A fully automated bone-ageing system

The automated Greulich and Pyle: a coming-of-age for segmental methods?

The well-known Greulich and Pyle (GP) method of bone age assessment (BAA) relies on comparing a hand X-ray against templates of discrete maturity classes collected in an atlas. Automated methods have recently shown great success with BAA, especially using deep learning. In this perspective, we first review the success and limitations of various automated BAA methods. We then offer a novel hypothesis: When networks predict bone age that is not aligned with a GP reference class, it is not simply statistical error (although there is that as well); they are picking up nuances in the hand X-ray that lie "outside that class." In other words, trained networks predict distributions around classes. This raises a natural question: How can we further understand the reasons for a prediction to deviate from the nominal class age? We claim that segmental aging, that is, ratings based on characteristic bone groups can be used to qualify predictions. This so-called segmental GP method has excellent properties: It can not only help identify differential maturity in the hand but also provide a systematic way to extend the use of the current GP atlas to various other populations.

Identifying Skeletal Maturity from X-rays using Deep Neural Networks

Skeletal maturity estimation is routinely evaluated by pediatrics and radiologists to assess growth and hormonal disorders. Methods integrated with regression techniques are incompatible with low-resolution digital samples and generate bias, when the evaluation protocols are implemented for feature assessment on coarse X-Ray hand images. This paper proposes a comparative analysis between two deep neural network architectures, with the base models such as Inception-ResNet-V2 and Xception-pre-trained networks. Based on 12,611 hand X-Ray images of RSNA Bone Age database, Inception-ResNet-V2 and Xception models have achieved R-Squared value of 0.935 and 0.942 respectively. Further, in the same order, the MAE accomplished by the two models are 12.583 and 13.299 respectively, when subjected to very few training instances with negligible chances of overfitting.

Traditional and New Methods of Bone Age Assessment-An Overview

Bone age is one of biological indicators of maturity used in clinical practice and it is a very important parameter of a child’s assessment, especially in paediatric endocrinology. The most widely used method of bone age assessment is by performing a hand and wrist radiograph and its analysis with Greulich-Pyle or Tanner-Whitehouse atlases, although it has been about 60 years since they were published. Due to the progress in the area of Computer-Aided Diagnosis and application of artificial intelligence in medicine, lately, numerous programs for automatic bone age assessment have been created. Most of them have been verified in clinical studies in comparison to traditional methods, showing good precision while eliminating inter- and intra-rater variability and significantly reducing the time of assessment. Additionally, there are available methods for assessment of bone age which avoid X-ray exposure, using modalities such as ultrasound or magnetic resonance imaging.

Automated bone age assessment: motivation, taxonomies, and challenges

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.

Bone age assessment: automated techniques coming of age?

Bone age determination from hand radiographs is one of the oldest radiographic procedures. The first atlas was published by Poland in 1898, and to date the Greulich Pyle atlas, although it dates from 1959, is still the most commonly used method. Bone age rating is time-consuming, suffers from an unsatisfactorily high rater variability, and therefore already 25 years ago it was proposed to replace the manual rating by an automated, computerized method, a field nowadays referred to as computer-aided diagnosis (CAD). The pursuit of this goal reached a first stage of accomplishment in 1992-1996 with the presentation of several systems. However, they had limited clinical value, and efforts in CAD research were increasingly focused on lesion detection for cancer screening. It was only in 2008 that a fully-automated bone age method was presented, which appears to be clinically acceptable. In this paper we consider the requirements that should be met by an automated bone age method and review the state of the art. Integration in PACS and saving time are important factors for radiologists. But it is the validation of the methods which poses the greatest challenge, because there is no gold standard for bone age rating, and the direct comparison to manual rating is therefore not sufficient for demonstrating that manual rating can be replaced by automated rating. One needs additional studies assessing the precision of a method and its accuracy when used for adult height prediction, which serves as an objective.

The BoneXpert method for automated determination of skeletal maturity

Bone age rating is associated with a considerable variability from the human interpretation, and this is the motivation for presenting a new method for automated determination of bone age (skeletal maturity). The method, called BoneXpert, reconstructs, from radiographs of the hand, the borders of 15 bones automatically and then computes "intrinsic" bone ages for each of 13 bones (radius, ulna, and 11 short bones). Finally, it transforms the intrinsic bone ages into Greulich Pyle (GP) or Tanner Whitehouse (TW) bone age. The bone reconstruction method automatically rejects images with abnormal bone morphology or very poor image quality. From the methodological point of view, BoneXpert contains the following innovations: 1) a generative model (active appearance model) for the bone reconstruction; 2) the prediction of bone age from shape, intensity, and texture scores derived from principal component analysis; 3) the consensus bone age concept that defines bone age of each bone as the best estimate of the bone age of the other bones in the hand; 4) a common bone age model for males and females; and 5) the unified modelling of TW and GP bone age. BoneXpert is developed on 1559 images. It is validated on the Greulich Pyle atlas in the age range 2-17 years yielding an SD of 0.42 years [0.37; 0.47] 95% conf, and on 84 clinical TW-rated images yielding an SD of 0.80 years [0.68; 0.93] 95% conf. The precision of the GP bone age determination (its ability to yield the same result on a repeated radiograph) is inferred under suitable assumptions from six longitudinal series of radiographs. The result is an SD on a single determination of 0.17 years [0.13; 0.21] 95% conf.

Preliminary report on the validation of a grammar-based computer system for assessing skeletal maturity with the Tanner-Whitehouse 2 method

A series of hand and wrist radiographs was assessed manually by two individuals and by a fully automated computer system for determining bone age. Assessments were repeated after 1 month to determine variability between and within the methods of bone age assessment. There was slight intra-observer variation, but complete reproducibility when assessments were made by computer. The variation between the human assessors was less than that between human and computer assessments. The difference between overall maturity scores made by the human observer and the computer system was, however, acceptably small, and the majority of assessments were the same. It is concluded this computer system for assessing bone age in normal children is reliable and accurate, but that it needs to be validated against a much larger set of radiographs.