Search Results (29)

Background: Central precocious puberty (CPP), defined as the onset of secondary sexual characteristics before age 8 in girls, is increasingly prevalent worldwide. CPP is often caused by early activation of the HPG axis, leading to accelerated growth and bone maturation. However, the diagnostic accuracy of standard bone age (BA) methods remains uncertain in this context. Objective: To compare the diagnostic accuracy of the Greulich–Pyle atlas (GPA) and Tanner–Whitehouse 3 (TW3) methods in estimating skeletal age in girls with CPP and to assess the predictive value of serum hormone levels for estimating chronological age (CA). Methods: An observational, cross-sectional diagnostic study was conducted, involving n = 109 girls aged 6–12 years with confirmed CPP (Ethics Committee approval: CHUC_2023_86; 13 July 2023). Left posteroanterior hand–wrist (PA–HW) radiographs were assessed using the GPA and TW3 methods. Anthropometric measurements were recorded, and serum concentrations of estradiol, LH, FSH, DHEA-S, cortisol, TSH, and free T4 were obtained. Comparisons between CA and BA estimates were conducted using repeated-measures ANOVA, and ANCOVA was applied to examine the hormonal predictors of CA. Results: Both GPA and TW3 overestimated CA between 7 and 12 years, with the GPA showing larger deviations (up to 4.8 months). The TW3 method provided more accurate estimations, particularly at advanced pubertal stages. Estradiol (η²_p = 0.188–0.197), LH (η²_p = 0.061–0.068), and FSH (η²_p = 0.008–0.023) emerged as the strongest endocrine predictors of CA, significantly enhancing the explanatory power of both radiological methods. Conclusions: The TW3 method demonstrated superior diagnostic accuracy over GPA in girls with CPP, especially between 7 and 12 years. Integrating estradiol, LH, and FSH into BA assessment significantly improved the accuracy, supporting a more individualized and physiologically grounded diagnostic approach. Full article

Background/Objectives: Cardiomegaly—defined as the abnormal enlargement of the heart—is a key radiological indicator of various cardiovascular conditions. Early detection is vital for initiating timely clinical intervention and improving patient outcomes. This study investigates the application of deep learning techniques for the automated diagnosis of cardiomegaly from chest X-ray (CXR) images, utilizing both convolutional neural networks (CNNs) and Vision Transformers (ViTs). Methods: We assembled one of the largest and most diverse CXR datasets to date, combining posteroanterior (PA) images from PadChest, NIH CXR, VinDr-CXR, and CheXpert. Multiple pre-trained CNN architectures (VGG16, ResNet50, InceptionV3, DenseNet121, DenseNet201, and AlexNet), as well as Vision Transformer models, were trained and compared. In addition, we introduced a novel stacking-based ensemble model—Combined Ensemble Learning Model (CELM)—that integrates complementary CNN features via a meta-classifier. Results: The CELM achieved the highest diagnostic performance, with a test accuracy of 92%, precision of 99%, recall of 89%, F1-score of 0.94, specificity of 92.0%, and AUC of 0.90. These results highlight the model’s high agreement with expert annotations and its potential for reliable clinical use. Notably, Vision Transformers offered competitive performance, suggesting their value as complementary tools alongside CNNs. Conclusions: With further validation, the proposed CELM framework may serve as an efficient and scalable decision-support tool for cardiomegaly screening, particularly in resource-limited settings such as intensive care units (ICUs) and emergency departments (EDs), where rapid and accurate diagnosis is imperative. Full article

Introduction: Bone-age assessment using posteroanterior left hand–wrist radiographs is indispensable in pediatric endocrinology and forensic age determination. Traditional methods—Greulich–Pyle atlas and Tanner–Whitehouse scoring—are time-consuming, operator-dependent, and prone to inter- and intra-observer variability. Aim: To systematically review the performance of AI-based models for automated bone-age estimation from left PA hand–wrist radiographs. Materials and Methods: A systematic review was carried out and previously registered in PROSPERO (CRD42024619808) in MEDLINE (PubMed), Google Scholar, ELSEVIER (Scopus), EBSCOhost, Cochrane Library, Web of Science (WoS), IEEE Xplore, and ProQuest for original studies published between 2019 and 2024. Two independent reviewers extracted study characteristics and outcomes, assessed methodological quality via the Newcastle–Ottawa Scale, and evaluated bias using ROBINS-E. Results: Seventy-seven studies met inclusion criteria, encompassing convolutional neural networks, ensemble and hybrid models, and transfer-learning approaches. Commercial systems (e.g., BoneXpert^®, Physis^®, VUNO Med^®-BoneAge) achieved mean absolute errors of 2–31.8 months—significantly surpassing Greulich–Pyle and Tanner–Whitehouse benchmarks—and reduced reading times by up to 87%. Common limitations included demographic bias, heterogeneous imaging protocols, and scarce external validation. Conclusions: AI-based approaches have substantially advanced automated bone-age estimation, delivering clinical-grade speed and mean absolute errors below 6 months. To ensure equitable, generalizable performance, future work must prioritize demographically diverse training cohorts, implement bias-mitigation strategies, and perform local calibration against region-specific standards. Full article

Background: Chest radiography remains a cornerstone of thoracic imaging; however, the influence of patient demographics and technical factors on radiologic findings is not yet fully understood. This study investigates how gender, age, and radiographic projection affect the prevalence of three common findings: atelectasis, pleural effusion, and pulmonary nodules. Methods: We analyzed 112,120 frontal chest radiographs from the publicly available NIH ChestX-ray8 dataset. Gender-specific prevalence rates were compared using chi-square tests and unadjusted odds ratios (ORs). Multivariable logistic regression was performed to assess the independent effects of gender, age, and projection (posteroanterior [PA] vs. anteroposterior [AP]), including interaction terms. Results: Atelectasis and nodules were more prevalent in male patients, with unadjusted rates of 10.9% and 5.8% versus 9.5% and 5.4% in females. While the difference in nodules’ prevalence was not statistically significant (OR = 0.96, p = 0.16), multivariable analysis showed a significant association (adjusted OR = 1.378, 95% CI 1.157–1.641; p = 0.0003). Pleural effusion showed no significant gender difference (11.7% vs. 12.1%; OR 0.97, p = 0.10). Increasing age was consistently associated with higher odds of all findings (ORs per year: 1.016–1.012; all p < 0.0001). The PA view reduced the odds of atelectasis (OR 0.59) and effusion (OR 0.60), but increased the odds of detecting nodules (OR 1.31). Interaction terms showed minor but statistically significant gender-related differences in age effects. Conclusions: Gender, age, and radiographic projection substantially affect the radiographic detection of frequently observed thoracic abnormalities. These findings are directly relevant for improving clinical diagnostic accuracy and for reducing demographic and technical biases in AI-based radiograph interpretation, particularly in critical care and screening settings. Full article

Background/Objectives: Bone age assessments are essential for evaluating the growth and skeletal development of children and adolescents, influenced by factors such as genetics, ethnicity, culture, and nutrition. Clinical standards for these assessments must be adapted to the specific populations under study. This study validates the use of the Tanner–Whitehouse 3 method for determining bone age in pediatric and adolescent populations in the Canary Islands. Methods: This cross-sectional study analyzed 214 posteroanterior radiographs of the left hand and wrist from 80 females and 134 males, aged between 5 and 18 years. The radiographs were independently evaluated by three raters: a Radiologist Specialist (Rater 1), a General Practitioner (Rater 2), and a Medical Student (Rater 3). Intra- and inter-rater reliability were assessed using intraclass correlation coefficients (ICCs). Accuracy was evaluated by comparing estimated bone age with chronological age, stratified by sex and developmental stage. Results: Strong intra-rater reliability was observed across all raters. Raters 1 and 2 demonstrated excellent consistency (ICCs: 0.990–0.996), while Rater 3 exhibited slightly lower yet robust reliability (ICCs: 0.921–0.976). Inter-rater agreement was high between Raters 1 and 2 but decreased with Rater 3, reflecting the influence of experience (ICCs: 0.812–0.912). Bone age was underestimated in preschool children (mean difference: 3.712 mos.; 95% CI: 1.290–6.130; p = 0.199) and school-age males (mean difference: 3.978 mos.; 95% CI: −12.550 to 4.590; p = 0.926), with minimal discrepancies in females. In teenagers, the Tanner–Whitehouse 3 method slightly overestimated bone age (mean difference: −0.360 mos.; 95% CI: −0.770 to −0.954; p = 0.299). Conclusions: In conclusion, the Tanner–Whitehouse 3 method demonstrates overall precision and reliability but requires caution, as it underestimates bone age in preschool children and school-age males, while slightly overestimating it in adolescents. Full article

Objectives: The scaphoid fat pad stripe (SFS) is a radiological sign first described in 1975 as a line of relative lucency lying parallel to the lateral border of the scaphoid, with slight convexity toward it, and it is optimally demonstrated on postero-anterior and oblique views with ulnar deviation of the carpus. The obliteration or displacement of this line is commonly present in acute fractures of the scaphoid, radial styloid process, and proximal first metacarpus. The aim of this observational study is to investigate the supportive value of the fat stripe sign (SFS) in the diagnosis of scaphoid fractures in the pediatric population. Methods: This is a monocentric, retrospective study of all patients referred to the Pediatric Traumatology Unit of a tertiary hospital from the Emergency Department with clinical suspicion of scaphoid fracture without visible fracture in the initial X-ray. Radiological reports for CT and MRIs were recorded, and the initial X-rays were blindly reviewed by a pediatric orthopedic fellowship-accredited surgeon for the presence of an abnormal scaphoid fat pad stripe sign and the presence of a fracture line in the initial X-rays. Results: The results of the blind interpretation of the initial X-rays for the fat stripe sign showed 86% sensitivity and 58% specificity, with the negative predictive value reaching 92%. Conclusions: The scaphoid fat stripe sign can be used as an adjacent in the diagnosis of an occult scaphoid fracture in children or adolescents. Its high negative predictive value, if confirmed in larger studies, can be an element used to exclude scaphoid fracture and consequently avoid unnecessary immobilizations and health costs. Full article

Dynamic digital radiography (DDR) is a recent imaging technique that allows for real-time visualization of thoracic and pulmonary movement in synchronization with the breathing cycle, providing useful clinical information. A 46-year-old male, a former smoker, was evaluated for unexplained dyspnea and reduced exercise tolerance. His medical history included a SARS-CoV-2 infection in 2021. On physical examination, decreased breath sounds were noted at the right-lung base. Spirometry showed results below predicted values. A standard chest radiograph revealed an elevated right hemidiaphragm, a finding not present in a previous CT scan performed during his SARS-CoV-2 infection. To better assess the diaphragmatic function, a posteroanterior DDR study was performed in the standing position with X-ray equipment (AeroDR TX, Konica Minolta Inc., Tokyo, Japan) during forced breath, with the following acquisition parameters: tube voltage, 100 kV; tube current, 50 mA; pulse duration of pulsed X-ray, 1.6 ms; source-to-image distance, 2 m; additional filter, 0.5 mm Al + 0.1 mm Cu. The exposure time was 12 s. The pixel size was 388 × 388 μm, the matrix size was 1024 × 768, and the overall image area was 40 × 30 cm. The dynamic imaging, captured at 15 frames/s, was then assessed on a dedicated workstation (Konica Minolta Inc., Tokyo, Japan). The dynamic acquisition showed a markedly reduced motion of the right diaphragm. The diagnosis of diaphragm dysfunction can be challenging due to its range of symptoms, which can vary from mild to severe dyspnea. The standard chest X-ray is usually the first exam to detect an elevated hemidiaphragm, which may suggest motion impairment or paralysis but fails to predict diaphragm function. Ultrasound (US) allows for the direct visualization of the diaphragm and its motion. Still, its effectiveness depends highly on the operator’s experience and could be limited by gas and abdominal fat. Moreover, ultrasound offers limited information regarding the lung parenchyma. On the other hand, high-resolution CT can be useful in identifying causes of diaphragmatic dysfunction, such as atrophy or eventration. However, it does not allow for the quantitative assessment of diaphragmatic movement and the differentiation between paralysis and dysfunction, especially in bilateral dysfunction, which is often overlooked due to the elevation of both hemidiaphragms. Dynamic Digital Radiography (DDR) has emerged as a valuable and innovative imaging technique due to its unique ability to evaluate diaphragm movement in real time, integrating dynamic functional information with static anatomical data. DDR provides both visual and quantitative analysis of the diaphragm’s motion, including excursion and speed, which leads to a definitive diagnosis. Additionally, DDR offers a range of post-processing techniques that provide information on lung movement and pulmonary ventilation. Based on these findings, the patient was referred to a thoracic surgeon and deemed a candidate for surgical plication of the right diaphragm. Full article

Bone age assessments measure the growth and development of children and adolescents by evaluating their skeletal maturity, which is influenced by various factors like heredity, ethnicity, culture, and nutrition. The clinical standards for this assessment should be up to date and appropriate for the specific population being studied. This study validates the GP-Canary Atlas for accurately predicting bone age by analyzing posteroanterior left hand and wrist radiographs of healthy children (80 females and 134 males) from the Canary Islands across various developmental stages and genders. We found strong intra-rater reliability among all three raters, with Raters 1 and 2 indicating very high consistency (intra-class coefficients = 0.990 to 0.996) and Rater 3 displaying slightly lower but still strong reliability (intra-class coefficients = 0.921 to 0.976). The inter-rater agreement was excellent between Raters 1 and 2 but significantly lower between Rater 3 and the other two raters, with intra-class coefficients of 0.408 and 0.463 for Rater 1 and 0.327 and 0.509 for Rater 2. The accuracy analysis revealed a substantial underestimation of bone age compared to chronological age for preschool- (mean difference = 17.036 months; p < 0.001) and school-age males (mean difference = 13.298 months; p < 0.001). However, this was not observed in females, where the mean difference was minimal (3.949 months; p < 0.239). In contrast, the Atlas showed greater accuracy for teenagers, showing only a slight overestimation (mean difference = 3.159 months; p = 0.823). In conclusion, the GP-Canary Atlas demonstrates overall precision but requires caution as it underestimates the BA in preschool children and overestimates it in school-age girls and adolescents. Full article

The Cobb angle (CA) serves as the principal method for assessing spinal deformity, but manual measurements of the CA are time-consuming and susceptible to inter- and intra-observer variability. While learning-based methods, such as SpineHRNet+, have demonstrated potential in automating CA measurement, their accuracy can be influenced by the severity of spinal deformity, image quality, relative position of rib and vertebrae, etc. Our aim is to create a reliable learning-based approach that provides consistent and highly accurate measurements of the CA from posteroanterior (PA) X-rays, surpassing the state-of-the-art method. To accomplish this, we introduce SpineHRformer, which identifies anatomical landmarks, including the vertices of endplates from the 7th cervical vertebra (C7) to the 5th lumbar vertebra (L5) and the end vertebrae with different output heads, enabling the calculation of CAs. Within our SpineHRformer, a backbone HRNet first extracts multi-scale features from the input X-ray, while transformer blocks extract local and global features from the HRNet outputs. Subsequently, an output head to generate heatmaps of the endplate landmarks or end vertebra landmarks facilitates the computation of CAs. We used a dataset of 1934 PA X-rays with diverse degrees of spinal deformity and image quality, following an 8:2 ratio to train and test the model. The experimental results indicate that SpineHRformer outperforms SpineHRNet+ in landmark detection (Mean Euclidean Distance: 2.47 pixels vs. 2.74 pixels), CA prediction (Pearson correlation coefficient: 0.86 vs. 0.83), and severity grading (sensitivity: normal-mild; 0.93 vs. 0.74, moderate; 0.74 vs. 0.77, severe; 0.74 vs. 0.7). Our approach demonstrates greater robustness and accuracy compared to SpineHRNet+, offering substantial potential for improving the efficiency and reliability of CA measurements in clinical settings. Full article

Objective: Prior studies on models based on deep learning (DL) and measuring the cardiothoracic ratio (CTR) on chest radiographs have lacked rigorous agreement analyses with radiologists or reader tests. We validated the performance of a commercially available DL-based CTR measurement model with various thoracic pathologies, and performed agreement analyses with thoracic radiologists and reader tests using a probabilistic-based reference. Materials and Methods: This study included 160 posteroanterior view chest radiographs (no lung or pleural abnormalities, pneumothorax, pleural effusion, consolidation, and n = 40 in each category) to externally test a DL-based CTR measurement model. To assess the agreement between the model and experts, intraclass or interclass correlation coefficients (ICCs) were compared between the model and two thoracic radiologists. In the reader tests with a probabilistic-based reference standard (Dawid–Skene consensus), we compared diagnostic measures—including sensitivity and negative predictive value (NPV)—for cardiomegaly between the model and five other radiologists using the non-inferiority test. Results: For the 160 chest radiographs, the model measured a median CTR of 0.521 (interquartile range, 0.446–0.59) and a mean CTR of 0.522 ± 0.095. The ICC between the two thoracic radiologists and between the model and two thoracic radiologists was not significantly different (0.972 versus 0.959, p = 0.192), even across various pathologies (all p-values > 0.05). The model showed non-inferior diagnostic performance, including sensitivity (96.3% versus 97.8%) and NPV (95.6% versus 97.4%) (p < 0.001 in both), compared with the radiologists for all 160 chest radiographs. However, it showed inferior sensitivity in chest radiographs with consolidation (95.5% versus 99.9%; p = 0.082) and NPV in chest radiographs with pleural effusion (92.9% versus 94.6%; p = 0.079) and consolidation (94.1% versus 98.7%; p = 0.173). Conclusion: While the sensitivity and NPV of this model for diagnosing cardiomegaly in chest radiographs with consolidation or pleural effusion were not as high as those of the radiologists, it demonstrated good agreement with the thoracic radiologists in measuring the CTR across various pathologies. Full article

This study aimed to investigate whether the morphology of the temporomandibular joint (TMJ) is associated with chewing patterns while considering skeletal morphology, sex, age, and symptoms of temporomandibular disorder (TMD). A cross-sectional observational study of 102 TMJs of 80 patients (age 16–40 years) was performed using pretreatment records of cone-beam computed tomography imaging of the TMJ, mandibular kinesiographic records of gum chewing, lateral and posteroanterior cephalometric radiographs, patient history, and pretreatment questionnaires. To select appropriate TMJ measurements, linear regression analyses were performed using TMJ measurements as dependent variables and chewing patterns as the independent variable with adjustment for other covariates, including Nasion-B plane (SNB) angle, Frankfort-mandibular plane angle (FMA), amount of lateral mandibular shift, sex, age, and symptoms of TMD. In multiple linear regression models adjusted for other covariates, the length of the horizontal short axis of the condyle and radius of the condyle at 135° from the medial pole were significantly (p < 0.05) associated with the chewing patterns in the frontal plane on the working side. “Non-bilateral grinding” displayed a more rounded shape of the mandibular condyle. Conversely, “bilateral grinding” exhibited a flatter shape in the anteroposterior aspect. These findings suggest that the mandibular condyle morphology might be related to skeletal and masticatory function, including chewing patterns. Full article

Cardiovascular diseases are among the major health problems that are likely to benefit from promising developments in quantum machine learning for medical imaging. The chest X-ray (CXR), a widely used modality, can reveal cardiomegaly, even when performed primarily for a non-cardiological indication. Based on pre-trained DenseNet-121, we designed hybrid classical–quantum (CQ) transfer learning models to detect cardiomegaly in CXRs. Using Qiskit and PennyLane, we integrated a parameterized quantum circuit into a classic network implemented in PyTorch. We mined the CheXpert public repository to create a balanced dataset with 2436 posteroanterior CXRs from different patients distributed between cardiomegaly and the control. Using k-fold cross-validation, the CQ models were trained using a state vector simulator. The normalized global effective dimension allowed us to compare the trainability in the CQ models run on Qiskit. For prediction, ROC AUC scores up to 0.93 and accuracies up to 0.87 were achieved for several CQ models, rivaling the classical–classical (CC) model used as a reference. A trustworthy Grad-CAM++ heatmap with a hot zone covering the heart was visualized more often with the QC option than that with the CC option (94% vs. 61%, p < 0.001), which may boost the rate of acceptance by health professionals. Full article

When doctors are fatigued, they often make diagnostic errors. Similarly, pharmacists may also make mistakes in dispensing medication. Therefore, object segmentation plays a vital role in many healthcare-related areas, such as symptom analysis in biomedical imaging and drug classification. However, many traditional deep-learning algorithms use a single view of an image for segmentation or classification. When the image is blurry or incomplete, these algorithms fail to segment the pathological area or the shape of the drugs accurately, which can then affect subsequent treatment plans. Consequently, we propose the Fuzzy DBNet, which combines the dual butterfly network and the fuzzy ASPP in a deep-learning network and processes images from both sides of an object simultaneously. Our experiments used multi-category pill and lung X-ray datasets for training. The average Dice coefficient of our proposed model reached 95.05% in multi-pill segmentation and 97.05% in lung segmentation. The results showed that our proposed model outperformed other state-of-the-art networks in both applications, demonstrating that our model can use multiple views of an image to obtain image segmentation or identification. Full article

Pectus excavatum (PE), a chest-wall deformity that can compromise cardiopulmonary function, cannot be detected by a radiologist through frontal chest radiography without a lateral view or chest computed tomography. This study aims to train a convolutional neural network (CNN), a deep learning architecture with powerful image processing ability, for PE screening through frontal chest radiography, which is the most common imaging test in current hospital practice. Posteroanterior-view chest images of PE and normal patients were collected from our hospital to build the database. Among them, 80% were used as the training set used to train the established CNN algorithm, Xception, whereas the remaining 20% were a test set for model performance evaluation. The performance of our diagnostic artificial intelligence model ranged between 0.976–1 under the receiver operating characteristic curve. The test accuracy of the model reached 0.989, and the sensitivity and specificity were 96.66 and 96.64, respectively. Our study is the first to prove that a CNN can be trained as a diagnostic tool for PE using frontal chest X-rays, which is not possible by the human eye. It offers a convenient way to screen potential candidates for the surgical repair of PE, primarily using available image examinations. Full article

COVID-19 is a transferable disease inherited from the SARS-CoV-2 virus. A total of 594 million people have been infected, and 6.4 million human beings have died due to COVID-19. The fastest way to diagnose the disease is by radiography. Deep learning has been the most popular technique for image classification during the last decade. This paper aims to examine the contributions of machine learning for the detection of COVID-19 using Deep Learning and explores the overall application of convolutional neural networks of some famous state-of-the-art deep learning pre-trained models. In this research, our objective is to explore the various image classification strategies for CXIs and the application of deep learning models for optimization and feature selection. The study presented in this article shows that the accuracy of deep learning models when detecting COVID-19 on the basis of chest X-ray images ranges from 93 percent to above 99 percent. Full article