Standardization of Weightage Assigned to Different Segments of the Hand X-ray for Assessment of Bone Age by the Greulich-Pyle Method.

Introduction: Bone age (BA) assessment is important in evaluating disorders of growth and puberty; the Greulich and Pyle atlas method (GP) is most used. We aimed to determine the weightage to be attributed by raters to various segments of the hand x-ray, namely, distal end of radius-ulna (RU), carpals, and short bones for rating bone age using the GP atlas method. Methods: 692 deidentified x-rays from a previous study (PUNE-dataset) and 400 from the Radiological Society of North America (RSNA-dataset) were included in the study. Mean of BA assessed by experienced raters was termed reference rating. Linear regression was used to model reference age as function of age ratings of the three segments. The root-mean-square-error (RMSE) of segmental arithmetic mean and weighted mean with respect to reference rating were computed for both datasets. Results: Short bones were assigned the highest weightage. Carpals were assigned higher weightage in pre-pubertal PUNE participants as compared to RSNA, vice-versa in RU segment of post-pubertal participants. The RMSE of weighted mean ratings was significantly lower than for the arithmetic mean in the PUNE dataset. Conclusion: We thus determined weightage to be attributed by raters to segments of the hand x-ray for assessment of bone age by the GP method.

Characterization of palmitic acid toxicity induced insulin resistance in HepG2 cells.

BACKGROUND: An etiology of palmitic acid (PA) induced insulin resistance (IR) is complex for which two mechanisms are proposed namely ROS induced JNK activation and lipid induced protein kinase-C (PKCε) activation. However, whether these mechanisms act alone or in consortium is not clear. METHODS AND RESULTS: In this study, we have characterized PA induced IR in liver cells. These cells were treated with different concentrations of PA for either 8 or 16 h. Insulin responsiveness of cells treated with PA for 8 h was found to be same as that of control. However, cells treated with PA for 16 h, showed increased glucose output both in the presence and in absence of insulin only at higher concentrations, indicating development of IR. In these, both JNK and PKCε were activated in response to increased ROS and lipid accumulation, respectively. Activated JNK and PKCε phosphorylated IRS1 at Ser-307 resulting in inhibition of AKT which in turn inactivated GSK3ß, leading to reduced glycogen synthase activity. Inhibition of AKT also reduced insulin suppression of hepatic gluconeogenesis by activating Forkhead box protein O1 (FOXO1) and increased expression of the gluconeogenic enzymes and their transcription factors. CONCLUSION: Thus, our data clearly demonstrate that both these mechanisms work simultaneously and more importantly, identified a threshold of HepG2 cells, which when crossed led to the pathological state of IR in response to PA.

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.

Adaptation and validation of an artificial intelligence based digital radiogrammetry tool for assessing bone health of indian children and youth with type-1 diabetes.

BACKGROUND AND OBJECTIVES: BoneXpert (BX) is an artificial intelligence software used primarily for bone age assessment. Besides, it can also be used to screen for bone health using the digital radiogrammetry tool called bone health index (BHI) for which normative reference values available are calculated from healthy European children. Due to ethnic difference in bone geometry, in a previous study, we generated reference curves based on healthy Indian children. The objectives of this study were: 1) To assess and compare bone health of Indian children with Type 1 diabetes (T1D) using both European and Indian BHI SDS reference data and 2) To identify determinants of poor bone health in Indian children and youth with T1D by using BHI tool (based on BHI-SDS Indian reference data) of BX. METHOD: The BHI was assessed retrospectively in 1159 subjects with T1D using digitalised left-hand x-rays and SDS were computed using European and Indian data. The demographic, anthropometric, clinical, biochemistry, dual x-ray absorptiometry (DXA) data and peripheral quantitative computed tomography (pQCT) data collection were performed using standard protocols and were extracted from hospital records. RESULTS: The BHI correlated well with DXA and pQCT parameters in subjects with T1D. BHI-SDS calculated using Indian reference data had better correlation with height and DXA parameters. 8.6% study participants had low (less than -2) BHI-SDS (Indian), with height SDS having significant effect. Subjects with low BHI-SDS were older, shorter and had higher duration of diabetes. They also had lower IGF1 and vitamin D concentrations, bone mineral density, and trabecular density. Female gender, increased duration of illness, poor glycaemic control, and vitamin D deficiency/insufficiency were significant predictors of poor BHI-SDS. CONCLUSION: Our study highlights the utility of digital radiogrammetry AI tool to screen for bone health of children with T1D and demonstrates and highlights the necessity of interpretation using ethnicity specific normative data.

BoneXpert-derived bone health index reference curves constructed on healthy Indian children and adolescents.

BACKGROUND: Artificial intelligence (AI)-based applications for the assessment of the paediatric musculoskeletal system like BoneXpert are not only useful to assess bone age (BA) but also to provide a bone health index (BHI) and a standard deviation score (SDS) for both. This allows comparison of the BHI with age- and sex-matched healthy Caucasian children. OBJECTIVE: We conducted this study with the objective of generating BHI curves using BoneXpert in healthy Indian children with BA between 2 and 17 years. METHOD: We retrospectively reviewed anthropometric parameters, BHI, and BHI SDS data of digitalized left-hand radiographs (joint photographic experts group [jpg] format) of a cohort of 788 paediatric patients from a previous study to which they were recruited to compare various methods of BA assessment. The recruited children represented all age groups for both sexes. The corrected BHI for jpg images was calculated using the formula corrected BHI=BHI*(stature/(avL*50))^0.33333 where stature is height of subject and avL is average length of metacarpal bones. The reference Indian BHI curves and centiles were generated using the Lambda-Mu-Sigma method. RESULT: The mean BHI and BHI SDS of the study group were 4.02±0.57 and -1.73±1.09, respectively. The average increase in median BHI from each age group was between 2.5% and 3% in both sexes up to age of 14 years after which it increased to 4.5% to 5%. The mean BHI of Indian children was lower than that of Caucasian children with maximum differences noted in boys at 16 years (21.7%) and girls at 14 years (16%). We report 8.4% SD of BHI for our study sample. Reference percentile curves for BHI according to BA were derived separately for boys and girls. CONCLUSION: Reference data has been provided for the screening of bone health status of Indian children and adolescents.

Corrigendum: Quantification of joint mobility limitation in adult type 1 diabetes.

[This corrects the article DOI: 10.3389/fendo.2023.1238825.].