Home-based virtual reality balance training and conventional balance training in Parkinson's disease: A randomized controlled trial.

BACKGROUND/PURPOSE: Virtual reality has the advantage to provide rich sensory feedbacks for training balance function. This study tested if the home-based virtual reality balance training is more effective than the conventional home balance training in improving balance, walking, and quality of life in patients with Parkinson's disease (PD). METHODS: Twenty-three patients with idiopathic PD were recruited and underwent twelve 50-minute training sessions during the 6-week training period. The experimental group (n = 11) was trained with a custom-made virtual reality balance training system, and the control group (n = 12) was trained by a licensed physical therapist. Outcomes were measured at Week 0 (pretest), Week 6 (posttest), and Week 8 (follow-up). The primary outcome was the Berg Balance Scale. The secondary outcomes included the Dynamic Gait Index, timed Up-and-Go test, Parkinson's Disease Questionnaire, and the motor score of the Unified Parkinson's Disease Rating Scale. RESULTS: The experimental and control groups were comparable at pretest. After training, both groups performed better in the Berg Balance Scale, Dynamic Gait Index, timed Up-and-Go test, and Parkinson's Disease Questionnaire at posttest and follow-up than at pretest. However, no significant differences were found between these two groups at posttest and follow-up. CONCLUSION: This study did not find any difference between the effects of the home-based virtual reality balance training and conventional home balance training. The two training options were equally effective in improving balance, walking, and quality of life among community-dwelling patients with PD.

Contrast enhancement and tissues classification of breast MRI using Kalman filter-based linear mixing method.

Much attention is currently focused on one of the newest breast examination techniques, breast MRI. Contrast-enhanced breast MRIs acquired by contrast injection have been shown to be very sensitive in the detection of breast cancer, but are also time-consuming and cause waste of medical resources. This paper therefore proposes the use of spectral signature detection technology, the Kalman filter-based linear mixing method (KFLM), which can successfully present the results as high-contrast images and classify breast MRIs into major tissues from four bands of breast MRIs. A series of experiments using phantom and real MRIs was conducted and the results compared with those of the commonly used c-means (CM) method and dynamic contrast-enhanced (DCE) breast MRIs for performance evaluation. After comparison with the CM algorithm and DCE breast MRIs, the experimental results showed that the high-contrast images generated by the spectral signature detection technology, the KFLM, were of superior quality.

Detecting ineffective triggering in the expiratory phase in mechanically ventilated patients based on airway flow and pressure deflection: feasibility of using a computer algorithm.

OBJECTIVE: Ineffective triggering (IT) is the most common manifestation of patient-ventilator asynchrony in mechanically ventilated patients. IT in the expiratory phase (ITE) accounts for the majority of IT and is associated with characteristic features of flow and airway pressure deflection, caused by ineffective effort from the patient. The purpose of this study was to quantify the characteristics of flow and airway pressure deflections of ITE and, using a computerized algorithm, to evaluate their usefulness in the detection of ITEs. DESIGN: Prospective, clinical study. SETTING: Medical intensive care unit in a 1,000-bed university hospital. PATIENTS: A total of 14 mechanically ventilated adult patients with patient-ventilator asynchrony. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: We analyzed 5,899 breaths and found that 1,831 were ITEs. The average values for maximum flow deflection (F(def)) and maximum airway pressure deflection (P(def)) in ITEs were 13.94 +/- 8.0 L/min and 1.91 +/- 0.97 cm H2O. With a starting value of 0.1 L/min for F(def) and 0.01 cm H2O for P(def), the area under the receiver operating characteristics curve of F(def) and P(def) for the detection of ITEs was 0.98 and 0.97, respectively. Sensitivity and specificity for the detection of ITEs were 91.5% and 96.2% for F(def), respectively, for a cutoff value of 5.45 L/min, and 93.3% and 92.9% for Pdef, for a cutoff value of 0.45 cm H2O. CONCLUSION: We conclude that accurately detecting and quantifying ITEs is feasible using a computerized algorithm based on F(def) and P(def). Such a computerized estimation of patient-ventilator interaction might be helpful for adjusting ventilator settings in an intensive care unit.

Classification of clustered microcalcifications using a Shape Cognitron neural network.

A new shape recognition-based neural network built with universal feature planes, called Shape Cognitron (S-Cognitron) is introduced to classify clustered microcalcifications. The architecture of S-Cognitron consists of two modules and an extra layer, called 3D figure layer lies in between. The first module contains a shape orientation layer, built with 20 cell planes of low level universal shape features to convert first-order shape orientations into numeric values, and a complex layer, to extract second-order shape features. The 3D figure layer is a feature extract-display layer that extracts the shape curvatures of an input pattern and displays them as a 3D figure. It is then followed by a second module made up of a feature formation layer and a probabilistic neural network-based classification layer. The system is evaluated by using Nijmegen mammogram database and experimental results show that sensitivity and specificity can reach 86.1 and 74.1%, respectively.