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      A simple method for bone age assessment: the capitohamate planimetry

      research-article
      , , ,
      European Radiology
      Springer Berlin Heidelberg
      Hand, Radiography, Child, Bone, Growth

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          Abstract

          Objectives

          To determine if the capitohamate (CH) planimetry could be a reliable indicator of bone age, and to compare it with Greulich-Pyle (GP) method.

          Methods

          This retrospective study included 391 children (age, 1–180 months). Two reviewers manually measured the areas of the capitate and hamate on plain radiographs. CH planimetry was defined as the measurement of the sum of areas of the capitate and hamate. Two reviewers independently applied the CH planimetry and GP methods in 109 children whose heights were at the 50th percentile of the growth chart.

          Results

          There was a strong positive correlation between chronological age and CH planimetry measurement (right, r = 0.9702; left, r = 0.9709). There was no significant difference in accuracy between CH planimetry (84.39–84.46 %) and the GP method (85.15–87.66 %) (p ≥ 0.0867). The interobserver reproducibility of CH planimetry (precision, 4.42 %; 95 % limits of agreement [LOA], −10.5 to 13.4 months) was greater than that of the GP method (precision, 8.45 %; LOA, −29.5 to 21.1 months).

          Conclusions

          CH planimetry may be a reliable method for bone age assessment.

          Key Points

          • Bone age assessment is important in the work-up of paediatric endocrine disorders.

          • Radiography of the left hand is widely used to estimate bone age.

          • Capitatohamate planimetry is a reliable and reproducible method for assessing bone age.

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          Most cited references33

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          Fully Automated Deep Learning System for Bone Age Assessment

          Skeletal maturity progresses through discrete phases, a fact that is used routinely in pediatrics where bone age assessments (BAAs) are compared to chronological age in the evaluation of endocrine and metabolic disorders. While central to many disease evaluations, little has changed to improve the tedious process since its introduction in 1950. In this study, we propose a fully automated deep learning pipeline to segment a region of interest, standardize and preprocess input radiographs, and perform BAA. Our models use an ImageNet pretrained, fine-tuned convolutional neural network (CNN) to achieve 57.32 and 61.40% accuracies for the female and male cohorts on our held-out test images. Female test radiographs were assigned a BAA within 1 year 90.39% and within 2 years 98.11% of the time. Male test radiographs were assigned 94.18% within 1 year and 99.00% within 2 years. Using the input occlusion method, attention maps were created which reveal what features the trained model uses to perform BAA. These correspond to what human experts look at when manually performing BAA. Finally, the fully automated BAA system was deployed in the clinical environment as a decision supporting system for more accurate and efficient BAAs at much faster interpretation time (<2 s) than the conventional method.
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            Deep learning for automated skeletal bone age assessment in X-ray images

            Skeletal bone age assessment is a common clinical practice to investigate endocrinology, genetic and growth disorders in children. It is generally performed by radiological examination of the left hand by using either the Greulich and Pyle (G&P) method or the Tanner-Whitehouse (TW) one. However, both clinical procedures show several limitations, from the examination effort of radiologists to (most importantly) significant intra- and inter-operator variability. To address these problems, several automated approaches (especially relying on the TW method) have been proposed; nevertheless, none of them has been proved able to generalize to different races, age ranges and genders. In this paper, we propose and test several deep learning approaches to assess skeletal bone age automatically; the results showed an average discrepancy between manual and automatic evaluation of about 0.8 years, which is state-of-the-art performance. Furthermore, this is the first automated skeletal bone age assessment work tested on a public dataset and for all age ranges, races and genders, for which the source code is available, thus representing an exhaustive baseline for future research in the field. Beside the specific application scenario, this paper aims at providing answers to more general questions about deep learning on medical images: from the comparison between deep-learned features and manually-crafted ones, to the usage of deep-learning methods trained on general imagery for medical problems, to how to train a CNN with few images.
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              Bone age: assessment methods and clinical applications

              Mari Satoh (2015)
              Abstract. The main bone age assessment methods are the Greulich-Pyle and Tanner-Whitehouse 2 methods, both of which involve left hand and wrist radiographs. Several other bone age assessment methods have been developed, including ultrasonographic, computerized, and magnetic resonance (MR) imaging methods. The ultrasonographic method appears unreliable in children with delayed and advanced bone age. MR imaging is noninvasive; however, bone age assessment using MR imaging is relatively new, and further examinations are needed. An automated method for determining bone age, named BoneXpert, has been validated for Caucasian children with growth disorders and children of various ethnic groups. Sex hormones are necessary for bone growth and maturation in children with a bone age corresponding to normal pubertal age, and estrogen is essential for growth plate closure. Bone age is an effective indicator for diagnosing and treating various diseases. A new method for adult height prediction based on bone age has been developed using BoneXpert, in addition to the commonly used Bayley-Pinneau and Tanner-Whitehouse mark II methods. Furthermore, bone age may become a predictor for the timing of peak height velocity and menarche.
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                Author and article information

                Contributors
                82-31-8086-2588 , yochoru@gmail.com
                Journal
                Eur Radiol
                Eur Radiol
                European Radiology
                Springer Berlin Heidelberg (Berlin/Heidelberg )
                0938-7994
                1432-1084
                30 January 2018
                30 January 2018
                2018
                : 28
                : 6
                : 2299-2307
                Affiliations
                ISNI 0000 0004 1790 2596, GRID grid.488450.5, Department of Radiology, , Hallym University Dongtan Sacred Heart Hospital, ; 7 Keunjaebong-gil, Hwaseong, 18450 Gyeonggi-do Republic of Korea
                Article
                5255
                10.1007/s00330-017-5255-4
                5938295
                29383523
                a7061e4d-8b29-4949-8aa0-cd9a8c212d34
                © The Author(s) 2018

                Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

                History
                : 28 September 2017
                : 4 December 2017
                : 18 December 2017
                Categories
                Paediatric
                Custom metadata
                © European Society of Radiology 2018

                Radiology & Imaging
                hand,radiography,child,bone,growth
                Radiology & Imaging
                hand, radiography, child, bone, growth

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