A Decision Support System to Provide an Ongoing Prediction of Robot-Assisted Rehabilitation Outcome in Stroke Survivors.

Clinicians often deal with complex robotic platform and serious games in stroke patients rehabilitation contexts, and they face two main problems: 1) the interpretation of either the performance in game or measures of a robotic system from the motor recovery point of view, and 2) the duration and complexity of clinical scales administration that makes repetitive assessments during the therapy unpractical. In this paper, a Random Tree Forest based system was trained and tested to provide a prediction of different clinical outcomes (i.e. FMA, ARAT, and MI) along the whole therapy duration, having non-clinical measures only as inputs, acting as a simulated decision support system. The dataset includes 30 post-stroke patients, that underwent a 30-session robot-assisted rehabilitation treatment. Results have shown that the system is able to produce very accurate and reliable predictions about the motor recovery of the patient at the end of the therapy, already in the first phases of the rehabilitation (i40% of therapy execution), just using robotic platform measures. Such a tool would provide a great benefit in terms of rehabilitation objectives planning, as a decision support tool for highly personalized rehabilitation treatments.

A Random Tree Forest decision support system to personalize upper extremity robot-assisted rehabilitation in stroke: a pilot study.

Robotic-based rehabilitation administered by means of serious games certainly represents the frontier of rehabilitation treatments, offering a high degree of customization of therapy, to meet individual patients' needs and to tailor a proper rehabilitation therapy. Despite the rush on developing complex rehabilitation systems, they often do not provide clinicians with long-term information about the outcome of rehabilitation, thus, not supporting them in the initial set-up phase of the therapy. In this paper, a Random-Forest based system was trained and tested to provide a prediction at discharge of several clinical scales outcomes (i.e. FMA, ARAT, and MI), having clinical scale scores and measures from the robotic system at the enrollment as inputs. The dataset includes 25 post-stroke patients from different clinics, that underwent a variable number of days of rehabilitation with a robotic treatment. Results have shown that the system is able to predict the final outcome with an accuracy ranging from 60% to 73% on the selected scales. Also results provide information on which variables are more relevant for the prediction of outcome of therapy, in particular clinical scales scores such as FMA, ARAT, MI, NRS, PCS, and MCS and robotic automatically extracted measurements related to patient's work expenditure and time. This supports the idea of using such a system in a clinical environment in a decision support tool for clinicians.

Simulation of real-time deformable soft tissues for computer assisted surgery.

The simulation of realistic surgical procedures requires specialized optimized algorithms for the models of organs and tissues, which should comply both with accuracy of results and run-time computation. This paper provides a general survey of methods and approaches used for the simulation of soft tissues in Computer Assisted Surgery, discussing the technological challenges to achieve realistic simulation of deformation.An application example is presented, referring to the simulation of a gastroenterology procedure, abdominal paracentesis for the treatment of ascites.

Discriminatory ability of quantitative ultrasound measurements is similar to dual-energy X-ray absorptiometry in a Brazilian women population with osteoporotic fracture.

The discriminating ability and relevance of clinical risk factors, quantitative ultrasound (QUS) variables, X-ray-based bone mineral density (BMD) and hip axis length (HAL) measurements to evaluate the risk of osteoporotic fracture in elderly Brazilian women were examined in this study. QUS at the calcaneus (Achilles +, Lunar), HAL and BMD measurements (DPX-L, Lunar) at several anatomical sites were performed in 275 postmenopausal Caucasian women. Patients with suspected secondary osteoporosis were excluded. One hundred twenty-two (44.4%) women had had previous osteoporotic fracture. All of the subjects were over 50 years old (range 53-93) and answered a questionnaire that included details concerning aspects of lifestyle, diet, hormonal factors and drug use. Lateral thoracic and lumbar radiographs were taken and an independent radiologist reviewed the X-rays for the presence of vertebral fractures. After adjustments for age, the most relevant risk factors to discriminate patients with osteoporotic fracture from normal non-fracture controls were Stiffness index (OR 2.8 per standard deviation; 95% confidence interval 2.3, 8.7), familial history of hip fracture (OR 2.6 per standard deviation; 95% confidence interval 2.2, 5.4), femoral neck BMD (OR 2.3 per standard deviation; 95% confidence interval 1.9, 4.2), age (OR 2.1 per standard deviation; 95% confidence interval 1.6, 2.8) and weight (OR 1.9 per standard deviation; 95% confidence interval 1.5, 2.6). HAL measurements did not associate significantly with the risk of hip fracture in this population. The ability of QUS measurements discriminate between patients with fractures from those without was similar to, if not better, than X-ray-based BMD measurements. However, a combination of QUS and BMD measurements did not significantly improve fracture discrimination compared with either technique alone. Association of clinical risk factors with QUS or BMD measurements seems, on the other hand, to increase the sensibility to identify patients at risk of osteoporotic fractures.