|تعداد مشاهده مقاله||12,896,337|
|تعداد دریافت فایل اصل مقاله||6,144,894|
Computer Assisted Bone Age Estimation Using Dimensions of Metacarpal Bones and Metacarpophalangeal Joints based on Neural Network
|Journal of Dentistry|
|مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 01 خرداد 1402 اصل مقاله (584.75 K)|
|نوع مقاله: Original Article|
|شناسه دیجیتال (DOI): 10.30476/dentjods.2023.95629.1882|
|Abdolaziz Haghnegahdar1؛ Hamid Reza Pakshir2؛ Mojtaba Zandieh3؛ Ilnaz Ghanbari* 4|
|1Dept. of Oral and Maxillofacial Radiology, School of Dentistry, Shiraz University of Medical Sciences, Shiraz, Iran.|
|2Dept. of Orthodontics, Orthodontic Research Center, School of Dentistry, Shiraz University of Medical Sciences, Shiraz, Iran.|
|3Artificial Intelligence, Shiraz University, Shiraz, Iran.|
|4Dept. of Oral and Maxillofacial Surgery, School of Dentistry, Shiraz University of Medical Sciences, Shiraz, Iran.|
|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.
|Bone age؛ Metacarpal bones؛ Metacarpophalangeal joints؛ Neural network|
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