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Malunions of the hand and wrist can result in complicated deformities with subsequent functional difficulties. The complexity of the hand and the requirement for such large arcs of motion for normal function can complicate the evaluation and treatment of these problems. Ultimately each bone malunion is evaluated with an eye toward improving overall pain and function as well as decreasing the likelihood of late complications such as tendon rupture and arthritis. In this chapter, we address several of these unique scenarios as well as the techniques and challenges in treating these complicated problems. Phalanx, metacarpal, carpal, and distal radius malunions are all addressed.

Objectives In the last 2 decades, surgical treatment of intertrochanteric (IT) femur fractures has shown a continuing trend towards the increased use of intramedullary nails (IMN) and decreased use of sliding hip screws (SHS). Recent trends in the United States regarding the use of these implants, including charges and reimbursement, have not been investigated. Methods A national database of Medicare patients (PearlDiver, Inc.) was queried using Current Procedural Terminology (CPT) codes for patients with surgical dates from 2005-2011. Results A total of 34,759 SHS or IMN procedures for intertrochanteric femur fractures were identified from 2005-2011. There was a significant increase in the percentage of IMN compared to SHS, from 46.9% IMN in 2005 to 79.1% in 2011. The average charges for IMN and SHS increased. Statistically higher rates of PE (pConclusions The previously recognised trend of increasing use of IMN for IT femur fractures has continued. The overall incidence of operative IT femur fractures is not increasing at this time. The cost of IMN remains higher than SHS.

To use a national insurance database to explore the association of obesity with the incidence of complications after elbow arthroscopy in a Medicare population.Using Current Procedural Terminology (CPT) and International Classification of Diseases, 9th Revision (ICD-9) procedure codes, we queried the PearlDiver database for patients undergoing elbow arthroscopy. Patients were divided into obese (body mass index [BMI]30) and nonobese (BMI30) cohorts using ICD-9 codes for BMI and obesity. Nonobese patients were matched to obese patients based on age, sex, tobacco use, diabetes, and rheumatoid arthritis. Postoperative complications were assessed with ICD-9 and Current Procedural Terminology codes, including infection, nerve injury, stiffness, and medical complications.A total of 2,785 Medicare patients who underwent elbow arthroscopy were identified from 2005 to 2012; 628 patients (22.5%) were coded as obese or morbidly obese, and 628 matched nonobese patients formed the control group. There were no differences between the obese patients and matched control nonobese patients regarding type of elbow arthroscopy, previous elbow fracture or previous elbow arthroscopy. Obese patients had greater rates of all assessed complications, including infection (odds ratio [OR] 2.8, P = .037), nerve injury (OR 5.4, P = .001), stiffness (OR 1.9, P = .016) and medical complications (OR 6.9, P.0001).Obesity is associated with significantly increased rates of all assessed complications after elbow arthroscopy in a Medicare population, including infection, nerve injury, stiffness, and medical complications.Therapeutic Level III, case-control study.

We present a new rotation invariant measure for trajectories of dynamically changing locations of mobile objects (vehicles), which appear naturally in applications such as video-tracking, motion capture etc. Similar motion patterns can also be expressed at different orientations. We have modeled each trajectory by its sequence of angles. The similarity measure is defined based on longest common subsequence (LCS) method. To evaluate a system, we have simulated the database consisting of common trajectories of moving vehicles in the cities. First, clustering based on agglomerative algorithm with new similarity measure is applied on the training dataset. To classify new samples, similarity to the median of the clusters is considered and based on the rates of the similarity to the median, some natural language sentences is produced, these sentences express the behavioural descriptions of the vehicles. Experimental results show the accuracy and efficiency of the technique.

With this rapid increase in generating and collecting images, image mining and knowledge discovery attract so much attention. In this paper, we present a framework for extracting knowledge from sequence of images in the form of natural languages. In another view point, this process summarizes the enormous visual information of sequence of images into some simple and small sentences with the aim of automatic traffic regulations checking. Sequences of images have been manually extracted from a video of moving vehicles in cities. The structure of the system composed of Image Analysis and Knowledge Processing modules. Image Analysis module works on raw images and tries to extract a set of records consisting of spatial-temporal characteristics of moving objects in the scene. These records are passed to knowledge processing phase for further processing, information transformation and summarization. Knowledge processing phase consists of two modules: Trajectory Detection and Fuzzy Rule base. A new similarity measure based on Longest Common Subsequence (LCS) is proposed and used in trajectory detection module. This module tries to form traffic behavioral classes. In fuzzy Rule Base module, traffic regulations have been modeled using fuzzy rules and fuzzy inference used for checking normality/abnormality of the actions in the scene. The output of the system in the form of Natural Languages clearly shows what happened in the scene.

We present a new method based on CMAC neural network, used as classifier in a frontal face detection system. The gray level and the position of the pixels of an input image are directly presented to the network. Due to the simple structure of CMAC, with only one trainable layer, the training phase is very fast. The proposed method has been tested on a data set containing 960 faces and 20000 non-faces, selected among difficult face and non-face patterns. The results of experimentations exhibit an error rate of 8.5%, which is a reasonable result considering the simple structure of system and the important number of difficult patterns in the test dataset.