Skeletal Age Assessment Research Articles

Anterior Cruciate Ligament Injury in Skeletally Immature Patients

The incidence of anterior cruciate ligament (ACL) injury continues to increase in the skeletally immature population. These injuries were historically treated with nonsurgical measures in this age group due to concerns for iatrogenic damage to the physis during ACL reconstruction. However, delayed surgery often led to recurrent instability, meniscal tears, and chondral damage. With the development of newer and safer surgical techniques with satisfactory outcomes, ACL reconstruction has become much more common in this age group. The patient's skeletal age is often used to determine remaining growth, which helps to decide the surgical technique chosen. These techniques include physeal sparing, partial transphyseal, and transphyseal surgical options, using soft-tissue autografts. Each technique has been shown to have favorable patient-reported outcomes, but no technique is without the risk of complications. This article will review the management of skeletally immature ACL injuries, including relevant anatomy, risk factors for injury, assessment of skeletal age, and different treatment options and outcomes.

Appraisal of skeletal maturity indicators with respect to non-invasive biomarkers

Non-invasive salivary biomarkers are being investigated as they may be helpful in predicting skeletal age. This study was conducted to evaluate the correlation between salivary insulin-like growth factor-1, salivary alkaline phosphatase, cervical vertebrae maturation stages and chronological age in the assessment of skeletal age during the growth period. This study also aims to find associations with mandibular base length and maxillary base length. Total 80 eligible subjects aged 7 to 21 years were selected and divided into two gender-specific groups (41 males, 39 females). A further subdivision was made on the basis of six cervical vertebral maturation stages from the lateral cephalogram. The chronological age was given for each subject, and The maxillary base length and the length of the mandibular base was calculated from the impressions on the lateral cephalogram. Saliva samples were collected from each subject to determine salivary IGF-1 and ALP levels using an enzyme-linked immunosorbent assay (ELISA). The data obtained was statistically analysed using SPSS (20.0) software. It was found that the mean IGF-1 activity in saliva was highest at CVMI stage 3 and the highest mean ALP levels in saliva occurred in males at CVMI stage 3 and in females at CVMI stage 4. Mean salivary ALP and IGF-1 levels correlate well with . Salivary ALP levels show a correlation with chronological age and could also be used as a diagnostic model to predict cervical stage. The diagnostic levels of certain biomarkers that appear in human saliva are representative of an individual's circumpubertal growth spurt. The collective pattern of increase and decrease in the sample during the circumpubertal stages is essential to study for orthodontic diagnosis and treatment planning.

Hand bone extraction and segmentation based on a convolutional neural network

Automatic segmentation of hand bone X-ray images is an important process in skeletal bone age assessment (BAA). The segmentation process is extremely difficult and complicated due to the complex structure and tiny size of hand bone features, which may lead to inaccurate feature localization and incomplete segmentation. A two-stage segmentation method is proposed in this paper. In the first stage, the OSA-YOLOv5 network is used to extract hand bones, and in the second stage, GRU-UNet is used to separate extracted hand bones. The hand bone X-ray dataset consists of a mixed dataset of 1000 left-hand bone X-ray images of adolescents from licenced local hospitals and 1000 left-hand bone X-ray images from the RSNA public dataset. The training efficiency of the OSA-YOLOv5 network is 28 % higher than that of the traditional network, and the proposed network exhibits with a higher extraction accuracy. The segmentation accuracy of the GRU-Unet segmentation model 14.70 % higher than that if Unet. The spatial sequence features with spatial rules are found to have a positive correlation in GRU-Unet. Experimental results show that the proposed segmentation method has good generalization and robustness, which provides a good foundation for subsequent BAA.

Correlations Between Eight Comprehensive Skeletal Maturity Systems in a Modern Peripubertal Pediatric Population.

Several skeletal maturity systems allow for accurate skeletal age assessment from a wide variety of joints. However, discrepancies in estimates have been noted when applying systems concurrently. The aims of our study were to (1) compare the agreement among 8 different skeletal maturity systems in modern pediatric patients and (2) compare these discrepancy trends qbetween modern and historic children. We performed a retrospective (January 2000 to May 2022) query of our picture archiving and communication systems and included peripubertal patients who had at least two radiographs of different anatomic regions obtained ≤3 months apart for 8 systems: (1) proximal humerus ossification system (PHOS), (2) olecranon apophysis ossification staging system (OAOSS), (3) lateral elbow system, (4) modified Fels wrist system, (5) Sanders Hand Classification, (6) optimized oxford hip system, (7) modified Fels knee system, and (8) calcaneal apophysis ossification staging system (CAOSS). Any abnormal (ie, evidence of fracture or congenital deformity) or low-quality radiographs were excluded. These were compared with a cohort from a historic longitudinal study. SEM skeletal age, representing the variance of skeletal age estimates, was calculated for each system and used to compare system precision. A total of 700 radiographs from 350 modern patients and 954 radiographs from 66 historic patients were evaluated. In the modern cohort, the greatest variance was seen in PHOS (SEM: 0.28y), Sanders Hand (0.26y), and CAOSS (0.25y). The modified Fels knee system demonstrated the smallest variance (0.20y). For historic children, the PHOS, OAOSS, and CAOSS were the least precise (0.20y for all). All other systems performed similarly in historic children with lower SEMs (range: 0.18 to 0.19y). The lateral elbow system was more precise than the OAOSS in both cohorts. The precision of skeletal maturity systems varies across anatomic regions. Staged, single-parameter systems (eg, PHOS, Sanders Hand, OAOSS, and CAOSS) may correlate less with other systems than those with more parameters. Level III-retrospective study.

Value of Magnetic Resonance Imaging for Skeletal Bone Age Assessment in Healthy Male Children.

Skeletal bone age assessment for medical reasons is usually performed by conventional x-ray with use of ionizing radiation. Few pilot studies have shown the possible use of magnetic resonance imaging (MRI). To comprehensively evaluate feasibility and value of MRI for skeletal bone age (SBA) assessment in healthy male children. In this prospective cross-sectional study, 63 male soccer athletes with mean age of 12.35 ± 1.1 years were examined. All participants underwent 3.0 Tesla MRI with coronal T1-weighted turbo spin echo (TSE), coronal proton density (PD)-weighted turbo spin echo (TSE), and T1-weighted three-dimensional (3D) volume interpolated breath-hold examination (VIBE) sequence. Subsequently, SBA was assessed by 3 independent blinded radiologists with different levels of experience using the common Greulich-Pyle (GP) atlas and the Tanner-Whitehouse (TW2) method. In a mean total acquisition time of 5:04 ± 0:47 min, MR image quality was sufficient in all cases. MRI appraisal was significantly faster ( P < 0.0001) by GP with mean duration of 1:22 ± 0:08 min vs. 7:39 ± 0:28 min by TW. SBA assessment by GP resulted in mean age of 12.8 ± 1.2 years, by TW 13.0 ± 1.4 years. Interrater reliabilities were excellent for both GP (ICC = 0.912 (95% confidence interval [CI] = 0.868-0.944) and TW (ICC = 0.988 (95% CI = 0.980-0.992) and showed statistical significance ( P < 0.001). Subdivided, for GP, ICCs were 0.822 (95% CI = 0.680-0.907) and 0.843 (95% CI = 0.713-0.919) in Under 12 and Under 14 group. For TW, ICCs were 0.978 (95% CI = 0.958-0.989) in Under 12 and 0.979 (95% CI = 0.961-0.989) in Under 14 group. MRI is a clinically feasible, rapidly evaluable method to assess skeletal bone age of healthy male children. Using the Greulich-Pyle (GP) atlas or the Tanner-Whitehouse (TW2) method, reliable results are obtained independent of the radiologist's experience level.

Retracted: The Application of Magnetic Resonance Imaging in Skeletal Age Assessment.

[This retracts the article DOI: 10.1155/2022/9607237.].

Postmortem computed tomography assessment of skeletal and dental age in Polish children, adolescents, and young adults

This paper presents a retrospective analysis of postmortem computed tomography (PMCT) scans of secondary ossification centers in the medial clavicular epiphysis, iliac crest apophysis, proximal humeral epiphysis, distal femoral epiphysis, proximal tibial epiphysis, and distal tibial epiphysis. At the same time, we analyzed PMCT scans of the maxillary and mandibular incisors, canines, premolars, and molars. We assessed 203 corpses, whose age ranged from 2 to 30 years, including 156 males and 47 females. The purpose of our study was to compare the processes of secondary ossification center fusion and permanent tooth maturation. Our research hypothesis was that certain stages of skeletal and dental maturation occur along consistent timelines that can be related to the chronological age. Secondary ossification center fusion was evaluated based on Kreitner and also McKern and Steward’s classifications. The process of permanent tooth maturation was evaluated with Demirjian’s method. Spearman’s correlation coefficients (Rho) were positive in all analyses, which indicates that epiphyseal fusion progresses with age. The strongest relationship between the age and the stages of ossification was observed in the proximal tibial epiphysis (p < 0.001; Rho = 0.93) in females and in the medial clavicular epiphysis (p < 0.001; Rho = 0.77) in males. Studies show the importance of concomitant analysis of skeletal and dental maturation with a subsequent comparison of the results to achieve a greater precision in age estimation. A comparison of the results obtained in the study population of Polish children, adolescents, and young adults with the results of other studies in populations of similar ages showed a number of similarities in the time windows of dental and skeletal maturation. These similarities may help in age estimation.

Assessment of Skeletal Age using Hand-Wrist Radiographs Following BJORK System Sample Size - Prospective Study.

The planning of effective orthodontic therapy greatly benefits from an understanding of the skeletal maturation and stage of growth of the patients seeking orthodontic treatment. However, the patient's various craniofacial structures have varying levels of growth potential. The patient is exposed to additional radiation when hand-wrist radiographs are regularly used to forecast growth. As an alternative, cervical vertebrae in the lateral cephalograph have been suggested. When arranging orthodontic treatment for growing children, it is important to take into account the pubertal growth spurt, which is a crucial time in therapy. Finding out how much growth a patient with skeletal discrepancy would experience during adolescence is one of the key goals of conducting a hand and wrist radiograph.

Automated Skeletal Bone Age Assessment with Two-Stage Convolutional Transformer Network Based on X-ray Images.

Human skeletal development is continuous and staged, and different stages have various morphological characteristics. Therefore, bone age assessment (BAA) can accurately reflect the individual's growth and development level and maturity. Clinical BAA is time consuming, highly subjective, and lacks consistency. Deep learning has made considerable progress in BAA in recent years by effectively extracting deep features. Most studies use neural networks to extract global information from input images. However, clinical radiologists are highly concerned about the ossification degree in some specific regions of the hand bones. This paper proposes a two-stage convolutional transformer network to improve the accuracy of BAA. Combined with object detection and transformer, the first stage mimics the bone age reading process of the pediatrician, extracts the hand bone region of interest (ROI) in real time using YOLOv5, and proposes hand bone posture alignment. In addition, the previous information encoding of biological sex is integrated into the feature map to replace the position token in the transformer. The second stage extracts features within the ROI by window attention, interacts between different ROIs by shifting the window attention to extract hidden feature information, and penalizes the evaluation results using a hybrid loss function to ensure its stability and accuracy. The proposed method is evaluated on the data from the Pediatric Bone Age Challenge organized by the Radiological Society of North America (RSNA). The experimental results show that the proposed method achieves a mean absolute error (MAE) of 6.22 and 4.585 months on the validation and testing sets, respectively, and the cumulative accuracy within 6 and 12 months reach 71% and 96%, respectively, which is comparable to the state of the art, markedly reducing the clinical workload and realizing rapid, automatic, and high-precision assessment.

Intra-observer reproducibility and inter-observer agreement of Fels skeletal age assessments among male tennis players 8–16 years

BackgroundSkeletal age (SA) is an estimate of biological maturity status that is commonly used in sport-related medical examinations. This study considered intra-observer reproducibility and inter-observer agreement of SA assessments among male tennis players.MethodsSA was assessed with the Fels method in 97 male tennis players with chronological ages (CA) spanning 8.7–16.8 years. Radiographs were evaluated by two independent trained observers. Based on the difference between SA and CA, players were classified as late, average or early maturing; if a player was skeletally mature, he was noted as such as an SA is not assigned.ResultsThe magnitude of intra-individual differences between repeated SA assessments were d = 0.008 year (observer A) and d = 0.001 year (observer B); the respective coefficients of variation were 1.11% and 1.75%. Inter-observer mean differences were negligible (t = 1.252, p = 0.210) and the intra-class correlation coefficient was nearly perfect (ICC = 0.995). Concordance of classifications of players by maturity status between observers was 90%.ConclusionFels SA assessments were highly reproducible and showed an acceptable level of inter-observer agreement between trained examiners. Classifications of players by skeletal maturity status based on assessments of the two observers were highly concordant, though not 100%. The results highlight the importance of experienced observers in skeletal maturity assessments.

Prediction of Fishman’s skeletal maturity indicators using artificial intelligence

The present study aimed to evaluate the performance of automated skeletal maturation assessment system for Fishman’s skeletal maturity indicators (SMI) for the use in dental fields. Skeletal maturity is particularly important in orthodontics for the determination of treatment timing and method. SMI is widely used for this purpose, as it is less time-consuming and practical in clinical use compared to other methods. Thus, the existing automated skeletal age assessment system based on Greulich and Pyle and Tanner-Whitehouse3 methods was further developed to include SMI using artificial intelligence. This hybrid SMI-modified system consists of three major steps: (1) automated detection of region of interest; (2) automated evaluation of skeletal maturity of each region; and (3) SMI stage mapping. The primary validation was carried out using a dataset of 2593 hand-wrist radiographs, and the SMI mapping algorithm was adjusted accordingly. The performance of the final system was evaluated on a test dataset of 711 hand-wrist radiographs from a different institution. The system achieved a prediction accuracy of 0.772 and mean absolute error and root mean square error of 0.27 and 0.604, respectively, indicating a clinically reliable performance. Thus, it can be used to improve clinical efficiency and reproducibility of SMI prediction.

Age estimation of archeological populations using secondary dentin analysis

The innermost dental tissue, termed secondary dentin (SD), has been established as a valuable parameter for aging, as its thickness is highly correlated with age and can be viewed in a non-invasive manner using micro-computerized tomography (μCT). Dental remnants show a high preservation rate in an archeological context and may be used as age indicators, thus possibly supplementing traditional skeletal-based demographics. As the SD age assessment method was based on unworn teeth, in the current study, we adjusted the approach to allow age estimation of highly worn teeth and later to explore prehistoric samples. In order to do so, 90 lower premolars from known age and gender individuals with various degrees of occlusal attrition were included. The teeth originated from the Hamann-Todd Osteologic Collection (Cleveland, OH, USA) and from the Tel Aviv University collection (Israel). Combining SD measurements and occlusal Attrition slightly improved the standard error of age estimate from ±7.7 years based on SD and ±9.5 years based on Attrition to a range of ±6.5 years.The SD-age method was used to evaluate the age of individuals from three Natufian and Neolithic sites using dental remains (Hayonim Cave, Ein Mallaha 14,900–11,750 cal BP, and Kfar Hahoresh ∼12,175–8250 cal BP). Population coverage increased substantially with the addition of forty-eight premolars to previous skeletal age assessments to create new life tables. The addition of dental age assessments, representing unaccounted individuals, shifted the age distributions and increased the expected lifespan between 4.7 and 1.8 years. Using the SD-age analysis, isolated, fragmented teeth can be included in the paleodemographic study, thus broadening the coverage of samples and providing better insight into archaic populations' structure.

Indications and Timing of Guided Growth Techniques for Pediatric Upper Extremity Deformities: A Literature Review.

Osseous deformities in children arise due to progressive angular growth or complete physeal arrest. Clinical and radiological alignment measurements help to provide an impression of the deformity, which can be corrected using guided growth techniques. However, little is known about timing and techniques for the upper extremity. Treatment options for deformity correction include monitoring of the deformity, (hemi-)epiphysiodesis, physeal bar resection, and correction osteotomy. Treatment is dependent on the extent and location of the deformity, physeal involvement, presence of a physeal bar, patient age, and predicted length inequality at skeletal maturity. An accurate estimation of the projected limb or bone length inequality is crucial for optimal timing of the intervention. The Paley multiplier method remains the most accurate and simple method for calculating limb growth. While the multiplier method is accurate for calculating growth prior to the growth spurt, measuring peak height velocity (PHV) is superior to chronological age after the onset of the growth spurt. PHV is closely related to skeletal age in children. The Sauvegrain method of skeletal age assessment using elbow radiographs is possibly a simpler and more reliable method than the method by Greulich and Pyle using hand radiographs. PHV-derived multipliers need to be developed for the Sauvegrain method for a more accurate calculation of limb growth during the growth spurt. This paper provides a review of the current literature on the clinical and radiological evaluation of normal upper extremity alignment and aims to provide state-of-the-art directions on deformity evaluation, treatment options, and optimal timing of these options during growth.

Reliability of salivary biomarkers as skeletal maturity indicators: A systematic review

To assess the reliability of different salivary biomarkers as skeletal maturity indicators when compared with other methods of skeletal maturity assessment. A comprehensive search was conducted on three electronic databases: PUBMED, Google scholar and Cochrane library for the articles published from 2000 to July 2021. Assessment of skeletal age on the basis of levels of different salivary biomarkers at different pubertal stages was considered as the primary outcome. Electronic search, data collection and risk of bias assessment were performed by two authors with conflict resolution by the third author. Total 158 articles were retrieved after screening of titles, abstracts and full texts of all articles, of which 15 articles were selected for qualitative synthesis. All these studies were cross-sectional in design. These studies compared the levels of different salivary biomarkers as Alkaline Phosphatase (ALP), Insulin-like Growth Factor - I (IGF-I), Insulin-like Growth Factor Binding Protein-3 (IGFBP-3), Cortisol, Indian Hedgehog (IHH) protein and Dehydroepiandrosterone sulphate (DHEAS) with other methods of skeletal age estimation. Out of these six biomarkers salivary IGF-1 is a reliable indicator for skeletal maturity assessment. The current evidence suggests that salivary biomarkers can be used as an adjunct for growth prediction during orthodontic treatment planning along with other methods of skeletal maturation assessment. Still there is need for further research with longitudinal studies in this field.

Individual dental and skeletal age assessment according to Demirjian and Baccetti: Updated norm values for Central-European patients.

Chronological age often differs from dental and skeletal age. With orthopantomograms and lateral cephalograms, dental and skeletal development can be determined according to the methods published by Demirjian etal. and Baccetti etal. However, gender and skeletal class as possible confounders were frequently not considered and available norm values are not up-to-date. This retrospective cross-sectional study thus aimed to evaluate effects of skeletal class and gender on dental and skeletal age of growing patients and to generate updated norm values for contemporary Central-European patients. A total of 551patients were included in the dental and 733 in the skeletal age assessment, respectively. Dental analysis was based on tooth mineralisation stages in orthopantomograms (Demirjian) and skeletal age was defined by cervical vertebrae maturation stages (CVMS) in lateral cephalograms (Baccetti). Skeletal class was determined by the individualised ANB angle of Panagiotidis/Witt. With nonlinear regression analysis aformula for determining dental age was established. Effects of gender and skeletal class were evaluated and updated norm values generated. Inter- and intrarater reliability tests revealed at least substantial measurement concordance for tooth mineralisation and CVMS. Demirjian stages and CVMS significantly depended on gender with girls developing earlier. Skeletal class significantly affected skeletal age only, but without clinical relevance. Updated norm values for dental age differed significantly from the original values of Demirjian and the values for skeletal age differed from those published by Baccetti. Optimised norms, separated by gender, increase precision in determining individual dental and skeletal age during orthodontic treatment planning. Further studies analysing the effect of skeletal class on dental and skeletal development are needed.

Radiographic Evaluation of Pediatric Patients with Patellofemoral Instability.

The purpose of this review is to highlight the radiographic assessments of utility in the evaluation of a pediatric patient with patellofemoral instability to facilitate a thorough work-up. Understanding of these measures is useful in understanding evolving research in this field, providing accurate patient risk assessment, and appropriately directing surgical decision-making. Recent literature has broadened the radiographic characterization of the pediatric patellar instability and its anatomic risk factors. Knee MRI can inform the assessment of skeletal maturity and novel axial alignment measurements may enhance our identification of patients at increased risk of recurrent instability. Additional improvements have been made in the objective measurement and classification of trochlear dysplasia. Knee MRI-based skeletal age assessments may obviate the need for hand bone age assessments in growing children with patellofemoral instability. Novel objective measures exist in the evaluation of pediatric patellar instability both in the assessment of axial alignment and trochlear dysplasia. Future work should focus on how these measures can aid in guiding surgical decision-making.

Reliability of biological maturation analyses performed by equations predicting skeletal age and peak height velocity with hand and wrist X-ray results.

Hand-wrist radiography is the gold standard for the assessment of skeletal age (SA) and the assignment of biological maturation (BM) status. However, its practical use in the field is questioned, prompting the development of anthropometric equations to estimate BM. However, there is no consensus on the reliability of these equations in relation to hand-wrist X-rays. To test the reliability of the BM estimated from predictive equations with the results of hand-wrist X-rays. One-hundred forty-four young athletes (38%-males, 62%-females; 12.3 ± 1.4 years) were recruited. SA was assessed by analyzing X-rays of the hand-wrist using the Greulich-Pyle protocol. SA was predicted using the anthropometric equations of Cabral and Macêdo. By subtracting chronological age from SA, a measure of BM or biological age (BA) was computed. In addition, a BA of years from peak height velocity (PHV) was predicted using the anthropometric equations of Mirwald and Moore. Relative to X-ray, Cabral's equations showed high reliability in estimating BM in both sexes (p < .0001) while Macêdo's equations showed substantial reliability or males (p < .0001) only. Mirwald and Moore's equations showed weak reliability (p < .05 and p=.043, respectively). The prediction of SA using anthropometric equations was found to be reliable in categorizing individuals as late, synchronized, and/or early maturers in both sexes. However, the predictive equations of years from PHV only reliably categorized late and synchronized maturation stages in males and the synchronized maturers in females.

Relative Skeletal Maturity and Performance Test Outcomes in Elite Youth Middle Eastern Soccer Players.

This study aimed to explore the influence of differences in relative skeletal maturity on performance test outcomes in elite youth soccer players from the Middle East. We integrated skeletal age and performance assessments using mixed-longitudinal data available for 199 outfield players (chronological age range, 11.7 to 17.8 yr) enrolled as academy student-athletes (annual screening range, 1 to 5 visits). Skeletal age was determined as per the Tanner-Whitehouse II protocol. Relative maturity was calculated as the difference (∆) between Tanner-Whitehouse II skeletal age minus chronological age. Performance test outcomes of interest were 10-m sprinting, 40-m sprinting, countermovement jump height, and maximal aerobic speed. Separate random-effects generalized additive models quantified differences in performance test outcomes by relative skeletal maturity. Estimated differences were deemed practically relevant based on the location of the confidence interval (95% CI) against minimal detectable change values for each performance test outcome. For 40-m sprinting, differences of +0.51 s (95% CI, +0.35 to +0.67 s) and +0.62 s (95% CI, +0.45 to +0.78 s) were practically relevant for relative maturity status of ∆ = -1.5 yr versus ∆ = +0.5 and ∆ = +1 yr, respectively. For countermovement jump height, a difference of -8 cm (95% CI, -10 to -5 cm) was practically relevant for ∆ = -1.5 yr versus ∆ = +1 yr relative maturity status comparison. Effects for 10-m sprinting and maximal aerobic speed were unclear. Integration of skeletal age and performance assessments indicated that conventional maturity status classification criteria were inconsistent to inform player development processes in our sample. Between-player differences in test performance may depend on a substantial delay in skeletal maturation (∆ ≤ -1.5 yr) and the performance outcome measure.

The Application of Magnetic Resonance Imaging in Skeletal Age Assessment.

Method The study includes 80 patients identified from an endocrine clinic, two males and two females from each of 5 age groups (<5, 5 to 7, 8 to 10, 11 to 13, and 14 to 16 years). Skeletal age as determined from an open MRI scanner and radiographs performed on the same day was compared for each child. Two observers assess the skeletal age from radiographs and MRI images independently. After a period of at least three weeks, observers determined the skeletal age of all patients independently. All of the images were in different and random orders, on both of the assessment occasions. The agreement was assessed using the interclass correlation coefficient and Bland Altman plots. Problem Statement. The recurrent use of left-hand radiography in children with chronic conditions might result in the patient being exposed to the same image several times throughout the course of their lives. Use of radiation-free methods such as magnetic resonance imaging (MRI) may be able to assist in reducing the risks associated with radiation exposure, if done properly. Results Patients' age ranged from 3 to 16 years, in which the mean of the chronological age was 9.3 years (±2.9) and 9.8 years (±2.7) in girls and boys, respectively. The interrater agreement for skeletal age determination was 0.984 for radiographs and 0.976 for MRI scans. Using the G&P technique, for Observer 1, intraobserver agreement for radiographs and DXA was 0.993 and 0.983, respectively, and 0.995 and 0.994, respectively, for Observer 2. Plotting the rater readings against the line of equality shows no significant differences between readings acquired from radiographs and MRI scans. Conclusion For the study contribution, it is possible to employ open compact MRI to determine the skeletal age of a person. Our results showed that left-hand MRI scans were of better quality than the radiographs.

Metaheuristic with Deep Learning Enabled Biomedical Bone Age Assessment and Classification Model

The skeletal bone age assessment (BAA) was extremely implemented in development prediction and auxiliary analysis of medicinal issues. X-ray images of hands were detected from the estimation of bone age, whereas the ossification centers of epiphysis and carpal bones are important regions. The typical skeletal BAA approaches remove these regions for predicting the bone age, however, few of them attain suitable efficacy or accuracy. Automatic BAA techniques with deep learning (DL) methods are reached the leading efficiency on manual and typical approaches. Therefore, this study introduces an intellectual skeletal bone age assessment and classification with the use of metaheuristic with deep learning (ISBAAC-MDL) model. The presented ISBAAC-MDL technique majorly focuses on the identification of bone age prediction and classification process. To attain this, the presented ISBAAC-MDL model derives a mask Region-related Convolutional Neural Network (Mask-RCNN) with MobileNet as baseline model to extract features. Followed by, the whale optimization algorithm (WOA) is implemented for hyperparameter tuning of the MobileNet method. At last, Deep Feed-Forward Module (DFFM) based age prediction and Radial Basis Function Neural Network (RBFNN) based stage classification approach is utilized. The experimental evaluation of the ISBAAC-MDL model is tested using benchmark dataset and the outcomes are assessed over distinct factors. The experimental outcomes reported the better performances of the ISBAAC-MDL model over recent approaches with maximum accuracy of 0.9920.