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• W1844860139 abstract "Background: Age estimation of individuals is important in human biology and has various medical and forensic applications. Recent interest in MR-based methods aims to investigate alternatives for established methods involving ionising radiation. Automatic, software-based methods additionally promise improved estimation objectivity.Aim: To investigate how informative automatically selected image features are regarding their ability to discriminate age, by exploring a recently proposed software-based age estimation method for MR images of the left hand and wrist.Subjects and methods: One hundred and two MR datasets of left hand images are used to evaluate age estimation performance, consisting of bone and epiphyseal gap volume localisation, computation of one age regression model per bone mapping image features to age and fusion of individual bone age predictions to a final age estimate.Results: Quantitative results of the software-based method show an age estimation performance with a mean absolute difference of 0.85 years (SD = 0.58 years) to chronological age, as determined by a cross-validation experiment. Qualitatively, it is demonstrated how feature selection works and which image features of skeletal maturation are automatically chosen to model the non-linear regression function.Conclusion: Feasibility of automatic age estimation based on MRI data is shown and selected image features are found to be informative for describing anatomical changes during physical maturation in male adolescents." @default.
• W1844860139 created "2016-06-24" @default.
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• W1844860139 date "2015-07-04" @default.
• W1844860139 modified "2023-09-26" @default.
• W1844860139 title "What automated age estimation of hand and wrist MRI data tells us about skeletal maturation in male adolescents" @default.
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• W1844860139 doi "https://doi.org/10.3109/03014460.2015.1043945" @default.
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