Evidence map›Paper›PMID 30402703›Full record

ArticleEuropean radiology2019

Forensic age estimation for pelvic X-ray images using deep learning.

Yuan Li, Zhizhong Huang, Xiaoai Dong, Weibo Liang, Hui Xue, Lin Zhang, Yi Zhang, Zhenhua Deng

Abstract read
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In one paragraph

Article in European radiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.

0numbers the graph read from it
0cells of the map it votes in
37citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

37 citing papers in PubMed.

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  18. Artificial intelligence in forensic medicine and forensic dentistry.The Journal of forensic odonto-stomatology · 2023
    Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Yuan LiDepartment of Forensic Genetics, West China School of Preclinical and Forensic Medicine, Sichuan University, Chengdu, 610041, Sichuan, People's Republic of China.
Zhizhong HuangCollege of Computer Science, Sichuan University, No.24 South Section 1, Yihuan Road, Chengdu, 610065, China.
Xiaoai DongDepartment of Forensic Pathology, West China School of Preclinical and Forensic Medicine, Sichuan University, No. three, 17 South Renmin Road, Wuhou District, Chengdu City, 610041, Sichuan, People's Republic of China.
Weibo LiangDepartment of Forensic Genetics, West China School of Preclinical and Forensic Medicine, Sichuan University, Chengdu, 610041, Sichuan, People's Republic of China.
Hui XueDepartment of Radiology, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, People's Republic of China.
Lin ZhangDepartment of Forensic Genetics, West China School of Preclinical and Forensic Medicine, Sichuan University, Chengdu, 610041, Sichuan, People's Republic of China.
Yi ZhangCollege of Computer Science, Sichuan University, No.24 South Section 1, Yihuan Road, Chengdu, 610065, China. yzhang@scu.edu.cn.
Zhenhua DengDepartment of Forensic Pathology, West China School of Preclinical and Forensic Medicine, Sichuan University, No. three, 17 South Renmin Road, Wuhou District, Chengdu City, 610041, Sichuan, People's Republic of China. fydzh63@163.com.ORCID http://orcid.org/0000-0002-0355-1006

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeTo develop a deep learning bone age assessment model based on pelvic radiographs for forensic age estimation and compare its performance to that of the existing cubic regression model. MATERIALS AND

methodA retrospective collection data of 1875 clinical pelvic radiographs between 10 and 25 years of age was obtained to develop the model. Model performance was assessed by comparing the testing results to estimated ages calculated directly using the existing cubic regression model based on ossification staging methods. The mean absolute error (MAE) and root-mean-squared error (RMSE) between the estimated ages and chronological age were calculated for both models.

resultsFor all test samples (between 10 and 25 years old), the mean MAE and RMSE between the automatic estimates using the proposed deep learning model and the reference standard were 0.94 and 1.30 years, respectively. For the test samples comparable to those of the existing cubic regression model (between 14 and 22 years old), the mean MAE and RMSE for the deep learning model were 0.89 and 1.21 years, respectively. For the existing cubic regression model, the mean MAE and RMSE were 1.05 and 1.61 years, respectively.

conclusionThe deep learning convolutional neural network model achieves performance on par with the existing cubic regression model, demonstrating predictive ability capable of automated skeletal bone assessment based on pelvic radiographic images. KEY POINTS: • The pelvis has considerable value in determining the bone age. • Deep learning can be used to create an automated bone age assessment model based on pelvic radiographs. • The deep learning convolutional neural network model achieves performance on par with the existing cubic regression model.

Indexed as

Deep LearningAdolescentAdultAge Determination by SkeletonChildFemaleHumansMalePelvic BonesRadiographyRetrospective StudiesYoung AdultAge determination by skeletonForensic anthropologyMachine learningPelvisRadiography

Identifiers

PMID30402703

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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.