Pawel Podsiadlo

Knee joint biomechanics following arthroscopic partial meniscectomy

We investigated spatiotemporal data, joint kinematics, and joint kinetics during gait in a group of subjects who had recently undergone arthroscopic partial meniscectomy and compared the results to those of healthy controls. Gait analysis was performed on 105 pain‐free meniscectomy patients and 47 controls, walking at a self‐selected speed. The meniscectomy population was comparable to controls in spatiotemporal parameters and knee kinematics. However, they had reduced range of motion (ROM) and lower peak moments in the sagittal plane on the operated limb compared to the nonoperated limb. Compared to controls, the meniscectomy patients had significantly larger knee adduction moments over stance, even after accounting for their greater body weight. These differences likely increase articular loads on the medial compartment of the tibiofemoral joint and may contribute to the high risk of knee osteoarthritis following arthroscopic meniscal surgery.

Tibio-femoral cartilage defects 3–5 years following arthroscopic partial medial meniscectomy

OBJECTIVE Arthroscopic partial medial meniscectomy (APMM) is a common procedure to treat a medial meniscal tear. Individuals who undergo APMM have a heightened risk of developing tibio-femoral osteoarthritis (OA). Cartilage defects scored from magnetic resonance imaging (MRI) scans predict cartilage loss over time. It is not known whether cartilage defects in the early years following APMM are more common or of greater severity than in age-matched controls. This study compared the prevalence and severity of tibio-femoral cartilage defects in patients 3-5 years post-APMM with that of age-matched controls. METHODS Twenty-five individuals who had undergone APMM in the previous 46.9+/-5.0 months and 24 age-matched controls participated in this study. Sagittal plane knee MRI scans were acquired from the operated knees of patients and from randomly assigned knees of the controls and graded (0-4) for tibio-femoral cartilage defects. Defect prevalence (score of >or=2 for any compartment) and severity of the cartilage from both tibio-femoral compartments were compared between the groups. RESULTS The APMM group had greater prevalence (77 vs 42%, P=0.012) and severity (4.1+/-1.9 vs 2.8+/-1.1, P=0.005) of tibio-femoral cartilage defects than controls. Age was positively associated with tibio-femoral cartilage defect severity for APMM, r=0.523, P=0.007, but not for controls, r=0.045, P=0.834. CONCLUSION Tibio-femoral joint cartilage defects are more prevalent and of greater severity in individuals who had undergone APMM approximately 44 months earlier than in age-matched controls.

Knee Strength and Knee Adduction Moments following Arthroscopic Partial Meniscectomy

PURPOSE This study investigated the relationship between muscular strength about the knee and knee joint moments during gait in patients who had undergone arthroscopic partial meniscectomy (APM). METHODS One hundred and two APM patients and 42 age-matched nonoperated controls underwent strength testing and three-dimensional gait analysis. Patients were divided into weak and normal subgroups and compared with controls for spatiotemporal, kinematic, and kinetic gait parameters. RESULTS Spatiotemporal parameters, kinematics, and sagittal plane kinetics were similar between APM patients and controls. The APM group displayed weaker concentric knee extension and flexion strength compared with controls. The weak APM subgroup had an increased average and peak knee adduction moments over stance compared with the APM subgroup with normal strength levels and controls. The normal strength APM subgroup had a larger peak knee adduction moment in early stance compared with controls. CONCLUSION Achieving normal lower limb muscle strength following APM appears important to resume normal frontal plane loading of the knee while walking.

A measure of competence based on random classification for dynamic ensemble selection

In this paper, a measure of competence based on random classification (MCR) for classifier ensembles is presented. The measure selects dynamically (i.e. for each test example) a subset of classifiers from the ensemble that perform better than a random classifier. Therefore, weak (incompetent) classifiers that would adversely affect the performance of a classification system are eliminated. When all classifiers in the ensemble are evaluated as incompetent, the classification accuracy of the system can be increased by using the random classifier instead. Theoretical justification for using the measure with the majority voting rule is given. Two MCR based systems were developed and their performance was compared against six multiple classifier systems using data sets taken from the UCI Machine Learning Repository and Ludmila Kuncheva Collection. The systems developed had typically the highest classification accuracies regardless of the ensemble type used (homogeneous or heterogeneous).

Prediction of progression of radiographic knee osteoarthritis using tibial trabecular bone texture

OBJECTIVE To develop a system for predicting the progression of radiographic knee osteoarthritis (OA) using tibial trabecular bone texture. METHODS We studied 203 knees with (n = 68) or without (n = 135) radiographic tibiofemoral OA in 105 subjects (90 men and 15 women with a mean age of 54 years) in whom 2 sets of knee radiographs were obtained 4 years apart. We determined medial and lateral compartment tibial trabecular bone texture using an automated region selection method. Three texture parameters were calculated: roughness, degree of anisotropy, and direction of anisotropy based on a signature dissimilarity measure method. We evaluated tibiofemoral OA progression using a radiographic semiquantitative outcome: an increase in the medial joint space narrowing (JSN) grade. We examined the predictive ability of trabecular bone texture in knees with and those without preexisting radiographic OA, with adjustment for age, sex, and body mass index, using logistic regression (generalized estimating equations) and receiver operating characteristic curves. RESULTS The prediction of increased medial JSN in knees with or without preexisting radiographic OA was the most accurate for medial trabecular bone texture; the area under the curve (AUC) was 0.77 and 0.75, respectively. For lateral trabecular bone texture, the AUC was 0.71 in knees with preexisting OA and 0.72 in knees without preexisting OA. CONCLUSION We have developed a system, based on analyzing tibial trabecular bone texture, which yields good prediction of loss of tibiofemoral joint space. The predictive ability of the system needs to be further validated.

Characterization and classification of wear particles and surfaces

Abstract Wear particles and surfaces are three-dimensional (3-D) objects and their numerical characterization and classification is still largely an unresolved problem. Usually a set of various parameters is employed to describe the surface topography. These parameters are of limited use, especially when dealing with anisotropic surfaces. To solve this problem a modified Hurst orientation transform (HOT) method has been developed and applied to characterize the surface anisotropy. However, despite the apparent success this method does not yet provide a full description of the surface topography. It is known that complex structures observed in nature can be described and modeled by a combination of simple mathematical rules. It is therefore reasonable to assume that, in principle, it should also be possible to describe any surface by a set of such rules. The problem is in finding those rules. For this purpose, a modified partitioned iterated function system (PIFS) was developed and applied to encode the 3-D surface topography information, i.e. to obtain full description of surface topography of wear particles and surfaces. Importantly, PIFS information gained from individual wear particles or surfaces allows to classify them in groups which are characteristic to a particular failure type. This, in turn, allows to ascribe an ‘unclassified’ particle or surface to a particular group/category which is characteristic to a specific failure type or wear mechanism. This forms the basis of a system, which when fully developed, would allow an automated recognition of particles and surface morphologies without the need for experts. The system then can be developed further to include diagnosis of the type of failure. In this paper an overview of recent advances and developments in the characterization, classification and recognition of wear particles and surfaces is presented.

Differences in trabecular bone texture between knees with and without radiographic osteoarthritis detected by directional fractal signature method

OBJECTIVE To evaluate differences in tibial trabecular bone (TB) texture between subjects with and without radiographic knee osteoarthritis (OA) using a variance orientation transform (VOT) method. DESIGN Subjects with knee OA (Kellgren & Lawrence grade > or =2) and controls without OA (both n=26, seven women) were matched by sex, age, body mass index and compartment. The VOT method was applied to TB X-ray images and fractal signature and dimension in horizontal (FS(H), FD(H)) and vertical (FS(V), FD(V)) directions and along the roughest part of TB (FS(Sta), FD(Sta)), texture aspect ratio (Str) and signature (StrS), and mean FD (FD(MEAN)) were calculated. The VOT method was compared against an augmented Hurst orientation transform (HOT) method using paired t tests, intraclass correlation coefficients (ICCs) and coefficients of variation (CVs%). Longitudinal sensitivity to OA bone changes was not assessed. RESULTS For the reproducibility of texture parameters, ICCs were >0.75 and CVs% were <8.2% for both methods. Compared with controls, FD(MEAN), FD(H), FD(V) and FD(Sta) for OA knees were lower (P<0.001), while Str was higher in both medial (P=0.03) and lateral (P=0.02) compartments. FS(H), FS(Sta) were lower for OA knees than for controls at sizes 0.3-0.7 mm (P<0.001) in both compartments. In lateral compartment, FS(V) for OA knees was lower than for controls at sizes 0.3-0.5 mm (P<0.001) and 0.55-0.70 mm (P<0.02), while in medial compartment at sizes 0.3-0.7 mm (P<0.001). Compared with controls, StrS for OA knees was higher at sizes 0.3, 0.55-0.70 mm in medial (P<0.03) and lateral (P<0.04) compartments. CONCLUSIONS The VOT method is comparable to HOT method in the reproducibility of texture parameters and the ability to discriminate between non-OA and OA TB textures. However, unlike the HOT method, it quantifies texture roughness along the roughest part of the tibial bone, texture anisotropy at individual trabecular sizes and it works over a larger range of trabecular sizes. The VOT method may be a valuable tool for studying OA changes in TB.

Characterization of surface topography of wear particles by SEM stereoscopy

A method of characterizing the surface topography of wear particles by the application of computer image analysis to scanning electron microscope (SEM) stereoscopic pairs of images is proposed and described in this paper. The image analysis consists of noise reduction, edge detection, thresholding, stereoscopic matching, calculation of elevation points and interpolation. An improved automatic stereoscopic matching algorithm has been developed. The algorithm was initially applied to computer-generated images of a polished marble sphere and a polished marble cube and later to SEM images of wear particles found in artificial and synovial joints. The results demonstrate that SEM stereoscopy can successfully be used in the characterization and measurements of wear particle surface topography.

3-D imaging of surface topography of wear particles found in synovial joints

Significant advances have recently been made in the numerical characterization of particle boundaries. However, progress in the numerical characterization of particle surfaces is slow. There are two fundamental issues associated with this problem; i.e., the acquisition of reliable three-dimensional (3-D) surface data and numerical description of the 3-D surface features. In this paper, the first issue is addressed while the second issue will be addressed in our next papers. Pairs of field emission scanning electron microscope (FESEM) stereo images of wear particles found in synovial joints have been acquired. A specially developed stereoscopy method was applied to the acquired FESEM images in order to obtain 3-D surface topography data of wear particles. The surface topography data obtained from the wear particles was then processed to produce: the range image, the contour map and the shaded image of a particle surface. The surface topography of wear particles was also shown in the colour stereo images. The results obtained in this study have clearly demonstrated that 3-D surface topography data of wear particles is very helpful in particle visualization and it can be used in numerical description of the surface.

Surface characterization of wear particles

Wear particles are three-dimensional objects. Recent advances, accelerated by the application of computer technology, allow numerical characterization of particle shape in two dimensions. However, three-dimensional (3D) characterization of wear particle surface topography is still largely an unresolved problem. There are two issues associated with this problem, i.e., the acquisition of accurate wear particle surface topography data and the numerical description of 3D surface features. The usually small size of wear particles restricts the techniques that could be used to acquire accurate data from the particle surface. Surface profilometers, e.g., Talysurf, the instruments traditionally used in surface topography imaging, cannot be used because of the small size of particles. The limitation of an atomic force microscope is its relatively small vertical range, while the horizontal resolution of laser confocal or interferometric microscopes is too low to obtain accurate particle surface topography data. The application of a combination of SEM and stereoscopy techniques seems to alleviate this problem. 3D surface topography data obtained using this technique can be processed and presented in many different ways. The usefulness of various methods of surface data representation in visualization and numerical characterization of wear particle surfaces is discussed. One of the major difficulties associated with the characterization of surface topographies is the accurate description of surface spatial properties, i.e., their anisotropy and directionality. Recently, a specially modified Hurst Orientation Transform (HOT), to suit wear particle surface data, has been developed and applied to characterize the surface topography of particles. The Hurst coefficients are related to fractal dimensions and are a measure of surface roughness, i.e., a rougher surface is represented by lower Hurst coefficients. It was found that the modified HOT can be applied to reveal the surface anisotropy of wear particles. Although none of the other methods developed so far allow such a thorough characterization of wear particle surfaces as does the modified HOT, this method still does not provide a full description of the surface topography. Therefore, it appears that a totally different approach is needed in order to make a fundamental breakthrough in the characterization of wear particle surfaces. Since many of the complex structures observed in nature can be described and modelled by a combination of simple mathematical rules, it may be possible to describe the surface of a particle by a set of such rules. In our first attempt, a Partitioned Iterated Function System (PIFS) was applied to encode the wear particle surface topography information. This information can then be used to calculate the relevant surface descriptors. In this paper, an overview of recent advances and developments in the numerical characterization of wear particle surfaces is presented.