A new formula for assessing skeletal age in growing infants and children by measuring carpals and epiphyses of radio and ulna

Phantom study of a self-shielded X-ray bone age assessment instrument against scattered radiation in children

Hand-wrist radiography is the most common and accurate method for evaluating children's bone age. To reduce the scattered radiation of radiosensitive organs in bone age assessment, we designed a small X-ray instrument with radioprotection function by adding metal enclosure for X-ray shielding. We used a phantom operator to compare the scattered radiation doses received by sensitive organs under three different protection scenarios (proposed instrument, radiation personal protective equipment, no protection). The proposed instrument showed greater reduction in the mean dose of a single exposure compared with radiation personal protective equipment especially on the left side which was proximal to the X-ray machine (≥80.0% in eye and thyroid,  ≥99.9% in breast and gonad). The proposed instrument provides a new pathway towards more convenient and efficient radioprotection.

The accuracy of Cameriere methods in Turkish children: chronological age estimation using developing teeth and carpals and epiphyses of the ulna and radius

The aim of the present study was to develop a specific formula by measuring the developing teeth, carpal bones, and epiphyses of the ulna and radius to determine the chronological age in Turkish children. The left developing permanent mandibular teeth were evaluated, and the number of teeth with closed apex was recorded. The distance between the inner sides of open apex/apices was measured by using the ImageJ program and divided by the tooth length. The sum of the normalized open apices was also calculated. The carpal area (Ca), covering the epiphyses of ulna and radius and the carpal bones, was measured on the X-rays of left hand. The areas of each carpal bone and epiphyses of the ulna and radius were measured, and these measurements were added together to obtain the bone area (Bo). The Bo/Ca ratio between the total area of carpal bones and the carpal area was calculated to normalize the measurements. The accuracy of the equations formulated by Cameriere was evaluated, and a new regression equation was developed accordingly. The new formula showed no statistically significant difference between the chronological and the estimated age for females, males, and total sample. The new formula, which hit the age with 72.80% accuracy, was more successful in predicting chronological age than other adjusted regression equations. The new regression model, created for the Turkish children by using both developing teeth and hand-wrist bones, was considerably successful in estimating the chronological age.

Deep Neural Networks-Based Age Estimation of Cadavers Using CT Imaging of Vertebrae

Although age estimation upon death is important in the identification of unknown cadavers for forensic scientists, to the best of our knowledge, no study has examined the utility of deep neural network (DNN) models for age estimation among cadavers. We performed a postmortem computed tomography (CT) examination of 1000 and 500 male and female cadavers, respectively. These CT slices were converted into 3-dimensional images, and only the thoracolumbar region was extracted. Eighty percent of them were categorized as training datasets and the others as test datasets for both sexes. We fine-tuned the ResNet152 models using the training datasets. We conducted 4-fold cross-validation, and the mean absolute error (MAE) of the test datasets was calculated using the ensemble learning of four ResNet152 models. Consequently, the MAE of the male and female models was 7.25 and 7.16, respectively. Our study shows that DNN models can be useful tools in the field of forensic medicine.

Use of Advanced Artificial Intelligence in Forensic Medicine, Forensic Anthropology and Clinical Anatomy

Three-dimensional convolutional neural networks (3D CNN) of artificial intelligence (AI) are potent in image processing and recognition using deep learning to perform generative and descriptive tasks. Compared to its predecessor, the advantage of CNN is that it automatically detects the important features without any human supervision. 3D CNN is used to extract features in three dimensions where input is a 3D volume or a sequence of 2D pictures, e.g., slices in a cone-beam computer tomography scan (CBCT). The main aim was to bridge interdisciplinary cooperation between forensic medical experts and deep learning engineers, emphasizing activating clinical forensic experts in the field with possibly basic knowledge of advanced artificial intelligence techniques with interest in its implementation in their efforts to advance forensic research further. This paper introduces a novel workflow of 3D CNN analysis of full-head CBCT scans. Authors explore the current and design customized 3D CNN application methods for particular forensic research in five perspectives: (1) sex determination, (2) biological age estimation, (3) 3D cephalometric landmark annotation, (4) growth vectors prediction, (5) facial soft-tissue estimation from the skull and vice versa. In conclusion, 3D CNN application can be a watershed moment in forensic medicine, leading to unprecedented improvement of forensic analysis workflows based on 3D neural networks.

Intelligent Bone Age Assessment: An Automated System to Detect a Bone Growth Problem Using Convolutional Neural Networks with Attention Mechanism

Skeletal bone age assessment using X-ray images is a standard clinical procedure to detect any anomaly in bone growth among kids and babies. The assessed bone age indicates the actual level of growth, whereby a large discrepancy between the assessed and chronological age might point to a growth disorder. Hence, skeletal bone age assessment is used to screen the possibility of growth abnormalities, genetic problems, and endocrine disorders. Usually, the manual screening is assessed through X-ray images of the non-dominant hand using the Greulich-Pyle (GP) or Tanner-Whitehouse (TW) approach. The GP uses a standard hand atlas, which will be the reference point to predict the bone age of a patient, while the TW uses a scoring mechanism to assess the bone age using several regions of interest information. However, both approaches are heavily dependent on individual domain knowledge and expertise, which is prone to high bias in inter and intra-observer results. Hence, an automated bone age assessment system, which is referred to as Attention-Xception Network (AXNet) is proposed to automatically predict the bone age accurately. The proposed AXNet consists of two parts, which are image normalization and bone age regression modules. The image normalization module will transform each X-ray image into a standardized form so that the regressor network can be trained using better input images. This module will first extract the hand region from the background, which is then rotated to an upright position using the angle calculated from the four key-points of interest. Then, the masked and rotated hand image will be aligned such that it will be positioned in the middle of the image. Both of the masked and rotated images will be obtained through existing state-of-the-art deep learning methods. The last module will then predict the bone age through the Attention-Xception network that incorporates multiple layers of spatial-attention mechanism to emphasize the important features for more accurate bone age prediction. From the experimental results, the proposed AXNet achieves the lowest mean absolute error and mean squared error of 7.699 months and 108.869 months2, respectively. Therefore, the proposed AXNet has demonstrated its potential for practical clinical use with an error of less than one year to assist the experts or radiologists in evaluating the bone age objectively.

Age estimation in the living: A scoping review of population data for skeletal and dental methods

Age estimation of living individuals has become a crucial part of the forensic practice, especially due to the global increase in cross-border migration. The low rate of birth registration in many countries, hence of identification documents of migrants, especially in Africa and Asia, highlights the importance of reliable methods for age estimation of living individuals. Despite the fact that a number of skeletal and dental methods for age estimation have been developed, their main limitation is that they are based on specific reference samples and there is still no consensus among researchers on whether these methods can be applied to all populations. Though this issue remains still unsolved, population information at a glance could be useful for forensic practitioners dealing with such issues. This study aims at presenting a scoping review and mapping of the current situation concerning population data for skeletal (hand-wrist and clavicle) and dental methods (teeth eruption and third molar formation) for age estimation in the living. Two hundred studies on the rate of skeletal maturation and four hundred thirty-nine on the rate of dental maturation were found, covering the period from 1952 and 2020 for a total of ninety-eight countries. For most of the western and central African countries there are currently no data on the rate of skeletal and dental maturation. The same applies to the countries of the Middle East, as well as the eastern European countries, especially as regard the skeletal development.

Time of appearance of ossification centers in carpal bones. A radiological retrospective study on Saudi children

OBJECTIVES

To find reference data for the time of appearance of ossification centers in carpal bones and the lower ends of the radius and ulna in the Saudi population. In addition, to check the sequence of appearance of carpal bones and the relation of this sequence to the appearance of distal epiphyses of the radius and ulna. Methods: A retrospective radiological study was carried out between 2012 to 2020 at King Fahad Hospital of the University, Al-Khobar, Saudi Arabia. A sample of 279 hand/wrist plain radiographs of Saudi children was analyzed.

RESULTS

The first bones at the wrist region to appear in Saudi children are the capitate, hamate, and distal epiphysis of the radius, and these appear during the first year of life. The other bones develop subsequently at yearly intervals, and the last one to appear is the pisiform, which arises at the end of the first decade of life.

CONCLUSION

The sequence of appearance of carpal bones in the Saudi population is similar to what is described in the literature. However, the time of appearance of some of these bones is earlier than that in other populations.

Analysis of carpal bones on MR images for age estimation: First results of a new forensic approach

Current multifactorial age estimation methods are based on radiography, however, in the forensic field there is growing interest in using magnetic resonance imaging (MRI). With regard to the carpal region, MRI provides more information for defining the individual ossification nuclei and the cartilage surrounding single bones. During the phase of bone growth, the progressive reduction of the cartilage layer is accompanied by the development of a cartilage-bone interface. The aim of our study was to create a new model for age estimation, based on the ratio between the area occupied by the nucleus of ossification (NO) and the surface of growth (SG) of each carpal bone, the latter derived by adding NO to the area of cartilage-bone interface. We analyzed 57 MRI scans of Italian subjects aged between 12 and 20 years, without growth diseases, endocrine disorders or osteodystrophy. Measurements of NO and SG areas were extracted using ImageJ software, and the ratio between the NO and SG of each bone (NOSG) was calculated. A multiple linear regression model was used to estimate the individual's age as a function of the variables: gender and wrist bone measurements. The results showed that the best model was obtained with 6 predictors (nvmax=6): Gender, and the NOSG of the Trapezoid, Trapezium, Scaphoid, Pisiform, and Capitate. The median of the residuals (observed age minus predicted age) was -0.025 years, with an IQR of 0.19 years. Thus a new forensic approach to age assessment using MRI is introduced in this paper, which gives the preliminary results.

Bone age assessment with various machine learning techniques: A systematic literature review and meta-analysis

BACKGROUND

The assessment of bone age and skeletal maturity and its comparison to chronological age is an important task in the medical environment for the diagnosis of pediatric endocrinology, orthodontics and orthopedic disorders, and legal environment in what concerns if an individual is a minor or not when there is a lack of documents. Being a time-consuming activity that can be prone to inter- and intra-rater variability, the use of methods which can automate it, like Machine Learning techniques, is of value.

OBJECTIVE

The goal of this paper is to present the state of the art evidence, trends and gaps in the research related to bone age assessment studies that make use of Machine Learning techniques.

METHOD

A systematic literature review was carried out, starting with the writing of the protocol, followed by searches on three databases: Pubmed, Scopus and Web of Science to identify the relevant evidence related to bone age assessment using Machine Learning techniques. One round of backward snowballing was performed to find additional studies. A quality assessment was performed on the selected studies to check for bias and low quality studies, which were removed. Data was extracted from the included studies to build summary tables. Lastly, a meta-analysis was performed on the performances of the selected studies.

RESULTS

26 studies constituted the final set of included studies. Most of them proposed automatic systems for bone age assessment and investigated methods for bone age assessment based on hand and wrist radiographs. The samples used in the studies were mostly comprehensive or bordered the age of 18, and the data origin was in most of cases from United States and West Europe. Few studies explored ethnic differences.

CONCLUSIONS

There is a clear focus of the research on bone age assessment methods based on radiographs whilst other types of medical imaging without radiation exposure (e.g. magnetic resonance imaging) are not much explored in the literature. Also, socioeconomic and other aspects that could influence in bone age were not addressed in the literature. Finally, studies that make use of more than one region of interest for bone age assessment are scarce.

Quantifying the ossification of the carpus in skeletal age estimation: Radiographic standards for Australian subadults

An evaluation of the development of a child's skeleton and estimation of bone age provides an insight into a child's overall maturation. This study aimed to introduce a contemporary method for assessing bone age of Australian children using formulae incorporating carpal areal measurements. The standards introduced in this study can be used to assess the developmental status of Australian children who may be affected by growth-related illnesses. Additionally, in situations where the living age of a subadult is unknown, methodologies to accurately estimate age are required, particularly in the Western world where knowledge of the age of an individual is necessary for legal reasons. The sample consisted of retrospective hand and wrist radiographs acquired from 541 children (females: 246, males: 295) aged from birth to 20 years. Using the DICOM viewer Weasis, the carpal area ratio (B.Ar/T.Ar) was calculated for each individual radiograph by measuring the carpal bone area (B.Ar) and total tissue area of the carpus (T.Ar). A changepoint regression model demonstrated that the model constructed in this study was the most accurate in the younger age groups and was able to accurately determine whether a child was under 12 years if female and 13 years if male. A rapid acceleration of growth was observed at approximately 12-13 years in our sample, which may represent the onset of the pubertal growth spurt; this resulted in a high data variance and low model prediction accuracy in female and male children older than 12 and 13 years, respectively.

Carpals and epiphyses of radius and ulna as age indicators using longitudinal data: a Bayesian approach

The aim of this study is to develop a new formula for age estimation in a longitudinal study of a sample from the radiological collection of wrist bones of growing infants, children, and adolescents recorded at the Burlington Growth Centre. A sample of 82 individuals (43 boys and 39 girls), aged between 3 and 16 years, were analyzed with a total of 623 X-rays of left hand-wrist bones by measuring the area of carpal bones and epiphyses of the ulna and radius (Bo) and carpal area (Ca). The intra-class correlation coefficient (ICC) and its 95% confidence interval were used to evaluate intra-observer agreement. Hierarchical Bayesian calibration has been adopted to exceed the bias deriving from the classical regression approach used for age estimation in forensic disciplines, since it tends to overestimate or underestimate the age of the individuals. Calibration distributions of the dataset obtained by the evaluation of BoCa (the ratio of Bo and Ca) suggested mean absolute errors (MAE) of 1.07 and 1.34 years in boys and girls, respectively. The mean interquartile range (MIQR) was 1.7 and 2.42 years in boys and girls, respectively. The respective bias of the estimates was β_ERR = - 0.025 and - 0.074. Furthermore, a correspondence between different BoCa values and estimated age with its standard deviation (SD) was calculated for boys and girls, respectively. In conclusion, the Bayesian calibration method appears to be suitable for assessing both age and its distribution in subadults, according to hand-wrist maturity. Furthermore, it can easily incorporate other age predictors, obtaining a distribution of the subjects with multivariate predictors.