ArticleEuropean radiology2021
Assessment of rapidly advancing bone age during puberty on elbow radiographs using a deep neural network model.
Article in European radiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
14 citing papers in PubMed, 15 citations in OpenAlex.
- Automated MRI-based framework combining deep learning localization and machine learning classification for pubertal olecranon bone age assessment.International journal of legal medicine · 2026Article
- Forensic age estimation and legal age thresholds classification based on the elbow MRI and data mining in a Chinese population.International journal of legal medicine · 2026Article
- Construction and Validation of a Simplified Bone Age Assessment Method Based on Three Key Bones from the TW3 system.Endocrine · 2026Article
- Development and evaluation of a predictive model for central precocious puberty in girls.Translational pediatrics · 2026Article
- AI-powered automated X-ray bone age analyzer for assisting bone age assessment in Chinese children and adolescents: accuracy, consistency, and time efficiency.European journal of pediatrics · 2025Article
- Comparison of adult height prediction using bone age and body composition for growth assessment in Korean children.Scientific reports · 2025Article
- Automated Distal Radius and Ulna Skeletal Maturity Grading from Hand Radiographs with an Attention Multi-Task Learning Method.Tomography (Ann Arbor, Mich.) · 2024Article
- Olecranon bone age assessment in puberty using a lateral elbow radiograph and a deep-learning model.European radiology · 2024Article
- An accurate pediatric bone age prediction model using deep learning and contrast conversion.Ewha medical journal · 2024Article
- Validation of an established TW3 artificial intelligence bone age assessment system: a prospective, multicenter, confirmatory study.Quantitative imaging in medicine and surgery · 2024Article
- Deep-Learning-Based Automated Rotator Cuff Tear Screening in Three Planes of Shoulder MRI.Diagnostics (Basel, Switzerland) · 2023Article
- Accuracy of predicted adult height using the Greulich-Pyle method and artificial intelligence medical device.Clinical and experimental pediatrics · 2023Article
- An overview of deep learning applications in precocious puberty and thyroid dysfunction.Frontiers in endocrinology · 2022Review
- A Comprehensive Review of Artificial Intelligence Methods in Bone Age Assessment.Healthcare technology lettersReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors at 3 institutions in 2 countries.
Funding
Abstract
objectivesBone age is considered an indicator for the diagnosis of precocious or delayed puberty and a predictor of adult height. We aimed to evaluate the performance of a deep neural network model in assessing rapidly advancing bone age during puberty using elbow radiographs.
methodsIn all, 4437 anteroposterior and lateral pairs of elbow radiographs were obtained from pubertal individuals from two institutions to implement and validate a deep neural network model. The reference standard bone age was established by five trained researchers using the Sauvegrain method, a scoring system based on the shapes of the lateral condyle, trochlea, olecranon apophysis, and proximal radial epiphysis. A test set (n = 141) was obtained from an external institution. The differences between the assessment of the model and that of reviewers were compared.
resultsThe mean absolute difference (MAD) in bone age estimation between the model and reviewers was 0.15 years on internal validation. In the test set, the MAD between the model and the five experts ranged from 0.19 to 0.30 years. Compared with the reference standard, the MAD was 0.22 years. Interobserver agreement was excellent among reviewers (ICC: 0.99) and between the model and the reviewers (ICC: 0.98). In the subpart analysis, the olecranon apophysis exhibited the highest accuracy (74.5%), followed by the trochlea (73.7%), lateral condyle (73.7%), and radial epiphysis (63.1%).
conclusionsAssessment of rapidly advancing bone age during puberty on elbow radiographs using our deep neural network model was similar to that of experts. KEY POINTS: • Bone age during puberty is particularly important for patients with scoliosis or limb-length discrepancy to determine the phase of the disease, which influences the timing and method of surgery. • The commonly used hand radiographs-based methods have limitations in assessing bone age during puberty due to the less prominent morphological changes of the hand and wrist bones in this period. • A deep neural network model trained with elbow radiographs exhibited similar performance to human experts on estimating rapidly advancing bone age during puberty.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.