An Automated System for Skeletal Maturity Assessment by Extreme Learning Machines

Can forensic radiological skeletal age estimation be performed by examining ischiopubic-ilioischial-iliopubic synchondrosis in computed tomography images?

Introduction

In this study, we evaluated whether it is possible to perform forensic radiological skeletal age estimation via radiological examination of the ilioischial, ischiopubic, and iliopubic synchondrosis regions of the pelvis.

Methods

This study was conducted by retrospectively examining the abdominopelvic images of individuals aged 8–16 who had applied to the hospital for any reason without having a chronic disorder and who had undergone computed tomography. Two radiologists retrospectively reviewed the images. The BT images of the pelvis ilioischial, ischiopubic, and iliopubic synchondrosis regions were evaluated as follows: 0: open, 1: semiclosed, and 2: closed. The data were evaluated using the SPSS 17 program.

Results

Two hundred sixty-three children (118 girls and 145 boys) between the ages of 8 and 16 years without any health problems participated. There was a significant difference between the groups for all the evaluated synchondrosis joints in girls and boys (p<0.001 for each group comparison). We observed that ilioischial, ischiopubic, and iliopubic synchondrosis closed earlier in girls than boys. In addition, we found that the joints were closed at the age of 15 and over in boys and at 14 and over in girls.

Discussion

Some studies have previously evaluated synchondrosis by using computed tomography. We showed that forensic radiological skeletal age estimation could be performed by examining ischiopubic-ilioischial-iliopubic synchondrosis in pelvis computed tomography images. The pelvis is more resistant to decay than other parts of the body. Furthermore, pelvis bones can withst and the effects of postmortem animal attacks for a longer period. Therefore, we believe that forensic age estimation can be made on corpses with no extremity, a damaged chest, or whose only pelvic bones are assessable through the method we use.

Conclusion

In our study, the ischiopubic-ilioischial-iliopubic joints were open in those aged nine and under and closed in those aged 15 and above. Ilioischial, ischiopubic, and iliopubic synchondrosis were observed to close earlier in girls than in boys. We consider that our study will be beneficial in the 8-16-year-old age group if used. In addition, our study can be used to determine the radiological bone age in cases with wrist bone abnormalities or wrist amputation.

Automated Bone Age Assessment Using Artificial Intelligence: The Future of Bone Age Assessment

Bone age assessments are a complicated and lengthy process, which are prone to inter- and intra-observer variabilities. Despite the great demand for fully automated systems, developing an accurate and robust bone age assessment solution has remained challenging. The rapidly evolving deep learning technology has shown promising results in automated bone age assessment. In this review article, we will provide information regarding the history of automated bone age assessments, discuss the current status, and present a literature review, as well as the future directions of artificial intelligence-based bone age assessments.

Determination of growth and development periods in orthodontics with artificial neural network

BACKGROUND

We aimed to determine the growth-development periods and gender from the cervical vertebrae using the artificial neural network (ANN).

SETTING AND SAMPLE POPULATION

The cephalometric and hand-wrist radiographs obtained from 419 patients aged between 8 and 17 years were included in our study.

MATERIALS AND METHODS

Our retrospective study consisted of 419 patients' cephalometric and hand-wrist radiographs. The cephalometric radiographs were divided into six cervical vertebrae stages (CVS). Correlations were evaluated between hand-wrist maturation level, CVS, and ages. Twenty-seven vertebral reference points are marked on the cephalometric radiograph, and 32 linear measurements were taken. With the combination of these measurements, 24 different data sets were formed to train ANN. Thus, 24 different ANN models for the determination of the growth-development periods were obtained. According to the results, seven ANN models that have a high success level and clinically applicable were selected. Also, an ANN model was done by all measurements and age for the determination of gender from cervical vertebrae.

RESULTS

Significantly positive correlations between hand-wrist maturation level, CVS and ages were detected. The ANN-7 model (32 linear measurements and age) accuracy value was found 0.9427. The highest model accuracy, 0.8687, with the least linear measurements, was obtained by drawing 13 linear measurements, using vertical measurements and indents. Gender was determined using ANN (0.8950) on cervical vertebrae data.

CONCLUSION

The growth-development periods and gender were determined from the cervical vertebrae by using ANN. The success of the ANN algorithm has been satisfactory. Further studies are needed for a fully automatic decision support system.

Automated Bone Age Assessment with Image Registration Using Hand X-ray Images

One of the methods for identifying growth disorder is by assessing the skeletal bone age. A child with a healthy growth rate will have approximately the same chronological and bone ages. It is important to detect any growth disorder as early as possible, so that mitigation treatment can be administered with less negative consequences. Recently, the most popular approach in assessing the discrepancy between bone and chronological ages is through the subjective protocol of Tanner–Whitehouse that assesses selected regions in the hand X-ray images. This approach relies heavily on the medical personnel experience, which produces a high intra-observer bias. Therefore, an automated bone age prediction system with image registration using hand X-ray images is proposed in order to complement the inexperienced doctors by providing the second opinion. The system relies on an optimized regression network using a novel residual separable convolution model. The regressor network requires an input image to be 299 × 299 pixels, which will be mapped to the predicted bone age through three modules of the Xception network. Moreover, the images will be pre-processed or registered first to a standardized and normalized pose using separable convolutional neural networks. Three steps image registration are performed by segmenting the hand regions, which will be rotated using angle calculated from four keypoints of interest, before positional alignment is applied to ensure the region of interest is located in the middle. The hand segmentation is based on DeepLab V3 plus architecture, while keypoints regressor for angle alignment is based on MobileNet V1 architecture, where both of them use separable convolution as the core operators. To avoid the pitfall of underfitting, synthetic data are generated while using various rotation angles, zooming factors, and shearing images in order to augment the training dataset. The experimental results show that the proposed method returns the lowest mean absolute error and mean squared error of 8.200 months and 121.902 months2, respectively. Hence, an error of less than one year is acceptable in predicting the bone age, which can serve as a good supplement tool for providing the second expert opinion. This work does not consider gender information, which is crucial in making a better prediction, as the male and female bone structures are naturally different.

Applications of artificial intelligence and machine learning in orthodontics

Over the past two decades, artificial intelligence (AI) and machine learning (ML) have undergone considerable development. There have been various applications in medicine and dentistry. Their application in orthodontics has progressed slowly, despite promising results. The available literature pertaining to the orthodontic applications of AI and ML has not been adequately synthesized and reviewed. This review article provides orthodontists with an overview of AI and ML, along with their applications. It describes state-of-the-art applications in the areas of orthodontic diagnosis, treatment planning, growth evaluations, and in the prediction of treatment outcomes. AI and ML are powerful tools that can be utilized to overcome some of the clinical problems that orthodontists face daily. With the availability of more data, better AI and ML systems should be expected to be developed that will help orthodontists practice more efficiently and improve the quality of care.

A Supervised Incremental Learning Technique for Automatic Recognition of the Skeletal Maturity, or can a Machine Learn to Assess Bone Age Without Radiological Training from Experts?

Skeletal maturity estimation is an important medical procedure in the early diagnosis of growth disorders. Traditionally, it is performed by an expert physician or radiologist who determines it based on a visual subjective inspection, the approximated bone age of the child. This task is time consuming and is usually dependent on the judgment of each particular physician. Therefore, automated methods are extremely valuable and desirable. In this paper, we propose and describe an automatic method to estimate skeletal maturity through a supervised and incremental learning approach. Our method determines bone age by comparing aligned images with a [Formula: see text]–[Formula: see text] regression classifier. Here, we have solved the difficult task of image alignment by designing a radiological-hand specific Active Appearance Model, which was developed from a varied set of hand-labeled radiological images. By using this active model, our system constructs its own learned database by increasing a set of shape-aligned training images which are incrementally stored. Thus, when a test image arrives at the system, the alignment process is performed before the classification task takes place. For that purpose, we designed an original layout of landmarks to be located in representative regions of the radiographical image of the hand. Our results show that it is possible to use pixels directly as classification features as long as training and testing images have been previously aligned in shape and pose.

An Improved Ensemble of Random Vector Functional Link Networks Based on Particle Swarm Optimization with Double Optimization Strategy

For ensemble learning, how to select and combine the candidate classifiers are two key issues which influence the performance of the ensemble system dramatically. Random vector functional link networks (RVFL) without direct input-to-output links is one of suitable base-classifiers for ensemble systems because of its fast learning speed, simple structure and good generalization performance. In this paper, to obtain a more compact ensemble system with improved convergence performance, an improved ensemble of RVFL based on attractive and repulsive particle swarm optimization (ARPSO) with double optimization strategy is proposed. In the proposed method, ARPSO is applied to select and combine the candidate RVFL. As for using ARPSO to select the optimal base RVFL, ARPSO considers both the convergence accuracy on the validation data and the diversity of the candidate ensemble system to build the RVFL ensembles. In the process of combining RVFL, the ensemble weights corresponding to the base RVFL are initialized by the minimum norm least-square method and then further optimized by ARPSO. Finally, a few redundant RVFL is pruned, and thus the more compact ensemble of RVFL is obtained. Moreover, in this paper, theoretical analysis and justification on how to prune the base classifiers on classification problem is presented, and a simple and practically feasible strategy for pruning redundant base classifiers on both classification and regression problems is proposed. Since the double optimization is performed on the basis of the single optimization, the ensemble of RVFL built by the proposed method outperforms that built by some single optimization methods. Experiment results on function approximation and classification problems verify that the proposed method could improve its convergence accuracy as well as reduce the complexity of the ensemble system.

Automatic Region-Based Brain Classification of MRI-T1 Data

Image segmentation of medical images is a challenging problem with several still not totally solved issues, such as noise interference and image artifacts. Region-based and histogram-based segmentation methods have been widely used in image segmentation. Problems arise when we use these methods, such as the selection of a suitable threshold value for the histogram-based method and the over-segmentation followed by the time-consuming merge processing in the region-based algorithm. To provide an efficient approach that not only produce better results, but also maintain low computational complexity, a new region dividing based technique is developed for image segmentation, which combines the advantages of both regions-based and histogram-based methods. The proposed method is applied to the challenging applications: Gray matter (GM), White matter (WM) and cerebro-spinal fluid (CSF) segmentation in brain MR Images. The method is evaluated on both simulated and real data, and compared with other segmentation techniques. The obtained results have demonstrated its improved performance and robustness.