Pediatric Bone Age Assessment Using Deep Convolutional Neural Networks

Skeletal bone age assessment is a common clinical practice to diagnose endocrine and metabolic disorders in child development. In this paper, we describe a fully automated deep learning approach to the problem of bone age assessment using data from Pediatric Bone Age Challenge organized by RSNA 2017. The dataset for this competition is consisted of 12.6k radiological images of left hand labeled by the bone age and sex of patients. Our approach utilizes several deep learning architectures: U-Net, ResNet-50, and custom VGG-style neural networks trained end-to-end. We use images of whole hands as well as specific parts of a hand for both training and inference. This approach allows us to measure importance of specific hand bones for the automated bone age analysis. We further evaluate performance of the method in the context of skeletal development stages. Our approach outperforms other common methods for bone age assessment.Comment: 14 pages, 9 figure

A paediatric bone index derived by automated radiogrammetry

Hand radiographs are obtained routinely to determine bone age of children. This paper presents a method that determines a Paediatric Bone Index automatically from such radiographs. The Paediatric Bone Index is designed to have minimal relative standard deviation (7.5%), and the precision is determined to be 1.42%. Introduction We present a computerised method to determine bone mass of children based on hand radiographs, including a reference database for normal Caucasian children. Methods Normal Danish subjects (1,867), of ages 7-17, and 531 normal Dutch subjects of ages 5-19 were included. Historically, three different indices of bone mass have been used in radiogrammetry all based on A = pi TW(1 - T/W), where T is the cortical thickness and W the bone width. The indices are the metacarpal index A/W-2, DXR-BMD=A/W, and Exton-Smith's index A/(WL), where L is the length of the bone. These indices are compared with new indices of the form A/((WLb)-L-a), and it is argued that the preferred index has minimal SD relative to the mean value at each bone age and sex. Finally, longitudinal series of X-rays of 20 Japanese children are used to derive the precision of the measurements. Results The preferred index is A/((WL0.33)-L-1.33), which is named the Paediatric Bone Index, PBI. It has mean relative SD 7.5% and precision 1.42%. Conclusions As part of the BoneXpert method for automated bone age determination, our method facilitates retrospective research studies involving validation of the proposed index against fracture incidence and adult bone mineral densit

Estimating bone mass in children: can bone health index replace dual energy x-ray absorptiometry?

BACKGROUND: Bisphosphonates have been shown to increase metacarpal cortical width. Bone health index is computed from hand radiographs by measuring cortical thickness, width and length of the three middle metacarpals, and may potentially help predict fracture risk in children. OBJECTIVE: To compare bone health index with bone mineral density as measured from dual energy X-ray absorptiometry scans in patients with and without bisphosphonate treatment. MATERIALS AND METHODS: Two hundred ninety-three Caucasian patients (mean age: 11.5±3.7 years) were included. We documented absolute values and z-scores for whole-body less head and lumbar spine bone mineral density then correlated these with the bone health index, which were acquired on the same day, in different patient groups, depending on their ethnicity and diagnosis. RESULTS: Bone health index showed moderate to strong correlation with absolute values for whole-body (r=0.52) and lumbar spine (r=0.70) bone mineral density in those not treated with bisphosphonates and moderate correlation absolute values for whole-body (r=0.54) and lumber spine (r=0.51) bone mineral density for those treated with bisphosphonates. There was weak correlation of z-scores, ranging from r=0.11 to r=0.35 in both groups. CONCLUSION: The lack of a strong correlation between dual energy X-ray absorptiometry and bone health index suggests that they may be assessing different parameters

Prototypes for Content-Based Image Retrieval in Clinical Practice

Content-based image retrieval (CBIR) has been proposed as key technology for computer-aided diagnostics (CAD). This paper reviews the state of the art and future challenges in CBIR for CAD applied to clinical practice

Metacarpal thickness, width, length and medullary diameter in children--reference curves from the First Zürich Longitudinal Study

SUMMARY: Metacarpal thickness (T), width (W), length (L) and medullary diameter (M) were measured in 3,121 X-rays from 231 healthy Caucasian children aged 3 to 19 years and analysed for bone age, age, height, weight and gender-related characteristics, showing highly differentiated growth patterns with prepubertal dips. Reference data for the four metacarpal measures are presented. INTRODUCTION: The aim of the study was to create and explore a reference database for metacarpal T, W, L and M in children. METHODS: Three thousand one hundred twenty-one left-hand X-rays (1,661 from boys) from 231 healthy Caucasian subjects (119 boys) aged 3 to 19 years were analysed by BoneXpert, a programme for automatic analysis of hand X-rays and bone age (BA; in years). RESULTS: In boys, growth of T, W and L shows a prepubertal decrease from BA 7 to 13 and then accelerates again. In girls, the same is seen only for T starting from BA 8 to 11, whereas W and L grow at a declining rate. M shows steady growth until BA 10.5 in girls and BA 13.5 in boys and then grows smaller in both. W is greater in boys from BA 6 onwards, while L is greater in girls from BA 9 to 13 and T from BA 11 to 14. BA is reflected best by L until start of puberty and by T and L thereafter. CONCLUSION: T, W, L and M show highly differentiated growth patterns. These reference data provide a basis for further research into skeletal development and the management of hormone therapies in children