Background: Several authors have presented cardiac phantoms to mimic the particularities of the heart, making it suitable for medical training and surgical planning. Although the initial models were mainly focused on the ventricles, personalized phantoms of the atria were recently presented. However, such models are typically rigid, the atrial wall is not realistic and they are not compatible with ultrasound (US), being sub‐optimal for planning/training of several interventions.Methods: In this work, we propose a strategy to construct a patient‐specific atrial model. Specifically, the target anatomy is generated using a computed tomography (CT) dataset and then constructed using a mold‐cast approach. An accurate representation of the inter‐atrial wall (IAS) was ensured during the model generation, allowing its application for IAS interventions. Two phantoms were constructed using different flexible materials (silicone and polyvinyl alcohol cryogel, PVA‐C), which were then compared to assess their appropriateness for US acquisition and for the generation of complex anatomies.Results: Two experiments were set up to validate the proposed methodology. First, the accuracy of the manufacturing approach was assessed through the comparison between a post‐production CT and the virtual references. The results proved that the silicone‐based model was more accurate than the PVA‐C‐based one, with an error of 1.68 ± 0.79, 1.36 ± 0.94, 1.45 ± 0.77 mm for the left (LA) and right atria (RA) and IAS, respectively. Second, an US acquisition of each model was performed and the obtained images quantitatively and qualitatively assessed. Both models showed a similar performance in terms of visual evaluation, with an easy detection of the LA, RA, and the IAS. Furthermore, a moderate accuracy was obtained between the atrial surfaces extracted from the US and the ideal reference, and again a superior performance of the silicone‐based model against the PVA‐C phantom was observed.Conclusions: The proposed strategy proved to be accurate and feasible for the correct generation of complex personalized atrial models.

Landmark labeling in 3D head surfaces is an important and routine task in clinical practice to evaluate head shape, namely to analyze cranial deformities or growth evolution. However, manual labeling is still applied, being a tedious and time-consuming task, highly prone to intra-/inter-observer variability, and can mislead the diagnose. Thus, automatic methods for anthropometric landmark detection in 3D models have a high interest in clinical practice. In this paper, a novel framework is proposed to accurately detect landmarks in 3D infant's head surfaces. The proposed method is divided into two stages: (i) 2D representation of the 3D head surface; and (ii) landmark detection through a deep learning strategy. Moreover, a 3D data augmentation method to create shape models based on the expected head variability is proposed. The proposed framework was evaluated in synthetic and real datasets, achieving accurate detection results. Furthermore, the data augmentation strategy proved its added value, increasing the method's performance. Overall, the obtained results demonstrated the robustness of the proposed method and its potential to be used in clinical practice for head shape analysis.

Deformational plagiocephaly is a head deformity that occurs in newborns, treated in severe cases with helmets named cranial remodeling orthoses (CRO). Current CROs can fail to adapt to head growth, causing excessive pressure sores and other complications, and may lead to poor clinical results. In this work, we experimentally and numerically study the compressive behavior of a functionally graded lattice that may be used in future work as a potential inner lining for a CRO with a customized density distribution. This work is divided into five stages. First, we describe the design of all lattices involved in the study. Second, we measure the mechanical properties of the bulk material used in the manufacturing of the lattices. Third, we study the effect of unit cell size variation, testing three homogenous body-centered cubic (BCC) lattices, and creating a numerical model for the prediction of the compressive behavior of various lattices with different unit cell sizes. Fourth, we study the effects of truss diameter variation, designing and testing three homogenous lattices with different truss diameters. Finally, we design a BCC lattice with a truss diameter gradient and analyze compressive deformations in numerical and experimental compression studies. The numerical simulations of the compression of the homogenous and graded lattices agree with the experimental measurements, both in unit cell variation and in truss diameter variation. In the graded lattice, the compression displacements observed in each region are proportional to their density and agree with the numerical simulation. Truss diameter variation was found to have a wider range of compressive responses than unit cell size variation without major changes in the overall geometry of the lattice and found more suitable for the intended application. The studies showed the potential of the functionally graded lattice for use in the CRO.

According to the World Health Organization, 85% of the world population suffers from back pain, which accounts for over 50% of physical incapacity, permanent or temporary, among individuals in working-age. In most situations, this is caused by an incorrect posture, which causes changes in the spine structure. This paper proposes an instrumented vest for postural reeducation to address this issue. The vest has a set of inertial measurement unit (IMU) sensors strategically placed to provide an accurate characterization of the spine profile. The sensor readings are classified by a central processing unit. In case of an incorrect posture, users are alerted by an audio signal and through vibration. The wearable system works in stand-alone mode, but can also communicate with external systems through an API. Two applications were developed to communicate with the device through this API, one intended to run on a desktop computer and the other one for Android devices. These applications monitor spine profiles in real time and notify the user of incorrect postures, among other functionalities. The device prototype and the applications have been tested by 10 individuals in two different settings, first without any kind of feedback and then with feedback enabled. The tests demonstrate the usability, accuracy and robustness of the system, proving its high level of reliability in classifying postures and effectiveness for postural reeducation. In the future, the system is expected to be used as a platform for a serious game, to promote posture reeducation in a real world scenario.