Similarities and disparities between visual analysis and high-resolution electromyography of facial expressions.

Computer vision (CV) is widely used in the investigation of facial expressions. Applications range from psychological evaluation to neurology, to name just two examples. CV for identifying facial expressions may suffer from several shortcomings: CV provides indirect information about muscle activation, it is insensitive to activations that do not involve visible deformations, such as jaw clenching. Moreover, it relies on high-resolution and unobstructed visuals. High density surface electromyography (sEMG) recordings with soft electrode array is an alternative approach which provides direct information about muscle activation, even from freely behaving humans. In this investigation, we compare CV and sEMG analysis of facial muscle activation. We used independent component analysis (ICA) and multiple linear regression (MLR) to quantify the similarity and disparity between the two approaches for posed muscle activations. The comparison reveals similarity in event detection, but discrepancies and inconsistencies in source identification. Specifically, the correspondence between sEMG and action unit (AU)-based analyses, the most widely used basis of CV muscle activation prediction, appears to vary between participants and sessions. We also show a comparison between AU and sEMG data of spontaneous smiles, highlighting the differences between the two approaches. The data presented in this paper suggests that the use of AU-based analysis should consider its limited ability to reliably compare between different sessions and individuals and highlight the advantages of high-resolution sEMG for facial expression analysis.

E-Health Ultrasonic Diagnostic Monitoring for Analysis of Cardiac Insufficiency and Neuronal Regulation in Patients with Sepsis in Emergency Department under Image Reconstruction Algorithm.

An anisotropic diffusion filtering- (ADF-) ultrasound (ADF-U) for ultrasound reconstruction was constructed based on the ADF to explore the diagnostic application of ultrasound imaging based on electronic health (E-health) for cardiac insufficiency and neuronal regulation in patients with sepsis. The 144 patients with sepsis were divided into an experimental group (78 patients with cardiac insufficiency) and a control group (66 patients with normal cardiac function), and another 58 healthy people were included in a blank control. The ultrasound examination was performed on all patients. In addition, new ultrasound image reconstruction and diagnosis were performed based on ADF and E-health, and its reconstruction effects were compared with those of the Bilateral Filter-ultrasonic (BFU) algorithm and the Wavelet Threshold-ultrasonic (WTU) algorithm. The left and right ventricular parameters and neuropeptide levels were detected and recorded. The results show that the running time, average gradient (AG), and peak signal-to-noise ratio (SNR) (PSNR) of the ADF-U algorithm were greater than those of the Bilateral Filter-ultrasonic (BFU) and Wavelet Threshold-ultrasonic (WTU), but the mean square error (MSE) was opposite (P < 0.05); the left ventricular end-systolic volume (LVESV) and the vertical distance between the mitral valve E-point to septal separation (EPSS) in the experimental group were higher than those in the control and blank group, while the left ventricular ejection fraction (LVEF), stroke volume (SV), cardiac output (CO), and left ventricular fractional shortening (LVFS) were opposite (P < 0.05); the systolic peak velocity of right ventricular free wall tricuspid annulus (Sm) and pulmonary valve blood velocity (PVBV) in the experimental group were lower than those of the control group and blank group (P < 0.05); the messenger ribonucleic acid (mRNA) of Proopiomelanocortin (POMC) and Cocain and amphetamine-regulated transcript (CART) was higher than the mRNA IN control group and blank group (P < 0.05). In short, the ADF-U algorithm proposed in this study improved the resolution, SNR, and reconstruction efficiency of E-health ultrasound images and provided an effective reference value for the diagnosis of cardiac insufficiency and neuronal adjustment analysis in patients with sepsis in the emergency department.

Tertiary lymphoid structures (TLS) identification and density assessment on H&E-stained digital slides of lung cancer.

Tertiary lymphoid structures (TLS) are ectopic aggregates of lymphoid cells in inflamed, infected, or tumoral tissues that are easily recognized on an H&E histology slide as discrete entities, distinct from lymphocytes. TLS are associated with improved cancer prognosis but there is no standardised method available to quantify their presence. Previous studies have used immunohistochemistry to determine the presence of specific cells as a marker of the TLS. This has now been proven to be an underestimate of the true number of TLS. Thus, we propose a methodology for the automated identification and quantification of TLS, based on H&E slides. We subsequently determined the mathematical criteria defining a TLS. TLS regions were identified through a deep convolutional neural network and segmentation of lymphocytes was performed through an ellipsoidal model. This methodology had a 92.87% specificity at 95% sensitivity, 88.79% specificity at 98% sensitivity and 84.32% specificity at 99% sensitivity level based on 144 TLS annotated H&E slides implying that the automated approach was able to reproduce the histopathologists' assessment with great accuracy. We showed that the minimum number of lymphocytes within TLS is 45 and the minimum TLS area is 6,245µm2. Furthermore, we have shown that the density of the lymphocytes is more than 3 times those outside of the TLS. The mean density and standard deviation of lymphocytes within a TLS area are 0.0128/µm2 and 0.0026/µm2 respectively compared to 0.004/µm2 and 0.001/µm2 in non-TLS regions. The proposed methodology shows great potential for automated identification and quantification of the TLS density on digital H&E slides.

Impact of chart image characteristics on stock price prediction with a convolutional neural network.

Stock price prediction has long been the subject of research because of the importance of accuracy of prediction and the difficulty in forecasting. Traditionally, forecasting has involved linear models such as AR and MR or nonlinear models such as ANNs using standardized numerical data such as corporate financial data and stock price data. Due to the difficulty of securing a sufficient variety of data, researchers have recently begun using convolutional neural networks (CNNs) with stock price graph images only. However, we know little about which characteristics of stock charts affect the accuracy of predictions and to what extent. The purpose of this study is to analyze the effects of stock chart characteristics on stock price prediction via CNNs. To this end, we define the image characteristics of stock charts and identify significant differences in prediction performance for each characteristic. The results reveal that the accuracy of prediction is improved by utilizing solid lines, color, and a single image without axis marks. Based on these findings, we describe the implications of making predictions only with images, which are unstructured data, without using large amounts of standardized data. Finally, we identify issues for future research.

Synergistic tomographic image reconstruction: part 1.

This special issue focuses on synergistic tomographic image reconstruction in a range of contributions in multiple disciplines and various application areas. The topic of image reconstruction covers substantial inverse problems (Mathematics) which are tackled with various methods including statistical approaches (e.g. Bayesian methods, Monte Carlo) and computational approaches (e.g. machine learning, computational modelling, simulations). The issue is separated in two volumes. This volume focuses mainly on algorithms and methods. Some of the articles will demonstrate their utility on real-world challenges, either medical applications (e.g. cardiovascular diseases, proton therapy planning) or applications in material sciences (e.g. material decomposition and characterization). One of the desired outcomes of the special issue is to bring together different scientific communities which do not usually interact as they do not share the same platforms (such as journals and conferences). This article is part of the theme issue 'Synergistic tomographic image reconstruction: part 1'.

Magnetic resonance scoring system for assessment of adnexal masses: added value of diffusion-weighted imaging including apparent diffusion coefficient map.

OBJECTIVES: To validate prospectively the ADNEX magnetic resonance (MR) scoring system to assess adnexal masses and to evaluate a new, modified ADNEX MR scoring system that incorporates diffusion-weighted imaging (DWI) with apparent diffusion coefficient (ADC) mapping. METHODS: Between January 2015 and September 2018, 323 consecutive women with adnexal masses diagnosed on transvaginal ultrasound (TVS) underwent standardized MR imaging (MRI) including diffusion and dynamic contrast-enhanced sequences. Of these, 131 underwent subsequent surgery. For interpretation of the MRI examinations, we applied the five-category ADNEX MR scoring system, along with a modified scoring system including DWI with ADC mapping. For both scoring systems, a score was given for all adnexal masses. Histological diagnosis was considered as the gold standard and lesions were classified as benign or malignant. The difference between the predictive values for diagnosing malignancy of the classical and modified scoring systems was assessed on the basis of the areas under the receiver-operating-characteristics (AUC) curves. The sensitivity and specificity for diagnosing malignancy of each score were also calculated. RESULTS: Among the 131 women with adnexal mass(es) diagnosed on TVS who underwent MRI and subsequent surgery, the surgery revealed 161 adnexal masses in 126 women; five women had no mass. Histological examination confirmed 161 adnexal masses, of which all had been detected on MRI: 32 malignant tumors, 15 borderline tumors, which were classified as part of the malignant group (n = 47), and 114 benign lesions. The AUC for prediction of a malignant lesion was 0.938 (95% CI, 0.902-0.975) using the classical ADNEX MR scoring system and 0.974 (95% CI, 0.953-0.996) using the modified scoring system. Pairwise comparison of these AUCs revealed a significant difference (P = 0.0032). The sensitivity and specificity for diagnosing malignancy with an ADNEX MR score of 4 or more were 95.5% and 86.6%, respectively, using the classic scoring system, and 95.7% and 93.3%, respectively, using the modified scoring system. CONCLUSION: DWI with ADC mapping could be integrated into the ADNEX MR scoring system to improve specificity, thereby potentially optimizing clinical management by avoiding unnecessary surgery. © 2020 International Society of Ultrasound in Obstetrics and Gynecology.

Group-representative functional network estimation from multi-subject fMRI data via MRF-based image segmentation.

BACKGROUND AND OBJECTIVE: There has been growing interest in using functional connectivity patterns, determined from fMRI data to characterize groups of individuals exhibiting common traits. However, the present challenge lies in efficient and accurate identification of distinct patterns observed consistently across multiple subjects. Existing approaches either impose strong assumptions, require aligning images before processing, or require data-intensive machine learning algorithms with manually labeled training datasets. In this paper, we propose a more principled and flexible approach to address this. METHODS: Our approach redefines the problem of estimating the group-representative functional network as an image segmentation problem. After employing an improved clustering-based ICA scheme to pre-process the dataset of individual functional network images, we use a maximum a posteriori-Markov random field (MAP-MRF) framework to solve the image segmentation problem. In this framework, we propose a probabilistic model of the individual pixels of the fMRI data, with the model involving a latent group-representative functional network image. Given an observed dataset, we apply a novel and efficient variational Bayes algorithm to recover the associated latent group image. Our methodology seeks to overcome limitations in more traditional schemes by exploiting spatial relationships underlying the connectivity maps and accounting for uncertainty in the estimation process. RESULTS: We validate our approach using synthetic, simulated and real data. First, we generate datasets from the proposed forward model with subject-specific binary masking and measurement noise, as well as from a variant of the model without measurement noise. We use both datasets to evaluate our model, along with two algorithms: coordinate-ascent algorithm and variational Bayes algorithm. We conclude that our proposed model with variational Bayes outperforms other competitors, even under model-misspecification. Using variational Bayes offers a significant improvement in performance, with almost no additional computational overhead. We next test our approach on simulated fMRI data. We show our approach is robust to initialization and can recover a solution close to the ground truth. Finally, we apply our proposed methodology along with baselines to a real dataset of fMRI recordings of individuals from two groups, a control group and a group suffering from depression, with recordings made while individuals were subjected to musical stimuli. Our methodology is able to identify group differences that are less clear under competing methods. CONCLUSIONS: Our model-based approach demonstrates the advantage of probabilistic models and modern algorithms that account for uncertainty in accurate identification of group-representative connectivity maps. The variational Bayes methodology yields highly accurate results without increasing the computational load compared to traditional methods. In addition, it is robust to model misspecification, and increases the ability to avoid local optima in the solution.

Inter-laboratory comparison of Ki-67 proliferating index detected by visual assessment and automated digital image analysis.

BACKGROUND: Proliferation rate is a major determinant of the biologic behavior of the tumor and provides information that can be used to guide treatment decisions. METHODS: This ring study included 27 pathologists from 14 Institutions, in order to assess inter-observer concordance between pathologists in Croatia. We analyzed Ki-67 proliferative index on ten randomly selected breast cancer samples comparing consistency between visual assessment using light microscopy compared to digital image analyses results from one central laboratory as a referral value. RESULTS: When we analyzed Ki-67 as numeric value high concordance rate was found between Ki-67 score visually assessed in all participating Institutions compared to referral value assessed by digital image analysis (ICC 0.76, 95% CI 0.58-0.91), and Krippendorff's alpha was 0.79 (95% CI 0.58-1.00). Concordance was better in slides with higher Ki-67 values. When we categorized Ki-67 values according to generally accepted 20% cut-off value we noticed the lower concordance rate among participants in our study. CONCLUSION: Proliferation remains one of the most important parameters for tumor characterization helpful in making clinical decisions, but it should be used with great caution. Standardization of the Ki-67 assessment is essential and proliferating index should be expressed as exact numeric value. For patients with proliferative index near the cut-off value, other factors must be considered in making clinical decisions.

Optimized protocol of multiple post-processing techniques improves diagnostic accuracy of multidetector computed tomography in assessment of small bowel obstruction compared with conventional axial and coronal reformations.

BACKGROUND: Axial and coronal reformations have been a widely used image post-processing protocol for the ordinary multidetector computed tomography (MDCT) examination of patients with small bowel obstruction (SBO) or other abdominal diseases. The diagnostic accuracy of MDCT for assessing SBO is expected to be further improved through the use of multiple post-processing techniques. AIM: To systemically evaluate the diagnostic accuracy and efficiency of an optimized protocol using multiple post-processing techniques for MDCT assessment of SBO and secondary bowel ischemia. METHODS: This retrospective cross-sectional study included 106 patients with clinically suspected SBO. Two readers applied three protocols to image post-processing and interpretation of patients' MDCT volume data. We compared the three protocols based on time spent, number of images, diagnostic self-confidence, agreement, detection rate, and accuracy of detection of SBO and secondary bowel ischemia. RESULTS: Protocol 2 resulted in more time spent and number of images than protocols 1 and 3 (P < 0.01), but the results of the two readers using the same protocol were not different (P > 0.05). Using protocol 3, both readers added multiple post-processing techniques at frequencies of 29.2% and 34.9%, respectively, for obstruction cause, and 32.1% and 30.2%, respectively, for secondary bowel ischemia. Protocols 2 and 3 had higher total detection rates of obstruction cause and secondary bowel ischemia than protocol 1 (P < 0.01), but no difference was detected between protocols 2 and 3 (P > 0.05). The accuracy, sensitivity, specificity, positive predictive value and negative predictive value of protocols 2 and 3 were superior to those of protocol 1 for evaluating obstruction cause and secondary bowel ischemia. CONCLUSION: Our optimized protocol of multiple post-processing techniques can both guarantee efficiency and improve diagnostic accuracy of MDCT for assessing SBO and secondary bowel ischemia.

Implementation and assessment of the black body bias correction in quantitative neutron imaging.

We describe in this paper the experimental procedure, the data treatment and the quantification of the black body correction: an experimental approach to compensate for scattering and systematic biases in quantitative neutron imaging based on experimental data. The correction algorithm is based on two steps; estimation of the scattering component and correction using an enhanced normalization formula. The method incorporates correction terms into the image normalization procedure, which usually only includes open beam and dark current images (open beam correction). Our aim is to show its efficiency and reproducibility: we detail the data treatment procedures and quantitatively investigate the effect of the correction. Its implementation is included within the open source CT reconstruction software MuhRec. The performance of the proposed algorithm is demonstrated using simulated and experimental CT datasets acquired at the ICON and NEUTRA beamlines at the Paul Scherrer Institut.

An accurate and cost-effective alternative method for measuring cell migration with the circular wound closure assay.

Cell migration is important in many physiological and pathological processes. Mechanisms of two-dimensional cell migration have been investigated most commonly by evaluating rates of cell migration into linearly scratched zones on the surfaces of culture plates. Here, we present a detailed description of a simple adaptation for the well-known and popular wound closure assay, using a circular wound instead of a straight line. This method demonstrates improved precision, reproducibility, and sampling objectivity for measurements of wound sizes as compared with classic scratch assays, enabling more accurate calculations of migration rate. The added benefits of the method are simplicity and low cost as compared with commercially available assays for generating circular wounds.

Reliability and validity of MicroScribe-3DXL system in comparison with radiographic cephalometric system: Angular measurements.

AIM: The aim of the study was to assess the reliability and validity of cephalometric variables from MicroScribe-3DXL. MATERIALS AND METHODS: Seven cephalometric variables (facial angle, ANB, maxillary depth, U1/FH, FMA, IMPA, FMIA) were measured by a dentist in 60 Malay subjects (30 males and 30 females) with class I occlusion and balanced face. Two standard images were taken for each subject with conventional cephalometric radiography and MicroScribe-3DXL. All the images were traced and analysed. SPSS version 2.0 was used for statistical analysis with P-value was set at P<0.05. RESULTS: The results revealed a significant statistic difference in four measurements (U1/FH, FMA, IMPA, FMIA) with P-value range (0.00 to 0.03). The difference in the measurements was considered clinically acceptable. The overall reliability of MicroScribe-3DXL was 92.7% and its validity was 91.8%. CONCLUSION: The MicroScribe-3DXL is reliable and valid to most of the cephalometric variables with the advantages of saving time and cost. This is a promising device to assist in diverse areas in dental practice and research.

Assessing Bone Type of Implant Recipient Sites by Stereomicroscopic Observation of Bone Core Specimens: A Comparison With the Assessment Using Dental Radiography.

BACKGROUND: The aim of the study is to determine if bone quality evaluation of surgically obtained bone core specimens using a stereomicroscope is reliable for determining bone quality at implant recipient sites. METHODS: Bone quality was presurgically assessed in 122 edentulous ridges obtained from 62 patients using periapical radiographs and categorized according to the Lekholm and Zarb classification. During surgery, bone specimens were trephined, and bone types were immediately classified using a stereomicroscope. Microarchitectural characteristics of bone cores were evaluated after being scanned using microcomputed tomography (micro-CT). RESULTS: Bone types of implant sites categorized from radiography and stereomicroscope had statistically similar distribution but poor interrater agreement. Using micro-CT, maxillae and mandibles showed significant differences in microarchitectural characteristics of bone cores. Bone volume (BV), total volume (TV), and trabecular thickness (Tb.Th) increased, whereas bone surface density (BS/BV) and open porosity (Po.[Op]) decreased in mandibular bone cores compared with those in maxillary bone cores. Moreover, micro-CT values of BV/TV and Po.(Op) statistically correlated with bone types assessed by stereomicroscopy, particularly in mandibles (adjusted means of BV/TV of Type 2 to 4 versus Type 1 decreasing from -9.88%, -15.09%, -29.31%; those of Po.(Op) ranged from 9.77%, 15.06%, 29.52% in an upward trend). However, such correlations were not found in maxillae or when bone types were classified using periapical radiographs. CONCLUSIONS: Caution is needed when using presurgical periapical radiographs to predict bone quality at implant recipient sites. Surgically preserved bone core specimens, whenever obtainable, might offer additional information to accurately assess bone quality, particularly at mandibular implant sites.

Simulation study of the backward-scattering effect in Compton imager.

In the field of nuclear medicine, nuclear security and astrophysics, Compton imaging is a promising technique for gamma-ray source imaging. We are developing a Compton imager using two layers of CdZnTe pixel array detectors. In this paper, the backward-scattering effect within such imagers is numerically studied using Geant4 Monte Carlo Package. From images reconstructed based on forward-scattering and backward-scattering imaging events, the imaging precision was investigated in a comparative analysis, in regard to energy resolution and position resolution. Furthermore, to establish a method to use backward-scattering imaging events properly so that the imaging efficiency can be significantly improved, the difference between reconstruction from forward-scattering and backward-scattering imaging events was analyzed to uncover a causal mechanism.

Let's Not Waste Time: Using Temporal Information in Clustered Activity Estimation with Spatial Adjacency Restrictions (CAESAR) for Parcellating FMRI Data.

We have proposed a Bayesian approach for functional parcellation of whole-brain FMRI measurements which we call Clustered Activity Estimation with Spatial Adjacency Restrictions (CAESAR). We use distance-dependent Chinese restaurant processes (dd-CRPs) to define a flexible prior which partitions the voxel measurements into clusters whose number and shapes are unknown a priori. With dd-CRPs we can conveniently implement spatial constraints to ensure that our parcellations remain spatially contiguous and thereby physiologically meaningful. In the present work, we extend CAESAR by using Gaussian process (GP) priors to model the temporally smooth haemodynamic signals that give rise to the measured FMRI data. A challenge for GP inference in our setting is the cubic scaling with respect to the number of time points, which can become computationally prohibitive with FMRI measurements, potentially consisting of long time series. As a solution we describe an efficient implementation that is practically as fast as the corresponding time-independent non-GP model with typically-sized FMRI data sets. We also employ a population Monte-Carlo algorithm that can significantly speed up convergence compared to traditional single-chain methods. First we illustrate the benefits of CAESAR and the GP priors with simulated experiments. Next, we demonstrate our approach by parcellating resting state FMRI data measured from twenty participants as taken from the Human Connectome Project data repository. Results show that CAESAR affords highly robust and scalable whole-brain clustering of FMRI timecourses.

Lung hyperaeration assessment by computed tomography: correction of reconstruction-induced bias.

BACKGROUND: Computed tomography (CT) reconstruction parameters, such as slice thickness and convolution kernel, significantly affect the quantification of hyperaerated parenchyma (VHYPER%). The aim of this study was to investigate the mathematical relation between VHYPER% calculated at different reconstruction settings, in mechanically ventilated and spontaneously breathing patients with different lung pathology. METHODS: In this retrospective observational study, CT scans of patients of the intensive care unit and emergency department were collected from two CT scanners and analysed with different kernel-thickness combinations (reconstructions): 1.25 mm soft kernel, 5 mm soft kernel, 5 mm sharp kernel in the first scanner; 2.5 mm slice thickness with a smooth (B41s) and a sharp (B70s) kernel on the second scanner. A quantitative analysis was performed with Maluna® to assess lung aeration compartments as percent of total lung volume. CT variables calculated with different reconstructions were compared in pairs, and their mathematical relationship was analysed by using quadratic and power functions. RESULTS: 43 subjects were included in the present analysis. Image reconstruction parameters influenced all the quantitative CT-derived variables. The most relevant changes occurred in the hyperaerated and normally aerated volume compartments. The application of a power correction formula led to a significant reduction in the bias between VHYPER% estimations (p < 0.001 in all cases). The bias in VHYPER% assessment did not differ between lung pathology nor ventilation mode groups (p > 0.15 in all cases). CONCLUSIONS: Hyperaerated percent volume at different reconstruction settings can be described by a fixed mathematical relationship, independent of lung pathology, ventilation mode, and type of CT scanner.

TLC Fingerprinting and Pattern Recognition Methods in the Assessment of Authenticity of Poplar-Type Propolis.

Propolis is a "natural" remedy with prominent biological activity, which is used as dietary supplement. In the absence of clinical studies that would substantiate these claims, information on the biological activity of propolis is valuable. This study comprises chromatographic, image processing and chemometric approach for phenolic profiling of Serbian, Croatian and Slovenian propolis test solutions. Modern thin-layer chromatography equipment in combination with software for image processing was applied for fingerprinting and data acquisition, whereas the principal component analysis was used as pattern recognition method. Characterization of phenolic profile was performed along with the determination of the botanical and geographical origin of propolis. High-performance thin-layer chromatograms reveal that Central and Southeastern European propolis samples are rich in flavonoids. In addition, phenolic compounds proved to be suitable markers for the determination of European propolis authenticity.

Juxtacortical Lesions in Multiple Sclerosis: Assessment of Gray Matter Involvement Using Phase Difference-enhanced Imaging (PADRE).

PURPOSE: In multiple sclerosis (MS), a juxtacortical lesion at the border between the gray matter (GM) and subcortical white matter (WM) may often involve the GM. A recently developed, phase-weighted magnetic resonance imaging (MRI) technique "phase difference enhanced imaging (PADRE)" can delineate the GM and WM clearly due to the difference in myelin concentration. We evaluated whether PADRE is useful for the detection of GM involvement in the juxtacortical MS lesions. METHODS: One neuroradiologist reviewed the conventional MRI in 13 MS patients and selected 48 juxtacortical lesions. At the first reading session with the conventional MRI alone (T2-weighted imaging, and two-dimensional and three-dimensional fluid-attenuated inversion recovery), two other neuroradiologists classified the lesions into three patterns according to their anatomical locations: (a) subcortical WM lesions involving the subcortical WM alone; (b) intracortical (IC) lesions involving the GM alone; (c) mixed GM/subcortical WM (mixed) lesions involving the both subcortical WM and GM. We defined the subcortical WM as a WM within a distance of 10 mm from inner edge of the GM. For the analyses, we excluded the white matter lesions further than 10 mm from inner edge of the GM. At the second reading session MRI and PADRE were available and the radiologists re-evaluated their prior classification. RESULTS: At the first reading session, 27 lesions were classified as (a), 1 as (b), and 20 as (c). Therefore, a total of 21 lesions (44%) were judged to involve the GM. At the second reading session, the classification of 15 (31%) lesions changed; all 15 lesions were judged to involve the GM on the PADRE. Interobserver agreement (kappa value) was 0.84 for the first- and 0.95 for the second reading session. CONCLUSION: PADRE is useful for detecting GM involvement of the juxtacortical MS lesions.

Diagnostic accuracy and measurement sensitivity of digital models for orthodontic purposes: A systematic review.

INTRODUCTION: Our objective was to assess the accuracy, validity, and reliability of measurements obtained from virtual dental study models compared with those obtained from plaster models. METHODS: PubMed, PubMed Central, National Library of Medicine Medline, Embase, Cochrane Central Register of Controlled Clinical trials, Web of Knowledge, Scopus, Google Scholar, and LILACs were searched from January 2000 to November 2014. A grading system described by the Swedish Council on Technology Assessment in Health Care and the Cochrane tool for risk of bias assessment were used to rate the methodologic quality of the articles. RESULTS: Thirty-five relevant articles were selected. The methodologic quality was high. No significant differences were observed for most of the studies in all the measured parameters, with the exception of the American Board of Orthodontics Objective Grading System. CONCLUSIONS: Digital models are as reliable as traditional plaster models, with high accuracy, reliability, and reproducibility. Landmark identification, rather than the measuring device or the software, appears to be the greatest limitation. Furthermore, with their advantages in terms of cost, time, and space required, digital models could be considered the new gold standard in current practice.

After Detection: The Improved Accuracy of Lung Cancer Assessment Using Radiologic Computer-aided Diagnosis.

RATIONALE AND OBJECTIVES: The aim of this study was to evaluate the improved accuracy of radiologic assessment of lung cancer afforded by computer-aided diagnosis (CADx). MATERIALS AND METHODS: Inclusion/exclusion criteria were formulated, and a systematic inquiry of research databases was conducted. Following title and abstract review, an in-depth review of 149 surviving articles was performed with accepted articles undergoing a Quality Assessment of Diagnostic Accuracy Studies (QUADAS)-based quality review and data abstraction. RESULTS: A total of 14 articles, representing 1868 scans, passed the review. Increases in the receiver operating characteristic (ROC) area under the curve of .8 or higher were seen in all nine studies that reported it, except for one that employed subspecialized radiologists. CONCLUSIONS: This systematic review demonstrated improved accuracy of lung cancer assessment using CADx over manual review, in eight high-quality observer-performance studies. The improved accuracy afforded by radiologic lung-CADx suggests the need to explore its use in screening and regular clinical workflow.