Development and clinical evaluation of laser-sintered ankle foot orthoses

Ankle foot orthoses (AFOs) are traditionally manufactured using vacuum thermoforming as shaping technology. Additive manufacturing has the potential to disruptively change the way these orthopaedic devices are produced. In this study, AFOs are developed which are virtually designed and produced with laser sintering as shaping technology. The mechanical and clinical performances of these laser-sintered AFOs are compared with traditionally manufactured AFO by asking seven patients (both children and adults) to walk with each type of AFO

Prediction of dose escalation for rheumatoid arthritis patients under infliximab treatment

Rheumatoid arthritis (RA) is a chronic inflammatory joint disease that leads to irreversible joint destruction. To prevent this, new biological therapies, such as infliximab, have been successfully developed. The present analysis is based on an expanded access program in which 511 RA patients with chronic refractory disease were treated with infliximab. They received a standard dose of 3 mg/kg on weeks 0, 2, 6, 14 and every 8 weeks thereafter. On week 22, the treating rheumatologist evaluated the situation of every patient and decided whether the current dose should be increased or not. This decision can be considered as a measure of insufficient response. In the present analysis, 3 machine-learning classification techniques-the self-organizing map (SOM), multilayered perceptron (MLP) and support vector machine (SVM)-are implemented to model the decision to give a dose increase. Their performance on increasingly multivariate real-life data will be studied and compared to classical statistics-linear discriminant analysis (LDA) and logistic regression (LR). Results show that the SOM is an excellent tool for data visualization but not for classification. All the remaining methods show good classification performance, if configured well. However, as the number of features increases, the performance decreases. The SVM suffers to a lesser degree from this curse of dimensionality. Expectation maximization (EM) comes out as a good method to cope with missing values in such real-life data. (c) 2006 Elsevier Ltd. All rights reserved