Document Type : Original Article

Authors

1 Dept. of Oral and Maxillofacial Radiology, School of Dentistry, Shiraz University of Medical Sciences, Shiraz, Iran.

2 Dept. of Orthodontics, Orthodontic Research Center, School of Dentistry, Shiraz University of Medical Sciences, Shiraz, Iran.

3 Artificial Intelligence, Shiraz University, Shiraz, Iran.

4 Dept. of Oral and Maxillofacial Surgery, School of Dentistry, Shiraz University of Medical Sciences, Shiraz, Iran.

10.30476/dentjods.2023.95629.1882

Abstract

Statement of the Problem: Bone age is a more accurate assessment for biologic development than chronological age. The most common method for bone age estimation is using Pyle and Greulich Atlas. Today, computer-based techniques are becoming more favorable among investigators. However, the morphological features in Greulich and Pyle method are difficult to be converted into quantitative measures. During recent years, metacarpal bones and metacarpophalangeal joints dimensions were shown to be highly correlated with skeletal age.
Purpose: In this study, we have evaluated the accuracy and reliability of a trained neural network for bone age estimation with quantitative and recently introduced related data, including chronological age, height, trunk height, weight, metacarpal bones, and metacarpophalangeal joints dimensions.
Materials and Method: In this cross sectional retrospective study, aneural network, using MATLAB, was utilized to determine bone age by employing quantitative features for 304 subjects. To evaluate the accuracy of age estimation software, paired t-test, and inter-class correlation was used.
Results: The difference between the mean bone ages determined by the radiologists and the mean bone ages assessed by the age estimation software was not significant (p Value= 0.119 in male subjects and p= 0.922 in female subjects). The results from the software and radiologists showed a strong correlation -ICC=0.990 in male subjects and ICC=0.986 in female subjects (p< 0.001).
Conclusion: The results have shown an acceptable accuracy in bone age estimation with training neural network and using dimensions of bones and joints.

Highlights

Hamid Reza Pakshir (Google Scholar)

Keywords

  • Poznanski A, Hernandez R, Guire K, Bereza U, Garn S. Carpal length in children–a useful measurement in the diagnosis of rheumatoid arthritis and some congenital malformation syndromes. 1978; 129: 661–668.
  • Tanner JM, Healy MJR, Goldstein H, Cameron N. Assessment of skeletal maturity and prediction of adult height (TW3 Method). 3rd ed. WB Saunders: London; 2001. p. 110-117
  • Greulich WW, Pyle SI. Radiographic Atlas of Skeletal Development of Hand Wrist. 1st ed. Stanford University Press; Stanford CA: 1971. p. 73-86.
  • Greulich WW, Pyle SI. Radiographic atlas of skeletal development of hand wrist. 2nd ed Stanford. CA: Stanford University Press; 1959. p. 61-64.
  • Roch AF, Rochman CG, Davila GH. Effect of training of replicability of assessment of skeletal maturity (Greulich-Pyle). Am J Roentgenol Radium Ther Nucl Med. 1970; 108: 511-515. 
  • King DG, Steventon DM, O'Sullivan MP, Cook AM, Hornsby VP, Jefferson IG, et al. Reproducibility of bone ages when performed by radiology registrars: an audit of Tanner and Whitehouse II versus Greulich and Pyle methods. Br J Radiol. 1994; 67:848–851.
  • Tanner JM, Whitehouse RH. Assessment of Skeletal Maturity and Prediction of Adult Height(TW2 Method). 2nd ed. London, UK: Academic Press; 1975.p. 92-98.
  • Tanner JM, Gibbons RD. A computerized image analysis system for estimating Tanner-Whitehouse 2 bone age. Horm Res. 1994; 42:282–287.
  • Tanner JM, Oshman D, Lindgren G, Grunbaum JA, Elsouki R, Labarthe DR. Reliability and validity of computer-assisted estimates of Tanner-Whitehouse skeletal maturity (CASAS): comparison with the manual method. Horm Res. 1994; 42:288–294.
  • Dickhaus H, Wastl S. Computer assisted bone age assessment. Med Info. 1995; 8:709-713.
  • Cao F, Huang HK, Pietka E, Gilsanz V. Digital hand atlas and web-based bone age assessment: system design and implementation. Comp Med Imag Graph. 2000; 24:297–307.
  • Pietka E, Pospiech S, Gertych A, Cao F, Huang HK, Gilsanz V. Computer automated approach to the extraction of epiphyseal regions in hand radiographs. J Digit Imaging.2001; 14:165–172.
  • Haghnegahdar A, Pakshir H, Ghanbari I. Correlation between skeletal age and metacarpal bones and metacarpop-halangealjoints dimensions. J Dent. 2019; 20: 159-164.
  • Thodberg HH, van Rijn RR, Tanaka T, Martin DD, Kreiborg S. A paediatric bone index derived by automated radiogrammetry. Osteoporos Int. 2010; 21: 1391- 1400.
  • Zhang A, Gertych A, Liu B, Huang H, Kurkowska-Pospiech S. Carpal Bone Segmentation and Features Analysis in Bone Age Assessment of Children. Radiological Society of North America 2005 Scientific Assembly and Annual Meeting. Available at: http://archive.rsna. org/2005/4415569.html
  • Zhang A, Gertych A, Liu BJ. Automatic bone age assessment for young children from newborn to 7-year-old using carpal bones. Comput Med Imaging Graph. 2007; 31: 299-310.
  • Image Processing Information Lab.org [homepage on the Internet]. Los Angeles: University of Southern California c2005-12: Available at: http//www.ipilab.org/BAAweb/
  • Larson DB, Chen MC, Lungren MP. Performance of a deep-learning neural networkmodel in assessing skeletal maturity on pediatric hand radiographs. 2018; 287: 313-322.
  • Michael DJ, Nelson AC. HANDX: a model-based system for automatic segmentation of bones from digital hand radiographs. IEEE Trans Med Imaging. 1989; 8: 64–69.
  • Pietka E, McNitt-Gray MF, Kuo ML, Huang HK. Computer-assisted phalangeal analysis in skeletal age assessment. IEEE Trans Med Imaging. 1991; 10: 616–620.
  • Van Rijn RR, Thodberg HH. Bone age assessment: auto-mated techniques coming of age? ActaRadiol. 2013; 54: 1024–1029.