Creation and Validation of a Shorthand Knee MRI Bone Age Assessment Tool as an Alternative for Skeletal Maturity Assessment (203)

Objectives: In managing pediatric knee conditions, an accurate bone age assessment is often critical for diagnostic, prognostic, and treatment purposes. Historically, the Greulich and Pyle Atlas (hand atlas) has been the gold standard bone age assessment tool. In 2013, a shorthand bone age assessment tool based on this atlas (hand shorthand) was established as a simpler and more efficient alternative. Recently, a knee MRI bone age atlas (MRI atlas) was created potentially to circumvent the need for a left hand radiograph. Our objective is to create a shorthand version of the magnetic resonance imaging atlas. Methods: A shorthand bone age method (Figure 1) was created utilizing the previously published MRI atlas, which utilizes several criteria that are visualized across a series of images. The MRI shorthand draws on the most characteristic criteria for each age that is best observed on a single MR image. For validation, we performed a retrospective assessment of skeletally immature patients that had a knee MRI and left hand radiograph within four weeks. Four readers who were familiar with the hand atlas, hand shorthand, MRI atlas, and MRI shorthand read each of the images in a blinded fashion. Inter- and intra-observer reliability was evaluated using intraclass correlation coefficient (ICC), variability among observers was evaluated using percent agreement. Results: 26 patients with a mean age of 13.6 years (range 9.0-16.9) met the inclusion criteria. The intra- and inter-observer reliability of all four assessment tools was excellent (ICC ≥ 0.8, p<0.001) (Table 1). When comparing the MRI shorthand to the MRI atlas, there was excellent agreement (ICC = 0.974), whereas the hand shorthand compared to the hand atlas had good agreement (ICC = 0.765). The MRI shorthand also had perfect agreement in 58% of reads among all four readers and 96% of reads had agreement within 1 year, whereas the hand shorthand had perfect agreement in 32% of reads and 77% agreement within 1 year (Table 2). Conclusions: The MRI shorthand is a simple and efficient means of assessing skeletal maturity of adolescent patients with a knee MRI. This bone age assessment technique has inter-observer and intra-observer reliability equivalent or better than the standard means utilizing a left hand radiograph.

Creation and Validation of a Shorthand Magnetic Resonance Imaging Bone Age Assessment Tool of the Knee as an Alternative Skeletal Maturity Assessment

Background: In managing pediatric knee conditions, an accurate bone age assessment is often critical for diagnostic, prognostic, and treatment purposes. Historically, the Greulich and Pyle atlas (hand atlas) has been the gold standard bone age assessment tool. In 2013, a shorthand bone age assessment tool based on this atlas (hand shorthand) was devised as a simpler and more efficient alternative. Recently, a knee magnetic resonance imaging (MRI) bone age atlas (MRI atlas) was created to circumvent the need for a left-hand radiograph. Purpose: To create a shorthand version of the knee MRI atlas. Study Design: Cohort study (diagnosis); Level of evidence, 2. Methods: A shorthand bone age assessment method was created utilizing the previously published MRI atlas, which utilizes several criteria that are visualized across a series of images. The MRI shorthand draws on characteristic criteria for each age that are best observed on a single MRI scan. For validation, we performed a retrospective assessment of skeletally immature patients. One reader performed the bone age assessment using the MRI atlas and the MRI shorthand on 200 patients. Then, 4 readers performed the bone age assessment with the hand atlas, hand shorthand, MRI atlas, and MRI shorthand on a subset of 22 patients in a blinded fashion. All 22 patients had a knee MRI scan and a left-hand radiograph within 4 weeks of each other. Interobserver and intraobserver reliability, as well as variability among observers, were evaluated. Results: A total of 200 patients with a mean age of 13.5 years (range, 9.08-17.98 years) were included in this study. Also, 22 patients with a mean age of 13.3 years (range, 9.0-15.6 years) had a knee MRI scan and a left-hand radiograph within 4 weeks. The intraobserver and interobserver reliability of all 4 assessment tools were acceptable (intraclass correlation coefficient [ICC] ≥ 0.8; P < .001). When comparing the MRI shorthand with the MRI atlas, there was excellent agreement (ICC = 0.989), whereas the hand shorthand compared with the hand atlas had good agreement (ICC = 0.765). The MRI shorthand also had perfect agreement in 50% of readings among all 4 readers, and 95% of readings had agreement within 1 year, whereas the hand shorthand had perfect agreement in 32% of readings and 77% agreement within 1 year. Conclusion: The MRI shorthand is a simple and efficient means of assessing the skeletal maturity of adolescent patients with a knee MRI scan. This bone age assessment technique had interobserver and intraobserver reliability equivalent to or better than the standard method of utilizing a left-hand radiograph.

Ensemble Learning with Multiclassifiers on Pediatric Hand Radiograph Segmentation for Bone Age Assessment

In the study of pediatric automatic bone age assessment (BAA) in clinical practice, the extraction of the object area in hand radiographs is an important part, which directly affects the prediction accuracy of the BAA. But no perfect segmentation solution has been found yet. This work is to develop an automatic hand radiograph segmentation method with high precision and efficiency. We considered the hand segmentation task as a classification problem. The optimal segmentation threshold for each image was regarded as the prediction target. We utilized the normalized histogram, mean value, and variance of each image as input features to train the classification model, based on ensemble learning with multiple classifiers. 600 left-hand radiographs with the bone age ranging from 1 to 18 years old were included in the dataset. Compared with traditional segmentation methods and the state-of-the-art U-Net network, the proposed method performed better with a higher precision and less computational load, achieving an average PSNR of 52.43 dB, SSIM of 0.97, DSC of 0.97, and JSI of 0.91, which is more suitable in clinical application. Furthermore, the experimental results also verified that hand radiograph segmentation could bring an average improvement for BAA performance of at least 13%.

An Automated System for Identification of Skeletal Maturity using Convolutional Neural Networks Based Mechanism

This paper puts forward a proposition of automated skeletal recognition system that takes an input of left hand-wrist-fingers radiograph and give us an output of the bone age prediction. This system is more reliable, if is successful and time-saving than those laborious, fallible and time-consuming manual diagnostic methods. Here, a Faster R-CNN takes the input of left-hand radiograph giving the detected DRU region from left-hand radiograph. This output is given as an input to a properly trained CNN model. The experiment section provides us with the details regarding the experiments conducted on 1101 radiographs of left hand and wrist datasets and accuracy of model when different optimization algorithms and training sample amounts were utilized. Finally, this proposed system achieves 92% (radius) and 90% (ulna) classification accuracy after the parameter optimization.