Applicability of two bone age assessment methods to children from Saudi Arabia

The impact of HIV infection on skeletal maturity in peripubertal children in Zimbabwe: a cross-sectional study

INTRODUCTION

HIV infection and its treatment compromises skeletal development (growth and maturation). Skeletal maturity is assessed as bone age (BA) on hand and wrist radiographs. BA younger than chronological age (CA) indicates delayed development. We conducted a cross-sectional study to determine differences between BA and CA (i.e., skeletal maturity deviation [SMD]), and risk factors associated with SMD in peripubertal children with and without HIV established on antiretroviral therapy (ART) including use of tenofovir disoproxil fumarate (TDF).

METHODS

Children with HIV taking ART for at least two years and a comparison group of HIV-negative children, aged 8-16 years and frequency-matched by age and sex, were recruited from HIV clinics and local schools in the same catchment area, in Harare, Zimbabwe. BA was assessed from non-dominant hand-wrist radiographs using the Tanner Whitehouse 3 method. Negative SMD values correspond to delayed development, i.e., BA younger than CA. Multivariable linear regression models determined factors associated with SMD overall, and in children with HIV.

RESULTS

In total, 534 participants (54% males) were included; by design CA was similar in males and females, whether living with or without HIV. Mean (SD) SMD was more negative in CWH than in HIV-negative children in both males [-1.4(1.4) vs. -0.4(1.1) years] and females [-1.1(1.3) vs. -0.0(1.2) years]. HIV infection and weight-for-age Z-score<-2 were associated with more negative SMD in both males and females after adjusting for socio-economic status, orphanhood, pubertal stage, and calcium intake. Age at ART initiation was associated with SMD in both males and females with those starting ART later more delayed: starting ART aged 4-8 years 1.14 (-1.84, -0.43), or over 8 years 1.47 (-2.30, -0.65) (p-value for trend < 0.001). Similar non-significant trends were seen in males. TDF exposure TDF exposure whether < 4years or ≥ 4 years was not associated with delayed development.

CONCLUSION

Perinatally-acquired HIV infection and being underweight were independently associated with delayed skeletal maturation in both males and females. Starting ART later was independently associated with skeletal maturation delay in CWH. Given the known effects of delayed development on later health, it is important to find interventions to ensure healthy weight gain through early years and in CWH to initiate ART as early as possible.

Precision and Accuracy of Radiological Bone Age Assessment in Children among Different Ethnic Groups: A Systematic Review

AIM

The aim was to identify, evaluate, and summarize the findings of relevant individual studies on the precision and accuracy of radiological BA assessment procedures among children from different ethnic groups.

MATERIALS AND METHODS

A qualitative systematic review was carried out following the MOOSE statement and previously registered in PROSPERO (CRD42023449512). A search was performed in MEDLINE (PubMed) (n = 561), the Cochrane Library (n = 261), CINAHL (n = 103), Web of Science (WOS) (n = 181), and institutional repositories (n = 37) using MeSH and free terms combined with the Booleans "AND" and "OR". NOS and ROBINS-E were used to assess the methodological quality and the risk of bias of the included studies, respectively.

RESULTS

A total of 51 articles (n = 20,100) on radiological BA assessment procedures were precise in terms of intra-observer and inter-observer reliability for all ethnic groups. In Caucasian and Hispanic children, the Greulich-Pyle Atlas (GPA) was accurate at all ages, but in youths, Tanner-Whitehouse radius-ulna-short bones 3 (TW3-RUS) could be an alternative. In Asian and Arab subjects, GPA and Tanner-Whitehouse 3 (TW3) overestimated the BA in adolescents near adulthood. In African youths, GPA overestimated the BA while TW3 was more accurate.

CONCLUSION

GPA and TW3 radiological BA assessment procedures are both precise but their accuracy in estimating CA among children of different ethnic groups can be altered by racial bias.

Evaluation of two methods of bone age assessment in peripubertal children in Zimbabwe

OBJECTIVES

Bone age (BA) measurement in children is used to evaluate skeletal maturity and helps in the diagnosis of growth disorders in children. The two most used methods are Greulich and Pyle (GP), and Tanner and Whitehouse 3 (TW3), both based upon assessment of a hand-wrist radiograph. To our knowledge no study has compared and validated the two methods in sub-Saharan Africa (SSA), and only a few have determined BA despite it being a region where skeletal maturity is often impaired for example by HIV and malnutrition. This study aimed to compare BA as measured by two methods (GP and TW3) against chronological age (CA) and determine which method is most applicable in peripubertal children in Zimbabwe.

METHODS

We conducted a cross-sectional study of boys and girls who tested negative for HIV. Children and adolescents were recruited by stratified random sampling from six schools in Harare, Zimbabwe. Non-dominant hand-wrist radiographs were taken, and BA assessed manually using both GP and TW3. Paired sample Student t-tests were used to calculate the mean differences between BA and chronological age (CA) in boys and girls. Bland-Altman plots compared CA to BA as determined by both methods, and agreement between GP and TW3 BA. All radiographs were graded by a second radiographer and 20 % of participants of each sex were randomly selected and re-graded by the first observer. Intraclass correlation coefficient assessed intra- and inter-rater reliability and coefficient of variation assessed precision.

RESULTS

We recruited 252 children (111 [44 %] girls) aged 8.0-16.5 years. The boys and girls were of similar mean ± SD CA (12.2 ± 2.4 and 11.7 ± 1.9 years) and BA whether assessed by GP (11.5 ± 2.8 and 11.5 ± 2.1 years) or TW3 (11.8 ± 2.5 and 11.8 ± 2.1 years). In boys BA was lower than CA by 0.76 years (95 % CI: -0.95, -0.57) when using GP, and by 0.43 years (95 % CI: -0.61, -0.24) when using TW3. Among the girls there was no difference between BA and CA by either GP [-0.19 years (95 % CI: -0.40, 0.03)] or TW3 [0.07 years (95 % CI: -0.16, 0.29)]. In both boys and girls, there were no systematic differences between CA and TW3 BA across age groups whereas agreement improved between CA and GP BA as children got older. Inter-operator precision was 1.5 % for TW3 and 3.7 % for GP (n = 252) and intra-operator precision was 1.5 % for TW3 and 2.4 % for GP (n = 52).

CONCLUSION

The TW3 BA method had better precision than GP and did not systematically differ from CA, meaning that TW3 is the preferred method of assessment of skeletal maturity in Zimbabwean children and adolescents. TW3 and GP methods do not agree for estimates of BA and therefore cannot be used interchangeably. The systematic differences in GP BA assessments over age means it is not appropriate for use in all age groups or stages of maturity in this population.

A comparison of bone age assessments using automated and manual methods in children of Indian ethnicity

BACKGROUND

Bone age is useful for pediatric endocrinologists in evaluating various disorders related to growth and puberty. Traditional methods of bone age assessment, namely Greulich and Pyle (GP) and Tanner-Whitehouse (TW), have intra- and interobserver variations. Use of computer-automated methods like BoneXpert might overcome these subjective variations.

OBJECTIVE

The aim of our study was to assess the validity of BoneXpert in comparison to manual GP and TW methods for assessing bone age in children of Asian Indian ethnicity.

MATERIALS AND METHODS

We extracted from a previous study the deidentified left hand radiographs of 920 healthy children aged 2-19 years. We compared bone age as determined by four well-trained manual raters using GP and TW methods with the BoneXpert ratings. We computed accuracy using root mean square error (RMSE) to assess how close the bone age estimated by BoneXpert was to the reference rating.

RESULTS

The standard deviations (SDs) of rating among the four manual raters were 0.52 years, 0.52 years and 0.47 years for GP, TW2 and TW3 methods, respectively. The RMSEs between the automated bone age estimates and the true ratings were 0.39 years, 0.41 years and 0.36 years, respectively, for the same methods. The RMSE values were significantly lower in girls than in boys (0.53, 0.5 and 0.47 vs. 0.39, 0.47 and 0.4) by all the methods; however, no such difference was noted in classification by body mass index. The best agreement between BoneXpert and manual rating was obtained by using 50% weight on carpals (GP50). The carpal bone age was retarded in Indian children, more so in boys.

CONCLUSION

BoneXpert was accurate and performed well in estimating bone age by both GP and TW methods in healthy Asian Indian children; the error was larger in boys. The GP50 establishes "backward compatibility" with manual rating.

Deep learning accurately classifies elbow joint effusion in adult and pediatric radiographs

Joint effusion due to elbow fractures are common among adults and children. Radiography is the most commonly used imaging procedure to diagnose elbow injuries. The purpose of the study was to investigate the diagnostic accuracy of deep convolutional neural network algorithms in joint effusion classification in pediatric and adult elbow radiographs. This retrospective study consisted of a total of 4423 radiographs in a 3-year period from 2017 to 2020. Data was randomly separated into training (n = 2672), validation (n = 892) and test set (n = 859). Two models using VGG16 as the base architecture were trained with either only lateral projection or with four projections (AP, LAT and Obliques). Three radiologists evaluated joint effusion separately on the test set. Accuracy, precision, recall, specificity, F1 measure, Cohen’s kappa, and two-sided 95% confidence intervals were calculated. Mean patient age was 34.4 years (1–98) and 47% were male patients. Trained deep learning framework showed an AUC of 0.951 (95% CI 0.946–0.955) and 0.906 (95% CI 0.89–0.91) for the lateral and four projection elbow joint images in the test set, respectively. Adult and pediatric patient groups separately showed an AUC of 0.966 and 0.924, respectively. Radiologists showed an average accuracy, sensitivity, specificity, precision, F1 score, and AUC of 92.8%, 91.7%, 93.6%, 91.07%, 91.4%, and 92.6%. There were no statistically significant differences between AUC's of the deep learning model and the radiologists (p value > 0.05). The model on the lateral dataset resulted in higher AUC compared to the model with four projection datasets. Using deep learning it is possible to achieve expert level diagnostic accuracy in elbow joint effusion classification in pediatric and adult radiographs. Deep learning used in this study can classify joint effusion in radiographs and can be used in image interpretation as an aid for radiologists.

Relationship between height age, bone age and chronological age in normal children in the context of nutritional and pubertal status

INTRODUCTION

Bone age (BA) is a quantitative determination of skeletal maturation. The role of puberty in variations in BA is poorly understood as hypothalamic-pituitary-gonadal (HPG) axis maturation and skeletal maturation are regulated in parallel but independently by multiple different factors. In countries like India where there is rapid nutrition transition and increase in prevalence of obesity, their impact on height and BA is not well understood.

OBJECTIVES

To study if in 2-17 year old healthy children, the difference between chronological age (CA), height age (HA) and BA is less than 1 year on either side of the chronological age and to assess relationship of BA with height, weight and BMI with special reference to gender and puberty.

METHODS

This cross-sectional study included 804 preschool/school-going Indian children. Anthropometric measurements and pubertal assessments were performed using standard protocols and were converted to age and sex standardized z-scores using Indian references while BA was estimated by Tanner-Whitehouse (TW3) method. p<0.05 was considered statistically significant.

RESULTS

The mean age and gender standardized z-scores for height, weight, body mass index (BMI) and BA were -0.3 ± 0.7, -0.7 ± 0.8, -0.1 ± 1.0, and -0.2 ± 0.9 respectively. HA was more delayed in girls while BA was more delayed in boys. The mean BA z-score increased with increasing BMI. After the onset of puberty, there was higher increment in BA in girls and HA in boys (p<0.05).

CONCLUSIONS

HA, BA and CA were tightly correlated in healthy Indian children with a significant role of nutritional status and puberty in causing variation in the same.

Current and emerging artificial intelligence applications for pediatric musculoskeletal radiology

Artificial intelligence (AI) is playing an ever-increasing role in radiology (more so in the adult world than in pediatrics), to the extent that there are unfounded fears it will completely take over the role of the radiologist. In relation to musculoskeletal applications of AI in pediatric radiology, we are far from the time when AI will replace radiologists; even for the commonest application (bone age assessment), AI is more often employed in an AI-assist mode rather than an AI-replace or AI-extend mode. AI for bone age assessment has been in clinical use for more than a decade and is the area in which most research has been conducted. Most other potential indications in children (such as appendicular and vertebral fracture detection) remain largely in the research domain. This article reviews the areas in which AI is most prominent in relation to the pediatric musculoskeletal system, briefly summarizing the current literature and highlighting areas for future research. Pediatric radiologists are encouraged to participate as members of the research teams conducting pediatric radiology artificial intelligence research.

Autonomous artificial intelligence in pediatric radiology: the use and perception of BoneXpert for bone age assessment

BACKGROUND

The autonomous artificial intelligence (AI) system for bone age rating (BoneXpert) was designed to be used in clinical radiology practice as an AI-replace tool, replacing the radiologist completely.

OBJECTIVE

The aim of this study was to investigate how the tool is used in clinical practice. Are radiologists more inclined to use BoneXpert to assist rather than replace themselves, and how much time is saved?

MATERIALS AND METHODS

We sent a survey consisting of eight multiple-choice questions to 282 radiologists in departments in Europe already using the software.

RESULTS

The 97 (34%) respondents came from 18 countries. Their answers revealed that before installing the automated method, 83 (86%) of the respondents took more than 2 min per bone age rating; this fell to 20 (21%) respondents after installation. Only 17/97 (18%) respondents used BoneXpert to completely replace the radiologist; the rest used it to assist radiologists to varying degrees. For instance, 39/97 (40%) never overruled the automated reading, while 9/97 (9%) overruled more than 5% of the automated ratings. The majority 58/97 (60%) of respondents checked the radiographs themselves to exclude features of underlying disease.

CONCLUSION

BoneXpert significantly reduces reporting times for bone age determination. However, radiographic analysis involves more than just determining bone age. It also involves identification of abnormalities, and for this reason, radiologists cannot be completely replaced. AI systems originally developed to replace the radiologist might be more suitable as AI assist tools, particularly if they have not been validated to work autonomously, including the ability to omit ratings when the image is outside the range of validity.

Tanner-Whitehouse and Modified Bayley-Pinneau Adult Height Predictions in Elite Youth Soccer Players from the Middle East

PURPOSE

To provide the first scrutiny of adult height prediction protocols based on automated Greulich-Pyle and Tanner-Whitehouse (TW) skeletal ages applied to elite youth soccer players from the Middle East.

METHODS

We examined the application of modified Bayley-Pinneau (BoneXpert®), TW-II, and TW-III protocols using mixed-longitudinal data available for 103 subjects (chronological age range, 19.4 to 27.9 yr) previously enrolled as academy student-athletes (annual screening range, one to six visits). Random-effects generalized additive models quantified the presence of systematic mean differences between actual versus predicted adult height. Effects were deemed practically equivalent based on the location of the confidence interval (95% CI) against a realistic difference value of Δ = ± 1 cm. Each model pooled residual standard deviation described the actual precision of height predictions and was used to calculate a 95% prediction interval.

RESULTS

The BoneXpert® method overpredicted adult height systematically at chronological ages in the range of approximately 13.5 to 14.5 yr (95% CI range, -1.9 to -1 cm) and Greulich-Pyle skeletal ages between 13.5 and 15 yr (95% CI range, -3.1 to -1 cm). Effects based on TW-II were practically equivalent across the chronological and skeletal age measurement ranges, with this protocol yielding adult height predictions with a precision (standard deviation) of approximately ±2.6 cm. The mean TW-III effects indicated systematic adult height overpredictions until the attainment of 14.5 and 15 yr of chronological age (95% CI range, -3.8 to -1.1 cm) and TW-III skeletal age (95% CI range: -5.2 to -2.3 cm), respectively.

CONCLUSIONS

Tanner-Whitehouse-II adult height prediction method provided more consistent estimates and can be considered the method of choice for talent development purposes in youth soccer players from the Middle East.

Automated Bone Age Assessment with Image Registration Using Hand X-ray Images

One of the methods for identifying growth disorder is by assessing the skeletal bone age. A child with a healthy growth rate will have approximately the same chronological and bone ages. It is important to detect any growth disorder as early as possible, so that mitigation treatment can be administered with less negative consequences. Recently, the most popular approach in assessing the discrepancy between bone and chronological ages is through the subjective protocol of Tanner–Whitehouse that assesses selected regions in the hand X-ray images. This approach relies heavily on the medical personnel experience, which produces a high intra-observer bias. Therefore, an automated bone age prediction system with image registration using hand X-ray images is proposed in order to complement the inexperienced doctors by providing the second opinion. The system relies on an optimized regression network using a novel residual separable convolution model. The regressor network requires an input image to be 299 × 299 pixels, which will be mapped to the predicted bone age through three modules of the Xception network. Moreover, the images will be pre-processed or registered first to a standardized and normalized pose using separable convolutional neural networks. Three steps image registration are performed by segmenting the hand regions, which will be rotated using angle calculated from four keypoints of interest, before positional alignment is applied to ensure the region of interest is located in the middle. The hand segmentation is based on DeepLab V3 plus architecture, while keypoints regressor for angle alignment is based on MobileNet V1 architecture, where both of them use separable convolution as the core operators. To avoid the pitfall of underfitting, synthetic data are generated while using various rotation angles, zooming factors, and shearing images in order to augment the training dataset. The experimental results show that the proposed method returns the lowest mean absolute error and mean squared error of 8.200 months and 121.902 months2, respectively. Hence, an error of less than one year is acceptable in predicting the bone age, which can serve as a good supplement tool for providing the second expert opinion. This work does not consider gender information, which is crucial in making a better prediction, as the male and female bone structures are naturally different.