Automation of bone age reading and a new prediction model improve adult height prediction in children with short stature

Adding Letrozole to Growth Hormone and Gonadotropin-Releasing Hormone Analog Increases Height in Girls With Short Stature: A Hospital Record-Based Retrospective Study

OBJECTIVE

There have been rare data on letrozole for height improvement in girls. This study aimed to clarify the efficacy and safety of combination therapy with recombinant human growth hormone (rhGH), GnRHa, and letrozole in improving the height of girls with short stature and advanced bone age.

METHODS

This was a hospital record-based retrospective study. Follow-up was conducted on girls with short stature who received treatment with rhGH, GnRHa, and letrozole in our hospital. The treatment group included a total of 29 participants. Before treatment, the mean age of the patients was 11.17 years, and the mean treatment duration was 17.31 months. The control group consisted of 29 short-statured girls who received rhGH/GnRHa treatment, with the mean age and treatment duration of 12.43 years and 16.59 months, respectively.

RESULTS

The predicted adult heights (PAHs) before and after treatment were 155.38 and 161.32 cm (P < .001). The ΔPAH in the treatment group was 4 cm higher than that in the control group (5.85 vs 1.82 cm, P < .001). Significant differences were noted in the height standard deviation scores of bone age (P < .001) and chronological age (P = .003) before and after treatment. There was an increasing body mass index during therapy (P = .039). The height gain was 8.71 ± 4.46 cm, and the growth rate was 6.78 ± 3.84 cm per year.

CONCLUSION

Combined treatment with GH, GnRHa, and letrozole can enhance the adult height and PAH in short-statured girls, and no significant side effects have been reported.

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.

Near-Adult Heights and Adult Height Predictions Using Automated and Conventional Greulich-Pyle Bone Age Determinations in Children with Chronic Endocrine Diseases

OBJECTIVES

To validate adult height predictions (BX) using automated and Greulich-Pyle bone age determinations in children with chronic endocrine diseases.

METHODS

Heights and near-adult heights were measured in 82 patients (48 females) with chronic endocrinopathies at the age of 10.45 ± 2.12 y and at time of transition to adult care (17.98 ± 3.02 y). Further, bone age (BA) was assessed using the conventional Greulich-Pyle (GP) method by three experts, and by BoneXpert™. PAH were calculated using conventional BP tables and BoneXpert™.

RESULTS

The conventional and the automated BA determinations revealed a mean difference of 0.25 ± 0.72 y (p = 0.0027). The automated PAH by BoneXpert™ were 156.26 ± 0.86 cm (SDS - 2.01 ± 1.07) in females and 171.75 ± 1.6 cm (SDS - 1.29 ± 1.06) in males, compared to 153.95 ± 1.12 cm (SDS - 2.56 ± 1.5) in females and 169.31 ± 1.6 cm (SDS - 1.66 ± 1.56) in males by conventional BP, respectively and in comparison to near-adult heights 156.38 ± 5.84 cm (SDS - 1.91 ± 1.15) in females and 168.94 ± 8.18 cm (SDS - 1.72 ± 1.22) in males, respectively.

CONCLUSION

BA ratings and adult height predictions by BoneXpert™ in children with chronic endocrinopathies abolish rater-dependent variability and enhance reproducibility of estimates thereby refining care in growth disorders. Conventional methods may outperform automated analyses in specific cases.

A Novel Method for Adult Height Prediction in Children with Idiopathic Short Stature Derived from a German-Dutch Cohort

Context

Prediction of adult height (AH) is important in clinical management of short children. The conventional methods of Bayley-Pinneau (BP) or Roche-Wainer-Thissen (RWT) have limitations.

Objective

We aimed to develop a set of algorithms for AH prediction in patients with idiopathic short stature (ISS) which are specific for combinations of predicting variables.

Methods

Demographic and auxologic data were collected in childhood (1980s) and at AH (1990s). Data were collected by Dutch and German referral centers for pediatric endocrinology. A total of 292 subjects with ISS (219 male, 73 female) were enrolled. The population was randomly split into modeling (n = 235) and validation (n = 57) cohorts. Linear multi-regression analysis was performed with predicted AH (PAH) as response variable and combinations of chronological age (CA), baseline height, parental heights, relative bone age (BA/CA), birth weight, and sex as exploratory variables.

Results

Ten models including different exploratory variables were selected with adjusted R² ranging from 0.84 to 0.78 and prediction errors from 3.16 to 3.68 cm. Applied to the validation cohort, mean residuals (PAH minus observed AH) ranged from −0.29 to −0.82 cm, while the conventional methods showed some overprediction (BP: +0.53 cm; RWT: +1.33 cm; projected AH: +3.81 cm). There was no significant trend of residuals with PAH or any exploratory variables, in contrast to BP and projected AH.

Conclusion

This set of 10 multi-regression algorithms, developed specifically for children with ISS, provides a flexible tool for AH prediction with better accuracy than the conventional methods.

Clinical application of artificial intelligence in longitudinal image analysis of bone age among GHD patients

OBJECTIVE

This study aims to explore the clinical value of artificial intelligence (AI)-assisted bone age assessment (BAA) among children with growth hormone deficiency (GHD).

METHODS

A total of 290 bone age (BA) radiographs were collected from 52 children who participated in the study at Sun Yat-sen Memorial Hospital between January 2016 and August 2017. Senior pediatric endocrinologists independently evaluated BA according to the China 05 (CH05) method, and their consistent results were regarded as the gold standard (GS). Meanwhile, two junior pediatric endocrinologists were asked to assessed BA both with and without assistance from the AI-based BA evaluation system. Six months later, around 20% of the images assessed by the junior pediatric endocrinologists were randomly selected to be re-evaluated with the same procedure half a year later. Root mean square error (RMSE), mean absolute error (MAE), accuracy, and Bland-Altman plots were used to compare differences in BA. The intra-class correlation coefficient (ICC) and one-way repeated ANOVA were used to assess inter- and intra-observer variabilities in BAA. A boxplot of BA evaluated by different raters during the course of treatment and a mixed linear model were used to illustrate inter-rater effect over time.

RESULTS

A total of 52 children with GHD were included, with mean chronological age and BA by GS of 6.64 ± 2.49 and 5.85 ± 2.30 years at baseline, respectively. After incorporating AI assistance, the performance of the junior pediatric endocrinologists improved (P < 0.001), with MAE and RMSE both decreased by more than 1.65 years (Rater 1: ΔMAE = 1.780, ΔRMSE = 1.655; Rater 2: ΔMAE = 1.794, ΔRMSE = 1.719), and accuracy increasing from approximately 10% to over 91%. The ICC also increased from 0.951 to 0.990. During GHD treatment (at baseline, 6-, 12-, 18-, and 24-months), the difference decreased sharply when AI was applied. Furthermore, a significant inter-rater effect (P = 0.002) also vanished upon AI involvement.

CONCLUSION

AI-assisted interpretation of BA can improve accuracy and decrease variability in results among junior pediatric endocrinologists in longitudinal cohort studies, which shows potential for further clinical application.

Artificial Intelligence-Assisted Bone Age Assessment to Improve the Accuracy and Consistency of Physicians With Different Levels of Experience

BACKGROUND

The accuracy and consistency of bone age assessments (BAA) using standard methods can vary with physicians' level of experience.

METHODS

To assess the impact of information from an artificial intelligence (AI) deep learning convolutional neural network (CNN) model on BAA, specialists with different levels of experience (junior, mid-level, and senior) assessed radiographs from 316 children aged 4-18 years that had been randomly divided into two equal sets-group A and group B. Bone age (BA) was assessed independently by each specialist without additional information (group A) and with information from the model (group B). With the mean assessment of four experts as the reference standard, mean absolute error (MAE), and intraclass correlation coefficient (ICC) were calculated to evaluate accuracy and consistency. Individual assessments of 13 bones (radius, ulna, and short bones) were also compared between group A and group B with the rank-sum test.

RESULTS

The accuracies of senior, mid-level, and junior physicians were significantly better (all P < 0.001) with AI assistance (MAEs 0.325, 0.344, and 0.370, respectively) than without AI assistance (MAEs 0.403, 0.469, and 0.755, respectively). Moreover, for senior, mid-level, and junior physicians, consistency was significantly higher (all P < 0.001) with AI assistance (ICCs 0.996, 0.996, and 0.992, respectively) than without AI assistance (ICCs 0.987, 0.989, and 0.941, respectively). For all levels of experience, accuracy with AI assistance was significantly better than accuracy without AI assistance for assessments of the first and fifth proximal phalanges.

CONCLUSIONS

Information from an AI model improves both the accuracy and the consistency of bone age assessments for physicians of all levels of experience. The first and fifth proximal phalanges are difficult to assess, and they should be paid more attention.

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.

Automatic Determination of the Greulich-Pyle Bone Age as an Alternative Approach for Chinese Children with Discordant Bone Age

BACKGROUND

Automated bone age (BA) rating using BoneXpert is being adopted worldwide. This study investigated whether manual matching of hand radiographs could be replaced by BoneXpert for BA ratings of Chinese children with delayed or advanced BA.

METHODS

482 left-hand radiographs from 482 children (aged 2-16 years) with discordant BA were evaluated by BoneXpert and manually by 4 radiology residents using the Greulich and Pyle atlas. Radiographs whose BoneXpert BA deviated by >1 year from manual assessment were rerated by 2 attending radiologists in a blinded manner.

RESULTS

Among all 482 radiographs, 46 (9.5%) radiographs were rerated and no radiographs were rejected. Differences between BoneXpert and manual rating of 28 (5.8%) cases were >1 year. The manual BAs of the 28 radiographs were all >10 years and greater than the BoneXpert BAs. The root mean square deviation between the residents and BoneXpert was 0.56 for these children (95% CI 0.53-0.61).

CONCLUSION

BoneXpert agreed with manual BA rating in 94.2% of the images. Therefore, BoneXpert could be used as an alternative for the radiology residents to make an initial BA estimation. Modification of BoneXpert should provide greater accuracy for the estimation of BA in children aged >10 years with discordant BA.

Validation of adult height prediction based on automated bone age determination in the Paris Longitudinal Study of healthy children

BACKGROUND

An adult height prediction model based on automated determination of bone age was developed and validated in two studies from Zurich, Switzerland. Varied living conditions and genetic backgrounds might make the model less accurate.

OBJECTIVE

To validate the adult height prediction model on children from another geographical location.

MATERIALS AND METHODS

We included 51 boys and 58 girls from the Paris Longitudinal Study of children born 1953 to 1958. Radiographs were obtained once or twice a year in these children from birth to age 18. Bone age was determined using the BoneXpert method. Radiographs in children with bone age greater than 6 years were considered, in total 1,124 images.

RESULTS

The root mean square deviation between the predicted and the observed adult height was 2.8 cm for boys in the bone age range 6-15 years and 3.1 cm for girls in the bone age range 6-13 years. The bias (the average signed difference) was zero, except for girls below bone age 12, where the predictions were 0.8 cm too low.

CONCLUSION

The accuracy of the BoneXpert method in terms of root mean square error was as predicted by the model, i.e. in line with what was observed in the Zurich studies.

Bone age: assessment methods and clinical applications

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.

Validation of automated Greulich-Pyle bone age determination in children with chronic renal failure?

Growth failure is a common problem in children with chronic kidney disease (CKD). The causes are multifactorial and are associated with increased mortality and morbidity. Standard deviations of bone age versus chronological age in children with CKD have not been developed to date. Accurate and early treatment of bone age is an important component of determining the utility of GH therapy. Improvements in bone age assessments are being evaluated to optimize the understanding of growth delay in CKD.

Age of menarche and near adult height after long-term gonadotropin-releasing hormone agonist treatment in girls with central precocious puberty

PURPOSE

Gonadotropin-releasing hormone agonist (GnRHa) is known for improving final adult height in patients with central precocious puberty (CPP). This study aimed to investigate the age of menarche and near adult height in girls with CPP who had been treated with GnRHa.

METHODS

In this retrospective study, we reviewed the medical records of 71 Korean girls with CPP who had started menarche or reached over 13 years of bone age after long-term GnRHa treatment. We estimated near adult height using the Bayley-Pinneau method and identified the age of menarche in girls with CPP.

RESULTS

Mean chronological and bone age at menarche were 11.9±0.7 and 12.8±0.4 years, respectively. The period between menarche and the end of treatment was 14.0±5.6 months. Posttreatment near adult height was 163.8±4.7 cm, which was significantly greater than pretreatment predicted adult height (158.7±4.1 cm).

CONCLUSION

GnRHa treatment in girls with CPP could improve final adult height and made the age of menarche close to that of the general population.