An evaluation of statistical models for age estimation and the assessment of the 18-year threshold using conventional pelvic radiographs

Fan, Fei; Dong, Xingbei; Wu, Xuemei; Li, Rui; Dai, Xinhua; Zhang, Kui; Huang, Feihu

doi:10.1016/j.forsciint.2020.110350

Cited by 11 publications

(9 citation statements)

References 35 publications

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“…Real femur images for each age category are separately distributed except for age [12][13][14][15] and [16][17][18][19] since bone growth becomes slower until it ceases altogether (Figure 5). Meanwhile, distributions of real phalange images for age [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19] highly overlap due to its smaller growth. BAPGAN-generated images have similar distributions to the real ones.…”

Section: T-sne Resultsmentioning

confidence: 99%

“…Because skeletal maturity progresses through discrete stages, pediatric medicine has correlated children's chronological age with bone age to investigate endocrinology, genetic, and growth disorders; but, time-consuming manual bone age assessment methods [1,2] suffer from intra-and interobserver variability. In this context, Convolutional Neural Networks have shown great promise in age assessment on various modalities and body regions, including hand/pelvic X-ray [3,4], clavicula Computed Tomography [5], and hand Magnetic Resonance Imaging [6].…”

Section: Introductionmentioning

confidence: 99%

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BAPGAN: GAN-based Bone Age Progression of Femur and Phalange X-ray Images

Nakazawa¹,

Han²,

Hasei³

et al. 2021

Preprint

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Convolutional Neural Networks play a key role in bone age assessment for investigating endocrinology, genetic, and growth disorders under various modalities and body regions. However, no researcher has tackled bone age progression/regression despite its valuable potential applications: bonerelated disease diagnosis, clinical knowledge acquisition, and museum education. Therefore, we propose Bone Age Progression Generative Adversarial Network (BAPGAN) to progress/regress both femur/phalange X-ray images while preserving identity and realism. We exhaustively confirm the BAPGAN's clinical potential via Fréchet Inception Distance, Visual Turing Test by two expert orthopedists, and t-Distributed Stochastic Neighbor Embedding.

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Section: T-sne Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

BAPGAN: GAN-based Bone Age Progression of Femur and Phalange X-ray Images

Nakazawa¹,

Han²,

Hasei³

et al. 2021

Preprint

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“…12 Within the pelvis, the iliac crest, a secondary ossification centre, displays a relatively longer development period and, thus, presents as a reliable age marker in sub-adults and young adults. 4,[23][24][25] Previously undertaken investigations with the iliac crest have indicated a good correlation between apophyseal ossification/ fusion changes and age, [26][27][28][29] signifying its utility as an age marker. Numerous scoring approaches for iliac crest ossification and fusion have been devised and researched in the past.…”

Section: Introductionmentioning

confidence: 99%

“…71 On the other hand, a handful of observational studies employed regression analysis 37,38,68 and Bayesian inference 37,64 for the age estimation from the iliac crest. Machine learning is presented as another efficient statistical tool, believed to yield more accurate estimates of age 28,67 by eliminating the influence of human bias. [72][73][74] Currently, studies utilizing machine learning for iliac crest age estimation are limited to the modified Kreitner-Kellinghaus scoring method.…”

Section: Introductionmentioning

confidence: 99%

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Machine learning and regression analysis for age estimation from the iliac crest based on computed tomographic explorations in an Indian population

Warrier,

Shedge,

Garg

et al. 2023

Med Sci Law

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Age estimation constitutes an integral parameter of identification. In children, sub-adults, and young adults, accurate age estimation is vital on various aspects of civil, criminal, and immigration law. The iliac crest presents as a suitable age marker within these age cohorts, and the modified Risser method constitutes a relatively novel and unexplored method for iliac crest age estimation. The present study attempted to ascertain the applicability of this modified method for age estimation in the Indian population, an aspect previously unexplored, through computed tomographic examination of the iliac crest. Computed tomography scans of consenting individuals undergoing routine examinations of the pelvis/ abdomen for various clinically indicated reasons were collected and scored using the modified Risser stages. Computed tomographic examinations of the iliac crest indicate that the recalibrated method accurately depicts the temporal progression of ossification and fusion changes. Different regression and machine learning models were subsequently derived and/or trained to evaluate the accuracy and precision associated with the method. Amongst the ten regression models derived herein, compound regression exhibited the lowest inaccuracy (4.78 years) and root mean squared error values (5.46 years). Machine learning yielded further reduced error rates, with decision tree regression achieving inaccuracy and root mean squared error values of 1.88 years and 2.28 years, respectively. A comparative evaluation of error computations obtained from regression analysis and machine learning illustrates the statistical superiority of machine learning for forensic age estimation. Error computations obtained with machine learning suggest that the modified Risser method is capable of permitting reliable age estimation within criminal and civil proceedings.

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