Classification of Chest CT Lung Nodules Using Collaborative Deep Learning Model.

Background: Early detection of lung cancer through accurate diagnosis of malignant lung nodules using chest CT scans offers patients the highest chance of successful treatment and survival. Despite advancements in computer vision through deep learning algorithms, the detection of malignant nodules faces significant challenges due to insufficient training datasets. Methods: This study introduces a model based on collaborative deep learning (CDL) to differentiate between cancerous and non-cancerous nodules in chest CT scans with limited available data. The model dissects a nodule into its constituent parts using six characteristics, allowing it to learn detailed features of lung nodules. It utilizes a CDL submodel that incorporates six types of feature patches to fine-tune a network previously trained with ResNet-50. An adaptive weighting method learned through error backpropagation enhances the process of identifying lung nodules, incorporating these CDL submodels for improved accuracy. Results: The CDL model demonstrated a high level of performance in classifying lung nodules, achieving an accuracy of 93.24%. This represents a significant improvement over current state-of-the-art methods, indicating the effectiveness of the proposed approach. Conclusion: The findings suggest that the CDL model, with its unique structure and adaptive weighting method, offers a promising solution to the challenge of accurately detecting malignant lung nodules with limited data. This approach not only improves diagnostic accuracy but also contributes to the early detection and treatment of lung cancer, potentially saving lives.

Lossless compression-based detection of osteoporosis using bone X-ray imaging.

BACKGROUND: Digital X-ray imaging is essential for diagnosing osteoporosis, but distinguishing affected patients from healthy individuals using these images remains challenging. OBJECTIVE: This study introduces a novel method using deep learning to improve osteoporosis diagnosis from bone X-ray images. METHODS: A dataset of bone X-ray images was analyzed using a newly proposed procedure. This procedure involves segregating the images into regions of interest (ROI) and non-ROI, thereby reducing data redundancy. The images were then processed to enhance both spatial and statistical features. For classification, a Support Vector Machine (SVM) classifier was employed to distinguish between osteoporotic and non-osteoporotic cases. RESULTS: The proposed method demonstrated a promising Area under the Curve (AUC) of 90.8% in diagnosing osteoporosis, benchmarking favorably against existing techniques. This signifies a high level of accuracy in distinguishing osteoporosis patients from healthy controls. CONCLUSIONS: The proposed method effectively distinguishes between osteoporotic and non-osteoporotic cases using bone X-ray images. By enhancing image features and employing SVM classification, the technique offers a promising tool for efficient and accurate osteoporosis diagnosis.

A computational approach for analysis of intratumoral heterogeneity and standardized uptake value in PET/CT images1.

BACKGROUND: By providing both functional and anatomical information from a single scan, digital imaging technologies like PET/CT and PET/MRI hybrids are gaining popularity in medical imaging industry. In clinical practice, the median value (SUVmed) receives less attention owing to disagreements surrounding what defines a lesion, but the SUVmax value, which is a semi-quantitative statistic used to analyse PET and PET/CT images, is commonly used to evaluate lesions. OBJECTIVE: This study aims to build an image processing technique with the purpose of automatically detecting and isolating lesions in PET/CT images, as well as measuring and assessing the SUVmed. METHODS: The pictures are separated into their respective lesions using mathematical morphology and the crescent region, which are both part of the image processing method. In this research, a total of 18 different pictures of lesions were evaluated. RESULTS: The findings of the study reveal that the threshold is satisfied by both the SUVmax and the SUVmed for most of the lesion types. However, in six instances, the SUVmax and SUVmed values are found to be in different courts. CONCLUSION: The new information revealed by this study needs to be further investigated to determine if it has any practical value in diagnosing and monitoring lesions. However, results of this study suggest that SUVmed should receive more attention in the evaluation of lesions in PET and CT images.

Generative and Discriminative Learning for Lung X-Ray Analysis Based on Probabilistic Component Analysis.

Introduction: The paper presents a hybrid generative/discriminative classification method aimed at identifying abnormalities, such as cancer, in lung X-ray images. Methods: The proposed method involves a generative model that performs generative embedding in Probabilistic Component Analysis (PrCA). The primary goal of PrCA is to model co-existing information within a probabilistic framework, with the intent to locate the feature vector space for X-ray data based on a defined kernel structure. A kernel-based classifier, grounded in information-theoretic principles, was employed in this study. Results: The performance of the proposed method is evaluated against nearest neighbour (NN) classifiers and support vector machine (SVM) classifiers, which use a diagonal covariance matrix and incorporate normal linear and non-linear kernels, respectively. Discussion: The method is found to achieve superior accuracy, offering a viable solution to the class of problems presented. Accuracy rates achieved by the kernels in the NN and SVM models were 95.02% and 92.45%, respectively, suggesting the method's competitiveness with state-of-the-art approaches.

The cranial capacity of the Saudi population measured using 3D computed tomography scans.

OBJECTIVES: To measure the cranial capacity of members of the Saudi adult population across ages and genders. METHODS: This was a retrospective cross-sectional study that used 488 Computed Tomography (CT) scans of heads (of which 275 males) to measure cranial volume. The CT slices 0.625 mm thick were uploaded using the freely available software "3D-Slicer", which then reconstructed the images and built a 3D module. RESULTS: The mean (±SD) cranial capacity of the males was 1481.6 (±110) cm3 (range: 1241-1723 cm3), whereas the cranial capacity of the females was 1375.4 (±104) cm3 (range: 1203-1678 cm3). This study showed that the males had a mean cranial capacity that was 7% greater than that of the females in this study. The average cranial capacity of the males between the ages of 31 and 40 years was statistically significantly larger to that of the males aged 61-80 (p<0.05). CONCLUSION: This study demonstrated that the average cranial capacity of the males was larger than that of the females. These study results can help to determine the normal cranial capacity of adults in Saudi Arabia. Further work should be carried out to aid in establishing reference data for the Saudi population.

Radiographers' knowledge and attitude toward informed consent / Conocimientos y actitud de los radiógrafos hacia el consentimiento informado / Conhecimento e atitudes de radiologistas sobre consentimento informado

Health professionals are obliged to provide the patient and/or his or her guardian with information about examinations and treatment. In the case of examinations involving the use of ionizing radiation, informed consent is essential to weigh the associated benefits and risks. This study evaluates the knowledge and attitude of radiographers regarding informed consent in the radiology department. It is a descriptive cross-sectional study using an online questionnaire, divided into three sections and containing 19 questions, detailing the demographics, knowledge, and attitude of radiographers. Participant responses were scored between 1 and 3, then calculated and averaged to determine mean scores. Student's t-test was used to determine the statistical significance between means. Mean scores for knowledge and attitudes ranged from 11.60 ±1.72 to 18.73 ±1.84. Radiographers with more experience showed a statistically significant difference (17.45 ±1.23) (p < 0.00) with those with less experience (15.34 ±1.52). Twenty-one percent of the respondents did not seek consent from patients prior to examination. This study demonstrated that these professionals are knowledgeable about informed consent; however, more emphasis should be placed on practice by providing training courses and disseminating correct practice.

Los profesionales sanitarios están obligados a facilitar al paciente y/o a su tutor información sobre los exámenes y el tratamiento. En el caso de los exámenes que implican el uso de radiaciones ionizantes, el consentimiento informado es esencial para sopesar los beneficios y riesgos asociados. Este estudio evalúa los conocimientos y la actitud de los radiógrafos en relación con el consentimiento informado en el departamento de radiología. Se trata de un estudio descriptivo transversal en el que se utilizó un cuestionario en línea, dividido en tres secciones y que contenía 19 preguntas, en las que se detallaban los datos demográficos, los conocimientos y la actitud de los radiógrafos. Las respuestas de los participantes se puntuaron entre 1 y 3, y luego se calcularon y promediaron para determinar las puntuaciones medias. Se utilizó la prueba t de Student para determinar la significación estadística entre las medias. Las puntuaciones medias de los conocimientos y las actitudes oscilaron entre 11,60 ±1,72 y 18,73 ±1,84. Los radiógrafos con más experiencia mostraron un mayor nivel de conocimientos y actitudes. Los radiógrafos con más experiencia mostraron una diferencia estadísticamente significativa (17,45 ±1,23) (p < 0,00) con los que tenían menos experiencia (15,34 ±1,52). El 21% de los encuestados no solicitó el consentimiento de los pacientes antes de la exploración. Este estudio demostró que estos profesionales tienen conocimientos sobre el consentimiento informado; sin embargo, se debería hacer más hincapié en la práctica impartiendo cursos de formación y difundiendo la práctica correcta.

Profissionais da saúde são obrigados a fornecer ao paciente ou seu guardião informações sobre exames e tratamentos. No caso de exames envolvendo o uso de radiação ionizante, consentimento informado é essencial para balancear os benefícios e riscos associados. Esse estudo avalia o conhecimento e atitudes de radiologistas a respeito do consentimento informado no departamento de radiologia. É um estudo transversal usando um questionário online, dividido em três seções e contendo 19 perguntas, detalhando a demografia, conhecimento e atitudes de radiologistas. As respostas dos participantes foram classificadas entre 1 e 3, e então calculadas e calculadas as médias para determinar os escores médios. O teste t de Student foi usado para determinar a significância estatística entre as médias. Escores médios para conhecimento e atitudes variaram de 11,60 ±1,72 a 18,73 ±1,84. Radiologistas com mais experiência mostraram uma diferença estatisticamente significante (17.45 ±1.23) (p < 0.00) daqueles com menos experiência (15.34 ±1.52). 21% dos respondentes não buscaram consentimento dos pacientes antes do exame. Esse estudo demonstrou que esses profissionais estão cientes do consentimento informado; entretanto, mais ênfase deve ser dada na prática ao fornecer cursos de treinamento e disseminando práticas corretas.

Diagnostic Validity and Reliability of Low-Dose Prospective ECG-Triggering Cardiac CT in Preoperative Assessment of Complex Congenital Heart Diseases (CHDs).

For the precise preoperative evaluation of complex congenital heart diseases (CHDs) with reduced radiation dose exposure, we assessed the diagnostic validity and reliability of low-dose prospective ECG-gated cardiac CT (CCT). Forty-two individuals with complex CHDs who underwent preoperative CCT as part of a prospective study were included. Each CCT image was examined independently by two radiologists. The primary reference for assessing the diagnostic validity of the CCT was the post-operative data. Infants and neonates were the most common age group suffering from complex CHDs. The mean volume of the CT dose index was 1.44 ± 0.47 mGy, the mean value of the dose-length product was 14.13 ± 5.4 mGy*cm, and the mean value of the effective radiation dose was 0.58 ± 0.13 mSv. The sensitivity, specificity, PPV, NPV, and accuracy of the low-dose prospective ECG-gated CCT for identifying complex CHDs were 95.6%, 98%, 97%, 97%, and 97% for reader 1 and 92.6%, 97%, 95.5%, 95.1%, and 95.2% for reader 2, respectively. The overall inter-reader agreement for interpreting the cardiac CCTs was good (κ = 0.74). According to the results of our investigation, low-dose prospective ECG-gated CCT is a useful and trustworthy method for assessing coronary arteries and making a precise preoperative diagnosis of complex CHDs.

The Use of Chest Radiographs and Machine Learning Model for the Rapid Detection of Pneumonitis in Pediatric.

Pneumonia is a common lung disease that is the leading cause of death worldwide. It primarily affects children, accounting for 18% of all deaths in children under the age of five, the elderly, and patients with other diseases. There is a variety of imaging diagnosis techniques available today. While many of them are becoming more accurate, chest radiographs are still the most common method for detecting pulmonary infections due to cost and speed. A convolutional neural network (CNN) model has been developed to classify chest X-rays in JPEG format into normal, bacterial pneumonia, and viral pneumonia. The model was trained using data from an open Kaggle database. The data augmentation technique was used to improve the model's performance. A web application built with NextJS and hosted on AWS has also been designed. The model that was optimized using the data augmentation technique had slightly better precision than the original model. This model was used to create a web application that can process an image and provide a prediction to the user. A classification model was developed that generates a prediction with 78 percent accuracy. The precision of this calculation could be improved by increasing the epoch, among other subjects. With the help of artificial intelligence, this research study was aimed at demonstrating to the general public that deep-learning models can be created to assist health professionals in the early detection of pneumonia.

AUTOMATED ESTIMATION OF PATIENT'S SIZE USING AUTOWED TOOL AND INDOSECT PROGRAM: A DOSIMETRIC STUDY FOR PAEDIATRIC HEAD CT EXAMINATIONS.

Size-specific dose estimate (SSDE), which can be calculated by measuring the effective diameter (De) or water equivalent diameter (Dw) of the patient, is one of the recent approaches for verifying the individual doses during computer tomography (CT) examinations. This work aimed to compare the Dw estimated by the AutoWED tool and IndoseCT software and to investigate CT axial (ARH) and paediatric head (PH) protocols used in southern Saudi Arabia to calculate the dose received by paediatric patients using metrics of volume CT dose index (CTDIvol) and SSDE. The distribution between the ARH and PH protocols was 57.8 and 42.2%, respectively. There was no significant difference in Dw values between the AutoWED tool and the IndoseCT program (0.13%). Including CT table or other objects during estimation of Dw can lead to variation up to 11.4%. The impact of selecting IndoseCT options to identify the border of the patient may be part of the explanation for these variations. A strong linear relationship was obtained between De and Dw in paediatric head size (R2 = 0.96). Using IndoseCT, for 0-1.5, 1.5-5 and 5.0-18 age groups (years), the Dw was found to be 13.2, 15.3 and 16.8 cm, respectively. The SSDE for the PH protocol was substantially lower than that of the ARH protocol. As a result, education of the individuals engaging in paediatric CT examinations is necessary for dose optimization.

Automation of Cephalometrics Using Machine Learning Methods.

Cephalometry is a medical test that can detect teeth, skeleton, or appearance problems. In this scenario, the patient's lateral radiograph of the face was utilised to construct a tracing from the tracing of lines on the lateral radiograph of the face of the soft and hard structures (skin and bone, respectively). Certain cephalometric locations and characteristic lines and angles are indicated after the tracing is completed to do the real examination. In this unique study, it is proposed that machine learning models be employed to create cephalometry. These models can recognise cephalometric locations in X-ray images, allowing the study's computing procedure to be completed faster. To correlate a probability map with an input image, they combine an Autoencoder architecture with convolutional neural networks and Inception layers. These innovative architectures were demonstrated. When many models were compared, it was observed that they all performed admirably in this task.

Ethical aspects of age estimation for forensic purposes in Saudi Arabia / Aspectos éticos de la estimación de la edad para propósitos forenses en Arabia Saudita / Aspectos éticos da estimativa de idade para propósitos forenses na Arábia Saudita

Abstract: In forensic and legal contexts, an assessment of skeletal maturation is required to predict the chronological age in certain circumstances in which the chronological age is undocumented or unable to be proven. Carrying such medical examinations will always pose an ethical concern, especially in cases of assessing sexual maturity as well as examinations involving ionizing radiation. The cultural and religious barriers are also discussed concerning the ethical standards set in Saudi Arabia. Several ethical issues concerning age estimation examinations including issues related to consent forms, privacy, and radiation hazards have been documented. While ethical issues regarding radiation hazards are more likely to be overcome by introducing other imaging modalities without radiation hazards, ethics related to consent forms and privacy are still to be improved.

Resumen: En contextos forenses y legales, se requiere la evaluación de la maduración del esqueleto para predecir la edad cronológica en ciertas circunstancias en que la edad cronológica no está documentada o no es posible de probar. Realizar tales exámenes médicos siempre significa una preocupación ética, especialmente en casos de evaluación de madurez sexual así como en exámenes que involucran radiaciones ionizantes. Se discuten también las barreras culturales y religiosas relacionadas con el contexto de estándares éticos en Arabia Saudita. Entre los varios temas éticos relacionados con la estimación de la edad, se documentan temas relacionados con los formularios de consentimiento, privacidad y riesgos de radiación. Mientras que los temas éticos relacionados con riesgos de radiación pueden ser resueltos al introducir otras modalidades de examen sin radiación, los problemas éticos relacionados con los formularios de consentimiento y privacidad todavía necesitan mejorarse.

Resumo: Em contextos forenses e legais, uma avaliação de maturação esquelética é requerida para predizer a idade cronológica em certas circunstâncias nas quais a idade cronológica não é documentada ou impossível de ser provada. Conduzir tais exames médicos sempre irá suscitar uma preocupação ética, especialmente em casos para avaliar maturidade sexual bem como exames envolvendo radiação ionizante. As barreiras culturais e religiosas também são discutidas no que diz respeito a padrões éticos estabelecidos na Arábia Saudita. Diversos aspectos éticos concernentes a exames de estimativa de idade incluindo aspectos relacionados a formulários de consentimento, privacidade e risco de radiação foram documentados. Ao passo que aspectos éticos que dizem respeito a risco de radiação serem mais provavelmente superáveis pela introdução de outras modalidades de exames de imagem sem risco de radiação, a ética relacionada a formulários de consentimento e privacidade ainda estão por serem melhorados.

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.

Enhancement of Mammographic Images Using Histogram-Based Techniques for Their Classification Using CNN.

In the world, one in eight women will develop breast cancer. Men can also develop it, but less frequently. This condition starts with uncontrolled cell division brought on by a change in the genes that regulate cell division and growth, which leads to the development of a nodule or tumour. These tumours can be either benign, which poses no health risk, or malignant, also known as cancerous, which puts patients' lives in jeopardy and has the potential to spread. The most common way to diagnose this problem is via mammograms. This kind of examination enables the detection of abnormalities in breast tissue, such as masses and microcalcifications, which are thought to be indicators of the presence of disease. This study aims to determine how histogram-based image enhancement methods affect the classification of mammograms into five groups: benign calcifications, benign masses, malignant calcifications, malignant masses, and healthy tissue, as determined by a CAD system of automatic mammography classification using convolutional neural networks. Both Contrast-limited Adaptive Histogram Equalization (CAHE) and Histogram Intensity Windowing (HIW) will be used (CLAHE). By improving the contrast between the image's background, fibrous tissue, dense tissue, and sick tissue, which includes microcalcifications and masses, the mammography histogram is modified using these procedures. In order to help neural networks, learn, the contrast has been increased to make it easier to distinguish between various types of tissue. The proportion of correctly classified images could rise with this technique. Using Deep Convolutional Neural Networks, a model was developed that allows classifying different types of lesions. The model achieved an accuracy of 62%, based on mini-MIAS data. The final goal of the project is the creation of an update algorithm that will be incorporated into the CAD system and will enhance the automatic identification and categorization of microcalcifications and masses. As a result, it would be possible to increase the possibility of early disease identification, which is important because early discovery increases the likelihood of a cure to almost 100%.

A Novel Method for COVID-19 Diagnosis Using Artificial Intelligence in Chest X-ray Images.

The Coronavirus disease 2019 (COVID-19) is an infectious disease spreading rapidly and uncontrollably throughout the world. The critical challenge is the rapid detection of Coronavirus infected people. The available techniques being utilized are body-temperature measurement, along with anterior nasal swab analysis. However, taking nasal swabs and lab testing are complex, intrusive, and require many resources. Furthermore, the lack of test kits to meet the exceeding cases is also a major limitation. The current challenge is to develop some technology to non-intrusively detect the suspected Coronavirus patients through Artificial Intelligence (AI) techniques such as deep learning (DL). Another challenge to conduct the research on this area is the difficulty of obtaining the dataset due to a limited number of patients giving their consent to participate in the research study. Looking at the efficacy of AI in healthcare systems, it is a great challenge for the researchers to develop an AI algorithm that can help health professionals and government officials automatically identify and isolate people with Coronavirus symptoms. Hence, this paper proposes a novel method CoVIRNet (COVID Inception-ResNet model), which utilizes the chest X-rays to diagnose the COVID-19 patients automatically. The proposed algorithm has different inception residual blocks that cater to information by using different depths feature maps at different scales, with the various layers. The features are concatenated at each proposed classification block, using the average-pooling layer, and concatenated features are passed to the fully connected layer. The efficient proposed deep-learning blocks used different regularization techniques to minimize the overfitting due to the small COVID-19 dataset. The multiscale features are extracted at different levels of the proposed deep-learning model and then embedded into various machine-learning models to validate the combination of deep-learning and machine-learning models. The proposed CoVIR-Net model achieved 95.7% accuracy, and the CoVIR-Net feature extractor with random-forest classifier produced 97.29% accuracy, which is the highest, as compared to existing state-of-the-art deep-learning methods. The proposed model would be an automatic solution for the assessment and classification of COVID-19. We predict that the proposed method will demonstrate an outstanding performance as compared to the state-of-the-art techniques being used currently.

Hyperkalemia Among Hospitalized Patients and Association Between Duration of Hyperkalemia and Outcomes.

Background Hyperkalemia is a serious life-threatening condition that leads to significant morbidity and mortality. Objectives The aim of this study is to investigate the association between the duration and outcomes in patients hospitalized with hyperkalemia, as well as associated risk factors and drug-induced hyperkalemia. Methods A three-year retrospective chart review study was conducted at a tertiary hospital at King Abdulaziz Medical City, Riyadh, Saudi Arabia, between January 2016 and April 2019. We included all hospitalized adults and patients with hyperkalemia. Pediatric patients and dialysis patients with end-stage renal failure were excluded. Results Out of a total of 300 hospitalized patients who were screened for hyperkalemia, only 142 patients were included. The majority of cases were males (56.3%), whereas 43.7% were females. Most patients were above 55 years old. Regarding comorbidities in patients with hyperkalemia, most of them suffered from high blood pressure or diabetes. The mean serum potassium value was 5.7 ± 0.51 mEq. The most frequent medications used in the study population were azole antifungal medication followed by nonsteroidal anti-inflammatory drugs, beta-blockers, and angiotensin-converting enzyme inhibitors. Around 54 patients were not treated with medication and were monitored for spontaneous correction of hyperkalemia. Insulin was the most used medication for the treatment of hyperkalemia. The mean duration for the resolution of hyperkalemia was 12 ±9.4 hours. Out of 142 patients, only 10 (7%) patients died with hyperkalemia. Conclusions Hospitalized patients are at risk of hyperkalemia. In our study, we found that patients who had hyperkalemia were significantly likely to have acute kidney injury or cardiovascular diseases, and azole antifungals and beta-blockers were the most commonly used medications.

Applicability of two commonly used bone age assessment methods to twenty-first century UK children.

OBJECTIVES: To assess the effect of secular change on skeletal maturation and thus on the applicability of the Greulich and Pyle (G&P) and Tanner and Whitehouse (TW3) methods. METHODS: BoneXpert was used to assess bone age from 392 hand trauma radiographs (206 males, 257 left). The paired sample t test was performed to assess the difference between mean bone age (BA) and mean chronological age (CA). ANOVA was used to assess the differences between groups based on socioeconomic status (taken from the Index of Multiple Deprivation). RESULTS: CA ranged from 2 to 15 years for females and 2.5 to 15 years for males. Numbers of children living in low, average and high socioeconomic areas were 216 (55%), 74 (19%) and 102 (26%) respectively. We found no statistically significant difference between BA and CA when using G&P. However, using TW3, CA was underestimated in females beyond the age of 3 years, with significant differences between BA and CA (- 0.43 years, SD 1.05, p = < 0.001) but not in males (0.01 years, SD 0.97, p = 0.76). Of the difference in females, 17.8% was accounted for by socioeconomic status. CONCLUSION: No significant difference exists between BoneXpert-derived BA and CA when using the G&P atlas in our study population. There was a statistically significant underestimation of BoneXpert-derived BA compared with CA in females when using TW3, particularly in those from low and average socioeconomic backgrounds. Secular change has not led to significant advancement in skeletal maturation within our study population. KEY POINTS: • The Greulich and Pyle method can be applied to the present-day United Kingdom (UK) population. • The Tanner and Whitehouse (TW3) method consistently underestimates the age of twenty-first century UK females by an average of 5 months. • Secular change has not advanced skeletal maturity of present-day UK children compared with those of the mid-twentieth century.

Is the Greulich and Pyle atlas applicable to all ethnicities? A systematic review and meta-analysis.

OBJECTIVE: To determine whether the Greulich and Pyle (G&P) atlas is applicable when applied to populations of different ethnicity. METHODS: A systematic review of studies published between 1959 and 15th February 2017 identified from the Embase, MEDLINE and Cochrane databases was undertaken. Quality of the studies was assessed using the National Institute for Health and Care Excellence tool. Meta-analysis used mean differences and standard deviations as summary statistics for the difference between bone age (BA) and chronological age (CA). RESULTS: A total of 49 studies were included of which 27 (55%) were related to Caucasian populations. Of the 49 eligible studies, 35 were appropriate for further meta-analysis. In African females, meta-analysis showed a significant mean difference between BA and CA of 0.37 years (95% CI 0.04, 0.69). In Asian males, meta-analysis showed significant differences between BA and CA of -1.08, -1.35, -1.07, -0.80 and 0.50 years for chronological ages of 6, 7, 8, 9 and 17 years, respectively. Meta-analysis showed no significant differences between BA and CA in African males, Asian females, Caucasians and Hispanics. CONCLUSIONS: The G&P standard is imprecise and should be used with caution when applied to Asian male and African female populations, particularly when aiming to determine chronological age for forensic/legal purposes. KEY POINTS: • In African females, bone age is significantly advanced when compared to the G&P standard. • In Asian males, bone age is significantly delayed between 6 and 9 years old inclusive and significantly advanced at 17 years old when compared to the G&P standard. • The G&P atlas should be used with caution when applied to Asian and African populations, particularly when aiming to determine chronological age for forensic/legal purposes.

Estimating bone mass in children: can bone health index replace dual energy x-ray absorptiometry?

BACKGROUND: Bisphosphonates have been shown to increase metacarpal cortical width. Bone health index is computed from hand radiographs by measuring cortical thickness, width and length of the three middle metacarpals, and may potentially help predict fracture risk in children. OBJECTIVE: To compare bone health index with bone mineral density as measured from dual energy X-ray absorptiometry scans in patients with and without bisphosphonate treatment. MATERIALS AND METHODS: Two hundred ninety-three Caucasian patients (mean age: 11.5±3.7 years) were included. We documented absolute values and z-scores for whole-body less head and lumbar spine bone mineral density then correlated these with the bone health index, which were acquired on the same day, in different patient groups, depending on their ethnicity and diagnosis. RESULTS: Bone health index showed moderate to strong correlation with absolute values for whole-body (r=0.52) and lumbar spine (r=0.70) bone mineral density in those not treated with bisphosphonates and moderate correlation absolute values for whole-body (r=0.54) and lumber spine (r=0.51) bone mineral density for those treated with bisphosphonates. There was weak correlation of z-scores, ranging from r=0.11 to r=0.35 in both groups. CONCLUSION: The lack of a strong correlation between dual energy X-ray absorptiometry and bone health index suggests that they may be assessing different parameters.