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Machine learning and deep learning enabled age estimation on medial clavicle CT images · Study

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ArticleMachine learning and deep learning enabled age estimation on medial clavicle CT images

Lirong Qiu2023

Article2023

Machine learning and deep learning enabled age estimation on medial clavicle CT images

Lirong Qiu · Xinhua Dai · Guangfeng Liu · Peng Zhao · Meng‐jun Zhan · Junhong Liu +5 more

19 citations·0/0 files stored·International Journal of Legal Medicine

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Forensic Anthropology and Bioarchaeology Studies Artificial Intelligence in Healthcare and Education Medical Imaging and Analysis

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Citations in this library

Stored only when both papers are in the corpus — 19 citations exist upstream.

References

2017Performance of a Deep-Learning Neural Network Model in Assessing Skeletal Maturity on Pediatric Hand Radiographs 2017Fully Automated Deep Learning System for Bone Age Assessment 2016Deep learning for automated skeletal bone age assessment in X-ray images 2004Studies on the time frame for ossification of the medial clavicular epiphyseal cartilage in conventional radiography 2010Enhanced possibilities to make statements on the ossification status of the medial clavicular epiphysis using an amplified staging scheme in evaluating thin-slice CT scans 2009Forensic age estimation in living subjects based on the ossification status of the medial clavicular epiphysis as revealed by thin-slice multidetector computed tomography 2012Magnetic resonance imaging of the sternal extremity of the clavicle in forensic age estimation: towards more sound age estimates 2013The value of sub-stages and thin slices for the assessment of the medial clavicular epiphysis: a prospective multi-center CT study

Cited by

2025A review of the literature on the applications of machine learning in forensic anthropology 2024A probability model for estimating age in young individuals relative to key legal thresholds: 15, 18 or 21-year

Details

Year2023

Typearticle

Published2023-11-08

VenueInternational Journal of Legal Medicine

ISSN-L0044-3433

Languageen

Citations19

Files0 public / 0

ShelfForensic Anthropology and Bioarchaeology Studies

Identifiers

doi10.1007/s00414-023-03115-w

openalex_workW4388483550

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