3D models are accurate replicas of the cardiovascular anatomy and improve the understanding of complex CHD. 3D models did not change the surgical decision in most of the cases (21 of 40 cases, 52.5% cases). However, in 19 of the 40 selected complex cases, 3D model helped redefining the surgical approach.

Objectives To evaluate whether three‐dimensional (3D) printed models can be used to improve interventional simulation and planning in patients with aortic arch hypoplasia. Background: Stenting of a hypoplastic transverse arch is technically challenging, and complications such as stent migration and partial obstruction of the origin of the head and neck vessels are highly dependent on operator skills and expertise. Methods: Using magnetic resonance imaging (MRI) data, a 3D model of a repaired aortic coarctation of a 15‐year‐old boy with hypoplastic aortic arch was printed. Simulation of the endovascular stenting of the hypoplastic arch was carried out under fluoroscopic guidance in the 3D printed model, and subsequently in the patient. A Bland–Altman analysis was used to evaluate the agreement between measurements of aortic diameter in the 3D printed model and the patient's MRI and X‐ray angiography. Results: The 3D printed model proved to be radio‐opaque and allowed simulation of the stenting intervention. The assessment of optimal stent position, size, and length was found to be useful for the actual intervention in the patient. There was excellent agreement between the 3D printed model and both MRI and X‐ray angiographic images (mean bias and standard deviation of 0.36 ± 0.45 mm). Conclusions: 3D printed models accurately replicate patients' anatomy and are helpful in planning endovascular stenting in transverse arch hypoplasia. This opens a door for potential simulation applications of 3D models in the field of catheterization and cardiovascular interventions. © 2015 Wiley Periodicals, Inc. © 2015 Wiley Periodicals, Inc.

Cardiovascular models constructed by rapid prototyping techniques are extremely helpful for planning corrective surgery in patients with complex congenital malformations. Therefore they may potentially reduce operative time and morbi-mortality.

NAOTherapist is a cognitive robotic architecture whose main goal is to develop non-contact upperlimb rehabilitation sessions autonomously with a social robot for patients with physical impairments. In order to achieve a fluent interaction and an active engagement with the patients, the system should be able to adapt by itself in accordance with the perceived environment. In this paper, we describe the interaction mechanisms that are necessary to supervise and help the patient to carry out the prescribed exercises correctly. We also provide an evaluation focused on the child-robot interaction of the robotic platform with a large number of schoolchildren and the experience of a first contact with three pediatric rehabilitation patients. The results presented are obtained through questionnaires, video analysis and system logs, and have proven to be consistent with the hypotheses proposed in this work. J. C. Pulido (first author) and J. C. González (second author) contributed equally to this work.

BackgroundNeurorehabilitation therapies exploiting the use-dependent plasticity of our neuromuscular system are devised to help patients who suffer from injuries or diseases of this system. These therapies take advantage of the fact that the motor activity alters the properties of our neurons and muscles, including the pattern of their connectivity, and thus their functionality. Hence, a sensor-motor treatment where patients makes certain movements will help them (re)learn how to move the affected body parts. But these traditional rehabilitation processes are usually repetitive and lengthy, reducing motivation and adherence to the treatment, and thus limiting the benefits for the patients.ObjectiveOur goal was to create innovative neurorehabilitation therapies based on THERAPIST, a socially assistive robot. THERAPIST is an autonomous robot that is able to find and execute plans and adapt them to new situations in real-time. The software architecture of THERAPIST monitors and determines the course of action, learns from previous experiences, and interacts with people using verbal and non-verbal channels. THERAPIST can increase the adherence of the patient to the sessions using serious games. Data are recorded and can be used to tailor patient sessions.MethodsWe hypothesized that pediatric patients would engage better in a therapeutic non-physical interaction with a robot, facilitating the design of new therapies to improve patient motivation. We propose RoboCog, a novel cognitive architecture. This architecture will enhance the effectiveness and time-of-response of complex multi-degree-of-freedom robots designed to collaborate with humans, combining two core elements: a deep and hybrid representation of the current state, own, and observed; and a set of task-dependent planners, working at different levels of abstraction but connected to this central representation through a common interface. Using RoboCog, THERAPIST engages the human partner in an active interactive process. But RoboCog also endows the robot with abilities for high-level planning, monitoring, and learning. Thus, THERAPIST engages the patient through different games or activities, and adapts the session to each individual.ResultsRoboCog successfully integrates a deliberative planner with a set of modules working at situational or sensorimotor levels. This architecture also allows THERAPIST to deliver responses at a human rate. The synchronization of the multiple interaction modalities results from a unique scene representation or model. THERAPIST is now a socially interactive robot that, instead of reproducing the phrases or gestures that the developers decide, maintains a dialogue and autonomously generate gestures or expressions. THERAPIST is able to play simple games with human partners, which requires humans to perform certain movements, and also to capture the human motion, for later analysis by clinic specialists.ConclusionsThe initial hypothesis was validated by our experimental studies showing that interaction with the robot results in highl...