International journal of legal medicine · 2025Article
- Utility of the morphological scoring of costal cartilage ossification in age estimation of adult Egyptians using multidetector computed tomography.Forensic sciences research · 2025Article
- Artificial intelligence in the practice of forensic medicine: a scoping review.International journal of legal medicine · 2024Article
- Adult age estimation from the sternum using maximum intensity projection images of CT and data mining in a Chinese population.International journal of legal medicine · 2024Article
- Deep learning enables automatic adult age estimation based on CT reconstruction images of the costal cartilage.European radiology · 2023Article
- The intervertebral discs' fibrocartilage as a DNA source for genetic identification in severely charred cadavers.Forensic science, medicine, and pathology · 2022Article
- Article
- Comparison of different iterative CT reconstruction techniques and filtered back projection for assessment of the medial clavicular epiphysis in forensic age estimation.International journal of legal medicine · 2020Article
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7 authors at 3 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
To establish population-specific age estimation models in adults from costal cartilage for contemporary Chinese by using three-dimensional volume-rendering technique. Five hundred and twelve individuals (254 females and 258 males) with documented ages between 20 and 85 years were retrospectively included. Their clinical CT examinations (1 mm slice thickness) were used to develop the sex-specific age prediction model. A validation sample comprising 26 female and 24 male individuals was then used to test the predictive accuracy of the established models. Simple linear regression (SLR), multiple linear regression (MLR), gradient boosting regression (GBR), support vector machine (SVM), and decision tree regression (DTR) were utilized to build the age diagnosis models from calibration samples. By comparison, the decision tree regression was the relatively more accurate age prediction model for male, with mean absolute error = 5.31 years, least absolute error = 0.10 years, correct percentage within 5 years = 54%, and the correct percentage within 10 years = 88%. The stepwise multiple linear regression equations was the relatively more accurate one for female, with mean absolute error = 6.72 years, least absolute error = 0.68 years, correct percentage within 5 years = 42%, and correct percentage within 10 years = 77%. Our results indicated that the present established age estimation model can be applied as an additional guidance for age estimation in adults.
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