Pairs Of Radiographs Research Articles

Measuring total hip arthroplasty liner wear using the EOS Imaging System: experimental and clinical results.

The low-dose EOS Imaging System is an emerging tool for 3-dimensional measurements in orthopedics. The clinical feasibility for measuring total hip arthroplasty (THA) liner wear has not yet been investigated. We aimed to evaluate the feasibility of using EOS to measure THA liner wear by examining the experimental accuracy using a THA phantom and clinical precision of patients with THA, considering a clinically relevant precision at the 95% repeatability limit to be 0.2 mm. An experimental THA phantom with movable stem and a fixed cup with a plastic liner was constructed to simulate progressive 3D liner wear. Series of 11 pairs of radiographs with 50 μm femoral movement in between were obtained for each 3D axis in EOS. 30 patients with a THA were scanned twice using EOS to assess precision. Model-based radiostereometric analysis (RSA) was used for wear measurement. The mean difference (true minus simulated wear) with standard deviation (SD) and 95% limits of agreement for experimental THA wear were 0.005 (0.037) and [-0.069 to 0.079] mm for the vertical (y) axis. The mean (SD) and 95% repeatability limit for precision for clinical measurement were -0.029 (0.105) and 0.218 mm. Experimental THA liner wear using EOS was within clinically relevant tolerances and without bias. The clinical precision was just outside our defined clinically relevant precision. Compared with conventional RSA, EOS is less accurate and precise but may still be of value for certain clinical applications, provided larger sample size or longer follow-up are available.

MSKI-RADS: An MRI-based Musculoskeletal Infection Reporting and Data System for the Diagnosis of Extremity Infections.

Background Current terms used to describe the MRI findings for musculoskeletal infections are nonspecific and inconsistent. Purpose To develop and validate an MRI-based musculoskeletal infection classification and scoring system. Materials and Methods In this retrospective cross-sectional internal validation study, a Musculoskeletal Infection Reporting and Data System (MSKI-RADS) was designed. Adult patients with radiographs and MRI scans of suspected extremity infections with a known reference standard obtained between June 2015 and May 2019 were included. The scoring categories were as follows: 0, incomplete imaging; I, negative for infection; II, superficial soft-tissue infection; III, deeper soft-tissue infection; IV, possible osteomyelitis (OM); V, highly suggestive of OM and/or septic arthritis; VI, known OM; and NOS (not otherwise specified), nonspecific bone lesions. Interreader agreement for 20 radiologists from 13 institutions (intraclass correlation coefficient [ICC]) and true-positive rates of MSKI-RADS were calculated and the accuracy of final diagnoses rendered by the readers was compared using generalized estimating equations for clustered data. Results Among paired radiographs and MRI scans from 208 patients (133 male, 75 female; mean age, 55 years ± 13 [SD]), 20 were category I; 34, II; 35, III; 30, IV; 35, V; 18, VI; and 36, NOS. Moderate interreader agreement was observed among the 20 readers (ICC, 0.70; 95% CI: 0.66, 0.75). There was no evidence of correlation between reader experience and overall accuracy (P = .94). The highest true-positive rate was for MSKI-RADS I and NOS at 88.7% (95% CI: 84.6, 91.7). The true-positive rate was 73% (95% CI: 63, 80) for MSKI-RADS V. Overall reader accuracy using MSKI-RADS across all patients was 65% ± 5, higher than final reader diagnoses at 55% ± 7 (P < .001). Conclusion MSKI-RADS is a valid system for standardized terminology and recommended management of imaging findings of peripheral extremity infections across various musculoskeletal-fellowship-trained reader experience levels. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Schweitzer in this issue.

Identification of TRISO pebbles at arbitrary orientation using pairs of X-ray radiographs

TRISO (tri-structural isotropic) fuel is anticipated to be one of the fuel types for future small modular reactors. A TRISO-fuelled pebble in a pebble bed reactor (PBR) consists of thousands of TRISO fuel particles embedded inside a graphite sphere. The diameter of each of the fuel particles is approximately 1 mm. A PBR can contain hundreds of thousands of pebbles, each with a diameter of approximately 6 cm. Unlike in traditional reactor designs, which have a relatively small number of discrete fuel assemblies, the large number of pebbles inside a PBR makes the tracking of individual fuel pebbles difficult. The distribution of TRISO particles within the pebble provides a unique fingerprint that identifies a pebble and can be used to track it. This fingerprint can be determined using a standard X-ray radiograph or a more time-consuming X-ray computed tomography scan. When an individual radiograph is used, the pebble image is compared to a library of reference images of the pebbles. To obtain a good match, the orientation of the pebble must be close to the orientation used for the reference image. As a pebble exits the reactor in a random orientation, the pebble must be reorientated before its identification can take place. This paper discusses the addition of a reference particle in the non-fuelled region of the pebble, as well as the simultaneous acquisition of pairs of radiographs at 90° with respect to each other, to aid pebble reorientation and the development of an identification framework. The framework follows a hybrid approach in which the pebble is first reorientated using machine learning techniques before being identified using traditional computer vision techniques. Other effects such as the impact of transmutation of the elements inside the fuel and the radioactivity of irradiated pebbles are also discussed.

Predicting osteoporosis from kidney-ureter-bladder radiographs utilizing deep convolutional neural networks

Osteoporosis is a common condition that can lead to fractures, mobility issues, and death. Although dual-energy X-ray absorptiometry (DXA) is the gold standard for osteoporosis, it is expensive and not widely available. In contrast, kidney-ureter-bladder (KUB) radiographs are inexpensive and frequently ordered in clinical practice. Thus, it is a potential screening tool for osteoporosis. In this study, we explored the possibility of predicting the bone mineral density (BMD) and classifying high-risk patient groups using KUB radiographs. We proposed DeepDXA-KUB, a deep learning model that predicts the BMD values of the left hip and lumbar vertebrae from an input KUB image. The datasets were obtained from Taiwanese medical centers between 2006 and 2019, using 8913 pairs of KUB radiographs and DXA examinations performed within 6 months. The images were randomly divided into training and validation sets in a 4:1 ratio. To evaluate the model's performance, we computed a confusion matrix and evaluated the sensitivity, specificity, accuracy, precision, positive predictive value, negative predictive value, F1 score, and area under the receiver operating curve (AUROC). Moderate correlations were observed between the predicted and DXA-measured BMD values, with a correlation coefficient of 0.858 for the lumbar vertebrae and 0.87 for the left hip. The model demonstrated an osteoporosis detection accuracy, sensitivity, and specificity of 84.7 %, 81.6 %, and 86.6 % for the lumbar vertebrae and 84.2 %, 91.2 %, and 81 % for the left hip, respectively. The AUROC was 0.939 for the lumbar vertebrae and 0.947 for the left hip, indicating a satisfactory performance in osteoporosis screening. The present study is the first to develop a deep learning model based on KUB radiographs to predict lumbar spine and femoral BMD. Our model demonstrated a promising correlation between the predicted and DXA-measured BMD in both the lumbar vertebrae and hip, showing great potential for the opportunistic screening of osteoporosis.

Defining and Differentiating Congenital Vertical Talus and Congenital Oblique Talus: It's Not Primarily About the Talus.

Congenital vertical talus (CVT) and congenital oblique talus (COT) are rocker-bottom foot deformities that have similar names and no objective definitions. This has led to confusion for practitioners, as well as scientific challenges for researchers. Our goal was to provide objective radiographic criteria to define and differentiate CVT and COT. We evaluated 62 pairs of maximum dorsiflexion and plantar flexion lateral radiographs of infant feet that had been clinically diagnosed with CVT. The dorsiflexion tibiotalar angle, the plantar flexion talus-first metatarsal angle, and the plantar flexion foot center of rotation of angulation (foot-CORA) were measured using transparent overlay tools. Freehand measurements were made on a subset of 10 pairs of radiographs to confirm clinical applicability. Nine contralateral pairs of radiographs of normal feet were measured for comparison. Specific values for the radiographic measurements were identified that, together, reliably differentiated the shapes of rocker-bottom feet with CVT, COT, and flexible flatfoot with a short tendo-Achilles (FFF-STA), as well as the shape of the normal foot. More severe and rigid rocker-bottom foot deformities were diagnosed with CVT. Less severe and more flexible deformities were diagnosed with COT. CVT, COT, FFF-STA, and normal feet can be reliably differentiated using 2 angular measurements and 1 bone position measurement on dorsiflexion and plantar flexion lateral radiographs. Our data indicated that the differentiation of CVT and COT is based primarily on the rigidity of the navicular dislocation rather than the verticality of the talus. The data further supported the proposition that COT is a foot deformity along a spectrum of valgus/eversion deformities of the hindfoot that requires early treatment. Application of these diagnostic criteria should lead to clinical studies that identify a specific treatment, treatment outcome, and prognosis for each deformity. Diagnostic Level II. See Instructions for Authors for a complete description of levels of evidence.

Revisiting the Trustworthiness of Saliency Methods in Radiology AI.

Purpose To determine whether saliency maps in radiology artificial intelligence (AI) are vulnerable to subtle perturbations of the input, which could lead to misleading interpretations, using prediction-saliency correlation (PSC) for evaluating the sensitivity and robustness of saliency methods. Materials and Methods In this retrospective study, locally trained deep learning models and a research prototype provided by a commercial vendor were systematically evaluated on 191 229 chest radiographs from the CheXpert dataset and 7022 MR images from a human brain tumor classification dataset. Two radiologists performed a reader study on 270 chest radiograph pairs. A model-agnostic approach for computing the PSC coefficient was used to evaluate the sensitivity and robustness of seven commonly used saliency methods. Results The saliency methods had low sensitivity (maximum PSC, 0.25; 95% CI: 0.12, 0.38) and weak robustness (maximum PSC, 0.12; 95% CI: 0.0, 0.25) on the CheXpert dataset, as demonstrated by leveraging locally trained model parameters. Further evaluation showed that the saliency maps generated from a commercial prototype could be irrelevant to the model output, without knowledge of the model specifics (area under the receiver operating characteristic curve decreased by 8.6% without affecting the saliency map). The human observer studies confirmed that it is difficult for experts to identify the perturbed images; the experts had less than 44.8% correctness. Conclusion Popular saliency methods scored low PSC values on the two datasets of perturbed chest radiographs, indicating weak sensitivity and robustness. The proposed PSC metric provides a valuable quantification tool for validating the trustworthiness of medical AI explainability. Keywords: Saliency Maps, AI Trustworthiness, Dynamic Consistency, Sensitivity, Robustness Supplemental material is available for this article. © RSNA, 2023 See also the commentary by Yanagawa and Sato in this issue.

Outcome of single-visit root canal treatment with or without MTAD: A randomized controlled clinical trial.

This study aimed to investigate 2-year changes in periapical trabecular patterns in single-rooted teeth with apical periodontitis using fractal analysis and periapical index (PAI) after root canal treatment performed with or without BioPure MTAD solution. In this randomized clinical trial, 100 patients were selected and randomized to either the BioPure MTAD or the control groups. Initial periapical radiographs were obtained for each participant before and 2 years after root canal treatment. The region of interest in the periapical lesion around the root apex was selected from the paired periapical radiographs, and then, the fractal dimension (FD) was calculated. With regards to the classification of periapical status, PAI was labelled as "healed" (PAI ≤ 2) or "unhealed" (PAI ≥ 3). After 24 months, 28 patients did not comply with the follow-up and the data of 72 patients were compared. When the initial and the follow-up PAI scores were compared, the decrease was statistically significant in 33 of 37 teeth (89.2%) and 32 of 35 teeth (91.4%) in the BioPure MTAD and control group, respectively. In both groups, statistically significant increases were observed in FD values after 2 years in all patients (p < .001). No significant difference was found between the two groups amongst decreased PAI scores and increased FD values. Root canal treatments with or without BioPure MTAD irrigation contributed to periapical healing in single-visit root canal treatment. Two years after root canal treatment, the extent of the periapical trabecular bone radiographically increased, as the FD and PAI data revealed.

Deep Learning for Automated Triaging of Stable Chest Radiographs in a Follow-up Setting.

Background Most artificial intelligence algorithms that interpret chest radiographs are restricted to an image from a single time point. However, in clinical practice, multiple radiographs are used for longitudinal follow-up, especially in intensive care units (ICUs). Purpose To develop and validate a deep learning algorithm using thoracic cage registration and subtraction to triage pairs of chest radiographs showing no change by using longitudinal follow-up data. Materials and Methods A deep learning algorithm was retrospectively developed using baseline and follow-up chest radiographs in adults from January 2011 to December 2018 at a tertiary referral hospital. Two thoracic radiologists reviewed randomly selected pairs of "change" and "no change" images to establish the ground truth, including normal or abnormal status. Algorithm performance was evaluated using area under the receiver operating characteristic curve (AUC) analysis in a validation set and temporally separated internal test sets (January 2019 to August 2021) from the emergency department (ED) and ICU. Threshold calibration for the test sets was conducted, and performance with 40% and 60% triage thresholds was assessed. Results This study included 3 304 996 chest radiographs in 329 036 patients (mean age, 59 years ± 14 [SD]; 170 433 male patients). The training set included 550 779 pairs of radiographs. The validation set included 1620 pairs (810 no change, 810 change). The test sets included 533 pairs (ED; 265 no change, 268 change) and 600 pairs (ICU; 310 no change, 290 change). The algorithm had AUCs of 0.77 (validation), 0.80 (ED), and 0.80 (ICU). With a 40% triage threshold, specificity was 88.4% (237 of 268 pairs) and 90.0% (261 of 290 pairs) in the ED and ICU, respectively. With a 60% triage threshold, specificity was 79.9% (214 of 268 pairs) and 79.3% (230 of 290 pairs) in the ED and ICU, respectively. For urgent findings (consolidation, pleural effusion, pneumothorax), specificity was 78.6%-100% (ED) and 85.5%-93.9% (ICU) with a 40% triage threshold. Conclusion The deep learning algorithm could triage pairs of chest radiographs showing no change while detecting urgent interval changes during longitudinal follow-up. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Czum in this issue.

THA-Net: A Deep Learning Solution for Next-Generation Templating and Patient-specific Surgical Execution

BackgroundThis study introduces THA-Net, a deep learning inpainting algorithm for simulating postoperative total hip arthroplasty (THA) radiographs from a single preoperative pelvis radiograph input, while being able to generate predictions either unconditionally (algorithm chooses implants) or conditionally (surgeon chooses implants). MethodsThe THA-Net is a deep learning algorithm which receives an input preoperative radiograph and subsequently replaces the target hip joint with THA implants to generate a synthetic yet realistic postoperative radiograph. We trained THA-Net on 356,305 pairs of radiographs from 14,357 patients from a single institution’s total joint registry and evaluated the validity (quality of surgical execution) and realism (ability to differentiate real and synthetic radiographs) of its outputs against both human-based and software-based criteria. ResultsThe surgical validity of synthetic postoperative radiographs was significantly higher than their real counterparts (mean difference: 0.8 to 1.1 points on 10-point Likert scale, P < .001), but they were not able to be differentiated in terms of realism in blinded expert review. Synthetic images showed excellent validity and realism when analyzed with already validated deep learning models. ConclusionWe developed a THA next-generation templating tool that can generate synthetic radiographs graded higher on ultimate surgical execution than real radiographs from training data. Further refinement of this tool may potentiate patient-specific surgical planning and enable technologies such as robotics, navigation, and augmented reality (an online demo of THA-Net is available at: https://demo.osail.ai/tha_net).

Impact of sex on spinal radiographic progression in axial spondyloarthritis: a longitudinal Swiss cohort analysis over a period of 10 years

ObjectiveTo investigate sex differences in spinal radiographic progression in axial spondyloarthritis (axSpA).MethodsAxSpA patients in the Swiss Clinical Quality Management cohort with available spinal radiographs every 2 years were included. Paired...

Development of Artificial Intelligence-Based Dual-Energy Subtraction for Chest Radiography

Recently, some facilities have utilized the dual-energy subtraction (DES) technique for chest radiography to increase pulmonary lesion detectability. However, the availability of the technique is limited to certain facilities, in addition to other limitations, such as increased noise in high-energy images and motion artifacts with the one-shot and two-shot methods, respectively. The aim of this study was to develop artificial intelligence-based DES (AI–DES) technology for chest radiography to overcome these limitations. Using a trained pix2pix model on clinically acquired chest radiograph pairs, we successfully converted 130 kV images into virtual 60 kV images that closely resemble the real images. The averaged peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) between virtual and real 60 kV images were 33.8 dB and 0.984, respectively. We also achieved the production of soft-tissue- and bone-enhanced images using a weighted image subtraction process with the virtual 60 kV images. The soft-tissue-enhanced images exhibited sufficient bone suppression, particularly within lung fields. Although the bone-enhanced images contained artifacts on and around the lower thoracic and lumbar spines, superior sharpness and noise characteristics were presented. The main contribution of our development is its ability to provide selectively enhanced images for specific tissues using only high-energy images obtained via routine chest radiography. This suggests the potential to improve the detectability of pulmonary lesions while addressing challenges associated with the existing DES technique. However, further improvements are necessary to improve the image quality.

Measurement of interspinous motion in dynamic cervical radiographs using a deep learning-based segmentation model.

Interspinous motion (ISM) is a representative method for evaluating the functional fusion status following anterior cervical discectomy and fusion (ACDF) surgery, but the associated measuring difficulty and potential errors in the clinical setting remain concerns. The aim of this study was to investigate the feasibility of a deep learning-based segmentation model for measuring ISM in patients who underwent ACDF surgery. This study is a retrospective analysis of flexion-extension dynamic cervical radiographs from a single institution and a validation of a convolutional neural network (CNN)-based artificial intelligence (AI) algorithm for measuring ISM. Data from 150 lateral cervical radiographs from the normal adult population were used to train the AI algorithm. A total of 106 pairs of dynamic flexion-extension radiographs from patients who underwent ACDF at a single institution were analyzed and validated for measuring ISM. To evaluate the agreement power between human experts and the AI algorithm, the authors assessed the interrater reliability using the intraclass correlation coefficient and root mean square error (RMSE) and performed a Bland-Altman plot analysis. They processed 106 pairs of radiographs from ACDF patients into the AI algorithm for autosegmenting the spinous process created using 150 normal population radiographs. The algorithm automatically segmented the spinous process and converted it to a binary large object (BLOB) image. The rightmost coordinate value of each spinous process from the BLOB image was extracted, and the pixel distance between the upper and lower spinous process coordinate value was calculated. The AI-measured ISM was calculated by multiplying the pixel distance by the pixel spacing value included in the DICOM tag of each radiograph. The AI algorithm showed a favorable prediction power for detecting spinous processes with an accuracy of 99.2% in the test set radiographs. The interrater reliability between the human and AI algorithm of ISM was 0.88 (95% CI 0.83-0.91), and its RMSE was 0.68. In the Bland-Altman plot analysis, the 95% limit of interrater differences ranged from 0.11 to 1.36 mm, and a few observations were outside the 95% limit. The mean difference between observers was 0.02 ± 0.68 mm. This novel CNN-based autosegmentation algorithm for measuring ISM in dynamic cervical radiographs showed strong agreement power to expert human raters and could help clinicians to evaluate segmental motion following ACDF surgery in clinical settings.

How Do Standing Neutral, Supine Lateral, Standing Flexion, and Standing Extension Radiographs Compare in Detecting the Presence and Magnitude of Stable and Dynamic Spondylolisthesis?

Clinical guidelines recommend standing radiographs as the most appropriate imaging for detecting degenerative spondylolisthesis, although reliable evidence about the standing position is absent. To our knowledge, no studies have compared different radiographic views and pairings to detect the presence and magnitude of stable and dynamic spondylolisthesis. (1) What is the percentage of new patients presenting with back or leg pain with stable (3 mm or greater listhesis on standing radiographs) and dynamic (3 mm or greater listhesis difference on standing-supine radiographs) spondylolisthesis? (2) What is the difference in the magnitude of spondylolisthesis between standing and supine radiographs? (3) What is the difference in the magnitude of dynamic translation among flexion-extension, standing-supine, and flexion-supine radiographic pairs? This cross-sectional, diagnostic study was performed at an urban, academic institution between September 2010 and July 2016; 579 patients 40 years or older received a standard radiographic three-view series (standing AP, standing lateral, and supine lateral radiographs) at a new patient visit. Of those individuals, 89% (518 of 579) did not have any of the following: history of spinal surgery, evidence of vertebral fracture, scoliosis greater than 30°, or poor image quality. In the absence of a reliable diagnosis of dynamic spondylolisthesis using this three-view series, patients may have had flexion and extension radiographs, and approximately 6% (31 of 518) had flexion and extension radiographs. A total of 53% (272 of 518) of patients were female, and the patients had a mean age of 60 ± 11 years. Listhesis distance (in mm) was measured by two raters as displacement of the posterior surface of the superior vertebral body in relation to the posterior surface of the inferior vertebral body from L1 to S1; interrater and intrarater reliability, assessed with intraclass correlation coefficients, was 0.91 and 0.86 to 0.95, respectively. The percentage of patients with and the magnitude of stable spondylolisthesis was estimated on and compared between standing neutral and supine lateral radiographs. The ability of common pairs of radiographs (flexion-extension, standing-supine, and flexion-supine) to detect dynamic spondylolisthesis was assessed. No single radiographic view or pair was considered the gold standard because stable or dynamic listhesis on any radiographic view is often considered positive in clinical practice. Among 518 patients, the percentage of patients with spondylolisthesis was 40% (95% CI 36% to 44%) on standing radiographs alone, and the percentage of patients with dynamic spondylolisthesis was 11% (95% CI 8% to 13%) on the standing-supine pair. Standing radiographs detected greater listhesis than supine radiographs did (6.5 ± 3.9 mm versus 4.9 ± 3.8 mm, difference 1.7 mm [95% CI 1.2 to 2.1 mm]; p < 0.001). Among 31 patients, no single radiographic pairing identified all patients with dynamic spondylolisthesis. The listhesis difference detected between flexion-extension was no different from the listhesis difference detected between standing-supine (1.8 ± 1.7 mm versus 2.0 ± 2.2 mm, difference 0.2 mm [95% CI -0.5 to 1.0 mm]; p = 0.53) and flexion-supine (1.8 ± 1.7 mm versus 2.5 ± 2.2 mm, difference 0.7 mm [95% CI 0.0 to 1.5]; p = 0.06). This study supports current clinical guidelines that lateral radiographs should be obtained with patients in the standing position, because all cases of stable spondylolisthesis of 3 mm or greater were detected on standing radiographs alone. Each radiographic pair did not detect different magnitudes of listhesis, and no single pair detected all cases of dynamic spondylolisthesis. Clinical concern for dynamic spondylolisthesis may justify standing neutral, supine lateral, standing flexion, and standing extension views. Future studies could identify and evaluate a set of radiographic views that provides the greatest capacity to diagnose stable and dynamic spondylolisthesis. Level III, diagnostic study.

Site-specific assessment of spinal radiographic progression improves detection of TNF blocker-associated disease modification in axial spondyloarthritis: longitudinal observational data from the Swiss Clinical Quality Management Registry

ObjectivesTo analyse whether time-varying treatment with tumour necrosis factor inhibitors (TNFi) in radiographic axial spondyloarthritis (r-axSpA) has a differential impact on structural damage progression on different spinal segments (cervical versus lumbar spine).MethodsPatients with r-axSpA in the Swiss Clinical Quality Management cohort were included if cervical and lumbar radiographs were available at intervals of 2 years for a maximum of 10 years. Paired radiographs were scored by two calibrated readers according to the modified Stoke Ankylosing Spondylitis Spine Score (mSASSS). The relationship between TNFi use and progression in the cervical and the lumbar spine was analysed using generalised estimating equation models and adjustment for potential confounding. Radiographic progression per spinal segment was defined as an increase of ≥ 1 mSASSS unit or by the formation of ≥ 1 new syndesmophyte over 2 years.ResultsMean ± SD symptom duration was 13.8 ± 9.8 years. Mean ± SD mSASSS progression per radiographic interval was 0.41 ± 1.69 units in the cervical spine and 0.45 ± 1.45 units in the lumbar spine (p = 0.66). Prior use of TNFi significantly reduced the odds of progression in the cervical spine by 68% (OR 0.32, 95% CI 0.14–0.72), but not in the lumbar spine (OR 0.99, 95% CI 0.52–1.88). A more restricted inhibition of progression in the lumbar spine was confirmed after multiple imputation of missing covariate data (OR 0.43, 95% CI 0.24–0.77 and 0.85, 95% CI 0.51–1.41, for the cervical and lumbar spine, respectively). It was also confirmed with progression defined as formation of ≥ 1 syndesmophyte (OR 0.31, 95% CI 0.12–0.80 versus OR 0.56, 95% CI 0.26–1.24 for the cervical and lumbar spine, respectively).ConclusionDisease modification by treatment with TNFi seems to more profoundly affect the cervical spine in this r-axSpA population with longstanding disease. Site-specific analysis of spinal progression might, therefore, improve detection of disease modification in clinical trials in axSpA.

Spatial distribution of the imaging dose and characterization of the scatter radiation contribution in CyberKnife radiosurgery

PurposeThe imaging dose for intra- and extra-cranial CyberKnife radiosurgery applications was calculated and the scattered radiation reaching the digital detectors was quantified and analyzed with regard to its origin. MethodsThe image guidance subsystem of the CyberKnife was modeled based on vendor-provided information. The emitted X-ray energy spectrum for 120 kV was estimated using the SpekPy software tool. Monte Carlo (MC) image acquisition simulations were performed to calculate the total, primary and scattered photon fluences reaching each detector as a function of the imaged object dimensions. MC calculations of the imaging dose were performed for intra- and extra-cranial applications assuming 120 kV and 10 mAs acquisition settings. ResultsThe amount of scattered radiation reaching each detector was found to depend on the dimensions of the imaged anatomical region, contributing more than 40 % to the total photon fluence for regions more than 20 cm thick. More than 20 % of this scattered radiation originates from the contralateral imaging field. A maximum organ dose of 1.5 mGy at the nasal bones and an average dose of 0.37 mGy to the eye lenses per image pair acquisition was calculated for head applications. An entrance imaging dose of 0.4 mGy was calculated for extracranial applications. ConclusionsScattered radiation reaching each detector in the skull and spine tracking applications can be reduced by acquiring the pair of radiographs sequentially instead of simultaneously. A dose of 3.7 cGy to the eye lenses is estimated assuming 100 image pair exposures required for treatment completion.

Development of the Clinical Gestalt Assessment: a visual clinical global impression scale for Proteus syndrome

BackgroundClinical outcome assessments are important tools for measuring the natural history of disease and efficacy of an intervention. The heterogenous phenotype and difficult to quantity features of Proteus syndrome present challenges to measuring clinical outcomes. To address these, we designed a global clinical assessment for Proteus syndrome, a rare mosaic overgrowth disorder. The Clinical Gestalt Assessment (CGA) aims to evaluate change over time in this phenotypically diverse disorder.ResultsWe gathered paired serial photographs and radiographs obtained at 12-to-36-month intervals from our natural history study of Proteus syndrome. The chronologic order of each set was blinded and presented to clinicians familiar with overgrowth disorders. They were asked to determine the chronologic order and, based on that response, rate global clinical change using a seven-point scale (Much Worse, Worse, Minimally Worse, No Change, Minimally Improved, Improved, Much Improved). Following a pilot, we tested the inter-rater reliability of the CGA using eight cases rated by eight clinicians. Raters identified the correct chronologic order in 53 of 64 (83%) of responses. There was low inter-rater variance and poor to moderate reliability with an intraclass correlation coefficient of 0.46 (95% CI 0.24–0.75). The overall estimate of global change was Minimally Worse over time, which is an accurate reflection of the natural history of Proteus syndrome.ConclusionsThe CGA is a tool to evaluate clinical change over time in Proteus syndrome and may be a useful adjunct to measure clinical outcomes in prospective therapeutic trials.

Statistical modelling of how the sagittal alignment of the cervical spine is affected by adolescent idiopathic scoliosis and how scoliosis surgery changes that.

The relationship between the sagittal shape of the cervical spine and that of the thoracolumbar spine is established in the normal spine. Adolescent idiopathic scoliosis (AIS) is recognised as a change in the shape of the spine in both the coronal and sagittal planes. The effects of AIS on the alignment of the cervical spine, including the effects of surgery, has been less well studied. The objective of this study was to identify, using regression analysis, the significant relationships between the alignment of the thoracolumbar spine, in both the coronal and sagittal planes, and the sagittal alignment of the cervical spine in AIS. This study used coronal and sagittal radiographic measures from a group with AIS, both pre and post-operatively, which were analysed using multiple linear regression methods to identify significant parameters that explain the sagittal shape of the cervical spine. There were 51 pairs of pre and post-operative radiographs analysed, 40 of which were Lenke 1 curves and 11 Lenke 3 curves. Posterior spinal fusion was performed for all. The significant parameters pre-operatively were T1 slope, thoracic kyphosis, lumbar lordosis and SVA with an R2 value of 78%. Post-operatively, the significant parameters were T1 slope, thoracic kyphosis, lumbar lordosis and thoracolumbar scoliosis with an R2 of 63%. The sagittal alignment of the cervical spine in AIS is related to the shape of key parameters in the rest of the spine. Changes in the cervical sagittal shape occur to compensate for changes in shape to the rest of the spine that occur as a consequence of surgery. This has implications for the understanding of how the compensatory mechanisms of the spine are used to maintain a horizontal gaze, along with prediction of the effects of surgery on the shape of the spine.

Zoledronic acid does not slow spinal radiographic progression of osteoarthritis in postmenopausal women with osteoporosis and radiographic osteoarthritis.

Introduction:Post hoc analyses of osteoporosis trials have suggested that alendronate and strontium ranelate may be associated with a reduction in the progression of spinal radiographic osteoarthritis (OA). We performed an analysis on a subgroup of participants in the horizon PFT trial (a 3-year randomized controlled trial (RCT) of yearly zoledronic acid (ZA) in postmenopausal women with osteoporosis), to evaluate the effect of ZA on the structural progression of spinal osteophytes (OPh) and disk space narrowing (DN).Methods:Paired lateral spinal X-rays (baseline and 36 months) were selected from the horizon PFT trial records restricted to those with radiographic OA at baseline. The X-rays were analyzed by two readers blinded to the treatment allocation. OPh and DN were scored separately using the Lane atlas (0–3 for increasing severity at each vertebral level) at all evaluable levels from T4–12 and L1–5.Results:A total of 504 sets of paired radiographs were included in the analysis, 245 in the ZA group and 259 in the placebo group. Overall, the rates of change of OPh and DN scores were low, and they were not statistically different between the groups (change in the whole spine OPh ZA 1.0 ± 1.6, placebo 0.8 ± 1.3, p = 0.1; DN ZA 0.3 ± 1.0, placebo 0.3 ± 0.8, p = 0.7).Conclusion:Yearly ZA for 3 years was not associated with a slowing of progression of OPh or DN in the thoracolumbar spine in patients with pre-existing radiographic OA.

SimChest: A Novel Similarity Measurement Agnostic to Change in Follow-Up Chest Radiograph Pairs Via a Supervised Contrastive Learning Model

SimChest: A Novel Similarity Measurement Agnostic to Change in Follow-Up Chest Radiograph Pairs Via a Supervised Contrastive Learning Model

The biasing impact of irrelevant contextual information on forensic odontology radiograph matching decisions

The potential biasing effect of irrelevant context information on the forensic odontology method of radiograph-based identification has never been empirically investigated despite being a recognized problem in other forensic science disciplines. This study examines the effect of irrelevant context information on the probability judgment of match (JOM) of practicing forensic odontologist and dentist participants who were asked to match pairs of dental radiographs supplemented with irrelevant case information.The irrelevant case information contained domain task-irrelevant context information which varied in strength (strong or weak). It suggested either supportive or contradictory bias relative to the actual match status of the radiograph pairs. The dental radiographs consisted of verified match and non-match radiographs pairs sampled and de-identified from actual forensic cases. Changes in accuracy and JOM between supportive and contradictory contexts conditions revealed a contextual bias. Mixed model analysis showed that strong supportive context increased the odds ratio of correct decisions by a factor of 2.4 [1.23, 4.46]; p = 0.0097. Consistent with the biasing effect, the JOM score differences between strong supportive and contradictory irrelevant context information were 1.03 and 0.43 respectively for the non-match and match decisions. The direction of context suggestion (p = 0.0067), the radiograph match status (p = 0.014), and their interactions (p = 0.0061), were all found to impact the participants’ decision. The weak context information was not strong enough to have a significant effect on accuracy or JOM scores.This study demonstrates that radiograph match judgment is affected and can be biased by strong irrelevant contextual information.