Percentage Of Outliers Research Articles

Measuring the Probabilistic Photometric Redshifts of X-ray Quasars Based on the Quantile Regression of Ensembles of Decision Trees

We present empirical machine learning algorithms for measuring the probabilistic photometric redshifts (photo-z) of X-ray quasars based on the quantile regression of ensembles of decision trees. Relying on the data of present-day photometric sky surveys (e.g., SDSS, GALEX, WISE, UKIDSS, 2MASS, FIRST), the proposed methods allow one to make high-quality photo-z point predictions for extragalactic objects, to estimate the confidence intervals, and to reconstruct the full probability distribution functions for all predictions. The quality of photo-z predictions has been tested on samples of X-ray quasars from the 1RASS and 3XMM DR7 surveys, which have spectroscopic redshift measurements in the SDSS DR14Q catalog. The proposed approaches have shown the following accuracy (the metrics are the normalized median absolute deviation σNMAD and the percentage of outliers n>0.15): σNMAD, n>0.15 = 0.043, 12% (SDSS + WISE), 0.037, 8% (SDSS + WISE + GALEX) and 0.032, 8.6% (SDSS + WISE + GALEX + UKIDSS) on the RASS sample; σNMAD, n>0.15 = 0.054, 13% (SDSS + WISE), 0.045, 7.6% (SDSS + WISE + GALEX), and 0.037, 6.6% (SDSS + WISE + GALEX + UKIDSS) on the 3XMM sample. The presented photo-z algorithms will become an important tool for analyzing the multi-wavelength data on X-ray quasars in the forthcoming Spectrum–Roentgen–Gamma sky survey.

Evaluation of the patellofemoral joint in total knee arthroplasty: Validation of the weight bearing merchant radiographic view

BackgroundStandard radiographic views for patellofemoral joint assessment do not reflect loading at which TKA patients may describe post-operative anterior symptoms. A novel weight bearing (WB) Merchant view has been described and demonstrated a number of tracking changes that correlated with clinical outcomes. In this study, we aim to validate the WB Merchant view and assess relationships with patient outcome scores. MethodsPatients were randomly allocated to receive one of the three commonly used patellar implants with a single TKA prosthesis. Patients were evaluated at six months post-operatively using both NWB and WB Merchant views. Indicators of patellar tracking were correlated with improvement in KOOS, WOMAC and Kujala scores. For reliability assessment, radiographs were assessed twice by two readers. ResultsThe WB Merchant view showed a reduction in the percentage of outliers of tracking indices in comparison to the NWB view (Congruence angle: NWB = 37%, WB = 24%; Displacement: NWB = 2%, WB = 0%; Tilt angle: NWB = 60%, WB = 56%). There was an increase in the lateral contact state with the WB Merchant view (Type I: NWB = 19%, WB = 28%; Type II: NWB = 3%, WB = 4%). The state of lateral contact had a consistent and statistically significant correlation with the improvement in KOOS, WOMAC and Kujala scores (p value = 0.01, 0.01 and 0.03, respectively). All radiographic indices had good reliability with accepted variability. ConclusionThe WB Merchant radiograph is an easy to perform and reliable view for the evaluation of patellar tracking and may provide additional information to the routinely used NWB view.

Space-time outlier identification in a large ground deformation data set

A novel application for outlier detection is in ground deformation monitoring. During any type of underground construction in urban settings, sensors are placed on the ground surface to monitor the vertical displacement with the goal of ensuring that there is no substantial heaving or settling of the ground. As a result, a large spatial-temporal data set is produced, but the sensors are often very sensitive, and spurious readings are commonly observed, resulting in both random and systematic outliers. In this work, we present a novel, fast spatial-temporal quality control procedure that is designed to remove these spurious readings prior to subsequent ground deformation monitoring. First, a robust kriging model is applied to the spatial ground deformations at each time point to remove systematic errors; next, an exponential moving average model is applied to the time series of ground deformations at each station to remove random outliers. A case study using ground deformation data when four subway tunnels are bored under a railyard in Queens, New York is used to illustrate the methodology. Methods used to construct outlier bounds are described, and the accuracy of our outlier detection approach is evaluated by calculating the percentages of outliers detected in an introduced artificial outlier set.

ФИЛЬТРАЦИЯ ВЫБРОСОВ В ЗАДАЧАХ СТАТИЧЕСКОЙ И ДИНАМИЧЕСКОЙ ОБРАБОТКИ ДАННЫХ В ЭТАЛОНАХ ВРЕМЕНИ И ЧАСТОТЫ

PURPOSE. Filtration of anomalous measurements (outliers) of frequency values of hydrogen standards which form a part of the group standards of time and frequency will allow to reduce an algorithmic estimation error of the state vector of group standards. METHODS. The study employs the methods of noise-immunity estimation in the mode of static and dynamic data processing. RESULTS. It is proposed to use α-trimmed estimates with an unknown percentage of outliers to filter the outliers in the static mode. In the case of dynamic data processing, the outliers are rejected if the estimates obtained at this stage go beyond the confidence intervals. The latter are calculated on the basis of the predictive models of frequency variation constructed at the stage of static processing of data. The proposed algorithms are implemented in Mathcad 15.0. The efficiency of the proposed methods has been verified by statistical modeling and confirmed by the processing of real data obtained as a result of VET 1-5 secondary standard operation. CONCLUSIONS. The received results form an important development stage of a formalized estimation procedure of the state vector of the time and frequency group standard in static and dynamic modes based on the use of predictive models.

Landmark-based algorithms for group average and pattern recognition

We introduce a class of mathematical algorithms with the aim of establishing a framework of finding a group average and extracting prominent features in a group of landmark represented shapes or image templates. A group average is an estimator that is said to best represent the common features of the group being studied. The proposed algorithms, as a tool of feature extraction, extract information about momentum at each landmark through the process of template matching. Once the convergence criterion is satisfied numerically, the algorithms produce a group average and a local coordinate system for each member of the observing group, in terms of the residual momentum. We present several examples to illustrate the use of the proposed algorithms for finding a group average. Using the metrics computed between the group average and each member of the group, we successfully run a cluster analysis for datasets that contain a heavy percentage of outliers. Finally, we apply the collected residual momenta computed in the proposed algorithms in some statistical methods to demonstrate a potential application of the algorithms for detecting structure abnormality.

Outlier classification performance of risk adjustment methods when profiling multiple providers

BackgroundWhen profiling multiple health care providers, adjustment for case-mix is essential to accurately classify the quality of providers. Unfortunately, misclassification of provider performance is not uncommon and can have grave implications. Propensity score (PS) methods have been proposed as viable alternatives to conventional multivariable regression. The objective was to assess the outlier classification performance of risk adjustment methods when profiling multiple providers.MethodsIn a simulation study based on empirical data, the classification performance of logistic regression (fixed and random effects), PS adjustment, and three PS weighting methods was evaluated when varying parameters such as the number of providers, the average incidence of the outcome, and the percentage of outliers. Traditional classification accuracy measures were considered, including sensitivity and specificity.ResultsFixed effects logistic regression consistently had the highest sensitivity and negative predictive value, yet a low specificity and positive predictive value. Of the random effects methods, PS adjustment and random effects logistic regression performed equally well or better than all the remaining PS methods for all classification accuracy measures across the studied scenarios.ConclusionsOf the evaluated PS methods, only PS adjustment can be considered a viable alternative to random effects logistic regression when profiling multiple providers in different scenarios.

An exponential-type kernel robust regression model for interval-valued variables

The presence of outliers is very common in regression problems and the use of robust regression methods is strongly recommended such that the bad fitted observations not affect the parameter estimates of the model. Interval-valued variables are becoming common in data analysis problems since this type of data represents either the uncertainty existing in an error measurement or the natural variability present in the data. Regarding the presence of outliers in interval-valued data sets, few robust regression methods have been proposed in literature. This paper introduces a new robust regression method for interval-valued variables that penalizes the presence of outliers in the midpoints and/or in the ranges of interval-valued observations through the use of exponential-type kernel functions. Thus, the weight given to the midpoint and range of each interval-valued observation is updated at each iteration in order to optimize a suitable objective function. The convergence of the parameter estimation algorithm is guaranteed with a low computational cost. A comparative study between the proposed method against some previous robust regression approaches for interval-valued variables is also considered. The performance of these methods are evaluated based on the bias and mean squared error (MSE) of the parameter estimates for the midpoints and ranges of the intervals, considering synthetic data sets with X-space outliers, Y-space outliers and leverage points, different sample sizes and percentage of outliers in a Monte Carlo framework. The results suggest that the proposed approach presents a competitive performance (or best), in comparison with the previous approaches, on interval-valued outliers scenarios that are comparable to those found in practices. Applications to real interval-valued data sets corroborates the usefulness of the proposed method.

On development of novel hybrid and robust adaptive models for net asset value prediction

Efficient prediction of net asset value (NAV) of various investment fund is crucial for both investors and fund management organisations. But prediction of such type of complex financial series is difficult because of uncertainty and influence by political and economical factors. In this paper a novel hybrid adaptive ensemble is developed and its performance is assessed both during training and testing phases using six different NAV data. Further a robust hybrid prediction model is proposed using minimisation of a robust norm and its prediction performance is evaluated and compared with its corresponding conventional ensemble model. Simulation based experimental results demonstrate superior prediction performance of proposed ensemble hybrid model compared to that of three individual component models. Statistical paired t-test is carried out to ensure the superiority of the proposed model in comparison to other three models. Further, it is observed that the proposed robust ensemble model outperforms its hybrid counterpart for all NAVs and for all percentage of outliers up to 10% present in the training samples. The same ensemble and robust models can also applied for efficient prediction of various NAVs for different day’s ahead prediction.

Methods for Estimating Item-Score Reliability.

Reliability is usually estimated for a test score, but it can also be estimated for item scores. Item-score reliability can be useful to assess the item’s contribution to the test score’s reliability, for identifying unreliable scores in aberrant item-score patterns in person-fit analysis, and for selecting the most reliable item from a test to use as a single-item measure. Four methods were discussed for estimating item-score reliability: the Molenaar–Sijtsma method (method MS), Guttman’s method , the latent class reliability coefficient (method LCRC), and the correction for attenuation (method CA). A simulation study was used to compare the methods with respect to median bias, variability (interquartile range [IQR]), and percentage of outliers. The simulation study consisted of six conditions: standard, polytomous items, unequal parameters, two-dimensional data, long test, and small sample size. Methods MS and CA were the most accurate. Method LCRC showed almost unbiased results, but large variability. Method consistently underestimated item-score reliabilty, but showed a smaller IQR than the other methods.

A robust pre-processing of BeadChip microarray images

Microarray images commonly used in gene expression studies are heavily contaminated by noise and/or outlying values (outliers). Unfortunately, standard methodology for the analysis of Illumina BeadChip microarray images turns out to be too vulnerable to data contamination by outliers. In this paper, an alternative approach to low-level pre-processing of images obtained by the BeadChip microarray technology is proposed. The novel approach robustifies the standard methodology in a complex way and thus ensures a sufficient robustness (resistance) to outliers. A gene expression data set from a cardiovascular genetic study is analyzed and the performance of the novel robust approach is compared with the standard methodology. The robust approach is able to detect and delete a larger percentage of outliers. More importantly, gene expressions are estimated more precisely. As a consequence, also the performance of a subsequently performed classification task to two groups (patients vs. control persons) is improved over the cardiovascular gene expression data set. A further improvement was obtained when considering weighted gene expression values, where the weights correspond to a robust estimate of variability of the measurements for each individual gene transcript.

Adaptive Image Mismatch Removal With Vector Field Interpolation Based on Improved Regularization and Gaussian Kernel Function

When the regularized kernel methods are utilized in the mismatch removal problem, the regularization coefficient and the choice of kernel function will seriously affect the performance of the methods. In this paper, we propose a method that combines an improved regularization and an adaptive Gaussian kernel function to interpolate the vector fields so as to overcome the issue. We formulated the problem as a modified maximum a posterior estimation of a Bayesian model. In this model, a two-order term of the regularization coefficient is introduced into the regularized risk function in order that the coefficient can be adaptively estimated in the expectation–maximization algorithm. In addition, an adaptive Gaussian kernel function also is imposed to construct the regularization, in which the width of the kernel function is adaptively determined by the diagonal length of the maximum enveloping rectangle of the sample set. Our experimental results verified that our method was robust to large outlier percentages and was slightly superior to some state-of-the-art methods in precision-recall tradeoff and efficiency. The evidence that the performance of our method was insensitive to the remaining inner parameters verified its good self-adaptability. Finally, airborne image pairs were used to demonstrate that our method can establish the feature correspondences even under a discontinuous vector field scene. In addition, we found that our method can obtain higher precision given a residual threshold for special applications such as robust epipolar geometry estimation in computer vision and photogrammetry.

Estimating photometric redshifts for X-ray sources in the X-ATLAS field using machine-learning techniques

We present photometric redshifts for 1,031 X-ray sources in the X-ATLAS field, using the machine learning technique TPZ (Carrasco Kind & Brunner 2013). X-ATLAS covers 7.1 deg2 observed with the XMM-Newton within the Science Demonstration Phase (SDP) of the H-ATLAS field, making it one of the largest contiguous areas of the sky with both XMMNewton and Herschel coverage. All of the sources have available SDSS photometry while 810 have additionally mid-IR and/or near-IR photometry. A spectroscopic sample of 5,157 sources primarily in the XMM/XXL field, but also from several X-ray surveys and the SDSS DR13 redshift catalogue, is used for the training of the algorithm. Our analysis reveals that the algorithm performs best when the sources are split, based on their optical morphology, into point-like and extended sources. Optical photometry alone is not enough for the estimation of accurate photometric redshifts, but the results greatly improve when, at least, mid-IR photometry is added in the training process. In particular, our measurements show that the estimated photometric redshifts for the X-ray sources of the training sample, have a normalized absolute median deviation, n_mad=0.06, and the percentage of outliers, eta=10-14 percent, depending on whether the sources are extended or point-like. Our final catalogue contains photometric redshifts for 933 out of the 1,031 X-ray sources with a median redshift of 0.9.

Analysis of lightning outliers in the EUCLID network

Abstract. Lightning data as observed by the European Cooperation for Lightning Detection (EUCLID) network are used in combination with radar data to retrieve the temporal and spatial behavior of lightning outliers, i.e., discharges located in a wrong place, over a 5-year period from 2011 to 2016. Cloud-to-ground (CG) stroke and intracloud (IC) pulse data are superimposed on corresponding 5 min radar precipitation fields in two topographically different areas, Belgium and Austria, in order to extract lightning outliers based on the distance between each lightning event and the nearest precipitation. It is shown that the percentage of outliers is sensitive to changes in the network and to the location algorithm itself. The total percentage of outliers for both regions varies over the years between 0.8 and 1.7 % for a distance to the nearest precipitation of 2 km, with an average of approximately 1.2 % in Belgium and Austria. Outside the European summer thunderstorm season, the percentage of outliers tends to increase somewhat. The majority of all the outliers are low peak current events with absolute values falling between 0 and 10 kA. More specifically, positive cloud-to-ground strokes are more likely to be classified as outliers compared to all other types of discharges. Furthermore, it turns out that the number of sensors participating in locating a lightning discharge is different for outliers versus correctly located events, with outliers having the lowest amount of sensors participating. In addition, it is shown that in most cases the semi-major axis (SMA) assigned to a lightning discharge as a confidence indicator in the location accuracy (LA) is smaller for correctly located events compared to the semi-major axis of outliers.

Background levels of some trace elements in calcareous soils of the Hamedan Province, Iran

Natural concentrations of trace elements are necessary for assessing trace elements contamination affected by anthropogenic activities in soils. This study was conducted to establish background concentrations of cadmium (Cd), cobalt (Co), chromium (Cr), copper (Cu), iron (Fe), manganese (Mn), nickel (Ni), lead (Pb) and zinc (Zn) in 50 unpolluted native soils with a total of 100 soil samples (surface and subsurface soil layers) in Hamedan province of Iran. Different methods have been used for the evaluation of background limits, and the relationships between soil properties and trace elements concentrations and among trace element concentrations have been analyzed. Values from Geometric Mean (GM) procedure had the lowest background limit, while the GM×GSD2 (geometric standard deviation) method produces the highest background limits. The percentage of outliers in Median+2MAD (median absolute deviation) ranged from 4.0 to 16.0% for natural data and from 0.0 to 12% for log-transformed data. The smallest outlier percentage was for Cd and Cr, and it was higher for Co, Cu, and Zn. The obtained geochemical baseline concentrations (mgkg−1) based on Median+2MAD were (log-transformed data) Cd 1.36, Co 15.42, Cr (natural data) 36.69, Cu 29.99, Fe 25.79 (gkg−1), Mn 368.68, Ni (natural data) 53.59, Pb 39.60, Zn 103.80. Carbonate calcium had significant negative correlations with Co, Cr, Cu, Fe, and Mn, indicating that trace elements are not included in the crystal structure of secondary carbonate minerals; consequently, accumulation of CaCO3 causes a dilution effect in the soils. The content of clay, silt and organic matter had significant role in the accumulation of Mn, Ni, and Pb in the soils of the studied area. Significant correlation was found among Co, Cr, Cu, Fe, Mn, and Ni and between Zn with Cd, Cr, Cu, Mn, and Ni. Factor analysis of trace elements explored six factors attributing 64.1% of the total variability. According to the results found by upper whisker method, correlation analysis and factor analysis, studied trace elements contaminated none of the soil samples, natural accumulation processes influenced their contents in soils and origin of trace elements may be lithogenic, but their sources may be different.

A COMPUTATIONAL TOOL TO EVALUATE THE SAMPLE SIZE IN MAP POSITIONAL ACCURACY

Abstract: In many countries, the positional accuracy control by points in Cartography or Spatial data corresponds to the comparison between sets of coordinates of well-defined points in relation to the same set of points from a more accurate source. Usually, each country determines a maximum number of points which could present error values above a pre-established threshold. In many cases, the standards define the sample size as 20 points, with no more consideration, and fix this threshold in 10% of the sample. However, the sampling dimension (n), considering the statistical risk, especially when the percentages of outliers are around 10%, can lead to a producer risk (to reject a good map) and a user risk (to accept a bad map). This article analyzes this issue and allows defining the sampling dimension considering the risk of the producer and of the user. As a tool, a program developed by us allows defining the sample size according to the risk that the producer / user can or wants to assume. This analysis uses 600 control points, each of them with a known error. We performed the simulations with a sample size of 20 points (n) and calculate the associated risk. Then we changed the value of (n), using smaller and larger sizes, calculating for each situation the associated risk both for the user and for the producer. The computer program developed draws the operational curves or risk curves, which considers three parameters: the number of control points; the number of iterations to create the curves; and the percentage of control points above the threshold, that can be the Brazilian standard or other parameters from different countries. Several graphs and tables are presented which were created with different parameters, leading to a better decision both for the user and for the producer, as well as to open possibilities for other simulations and researches in the future.

Revision of partial knee to total knee arthroplasty with use of patient-specific instruments results in acceptable femoral rotation.

Patient-specific instruments (PSI) were initially developed for the alignment of both total knee- (TKA) and partial knee arthroplasty (PKA). We hypothesize that CT-based PSI for PKA-to-TKA revision surgery can restore biomechanical limb alignment and prosthetic component positioning in vivo as calculated pre-operatively, resulting in a limited percentages of outliers. An imaging analysis was performed using CT-based 3D measurement methods based on a pre- and post-revision CT scan. Imaging data were gathered on 10 patients who were operated for PKA-to-TKA revision with the use of PSI based on CT imaging. The planned femur and tibia component position in vivo were compared with the pre-revision planned component position. Outliers were defined as deviations >3.0° from pre-revision planned position for the individual implant components. Adjustments (e.g. resection level and implant size) during surgery were recorded. The HKA axis was restored accurately in all patients with a mean post-operative HKA axis of 178.1° (1.4°). Five femoral (2 varus, 2 internal rotation and 1 extension) and 14 tibial guides (2 varus, 6 anterior slope, 3 internal rotation and 3 external rotation) on a total of 60 outcome measures were identified as outliers. During surgery, an intraoperative tibial resection of 2mm extra was performed in three patients. In 80 and 70% for, respectively, the femur and tibia, the surgeon-planned size was implanted during surgery. All patient-specific guides fitted well in all patients. No intraoperative or post-operative complications related to surgery were registered. This study introduced a unique new concept regarding PSI, PKA-to-TKA revision surgery. Based on the results, we were unable to fully confirm our hypothesis. PSI as a "new" tool for PKA-to-TKA revision surgery appears to be an accurate tool for the alignment of the TKA femur component. The tibial guide seems more susceptible to errors, resulting in a substantial percentage of outliers. Prospective cohort study, Level II.

Evolutionary Optimization for Robust Epipolar-Geometry Estimation and Outlier Detection

In this paper, a robust technique based on a genetic algorithm is proposed for estimating two-view epipolar-geometry of uncalibrated perspective stereo images from putative correspondences containing a high percentage of outliers. The advantages of this technique are three-fold: (i) replacing random search with evolutionary search applying new strategies of encoding and guided sampling; (ii) robust and fast estimation of the epipolar geometry via detecting a more-than-enough set of inliers without making any assumptions about the probability distribution of the residuals; (iii) determining the inlier-outlier threshold based on the uncertainty of the estimated model. The proposed method was evaluated both on synthetic data and real images. The results were compared with the most popular techniques from the state-of-the-art, including RANSAC (random sample consensus), MSAC, MLESAC, Cov-RANSAC, LO-RANSAC, StaRSAC, Multi-GS RANSAC and least median of squares (LMedS). Experimental results showed that the proposed approach performed better than other methods regarding the accuracy of inlier detection and epipolar-geometry estimation, as well as the computational efficiency for datasets majorly contaminated by outliers and noise.

Recovering the Properties of High-redshift Galaxies with Different JWST Broadband Filters

Abstract Imaging with the James Webb Space Telescope (JWST) will allow observations of the bulk of distant galaxies at the epoch of reionization. The recovery of their properties, such as age, color excess , specific star formation rate (sSFR), and stellar mass, will mostly rely on spectral energy distribution fitting, based on the data provided by JWST's two imager cameras, namely the Near Infrared Camera (NIRCam) and the Mid Infrared Imager (MIRI). In this work we analyze the effect of choosing different combinations of NIRCam and MIRI broadband filters, from 0.6 to 7.7 μm, on the recovery of these galaxy properties. We performed our tests on a sample of 1542 simulated galaxies, with known input properties, at z = 7–10. We found that, with only eight NIRCam broadbands, we can recover the galaxy age within 0.1 Gyr and the color excess within 0.06 mag for 70% of the galaxies. Additionally, the stellar masses and sSFR are recovered within 0.2 and 0.3 dex, respectively, at z = 7–9. Instead, at z = 10, no NIRCam band traces purely the λ > 4000 Å regime and the percentage of outliers in stellar mass (sSFR) increases by >20% (>90%), in comparison to z = 9. The MIRI F560W and F770W bands are crucial to improve the stellar mass and the sSFR estimation at z = 10. When nebular emission lines are present, deriving correct galaxy properties is challenging at any redshift and with any band combination. In particular, the stellar mass is systematically overestimated in up to 0.3 dex on average with NIRCam data alone and including MIRI observations only marginally improves the estimation.

Using ground observations of a digital camera in the VIS-NIR range for quantifying the phenology of Mediterranean woody species

The spectral reflectance of most plant species is quite similar, and thus the feasibility of identifying most plant species based on single date multispectral data is very low. Seasonal phenological patterns of plant species may enable to face the challenge of using remote sensing for mapping plant species at the individual level. We used a consumer-grade digital camera with near infra-red capabilities in order to extract and quantify vegetation phenological information in four East Mediterranean sites. After illumination corrections and other noise reduction steps, the phenological patterns of 1839 individuals representing 12 common species were analyzed, including evergreen trees, winter deciduous trees, semi-deciduous summer shrubs and annual herbaceous patches. Five vegetation indices were used to describe the phenology: relative green and red (green\\red chromatic coordinate), excess green (ExG), normalized difference vegetation index (NDVI) and green-red vegetation index (GRVI). We found significant differences between the phenology of the various species, and defined the main phenological groups using agglomerative hierarchical clustering. Differences between species and sites regarding the start of season (SOS), maximum of season (MOS) and end of season (EOS) were displayed in detail, using ExG values, as this index was found to have the lowest percentage of outliers. An additional visible band spectral index (relative red) was found as useful for characterizing seasonal phenology, and had the lowest correlation with the other four vegetation indices, which are more sensitive to greenness. We used a linear mixed model in order to evaluate the influences of various factors on the phenology, and found that unlike the significant effect of species and individuals on SOS, MOS and EOS, the sites' location did not have a direct significant effect on the timing of phenological events. In conclusion, the relative advantage of the proposed methodology is the exploitation of representative temporal information that is collected with accessible and simple devices, for the subsequent determination of optimal temporal acquisition of images by overhead sensors, for vegetation mapping over larger areas.

Intraoperative adjustment of alignment under valgus stress reduces outliers in patients undergoing medial opening-wedge high tibial osteotomy.

A considerable percentage of outliers with under- or over-correction continue to be reported despite precise preoperative planning and cautious intraoperative correction of lower limb alignment in medial opening-wedge high tibial osteotomy (MOWHTO). The purpose of this study was to determine whether our novel technique for the intraoperative adjustment of alignment under valgus stress reduces the number of outliers in patients undergoing MOWHTO compared to the conventional technique, which corrects alignment according to the cable method only. One hundred seventeen consecutive knees were enrolled in this case-control study. The first 52 knees (51 patients) were corrected in accordance with preoperative plans using the Dugdale method with modification with an intraoperative cable (group 1). In the other 65 knees (60 patients), the angle was corrected using the Dugdale method and limb alignment was adjusted using the intraoperative cable technique by applying valgus stress to the knee joint (group 2). The postoperative weight bearing line ratios and mechanical axis of the lower limb were compared at postoperative one year. Each knee was evaluated according to the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) score preoperatively and at postoperative one year. A significant reduction in the number of outliers was seen in group 2 compared to group 1 (group 1=48.1%, group 2=9.2%, p<0.001). Nineteen of 52 knees (36.5%) were under-corrected in group 1, whereas 6 of 65 knees (9.2%) were under-corrected in group 2 (p<0.001). Six of 52 knees (11.6%) were over-corrected in group 1, whereas 0 of 65 knees (0.0%) were over-corrected in group 2 (p=0.005). At one -year after operation, group 2 showed significantly lower WOMAC score than group 1 (p=0.014). Intraoperative adjustment of alignment under valgus stress significantly reduced the number of outliers compared to a technique that corrected alignment using the cable method in patients undergoing MOWHTO. Level III, retrospective comparative study.