Neila Mezghani

Feature selection using a principal component analysis of the kinematics of the pivot shift phenomenon

The pivot shift test reproduces a complex instability of the knee joint following rupture of the anterior cruciate ligament. The grade of the pivot shift test has been shown to correlate to subjective criteria of knee joint function, return to physical activity and long-term outcome. This severity is represented by a grade that is attributed by a clinician in a subjective manner, rendering the pivot shift test poorly reliable. The purpose of this study was to unveil the kinematic parameters that are evaluated by clinicians when they establish a pivot shift grade. To do so, eight orthopaedic surgeons performed a total of 127 pivot shift examinations on 70 subjects presenting various degrees of knee joint instability. The knee joint kinematics were recorded using electromagnetic sensors and principal component analysis was used to determine which features explain most of the variability between recordings. Four principal components were found to account for most of this variability (69%), with only the first showing a correlation to the pivot shift grade (r = 0.55). Acceleration and velocity of tibial translation were found to be the features that best correlate to the first principal component, meaning they are the most useful for distinguishing different recordings. The magnitudes of the tibial translation and rotation were amongst those that accounted for the least variability. These results indicate that future efforts to quantify the pivot shift should focus more on the velocity and acceleration of tibial translation and less on the traditionally accepted parameters that are the magnitudes of posterior translation and external tibial rotation.

On-line recognition of handwritten Arabic characters using a Kohonen neural network

Neural networks have been applied to various pattern classification and recognition problems for their learning ability, discrimination power and generalization ability The neural network most referenced in the pattern recognition literature are the multi-layer perceptron, the Kohonen associative memory and the Capenter-Grossberg ART network. The Kohonen memory runs an unsupervised clustering algorithm. It is easily trained and has attractive properties such as topological ordering and good generalization. In this study an on-line system for the recognition of handwriting Arabic characters using a Kohonen network is investigated. The input of the neural network is a feature vector of elliptic Fourier coefficients extracted from the handwritten dynamic representation. Experimental results show that the network successfully recognizes both clearly and roughly written characters with good performance.

Bayes Classification of Online Arabic Characters by Gibbs Modeling of Class Conditional Densities

This study investigates Bayes classification of online Arabic characters using histograms of tangent differences and Gibbs modeling of the class-conditional probability density functions. The parameters of these Gibbs density functions are estimated following the Zhu et al. constrained maximum entropy formalism, originally introduced for image and shape synthesis. We investigate two partition function estimation methods: one uses the training sample, and the other draws from a reference distribution. The efficiency of the corresponding Bayes decision methods, and of a combination of these, is shown in experiments using a database of 9,504 freely written samples by 22 writers. Comparisons to the nearest neighbor rule method and a Kohonen neural network method are provided.

Combination of pruned Kohonen maps for on-line arabic characters recognition

The purpose of this study is to investigate a methodfor high performance on-line Arabic characters recognition.This method is based on the use of Kohonen mapsand their corresponding confusion matrices which serve toprune them of error-causing nodes, and to combine themconsequently. We use two Kohonen maps obtained usingtwo distinct character representations, namely, Fourier descriptorsand tangents extracted along the characters on-linesignals. The two Kohonen maps are then combined usinga majority vote decision rule that, for each character,favors the most reliable map. This combination, withoutadding in any significant way to the process complexity, affordsa much better recognition rate.

Automatic Classification of Asymptomatic and Osteoarthritis Knee Gait Patterns Using Kinematic Data Features and the Nearest Neighbor Classifier

The aim of this work is to develop an automatic computer method to distinguish between asymptomatic (AS) and osteoarthritis (OA) knee gait patterns using 3-D ground reaction force (GRF) measurements. GRF features are first extracted from the force vector variations as a function of time and then classified by the nearest neighbor rule. We investigated two different features: the coefficients of a polynomial expansion and the coefficients of a wavelet decomposition. We also analyzed the impact of each GRF component (vertical, anteroposterior, and medial lateral) on classification. The best discrimination rate (91%) was achieved with the wavelet decomposition using the anteroposterior and the medial lateral components. These results demonstrate the validity of the representation and the classifier for automatic classification of AS and OA knee gait patterns. They also highlight the relevance of the anteroposterior and medial lateral force components in gait pattern classification.

Objective grading of the pivot shift phenomenon using a support vector machine approach

The pivot shift test is the only clinical test that has been shown to correlate with subjective criteria of knee joint function following rupture of the anterior cruciate ligament. The grade of the pivot shift is important in predicting short- and long-term outcome. However, because this grade is established by a clinician in a subjective manner, the pivot shifts value as a clinical tool is reduced. The purpose of this study was to develop a system that will objectively grade the pivot shift test based on recorded knee joint kinematics. Fifty-six subjects with different degrees of knee joint stability had the pivot shift test performed by one of eight different orthopaedic surgeons while their knee joint kinematics were recorded. A support vector machine based algorithm was used to objectively classify these recordings according to a clinical grade. The grades established by the surgeons were used as the gold standard for the development of the classifier. There was substantial agreement between our classifier and the surgeons in establishing the grade (weighted kappa=0.68). Seventy-one of 107 recordings (66%) were given the same grade and 96% of the time our classifier was within one grade of that given by the surgeons. Moreover, grades 0 and 1 were distinguished from grade 2 to 3 with 86% sensitivity and 90% specificity. Our results show the feasibility of automatically grading the pivot shift in a manner similar to that of an experienced clinician, based on knee joint kinematics.

A new representation of shape and its use for high performance in online Arabic character recognition by an associative memory

The purpose of this study is to investigate a new representation of shape and its use in handwritten online character recognition by a Kohonen associative memory. This representation is based on the empirical distribution of features such as tangents and tangent differences at regularly spaced points along the character signal. Recognition is carried out by a Kohonen neural network trained using the representation. In addition to the Euclidean distance traditionally used in the Kohonen training algorithm to measure the similarities among feature vectors, we also investigate the Kullback–Leibler divergence and the Hellinger distance, functions that measure distance between distributions. Furthermore, we perform operations (pruning and filtering) on the trained memory to improve its classification potency. We report on extensive experiments using a database of online Arabic characters produced without constraints by a large number of writers. Comparative results show the pertinence of the representation and the superior performance of the scheme.

Computer algorithms and applications used to assist the evaluation and treatment of adolescent idiopathic scoliosis: a review of published articles 2000-2009.

Adolescent idiopathic scoliosis (AIS) is a complex spinal deformity whose assessment and treatment present many challenges. Computer applications have been developed to assist clinicians. A literature review on computer applications used in AIS evaluation and treatment has been undertaken. The algorithms used, their accuracy and clinical usability were analyzed. Computer applications have been used to create new classifications for AIS based on 2D and 3D features, assess scoliosis severity or risk of progression and assist bracing and surgical treatment. It was found that classification accuracy could be improved using computer algorithms that AIS patient follow-up and screening could be done using surface topography thereby limiting radiation and that bracing and surgical treatment could be optimized using simulations. Yet few computer applications are routinely used in clinics. With the development of 3D imaging and databases, huge amounts of clinical and geometrical data need to be taken into consideration when researching and managing AIS. Computer applications based on advanced algorithms will be able to handle tasks that could otherwise not be done which can possibly improve AIS patients’ management. Clinically oriented applications and evidence that they can improve current care will be required for their integration in the clinical setting.

A decision tree can increase accuracy when assessing curve types according to Lenke classification of adolescent idiopathic scoliosis

Study Design. The assignment of adolescent idiopathic scoliosis (AIS) curves into curve types (1–6), as described by Lenke et al, was evaluated by 12 independent observers using the original description versus a decisional tree algorithm. Objective. To determine whether a decision tree algorithm can improve classification accuracy using the Lenke classification for AIS. Summary of Background Data. Curve type classification in AIS relies on several parameters to consider, and its relative complexity has lead to conflicting studies that reported fair-to-excellent interobserver reliability. Kings classification reliability was shown to be improved using a rule-based automated algorithm. No similar algorithm for Lenkes classification currently exists. Methods. A clinical diagram derived from a decision tree was developed to help clinicians classify AIS curves. Twelve clinicians and research assistants were asked to classify AIS curves using 2 methods: the original Lenke chart alone and the decision tree diagram in addition to the Lenke Chart. Wilcoxon ranking tests were used to evaluate any difference in classification accuracy and speed for both methods. Mann-Whitney tests were used to compare experts and nonexperts results. Pearson correlation was calculated to evaluate the relationship between accuracy and time taken to classify. Results. Use of the decision tree for curve type determination improved classification accuracy from 77.2% to 92.9% (P = 0.005) without requiring more time to classify. This improvement was statistically significant (P < 0.05). A statistically significant correlation between accuracy and time spent classifying when the decision tree is used was also observed (R = 0.62, P = 0.032). Conclusion. Transfer of a computer algorithm, a decision tree, to a clinical diagram improved both accuracy ofAIS classification. Algorithmic diagrams could prove beneficial to increase classification reliability due to their systematic approach.

Artificial neural networks assessing adolescent idiopathic scoliosis: comparison with Lenke classification

BACKGROUND CONTEXT Variability in classifying and selecting levels of fusion in adolescent idiopathic scoliosis (AIS) has been repeatedly documented. Several computer algorithms have been used to classify AIS based on the geometrical features, but none have attempted to analyze its treatment patterns. PURPOSE To use self-organizing maps (SOM), a kind of artificial neural networks, to reliably classify AIS cases from a large database. To analyze surgeons treatment pattern in selecting curve regions to fuse in AIS using Lenke classification and SOM. STUDY DESIGN This is a technical concept article on the possibility and benefits of using neural networks to classify AIS and a retrospective analysis of AIS curve regions selected for fusion. PATIENT SAMPLE A total of 1,776 patients surgically treated for AIS were prospectively enrolled in a multicentric database. Cobb angles were measured on AIS patient spine radiographies, and patients were classified according to Lenke classification. OUTCOME MEASURES For each patient in the database, surgical approach and levels of fusion selected by the treating surgeon were recorded. METHODS A Kohonen SOM was generated using 1,776 surgically treated AIS cases. The quality of the SOM was tested using topological error. Percentages of prediction of fusion based on Lenke classification for each patient in the database and for each node in the SOM were calculated. Lenke curve types, treatment pattern, and kappa statistics for agreement between fusion realized and fusion recommended by Lenke classification were plotted on each node of the map. RESULTS The topographic error for the SOM generated was 0.02, which demonstrates high accuracy. The SOM differentiates clear clusters of curve type nodes on the map. The SOM also shows epicenters for main thoracic, double thoracic, and thoracolumbar/lumbar curve types and transition zones between clusters. When cases are taken individually, Lenke classification predicted curve regions fused by the surgeon in 46% of cases. When those cases are reorganized by the SOM into nodes, Lenke classification predicted the curve regions to fuse in 82% of the nodes. Agreement with Lenke classification principles was high in epicenters for curve types 1, 2, and 5, moderate in cluster for curve types 3, 4, and 6, and low in transition zones between curve types. CONCLUSIONS An AIS SOM with high accuracy was successfully generated. Lenke classification principles are followed in 46% of the cases but in 82% of the nodes on the SOM. The SOM highlights the tendency of surgeons to follow Lenke classification principles for similar curves on the SOM. Self-organizing map classification of AIS could be valuable to surgeons because it bypasses the limitations imposed by rigid classification such as cutoff values on Cobb angle to define curve types. It can extract similar cases from large databases to analyze and guide treatment.