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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.

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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. PURPOSE: 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. MATERIAL AND 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. CONCLUSION: 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.

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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.

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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.

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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.

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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.

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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.

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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.

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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. MATERIALS AND 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.

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OBJECTIVE: Spinal involvement in rheumatoid arthritis (RA) is limited to the upper cervical spine, leading to cervical spine instability. This study aimed to evaluate the prevalence of anterior atlantoaxial subluxation (aAAS) and its associated risk factors in patients with RA. METHOD: This single-centre cross-sectional study 240 patients consecutively were recruited. Radiographs of the cervical spine were obtained in the flexion and neutral neck positions and read by two blinded observers. The diagnosis of aAAS was based on the distance between the anterior aspect of the dens and the posterior aspect of the anterior arch of the atlas, which was >3 mm during flexion. Statistical analysis was performed to determine the predictive factors of aAAS. RESULTS: Two hundred and forty patients with a mean ± SD age of 56.4 ± 11.4 years were recruited, and 191 (78%) were female. The mean ± SD duration of the disease was 10.2 ± 8.5 years. Of all 25 cases (10.4%) diagnosed with aAAS, the mean anterior atlantodental interval in patients with AAS was 4.19 ± 1.20 mm. One in three patients with aAAS had no neck pain. Patients with aAAS had longer disease duration, lower age at diagnosis, lower body mass index, higher anti-cyclic citrullinated peptide autoantibodies (anti-CCP), more frequent erosion, joint restriction, and joint prostheses. In the multivariate regression model, joint limitation, history of joint prostheses, low BMI, and higher anti-CCP levels were independent predictors of the aAAS. CONCLUSION: Thirty-three percent of patients with cervical involvement do not experience neck pain. Cervical involvement should be considered even without neck pain, particularly in established diseases.

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Tuberculosis, which primarily affects developing countries, remains a significant global health concern. Since the 2010s, the role of chest radiography has expanded in tuberculosis triage and screening beyond its traditional complementary role in the diagnosis of tuberculosis. Computer-aided diagnosis (CAD) systems for tuberculosis detection on chest radiographs have recently made substantial progress in diagnostic performance, thanks to deep learning technologies. The current performance of CAD systems for tuberculosis has approximated that of human experts, presenting a potential solution to the shortage of human readers to interpret chest radiographs in low- or middle-income, high-tuberculosis-burden countries. This article provides a critical appraisal of developmental process reporting in extant CAD software for tuberculosis, based on the Checklist for Artificial Intelligence in Medical Imaging. It also explores several considerations to scale up CAD solutions, encompassing manufacturer-independent CAD validation, economic and political aspects, and ethical concerns, as well as the potential for broadening radiography-based diagnosis to other nontuberculosis diseases. Collectively, CAD for tuberculosis will emerge as a representative deep learning application, catalyzing advances in global health and health equity. Keywords: Computer-aided Diagnosis (CAD), Conventional Radiography, Thorax, Lung, Machine Learning Supplemental material is available for this article. © RSNA, 2024.

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Tuberculosis (TB) represents a major public health problem worldwide. Any tissue may be infected. Involvement of the musculoskeletal (MSK) system account for 1-3% of all tuberculous infections. MSK TB may manifest as tuberculous spondylitis, arthritis, osteomyelitis, and soft tissue infections. Although TB spondylitis may present with distinctive imaging features compared to pyogenic infections of the spine, the imaging semiology of extra-spinal TB infections is mostly nonspecific and may mimic other lesions. TB infections should therefore always be considered in the differential diagnosis, particularly in immunocompromised patients. The aim of this article is to review the imaging features of spinal and extra-spinal MSK TB. Magnetic resonance imaging is considered the modality of choice to make the diagnosis and to evaluate the extent of the disease.

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Keywords: MRI, Imaging Sequences, Ultrasound, Mammography, CT, Angiography, Conventional Radiography Published under a CC BY 4.0 license. See also the commentary by Whitman and Vining in this issue.

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In recent years, imaging has become increasingly important to confirm diagnosis, monitor disease activity, and predict disease course and outcome in children with juvenile idiopathic arthritis (JIA). Over the past few decades, great efforts have been made to improve the quality of diagnostic imaging and to reach a consensus on which methods and scoring systems to use. However, there are still some critical issues, and the diagnosis, course, and management of JIA are closely related to clinical assessment. This review discusses the main indications for conventional radiography (XR), musculoskeletal ultrasound (US), and magnetic resonance imaging (MRI), while trying to maintain a clinical perspective. The diagnostic-therapeutic timing at which one or the other method should be used, depending on the disease/patient phenotype, will be assessed, considering the main advantages and disadvantages of each imaging modality according to the currently available literature. Some brief clinical case scenarios on the most frequently and severely involved joints in JIA are also presented.

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Radiographic markers contain protected health information that must be removed before public release. This work presents a deep learning algorithm that localizes radiographic markers and selectively removes them to enable de-identified data sharing. The authors annotated 2000 hip and pelvic radiographs to train an object detection computer vision model. Data were split into training, validation, and test sets at the patient level. Extracted markers were then characterized using an image processing algorithm, and potentially useful markers (eg, "L" and "R") without identifying information were retained. The model achieved an area under the precision-recall curve of 0.96 on the internal test set. The de-identification accuracy was 100% (400 of 400), with a de-identification false-positive rate of 1% (eight of 632) and a retention accuracy of 93% (359 of 386) for laterality markers. The algorithm was further validated on an external dataset of chest radiographs, achieving a de-identification accuracy of 96% (221 of 231). After fine-tuning the model on 20 images from the external dataset to investigate the potential for improvement, a 99.6% (230 of 231, P = .04) de-identification accuracy and decreased false-positive rate of 5% (26 of 512) were achieved. These results demonstrate the effectiveness of a two-pass approach in image de-identification. Keywords: Conventional Radiography, Skeletal-Axial, Thorax, Experimental Investigations, Supervised Learning, Transfer Learning, Convolutional Neural Network (CNN) Supplemental material is available for this article. © RSNA, 2023 See also the commentary by Chang and Li in this issue.

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Purpose: To train an explainable deep learning model for patient reidentification in chest radiograph datasets and assess changes in model-perceived patient identity as a marker for emerging radiologic abnormalities in longitudinal image sets. Materials and Methods: This retrospective study used a set of 1 094 537 frontal chest radiographs and free-text reports from 259 152 patients obtained from six hospitals between 2006 and 2019, with validation on the public ChestX-ray14, CheXpert, and MIMIC-CXR datasets. A deep learning model was trained for patient reidentification and assessed on patient identity confirmation, retrieval of patient images from a database based on a query image, and radiologic abnormality prediction in longitudinal image sets. The representation learned was incorporated into a generative adversarial network, allowing visual explanations of the relevant features. Performance was evaluated with sensitivity, specificity, F1 score, Precision at 1, R-Precision, and area under the receiver operating characteristic curve (AUC) for normal and abnormal prediction. Results: Patient reidentification was achieved with a mean F1 score of 0.996 ± 0.001 (2 SD) on the internal test set (26 152 patients) and F1 scores of 0.947-0.993 on the external test data. Database retrieval yielded a mean Precision at 1 score of 0.976 ± 0.005 at 299 × 299 resolution on the internal test set and Precision at 1 scores between 0.868 and 0.950 on the external datasets. Patient sex, age, weight, and other factors were identified as key model features. The model achieved an AUC of 0.73 ± 0.01 for abnormality prediction versus an AUC of 0.58 ± 0.01 for age prediction error. Conclusion: The image features used by a deep learning patient reidentification model for chest radiographs corresponded to intuitive human-interpretable characteristics, and changes in these identifying features over time may act as markers for an emerging abnormality.Keywords: Conventional Radiography, Thorax, Feature Detection, Supervised Learning, Convolutional Neural Network, Principal Component Analysis Supplemental material is available for this article. © RSNA, 2023See also the commentary by Raghu and Lu in this issue.

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Purpose: To analyze a recently published chest radiography foundation model for the presence of biases that could lead to subgroup performance disparities across biologic sex and race. Materials and Methods: This Health Insurance Portability and Accountability Act-compliant retrospective study used 127 118 chest radiographs from 42 884 patients (mean age, 63 years ± 17 [SD]; 23 623 male, 19 261 female) from the CheXpert dataset that were collected between October 2002 and July 2017. To determine the presence of bias in features generated by a chest radiography foundation model and baseline deep learning model, dimensionality reduction methods together with two-sample Kolmogorov-Smirnov tests were used to detect distribution shifts across sex and race. A comprehensive disease detection performance analysis was then performed to associate any biases in the features to specific disparities in classification performance across patient subgroups. Results: Ten of 12 pairwise comparisons across biologic sex and race showed statistically significant differences in the studied foundation model, compared with four significant tests in the baseline model. Significant differences were found between male and female (P < .001) and Asian and Black (P < .001) patients in the feature projections that primarily capture disease. Compared with average model performance across all subgroups, classification performance on the "no finding" label decreased between 6.8% and 7.8% for female patients, and performance in detecting "pleural effusion" decreased between 10.7% and 11.6% for Black patients. Conclusion: The studied chest radiography foundation model demonstrated racial and sex-related bias, which led to disparate performance across patient subgroups; thus, this model may be unsafe for clinical applications.Keywords: Conventional Radiography, Computer Application-Detection/Diagnosis, Chest Radiography, Bias, Foundation Models Supplemental material is available for this article. Published under a CC BY 4.0 license.See also commentary by Czum and Parr in this issue.

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Purpose: To externally test four chest radiograph classifiers on a large, diverse, real-world dataset with robust subgroup analysis. Materials and Methods: In this retrospective study, adult posteroanterior chest radiographs (January 2016-December 2020) and associated radiology reports from Trillium Health Partners in Ontario, Canada, were extracted and de-identified. An open-source natural language processing tool was locally validated and used to generate ground truth labels for the 197 540-image dataset based on the associated radiology report. Four classifiers generated predictions on each chest radiograph. Performance was evaluated using accuracy, positive predictive value, negative predictive value, sensitivity, specificity, F1 score, and Matthews correlation coefficient for the overall dataset and for patient, setting, and pathology subgroups. Results: Classifiers demonstrated 68%-77% accuracy, 64%-75% sensitivity, and 82%-94% specificity on the external testing dataset. Algorithms showed decreased sensitivity for solitary findings (43%-65%), patients younger than 40 years (27%-39%), and patients in the emergency department (38%-60%) and decreased specificity on normal chest radiographs with support devices (59%-85%). Differences in sex and ancestry represented movements along an algorithm's receiver operating characteristic curve. Conclusion: Performance of deep learning chest radiograph classifiers was subject to patient, setting, and pathology factors, demonstrating that subgroup analysis is necessary to inform implementation and monitor ongoing performance to ensure optimal quality, safety, and equity.Keywords: Conventional Radiography, Thorax, Ethics, Supervised Learning, Convolutional Neural Network (CNN), Machine Learning Algorithms Supplemental material is available for this article. © RSNA, 2023See also the commentary by Huisman and Hannink in this issue.

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INTRODUCTION: The proportion of diagnostic images not applied for diagnostic purposes is an indicator of image quality, safety, and efficiency in radiography. Despite increased awareness, image reject is still a substantial problem and needs continued observation and targeted measures. Accordingly, the objective of this study is to estimate the extent, variation, and characteristics of image rejects, in order to improve the quality, safety, and efficiency in radiography. METHODS: All skeletal images at two digital X-ray rooms at two public hospitals in Norway were reviewed for four weeks in 2020. The number of exposed images, type of examination, and number of deleted images were registered. For each deleted image the deduced reasons for deleting the image were recorded. RESULTS: 2183 and 1467 X-ray images were taken at Hospital 1 and 2 respectively. The corresponding reject rates were 14.2% and 9.1%. The reject rate varied greatly from day to day (from 0% to 22%), and the examinations with the highest reject rate were X-ray of extremities (knee, elbow, ankle, wrist) (12-25%) and of the spine (14-19%). The two clearly dominating reasons for image rejects were positioning and centering errors. CONCLUSION: The reject rate is high and reduces quality, safety, and efficiency of imaging services. The reasons for image rejects are typical professionally reducible errors indicating great potential for improvement. IMPLICATIONS FOR PRACTICE: Monitoring and assessing image rejects are of great importance to management, training, education, patient safety, and for quality improvement of imaging services.

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OBJECTIVE: To compare conventional radiography (CR) and magnetic resonance imaging (MRI) of the left hand/wrist and both clavicles for forensic age estimation of adolescents and young adults. MATERIALS AND METHODS: CR and MRI were prospectively conducted in 108 healthy Caucasian volunteers (52 males, 56 females) aged 16 to 21 years. Skeletal development was assessed by allocating stages (wrist, clavicles) and atlas standards (hand/wrist). Inter- and intra-observer agreements were quantified using linear weighted Cohen's kappa, and descriptive statistics regarding within-stage/standard age distributions were reported. RESULTS: Inter- and intra-observer agreements for hand/wrist CR (staging technique: 0.840-0.871 and 0.877-0.897, respectively; atlas method: 0.636-0.947 and 0.853-0.987, respectively) and MRI (staging technique: 0.890-0.932 and 0.897-0.952, respectively; atlas method: 0.854-0.941 and 0.775-0.978, respectively) were rather similar. The CR atlas method was less reproducible than the staging technique. Inter- and intra-observer agreements for clavicle CR (0.590-0.643 and 0.656-0.770, respectively) were lower than those for MRI (0.844-0.852 and 0.866-0.931, respectively). Furthermore, although shifted, wrist CR and MRI within-stage age distribution spread were similar, as were those between staging techniques and atlas methods. The possibility to apply (profound) substages to clavicle MRI rendered a more gradual increase of age distributions with increasing stages, compared to CR. CONCLUSIONS: For age estimation based on the left hand/wrist and both clavicles, reference data should be considered anatomical structure- and imaging modality-specific. Moreover, CR is adequate for hand/wrist evaluation and a wrist staging technique seems to be more useful than an atlas method. By contrast, MRI is of added value for clavicle evaluation.

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