BACKGROUND: The management of patients with slipped capital femoral epiphysis (SCFE) requires imaging diagnostics of good quality and accurate measurement of the degree of slippage. In Sweden, three different radiological methods are commonly used: the calcar femorale method; the Billing method; and the Head-shaft angle described by Southwick.
OBJECTIVE: To evaluate whether any of the three most common methods used in Sweden to measure the slip angle was more useful and reproducible than the others.
METHODS: Two experienced orthopaedists measured the slip angle in preoperative hip radiographs. Intra- and inter-observer variability between the two experienced observers and the reported value by clinicians who treated the child with SCFE was evaluated.
RESULTS: The intraclass correlation coefficient (ICC) confidence interval (CI) between the two experienced observers and the reporting clinicians overlapped for the three methods. In 37% of the cases, the difference was more than 5° between the experienced observers\' measurement and the reported value by clinicians. The two experienced orthopaedists\' intra- and inter-observer variability was low.
CONCLUSIONS: The observer\'s experience is more important than the method of choice when measuring the slip angle in SCFE. The research group recommends the calcar femorale method due to its feasibility on the versatile and commonly used frog leg lateral view.

Age estimation in forensic odontology is mainly based on the development of permanent teeth. To register the developmental status of an examined tooth, staging techniques were developed. However, due to inappropriate calibration, uncertainties during stage allocation, and lack of experience, non-uniformity in stage allocation exists between expert observers. As a consequence, related age estimation results are inconsistent. An automated staging technique applicable to all tooth types can overcome this drawback.This study aimed to establish an integrated automated technique to stage the development of all mandibular tooth types and to compare their staging performances.Calibrated observers staged FDI teeth 31, 33, 34, 37 and 38 according to a ten-stage modified Demirjian staging technique. According to a standardised bounding box around each examined tooth, the retrospectively collected panoramic radiographs were cropped using Photoshop CC 2021® software (Adobe®, version 23.0). A gold standard set of 1639 radiographs were selected (n31 = 259, n33 = 282, n34 = 308, n37 = 390, n38 = 400) and input into a convolutional neural network (CNN) trained for optimal staging accuracy. The performance evaluation of the network was conducted in a five-fold cross-validation scheme. In each fold, the entire dataset was split into a training and a test set in a non-overlapping fashion between the folds (i.e., 80% and 20% of the dataset, respectively). Staging performances were calculated per tooth type and overall (accuracy, mean absolute difference, linearly weighted Cohen\'s Kappa and intra-class correlation coefficient). Overall, these metrics equalled 0.53, 0.71, 0.71, and 0.89, respectively. All staging performance indices were best for 37 and worst for 31. The highest number of misclassified stages were associated to adjacent stages. Most misclassifications were observed in all available stages of 31.Our findings suggest that the developmental status of mandibular molars can be taken into account in an automated approach for age estimation, while taking incisors into account may hinder age estimation.

MRI, ultrasound, and conventional radiography each play distinct roles in the evaluation of juvenile idiopathic arthritis (JIA), with MRI being the preferred imaging modality of choice for assessing both inflammatory and destructive changes. These various imaging modalities provide valuable insights into JIA in pediatric patients. However, challenges persist in terms of achieving precision, ensuring validity, and distinguishing between pathologic findings and normal anatomic variations. Establishing normal reference values and implementing scoring systems can aid in the precise evaluation of disease activity and provide information to assist treatment decisions for children with JIA. Ongoing advancements in imaging techniques and standardization initiatives aim to bolster the accuracy of JIA diagnosis and assessment, ultimately leading to enhanced patient care and treatment outcomes.

Purpose To assess the prognostic value of a deep learning-based chest radiographic age (hereafter, CXR-Age) model in a large external test cohort of Asian individuals. Materials and Methods This single-center, retrospective study included chest radiographs from consecutive, asymptomatic Asian individuals aged 50-80 years who underwent health checkups between January 2004 and June 2018. This study performed a dedicated external test of a previously developed CXR-Age model, which predicts an age adjusted based on the risk of all-cause mortality. Adjusted hazard ratios (HRs) of CXR-Age for all-cause, cardiovascular, lung cancer, and respiratory disease mortality were assessed using multivariable Cox or Fine-Gray models, and their added values were evaluated by likelihood ratio tests. Results A total of 36 924 individuals (mean chronological age, 58 years ± 7 [SD]; CXR-Age, 60 years ± 5; 22 352 male) were included. During a median follow-up of 11.0 years, 1250 individuals (3.4%) died, including 153 cardiovascular (0.4%), 166 lung cancer (0.4%), and 98 respiratory (0.3%) deaths. CXR-Age was a significant risk factor for all-cause (adjusted HR at chronological age of 50 years, 1.03; at 60 years, 1.05; at 70 years, 1.07), cardiovascular (adjusted HR, 1.11), lung cancer (adjusted HR for individuals who formerly smoked, 1.12; for those who currently smoke, 1.05), and respiratory disease (adjusted HR, 1.12) mortality (P < .05 for all). The likelihood ratio test demonstrated added prognostic value of CXR-Age to clinical factors, including chronological age for all outcomes (P < .001 for all). Conclusion Deep learning-based chest radiographic age was associated with various survival outcomes and had added value to clinical factors in asymptomatic Asian individuals, suggesting its generalizability. Keywords: Conventional Radiography, Thorax, Heart, Lung, Mediastinum, Outcomes Analysis, Quantification, Prognosis, Convolutional Neural Network (CNN) Supplemental material is available for this article. © RSNA, 2024 See also the commentary by Adams and Bressem in this issue.

Purpose To develop and evaluate a publicly available deep learning model for segmenting and classifying cardiac implantable electronic devices (CIEDs) on Digital Imaging and Communications in Medicine (DICOM) and smartphone-based chest radiographs. Materials and Methods This institutional review board-approved retrospective study included patients with implantable pacemakers, cardioverter defibrillators, cardiac resynchronization therapy devices, and cardiac monitors who underwent chest radiography between January 2012 and January 2022. A U-Net model with a ResNet-50 backbone was created to classify CIEDs on DICOM and smartphone images. Using 2321 chest radiographs in 897 patients (median age, 76 years [range, 18-96 years]; 625 male, 272 female), CIEDs were categorized into four manufacturers, 27 models, and one \"other\" category. Five smartphones were used to acquire 11 072 images. Performance was reported using the Dice coefficient on the validation set for segmentation or balanced accuracy on the test set for manufacturer and model classification, respectively. Results The segmentation tool achieved a mean Dice coefficient of 0.936 (IQR: 0.890-0.958). The model had an accuracy of 94.36% (95% CI: 90.93%, 96.84%; 251 of 266) for CIED manufacturer classification and 84.21% (95% CI: 79.31%, 88.30%; 224 of 266) for CIED model classification. Conclusion The proposed deep learning model, trained on both traditional DICOM and smartphone images, showed high accuracy for segmentation and classification of CIEDs on chest radiographs. Keywords: Conventional Radiography, Segmentation Supplemental material is available for this article. © RSNA, 2024 See also the commentary by Júdice de Mattos Farina and Celi in this issue.

Imaging methods capable of detecting inflammation, such as MR imaging and ultrasound, are of paramount importance in rheumatic disease management, not only for diagnostic purposes but also for monitoring disease activity and treatment response. However, more advanced stages of arthritis, characterized by findings of cumulative structural damage, have traditionally been accomplished by radiographs and computed tomography. The purpose of this review is to provide an overview of imaging of some of the most prevalent inflammatory rheumatic diseases affecting the lower limb (osteoarthritis, rheumatoid arthritis, and gout) and up-to-date recommendations regarding imaging diagnostic workup.

Pseudolesions in bone and muscle are encountered mostly incidentally in routine imaging studies, especially due to the recent advancements on many different imaging modalities. These lesions can be categorized into the following categories: normal variants; congenital; iatrogenic; degenerative; and postoperative. In this review, we discuss the many different radiological characteristics of musculoskeletal pseudolesions that appear on imaging, which can prevent non-essential additional studies.

Monostotic fibrous dysplasia is a benign asymptomatic lesion that affects only one bone, which is replaced by amorphous connective tissue. Clinically there is an increase in the volume of the affected area, which is observed by imaging as a radiopaque area with diffuse non-corticalized limits capable of expanding to neighboring structures, and it is histologically evidenced as \"resembling Chinese characters\". The lesion is seen as a radiopaque image with diffuse borders in conventional or digital radiography, while cone beam computed tomography identifies the exact location and extension of an isodense, mixed or hyperdense image of non-corticalized edges. Magnetic resonance imaging is also used when the lesion involves soft tissues or nerves, and bone scintigraphy is performed in order to systemically observe bone quality. The objective of this article was to describe the new technologies in oral radiology for the diagnosis of monostotic fibrous dysplasia and the importance of the current imaging methods in achieving an adequate diagnosis. These techniques range from conventional radiography to bone scans, which provide images of higher quality, clarity and better precision with less invasive techniques to the patient. This review of the literature helps to expand the knowledge of dental professionals in relation to the clinical and imaging characteristics of monostotic fibrous dysplasia.
La displasia fibrosa monostótica es una lesión benigna y asintomática que afecta solo a un hueso, que es reemplazado por tejido conectivo amorfo. Clínicamente, existe un aumento del volumen de la zona afectada, que se observa en la imagen como un área radiopaca con límites difusos no corticalizados capaces de expandirse a estructuras vecinas y se evidencia histológicamente como “semejanza de caracteres chinos”. La lesión se ve como una imagen radiopaca con bordes difusos en una radiografía convencional o digital, mientras que la tomografía computarizada de haz cónico identifica la ubicación exacta y la extensión de una imagen isodensa, mixta o hiperdensa de bordes no corticalizados. La resonancia magnética también se usa cuando la lesión involucra tejidos blandos o nervios, y se realiza una gammagrafía ósea para observar sistémicamente la calidad del hueso. El objetivo de este artículo fue describir las nuevas tecnologías en radiología oral para el diagnóstico de la displasia fibrosa monostótica y la importancia de los métodos de imagen actuales para lograr un diagnóstico adecuado. Estas técnicas van desde la radiografía convencional hasta las gammagrafías óseas, que brindan imágenes de mayor calidad, claridad y mejor precisión con técnicas menos invasivas para el paciente. Esta revisión de la literatura ayuda a ampliar el conocimiento de los profesionales de la odontología en relación con las características clínicas y de imagen de la displasia fibrosa monostótica.

Purpose To develop an artificial intelligence (AI) system for humeral tumor detection on chest radiographs (CRs) and evaluate the impact on reader performance. Materials and Methods In this retrospective study, 14 709 CRs (January 2000 to December 2021) were collected from 13 468 patients, including CT-proven normal (n = 13 116) and humeral tumor (n = 1593) cases. The data were divided into training and test groups. A novel training method called false-positive activation area reduction (FPAR) was introduced to enhance the diagnostic performance by focusing on the humeral region. The AI program and 10 radiologists were assessed using holdout test set 1, wherein the radiologists were tested twice (with and without AI test results). The performance of the AI system was evaluated using holdout test set 2, comprising 10 497 normal images. Receiver operating characteristic analyses were conducted for evaluating model performance. Results FPAR application in the AI program improved its performance compared with a conventional model based on the area under the receiver operating characteristic curve (0.87 vs 0.82, P = .04). The proposed AI system also demonstrated improved tumor localization accuracy (80% vs 57%, P < .001). In holdout test set 2, the proposed AI system exhibited a false-positive rate of 2%. AI assistance improved the radiologists\' sensitivity, specificity, and accuracy by 8.9%, 1.2%, and 3.5%, respectively (P < .05 for all). Conclusion The proposed AI tool incorporating FPAR improved humeral tumor detection on CRs and reduced false-positive results in tumor visualization. It may serve as a supportive diagnostic tool to alert radiologists about humeral abnormalities. Keywords: Artificial Intelligence, Conventional Radiography, Humerus, Machine Learning, Shoulder, Tumor Supplemental material is available for this article. © RSNA, 2024.

OBJECTIVE: Distances and angles measured from long-leg radiographs (LLR) are important for surgical decision-making. However, projectional radiography suffers from distortion, potentially generating differences between measurement and true anatomical dimension. These phenomena are not uniform between conventional radiography (CR) digital radiography (DR) and fan-beam technology (EOS). We aimed to identify differences between these modalities in an experimental setup.
METHODS: A hemiskeleton was stabilized using an external fixator in neutral, valgus and varus knee alignment. Ten images were acquired for each alignment and each modality: one CR setup, two different DR systems, and an EOS. A total of 1680 measurements were acquired and analyzed.
RESULTS: We observed great differences for dimensions and angles between the 4 modalities. Femoral head diameter measurements varied in the range of > 5 mm depending on the modality, with EOS being the closest to the true anatomical dimension. With functional leg length, a difference of 8.7% was observed between CR and EOS and with the EOS system being precise in the vertical dimension on physical-technical grounds, this demonstrates significant projectional magnification with CR-LLR. The horizontal distance between the medial malleoli varied by 20 mm between CR and DR, equating to 21% of the mean.
CONCLUSIONS: Projectional distortion resulting in variations approaching 21% of the mean indicate, that our confidence on measurements from standing LLR may not be justified. It appears likely that among the tested equipment, EOS-generated images are closest to the true anatomical situation most of the time.