Bone age cluster assessment and feature clustering analysis based on phalangeal image rough segmentation

Bone age can reflect the true growth and development status of a child; thus, it plays a critical role in evaluating growth and endocrine disorders. This study established and validated an optimized Tanner-Whitehouse 3 artificial intelligence (TW3-AI) bone age assessment (BAA) system based on a convolutional neural network (CNN). A data set of 9,059 clinical radiographs of the left hand was obtained from the picture archives and communication systems (PACS) between January 2012 and December 2016. Among these, 8,005/9,059 (88%) samples were treated as the training set for model implementation, 804/9,059 (9%) samples as the validation set for parameters optimization, and the remaining 250/9,059 (3%) samples were used to verify the accuracy and reliability of the model compared to that of 4 experienced endocrinologists and 2 experienced radiologists. The overall variation of TW3-metacarpophalangeal, radius, ulna and short bones (RUS) and TW3-Carpal bone score, as well as each bone (13 RUS + 7 Carpal) between reviewers and the AI, were compared by Bland-Altman (BA) chart and Kappa test, respectively. Furthermore, the time consumption between the model and reviewers was also compared. The performance of TW3-AI model was highly consistent with the reviewers' overall estimation, and the root mean square (RMS) was 0.50 years. The accuracy of the BAA of the TW3-AI model was better than the estimate of the reviewers. Further analysis revealed that human interpretations of the male capitate, hamate, the first distal and fifth middle phalanx and female capitate, the trapezoid, and the third and fifth middle phalanx, were most inconsistent. The average image processing time was 1.5±0.2 s in the TW3-AI model, which was significantly shorter than manual interpretation. The diagnostic performance of CNN-based TW3 BAA was accurate and timesaving, and possesses better stability compared to diagnostics made by experienced experts.

A quantitative study was conducted to direct the design and development of a computerized system for bone age assessment (BAA) in University of Malaya Medical Center (UMMC). Bone age assessment is a clinical procedure performed in pediatric radiology for evaluation the stage of skeletal maturation. It is usually performed by comparing an x-ray of a child’s left hand with a standard of known samples. The current methods utilized in clinical environment to estimate bone age are time consuming and prone to observer variability. This is motivation for developing a computerized method for BAA. A primary analysis shows the current method used by UMMC radiologists for bone age assessment, their feedbacks, problems encountered and their opinions about new approach for BAA. Our study also extracts user requirements for designing and developing a computerized method for BAA.KeywordsComputerized bone age assessmentQuantitative studyBone ageRadiographyBone age assessment

Bone age assessment is challenging in clinical practice due to the complicated bone age assessment process. Current automatic bone age assessment methods were designed with rare consideration of the diagnostic logistics and thus may yield certain uninterpretable hidden states and outputs. Consequently, doctors can find it hard to cooperate with such models harmoniously because it is difficult to check the correctness of the model predictions. In this work, we propose a new graph-based deep learning framework for bone age assessment with hand radiographs, called Doctor Imitator (DI). The architecture of DI is designed to learn the diagnostic logistics of doctors using the scoring methods (e.g., the Tanner-Whitehouse method) for bone age assessment. Specifically, the convolutions of DI capture the local features of the anatomical regions of interest (ROIs) on hand radiographs and predict the ROI scores by our proposed Anatomy-based Group Convolution, summing up for bone age prediction. Besides, we develop a novel Dual Graph-based Attention module to compute patient-specific attention for ROI features and context attention for ROI scores. As far as we know, DI is the first automatic bone age assessment framework following the scoring methods without fully supervised hand radiographs. Experiments on hand radiographs with only bone age supervision verify that DI can achieve excellent performance with sparse parameters and provide more interpretability.

To explore the integration method and technical realization of artificial intelligence bone age assessment system with the hospital RIS-PACS network and workflow. Two sets of artificial intelligence based on bone age assessment systems (CHBoneAI 1.0/2.0) were developed. The intelligent system was further integrated with RIS-PACS based on the http protocol in Python flask web framework. The two sets of systems were successfully integrated into the local network and RIS-PACS in hospital. The deployment has been smoothly running for nearly 3 years. Within the current network setting, it takes less than 3 s to complete bone age assessment for a single patient. The artificial intelligence based bone age assessment system has been deployed in clinical RIS-PACS platform and the "running in parallel", which is marking a success of Stage-I and paving the way to Stage-II where the intelligent systems can evolve to become more powerful in particular of the system self-evolution and the "running alternatively".

Bone age assessment is of great significance to genetic diagnosis and endocrine diseases. Traditional bone age diagnosis mainly relies on experienced radiologists to examine the regions of interest in hand radiography, but it is time-consuming and may even lead to a vast error between the diagnosis result and the reference. The existing computer-aided methods predict bone age based on general regions of interest but do not explore specific regions of interest in hand radiography. This paper aims to solve such problems by performing bone age prediction on the articular surface and epiphysis from hand radiography using deep convolutional neural networks. The articular surface and epiphysis datasets are established from the Radiological Society of North America (RSNA) pediatric bone age challenge, where the specific feature regions of the articular surface and epiphysis are manually segmented from hand radiography. Five convolutional neural networks, i.e., ResNet50, SENet, DenseNet-121, EfficientNet-b4, and CSPNet, are employed to improve the accuracy and efficiency of bone age diagnosis in clinical applications. Experiments show that the best-performing model can yield a mean absolute error (MAE) of 7.34 months on the proposed articular surface and epiphysis datasets, which is more accurate and fast than the radiologists. The project is available at https://github.com/YameiDeng/BAANet/, and the annotated dataset is also published at https://doi.org/10.5281/zenodo.7947923.

Taking Matters into Your Own Hands.

Bone age assessment is an important indicator of child maturity. Generally, a person evaluated for bone age is mostly in the puberty stage. Compared to toddlers and post-puberty stages, the data of bone age at the puberty stage are hence much easier to obtain. As a result, the amounts of bone age data collected at the toddlers and post-puberty stages are often less than that of bone age data at the pulpy stage. This causes a data imbalance problem that affects the prediction accuracy. To deal with this problem, a data imbalance immunity bone age assessment (DIIBAA) system is proposed which integrates a CNN-based autoencoder and CNN-based scoring network. The DIIBAA system builds three autoencoders for the bone age data of toddlers, puberty, and post-puberty stages. Since the three types of autoencoders do not interfere with each other, there is no data imbalance problem in the DIIBAA system. After that, the outputs of the three autoencoders are input to the scoring network, and the auto-encoder produced the image with the highest score is regarded as the final prediction result. In the experiments, imbalanced training data with a positive and negative sample ratio of 1:2 were used, which has been alleviated compared to the original very imbalanced data. Meanwhile, the DenseNet169-based autoencoders are employed in the experiments, and the obtained evaluation accuracies are improved.

IntroductionA deep learning-based automatic bone age identification system (ABAIs) was introduced in medical imaging. This ABAIs enhanced accurate, consistent, and timely clinical diagnostics and enlightened research fields of deep learning and artificial intelligence (AI) in medical imaging.AimThe goal of this study was to use the Deep Neural Network (DNN) model to assess bone age in months based on a database of pediatric left-hand radiographs.MethodsThe Inception Resnet V2 model with a Global Average Pooling layer to connect to a single fully connected layer with one neuron using the Rectified Linear Unit (ReLU) activation function consisted of the DNN model for bone age assessment (BAA) in this study. The medical data in each case contained posterior view of X-ray image of left hand, information of age, gender and weight, and clinical skeletal bone assessment.ResultsA database consisting of 8,061 hand radiographs with their gender and age (0–18 years) as the reference standard was used. The DNN model’s accuracies on the testing set were 77.4%, 95.3%, 99.1% and 99.7% within 0.5, 1, 1.5 and 2 years of the ground truth respectively. The MAE for the study subjects was 0.33 and 0.25 year for male and female models, respectively.ConclusionIn this study, Inception Resnet V2 model was used for automatic interpretation of bone age. The convolutional neural network based on feature extraction has good performance in the bone age regression model, and further improves the accuracy and efficiency of image-based bone age evaluation. This system helps to greatly reduce the burden on clinical personnel.

This study aimed to evaluate the clinical accuracy of automated BoneXpert (BX) vs. clinician-based Greulich-Pyle (GP) bone age (BA) assessments by comparing their respective predicted adult heights (PAH) with near-final height (NFH) in patients with congenital adrenal hyperplasia (CAH). Forty-seven patients with classic salt-wasting CAH diagnosed in the neonatal period who had reached NFH were included. Left hand-wrist radiographs obtained during the prepubertal period (6-9 years) were evaluated manually by two clinicians using the GP atlas and by the BX-GP method. PAH was calculated for both sexes using the Bayley-Pinneau (BP) method based on clinician- and BX-derived BA. PAH standard deviation scores (SDS) were compared with NFH-SDS and target height (TH)-SDS. Agreement was assessed using Bland-Altman analysis. Thirty patients (63.8 %) were female. Mean ages at bone age assessment and NFH were 7.3 and 16.1 years, respectively. Mean prepubertal height-SDS was 0.0 in girls and-0.25 in boys, while NFH-SDS was-0.79 and-1.14, respectively. Mean bone age according to clinician-GP was 7.80 years in girls and 8.16 in boys, and 7.69 and 8.14 years according to BX-GP. Clinician-based PAH-SDS was-1.0 in girls and-1.18 in boys, whereas BX-based PAH-SDS was-0.86 and-1.17, respectively. BX-based PAH was closer to NFH in girls (p<0.001), with no significant difference in boys. In both sexes, PAH-SDS and NFH-SDS were significantly lower than TH-SDS (p<0.001). PAH calculations using the BP method based on BX-derived GP bone age readings more accurately predicted NFH in patients with CAH.

ObjectivesThis study aimed to evaluate the performance of artificial intelligence (AI) software in bone age (BA) assessment, according to the Greulich and Pyle (G&P) method in a German pediatric cohort.Materials and methodsHand radiographs of 306 pediatric patients aged 1–18 years (153 boys, 153 girls, 18 patients per year of life)—including a subgroup of patients in the age group for which the software is declared (243 patients)—were analyzed retrospectively. Two pediatric radiologists and one endocrinologist made independent blinded BA reads. Subsequently, AI software estimated BA from the same images. Both agreements, accuracy, and interchangeability between AI and expert readers were assessed.ResultsThe mean difference between the average of three expert readers and AI software was 0.39 months with a mean absolute difference (MAD) of 6.8 months (1.73 months for the mean difference and 6.0 months for MAD in the intended use subgroup). Performance in boys was slightly worse than in girls (MAD 6.3 months vs. 5.6 months). Regression analyses showed constant bias (slope of 1.01 with a 95% CI 0.99–1.02). The estimated equivalence index for interchangeability was − 14.3 (95% CI −27.6 to − 1.1).ConclusionIn terms of BA assessment, the new AI software was interchangeable with expert readers using the G&P method.Clinical relevance statementThe use of AI software enables every physician to provide expert reader quality in bone age assessment.Key Points• A novel artificial intelligence–based software for bone age estimation has not yet been clinically validated.• Artificial intelligence showed a good agreement and high accuracy with expert radiologists performing bone age assessment.• Artificial intelligence showed to be interchangeable with expert readers.

Bone age assessment (BAA) evaluates individual skeletal maturity by comparing the characteristics of skeletal development to the standard in a specific population. The X-ray image examination for bone age is tedious and subjective, and it requires high professional skills. Therefore, AI techniques are desired to innovate and improve BAA methods. Most of the BAA method use the whole X-ray image in an end-to-end model directly. Such whole-image-based approaches fail to characterize local changes and provide limited aid for diagnosis and understanding disease progress. To address these issues, we collected and curated a dataset of 2129 cases for the study of BAA with fine-grained skeletal maturity level labels of the 13 ROIs in hand bone based on the expert knowledge from TW method. We designed a four-stage automatic BAA model based on recursive feature pyramid network. Firstly, the palm region was segmented using U-Net, followed by the extraction of multi-target ROIs of hand bone using a recursive feature pyramid network. Given the extracted ROIs, we employed a transfer learning model with attention mechanism to predict the skeletal maturity level of each ROI. Finally, the bone age is assessed based on the percentile curve of bone maturity. The proposed BAA model can automate the BAA. In addition, it provides the detection result of the 13 ROIs and their ROI-level skeletal maturity. The MAE can reach 0.61 years on the dataset with the labeling precision of one year. All the data and annotations used in this paper are released publicly.

Bone age assessment (BAA) is a technique for assessing the maturity of individual skeletal development, and it is the most accurate and objective method for assessing the deviation of individual development in clinical practice. It is used in the diagnosis of pediatric endocrine diseases, age determination of suspects in juvenile delinquency cases, height prediction, and athlete selection. Early computer-aided BAA mainly segments the skeleton of the hand and gives a final bone age by comparing the morphological descriptions of bones to standard atlas. In recent years, deep learning methods have developed rapidly, and a large number of end-to-end BAA methods based on deep learning have emerged, which have brought new development to the automatic BAA technology. This paper summarizes the technical basis, research status of automatic BAA. Firstly, the basic theory of medical BAA is introduced, then the commonly used traditional segmentation methods in BAA are analyzed. After that, we summarized the most popular methods of BAA based on deep learning, including the network model, data set and the assessment results. This survey also draws attention to a number of research challenges in the fully automatic BAA with deep learning.

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. 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. 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. 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.

Bone age is an indicator of bone maturity and is useful for the treatment of different pediatric conditions as well as for legal issues. Bone age can be assessed by the analysis of different skeletal segments and teeth and through several methods; however, traditional bone age assessment is a complicated and time-consuming process, prone to inter- and intra-observer variability. There is a high demand for fully automated systems, but creating an accurate and reliable solution has proven difficult. Deep learning technology, machine learning, and Convolutional Neural Networks-based systems, which are rapidly evolving, have shown promising results in automated bone age assessment. We provide the background of bone age estimation, its usefulness and traditional methods of assessment, and review the currently artificial-intelligence-based solutions for bone age assessment and the future perspectives of these applications.

The aim of the study was to validate dual-energy X-ray absorptiometry (DXA) as a method to assess bone age in children. Paired dual-energy X-ray absorptiometry (DXA) scans and X-rays of the left hand were performed in 95 children who attended the paediatric endocrinology outpatient clinic of University Hospital Rotterdam, the Netherlands. We compared bone age assessments by DXA scan with those performed by X-ray. Bone age assessment was performed by two blinded observers according to the reference method of Greulich and Pyle. Intra-observer and interobserver reproducibility were investigated using the intraclass correlation coefficient (ICC), and agreement was tested using Bland and Altman plots. The intra-observer ICCs for both observers were 0.997 and 0.991 for X-ray and 0.993 and 0.987 for DXA assessments. The interobserver ICC was 0.993 and 0.991 for X-ray and DXA assessments, respectively. The mean difference between bone age assessed by X-ray and DXA was 0.11 years. The limits of agreement ranged from -0.82 to 1.05 years, which means that 95% of all differences between the methods were covered by this range. Results of bone age assessment by DXA scan are similar to those obtained by X-ray. The DXA method seems to be an alternative for assessing bone age in a paediatric hospital-based population.