Applicability of Magnetic Resonance Imaging for Bone Age Estimation in the Context of Medical Issues

Objective The determination of bone age is a method for analyzing biological age and structural maturity. Bone age estimation is predominantly used in the context of medical issues, for example in endocrine diseases or growth disturbance. As a rule, conventional X-ray images of the left wrist and hand are used for this purpose. The aim of the present study is to investigate the extent to which MRI can be used as a radiation-free alternative for bone age assessment. Methods In 50 patients, 19 females and 31 males, in addition to conventional left wrist and hand radiographs, MRI was performed with T1-VIBE (n = 50) and T1-TSE (n = 34). The average age was 11.87 years (5.08 to 17.50 years). Bone age assessment was performed by two experienced investigators blinded for chronological age according to the most widely used standard of Greulich and Pyle. This method relies on a subjective comparison of hand radiographs with gender-specific reference images from Caucasian children and adolescents. In addition to interobserver and intraobserver variability, the correlation between conventional radiographs and MRI was determined using the Pearson correlation coefficient. Results Between the bone age determined from the MRI data and the results of the conventional X-ray images, a very good correlation was found for both T1-VIBE with r = 0.986 and T1-TSE with r = 0.982. Gender differences did not arise. The match for the interobserver variability was very good: r = 0.985 (CR), 0.966 (T1-VIBE) and 0.971 (T1-TSE) as well as the match for the intraobserver variability for investigator A (CR = 0.994, T1-VIBE = 0.995, T1-TSE = 0.998) and for investigator B (CR = 0.994, T1-VIBE = 0.993, T1-TSE = 0.994). Conclusion The present study shows that MRI of the left wrist and hand can be used as a possible radiation-free alternative to conventional X-ray imaging for bone age estimation in the context of medical issues. Key points: Citation Format

Download Full-text

Intelligent Bone Age Assessment: An Automated System to Detect a Bone Growth Problem Using Convolutional Neural Networks with Attention Mechanism

Diagnostics ◽

10.3390/diagnostics11050765 ◽

2021 ◽

Vol 11 (5) ◽

pp. 765

Author(s):

Mohd Asyraf Zulkifley ◽

Nur Ayuni Mohamed ◽

Siti Raihanah Abdani ◽

Nor Azwan Mohamed Kamari ◽

Asraf Mohamed Moubark ◽

...

Keyword(s):

Bone Growth ◽

Bone Age ◽

Attention Mechanism ◽

Assessment System ◽

Dominant Hand ◽

Age Assessment ◽

Image Normalization ◽

X Ray ◽

Bone Age Assessment ◽

Skeletal Bone

Skeletal bone age assessment using X-ray images is a standard clinical procedure to detect any anomaly in bone growth among kids and babies. The assessed bone age indicates the actual level of growth, whereby a large discrepancy between the assessed and chronological age might point to a growth disorder. Hence, skeletal bone age assessment is used to screen the possibility of growth abnormalities, genetic problems, and endocrine disorders. Usually, the manual screening is assessed through X-ray images of the non-dominant hand using the Greulich–Pyle (GP) or Tanner–Whitehouse (TW) approach. The GP uses a standard hand atlas, which will be the reference point to predict the bone age of a patient, while the TW uses a scoring mechanism to assess the bone age using several regions of interest information. However, both approaches are heavily dependent on individual domain knowledge and expertise, which is prone to high bias in inter and intra-observer results. Hence, an automated bone age assessment system, which is referred to as Attention-Xception Network (AXNet) is proposed to automatically predict the bone age accurately. The proposed AXNet consists of two parts, which are image normalization and bone age regression modules. The image normalization module will transform each X-ray image into a standardized form so that the regressor network can be trained using better input images. This module will first extract the hand region from the background, which is then rotated to an upright position using the angle calculated from the four key-points of interest. Then, the masked and rotated hand image will be aligned such that it will be positioned in the middle of the image. Both of the masked and rotated images will be obtained through existing state-of-the-art deep learning methods. The last module will then predict the bone age through the Attention-Xception network that incorporates multiple layers of spatial-attention mechanism to emphasize the important features for more accurate bone age prediction. From the experimental results, the proposed AXNet achieves the lowest mean absolute error and mean squared error of 7.699 months and 108.869 months2, respectively. Therefore, the proposed AXNet has demonstrated its potential for practical clinical use with an error of less than one year to assist the experts or radiologists in evaluating the bone age objectively.

Download Full-text

Doctor Imitator: A Graph-Based Bone Age Assessment Framework Using Hand Radiographs

Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 - Lecture Notes in Computer Science ◽

10.1007/978-3-030-59725-2_74 ◽

2020 ◽

pp. 764-774

Author(s):

Jintai Chen ◽

Bohan Yu ◽

Biwen Lei ◽

Ruiwei Feng ◽

Danny Z. Chen ◽

...

Keyword(s):

Bone Age ◽

Assessment Framework ◽

Age Assessment ◽

Bone Age Assessment ◽

Hand Radiographs

Download Full-text

Bone age assessment by dual-energy X-ray absorptiometry in children: an alternative for X-ray?

British Journal of Radiology ◽

10.1259/bjr/23858213 ◽

2012 ◽

Vol 85 (1010) ◽

pp. 114-120 ◽

Cited By ~ 7

Author(s):

D H M Heppe ◽

H R Taal ◽

G D S Ernst ◽

E L T Van Den Akker ◽

M M H Lequin ◽

...

Keyword(s):

Bone Age ◽

Dual Energy ◽

Age Assessment ◽

X Ray ◽

Bone Age Assessment

Download Full-text

A Localization and Extraction Method Research for Phalange ROI and Carpal ROI from Left Hand Radiography

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.340.259 ◽

2011 ◽

Vol 340 ◽

pp. 259-265

Author(s):

Long Ke Ran ◽

Ling He ◽

Zhong Chen

Keyword(s):

Prediction Method ◽

Bone Age ◽

Third Order ◽

Shape Information ◽

Age Assessment ◽

Left Hand ◽

Key Points ◽

Order Polynomial ◽

Bone Age Assessment ◽

Hand Radiographs

In the research of Automatic bone age assessment,the most efficient location and successful extraction of regions of interest(ROI) from hand radiographs is one of the most difficult and important key technologies. Based on using shape information for phalanges and carpals, a background prediction method is propoesd , which uses a two-dimensional third order polynomial linear regression to fit background. And we also localize the key points of carpal and phalange ROI by usingK-cosine algorithm, finally we extract the carpal and phalange ROI successfully and properly. Through experiments, the proposed method resulted in over 93% correct extraction from more than 60 left hand radiograph data. The proposed method is robust to gray value variation of background and the position and orientation of the hand, so it can be used directly for automatic bone age assessment in the following study.

Download Full-text

Automated Bone Age Assessment: Motivation, Taxonomies, and Challenges

Computational and Mathematical Methods in Medicine ◽

10.1155/2013/391626 ◽

2013 ◽

Vol 2013 ◽

pp. 1-11 ◽

Cited By ~ 21

Author(s):

Marjan Mansourvar ◽

Maizatul Akmar Ismail ◽

Tutut Herawan ◽

Ram Gopal Raj ◽

Sameem Abdul Kareem ◽

...

Keyword(s):

Bone Age ◽

Future Research ◽

Age Assessment ◽

X Ray ◽

Left Hand ◽

Bone Age Assessment ◽

Manual Methods ◽

Biological Maturity ◽

Automated Methods ◽

Experimental Stage

Bone age assessment (BAA) of unknown people is one of the most important topics in clinical procedure for evaluation of biological maturity of children. BAA is performed usually by comparing an X-ray of left hand wrist with an atlas of known sample bones. Recently, BAA has gained remarkable ground from academia and medicine. Manual methods of BAA are time-consuming and prone to observer variability. This is a motivation for developing automated methods of BAA. However, there is considerable research on the automated assessment, much of which are still in the experimental stage. This survey provides taxonomy of automated BAA approaches and discusses the challenges. Finally, we present suggestions for future research.

Download Full-text