Hand radiographs are routinely used by pediatric endocrinologists to assess bone age in children presenting with atypical growth, pubertal disturbances, or other disorders of physical maturation. However, their diagnostic utility beyond skeletal maturation, particularly for differential diagnosis of growth disorders, remains largely untapped. Here, we introduce novel and well-defined morphometric measurements in hand radiographs and present algorithms for their automatic extraction. We retrospectively analyzed pediatric hand radiographs from 9 conditions (n = 1283), representing a spectrum of growth-affecting endocrine, metabolic, and genetic disorders with variable skeletal involvement (such as SHOX deficiency, Noonan syndrome, and Ullrich-Turner syndrome) and skeletal dysplasias with pronounced hand dysmorphology (such as mucopolysaccharidosis and achondroplasia). From these images, we extracted 550 phenotypical features from metacarpal and phalangeal bones, which were further processed via age- and sex-independent z-scores based on unaffected (UA) controls (n = 1353) to generate a high-dimensional standardized phenotypic space. Using this feature space, a binary screening based solely on the largely available UA training data achieved 82% specificity on an independent UA test set and condition-dependent sensitivities 48.3%-98.9%, enabling robust detection of growth disorders without requiring affected training samples. As a secondary demonstration, a 10-class classifier achieved a balanced test-set accuracy of 75%, with particularly high accuracy for UA controls (94%). Our findings demonstrate that these new measurements provide a low-cost, interpretable, and reproducible method that can be integrated into routine bone age evaluation for growth disorder screening and clinical decision support.

Objective Bone age (BA) assessment is essential for monitoring growth and maturation and guiding therapeutic interventions. While deep learning (DL) models offer high-speed automated BA prediction, their generalizability to rare pathological and diagnostically complex populations remains a significant concern. This study aims to validate the open-source DL system Deeplasia on external data from pediatric patients with various syndromic, endocrine, and lysosomal storage disorders (LSDs) and to compare its accuracy and consistency against multiple expert human raters.Methods We retrospectively assembled 1,138 hand radiographs from multiple centers, including patients with SHOX deficiency; Noonan syndrome; Silver-Russell syndrome; Ullrich-Turner syndrome; pseudohypoparathyroidism; congenital adrenal hyperplasia (CAH); precocious puberty and precocious pseudopuberty (cohort 1); mucopolysaccharidosis types I, II, III, IV, and VI; alpha-mannosidosis; and unclassified LSDs (cohort 2). For each radiograph, BA was evaluated using the Greulich and Pyle method by two to five human experts to obtain a mean BA reference. Model performance was assessed using the mean absolute error (MAE), root mean squared error (RMSE), and 1-year accuracy for each cohort and underlying conditions, sex, and age groups. Furthermore, Deeplasia's performance was compared with that of individual raters by testing each rater and the model against the remaining experts.Results Deeplasia achieved a mean MAE of 5.95 months, an RMSE of 8.01 months, and a 1-year accuracy of 89.9% for cohort 1 (endocrine and syndromic conditions). For cohort 2 (lysosomal storage disorders), Deeplasia achieved a mean MAE of 7.13 months, an RMSE of 9.56 months, and a 1-year accuracy of 81.2%. In direct comparisons between Deeplasia and individual raters tested against the remaining experts, Deeplasia outperformed all human raters.Conclusion Deeplasia was validated as a highly consistent, robust, and reliable tool for BA assessment in complex cases. It demonstrated superior accuracy compared with individual human raters and may assist clinicians in BA evaluation.

Background Skeletal dysplasias collectively affect a large number of patients worldwide. The majority of these disorders cause growth anomalies. Hence, assessing skeletal maturity via determining the bone age (BA) is one of the most valuable tools for their diagnoses. Moreover, consecutive BA assessments are crucial for monitoring the pediatric growth of patients with such disorders, especially for timing hormone treatments or orthopedic interventions. However, manual BA assessment is time-consuming and suffers from high intra-and inter-rater variability. This is further exacerbated by genetic disorders causing severe skeletal malformations. While numerous approaches to automatize BA assessment were proposed, few were validated for BA assessment on children with abnormal development. Objective We design and present Deeplasia, an open-source prior-free deep-learning approach for pediatric bone age assessment specifically validated on patients with skeletal dysplasias. Materials and methods We extensively experiment with training multiple convolutional neural network models under various conditions and select three to build a precise model ensemble. We utilize the public RSNA BA dataset consisting of training, validation, and test subsets each containing 12,611, 1,425, and 200 hand X-rays, respectively. For testing the performance of our model ensemble on dysplastic hands, we retrospectively collected 568 X-ray images from 189 patients with molecularly confirmed diagnoses of seven different genetic bone disorders including Achondroplasia and Hypochondroplasia. Results On the public RSNA test set, we achieve state-of-the-art performance with a mean absolute difference (MAD) of 3.87 months based on the average of six different reference ratings. We demonstrate the generalizability of Deeplasia to the dysplastic hands (unseen by the models) achieving a MAD of 5.84 months w.r.t. to the average of two reference ratings. Further, using longitudinal data from a subset of the dysplastic cohort (149 images), we estimate the test-retest precision of our model ensemble to be at least at the human expert level (2.74 months). Conclusion We conclude that Deeplasia suits assessing and monitoring the BA in patients with skeletal dysplasia. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This study has been supported by the European Reference Network on Rare Congenital Malformations and Rare Intellectual Disability (ERN-ITHACA). ERN-ITHACA is funded by the EU4Health Program of the European Union, under the Grant Agreement Nr. 101085231. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The study was approved by the ethics committee of the medical faculties of the universities Magdeburg (vote 27/22) and Leipzig (vote 121/22-ek). I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes

Skeletal dysplasias collectively affect a large number of patients worldwide. Most of these disorders cause growth anomalies. Hence, evaluating skeletal maturity via the determination of bone age (BA) is a useful tool. Moreover, consecutive BA measurements are crucial for monitoring the growth of patients with such disorders, especially for timing hormonal treatment or orthopedic interventions. However, manual BA assessment is time-consuming and suffers from high intra- and inter-rater variability. This is further exacerbated by genetic disorders causing severe skeletal malformations. While numerous approaches to automate BA assessment have been proposed, few are validated for BA assessment on children with skeletal dysplasias. We present Deeplasia, an open-source prior-free deep-learning approach designed for BA assessment specifically validated on patients with skeletal dysplasias. We trained multiple convolutional neural network models under various conditions and selected three to build a precise model ensemble. We utilized the public BA dataset from the Radiological Society of North America (RSNA) consisting of training, validation, and test subsets containing 12,611, 1,425, and 200 hand and wrist radiographs, respectively. For testing the performance of our model ensemble on dysplastic hands, we retrospectively collected 568 radiographs from 189 patients with molecularly confirmed diagnoses of seven different genetic bone disorders including achondroplasia and hypochondroplasia. A subset of the dysplastic cohort (149 images) was used to estimate the test–retest precision of our model ensemble on longitudinal data. The mean absolute difference of Deeplasia for the RSNA test set (based on the average of six different reference ratings) and dysplastic set (based on the average of two different reference ratings) were 3.87 and 5.84 months, respectively. The test–retest precision of Deeplasia on longitudinal data (2.74 months) is estimated to be similar to a human expert. We demonstrated that Deeplasia is competent in assessing the age and monitoring the development of both normal and dysplastic bones.

Aside from clinical endpoints like height gain, health-related quality of life has also become an important outcome indicator in the medical field. However, the data on short stature and health-related quality of life is inconsistent. Therefore, we examined changes in health-related quality of life in German children with idiopathic growth hormone deficiency or children born small for gestational age before and after 12 months of human growth hormone treatment. Children with idiopathic short stature without treatment served as a comparison group. At baseline, health-related quality of life data of 154 patients with idiopathic growth hormone deficiency (n = 65), born small for gestational age (n = 58) and idiopathic short stature (n = 31) and one parent each was collected. Of these, 130 completed health-related quality of life assessments after 1-year of human growth hormone treatment. Outcome measures included the Quality of Life in Short Stature Youth questionnaire, as well as clinical and sociodemographic data. Our results showed that the physical, social, and emotional health-related quality of life of children treated with human growth hormone significantly increased, while untreated patients with idiopathic short stature reported a decrease in these domains. Along with this, a statistically significant increase in height in the treated group can be observed, while the slight increase in the untreated group was not significant. In conclusion, the results showed that human growth hormone treatment may have a positive effect not only on height but also in improving patient-reported health-related quality of life of children with idiopathic growth hormone deficiency and children born small for gestational age.