Rotaglide total knee arthroplasty: a long-term follow-up study.

Background: Mobile-bearing knee designs represent an alternative to conventional fixed-bearing implants in total knee arthroplasty. The purpose of this study was to determine the clinical results of a mobile-bearing knee implant.

Methods: From 1990 to 1998, 326 primary consecutive mobile-bearing total knee prostheses were implanted in 260 patients who had a mean age and standard deviation of 66.

A novel intraoperative sensor for soft tissue balancing in total knee arthroplasty.

Ligament balancing during total knee arthroplasty (TKA) is a controllable and critical procedure necessary for the longevity of the prosthesis. Intraoperative knowledge of the magnitude and location of tibiofemoral forces, can guide the surgeon to an accurate balancing throughout the operation. We developed a novel sensor device in order to monitor tibiofemoral forces during TKA ligament balancing procedure.

Computational intelligence for the detection and classification of malignant lesions in screening mammography.

This report deals with the discussion of the findings obtained from the application of two computational intelligence methodologies for the detection of microcalcifications in screening mammography data. Genetic programming and inductive machine learning have been applied, in order to produce meaningful diagnostic rules for the medical staff. The data used in the experiments correspond to information acquired from two images of each breast of the patient, along with some associated patient information such as the age at time of study.

Breast tumor malignancy modelling using evolutionary neural logic networks.

The present work proposes a computer assisted methodology for the effective modelling of the diagnostic decision for breast tumor malignancy. The suggested approach is based on innovative hybrid computational intelligence algorithms properly applied in related cytological data contained in past medical records. The experimental data used in this study were gathered in the early 1990s in the University of Wisconsin, based in post diagnostic cytological observations performed by expert medical staff.

Automated identification of cancerous smears using various competitive intelligent techniques.

In this study the performance of various intelligent methodologies is compared in the task of pap-smear diagnosis. The selected intelligent methodologies are briefly described and explained, and then, the acquired results are presented and discussed for their comprehensibility and usefulness to medical staff, either for fault diagnosis tasks, or for the construction of automated computer-assisted classification of smears. The intelligent methodologies used for the construction of pap-smear classifiers, are different clustering approaches, feature selection, neuro-fuzzy systems, inductive machine learning, genetic programming, and second order neural networks.