The aim of this work was to assess the accuracy, repeatability, and reproducibility of a hand-held, structured-light 3D scanner (EINScan Pro 2X Plus with High Definition Prime Pack, SHINING 3D Tech. Co., Ltd., Hangzhou, China), to support its potential use in multi-site settings on lower limb prosthetics. Four limb models with different shapes were fabricated and scanned with a metrological 3D scanner (EINScan Laser FreeScan 5X, SHINING 3D Tech. Co., Ltd., Hangzhou, China) by a professional operator (OP0). Limb models were then mailed to three sites where two operators (OP1, OP2) scanned them using their own structured-light 3D scanner (same model). OP1 scanned limb models twice (OP1-A, OP1-B). OP0, OP1-A, and OP2 scans were compared for accuracy, OP1-A and OP1-B for repeatability, and OP1-A and OP2 for reproducibility. Among all comparisons, the mean radial error was <0.25 mm, mean angular error was <4°, and root mean square error of the radial distance was <1 mm. Moreover, limits of agreement were <3.5% for perimeters and volumes. By comparing these results with respect to clinically-relevant thresholds and to the literature available on other 3D scanners, we conclude that the EINScan Pro 2X Plus 3D Scanner with High Definition Prime Pack has good accuracy, repeatability, and reproducibility, supporting its use in multi-site settings.

The socket is the most important, patient-specific element of a prosthesis. Conventionally, the process of making a custom socket involves manually rectifying a plaster model of the residual limb. This process is time-consuming and often inconsistent among prosthetists because it is based on implicit knowledge. Hence, the aim of this work was to describe a novel process of generating a prosthetist-specific, digital “global” template and to illustrate that it can be automatically applied to rectify the shape of a transtibial residual limb. The process involved (1) the acquisition of a “training” dataset of unrectified and rectified positive models through manual data collection and digital 3D scanning, and (2) the unsupervised learning of the prosthetist’s rectifications by an artificial intelligence (AI) algorithm. The assessment of the process involved (1) evaluating whether the rectification rule learned by the AI was consistent with the prosthetist’s expectations, and (2) evaluating the template feasibility by applying the AI rectification process to a new residual limb and comparing the results to the prosthetist’s manual rectification for the same residual limb. The results suggest that the AI-rectified positive was consistent with the approach described by the prosthetist, with only small radial and angle errors and similar dimensions (volume and cross-sectional perimeters) as the hand-rectified positive. This study provides a proof-of-concept of the ability to integrate an AI algorithm into the fabrication process for transtibial prosthetic sockets. Once refined, this approach may provide a time-saving tool for prosthetists by automatically implementing typical rectifications and providing a good starting socket fit for individuals with amputation.

Scleroderma is a chronic and progressive autoimmune disorder of connective tissues often causing lesions and deformities of the hands. Individuals affected by this condition experience daily life limitations and are typically unable to take part in sport activities that involve impacts on the hands. In this article we describe the design and manufacturing of custom-made hand orthoses to play sitting volleyball, for an elite paralympic athlete affected by scleroderma. The devices consist of a carbon fibre shell with an internal silicone padding and an external polymeric multilayer cover. The manufacturing of the orthoses involves digital modelling, 3D printing, composite lamination and an innovative method to create a strong and durable chemical bonding between silicone and carbon fibre. The internal silicone padding proved to be effective in hosting and protecting the hands, whereas the external shell with polymeric multilayer cover allowed to dampen the ball shocks while effectively hitting the ball. Indeed, these devices allowed the athlete to take part in the 2020 Tokyo Paralympic games and were used for two years without showing any damage.

A well-fitting socket and a fine-tuned foot alignment are crucial elements in a running-specific prosthesis to allow Paralympic athletes with below-knee amputation to express their full competitive potential. For this reason, once a satisfactory socket-foot configuration is established after dynamic alignment, it is fundamental to reproduce the same conditions when constructing the definitive carbon fiber socket, and when renewing or constructing a back-up prosthesis, without dismantling the original. In addition, to cope with emerging needs of the athlete, it would be beneficial to implement fine-tuning adjustments of the alignment in a very controlled manner. At present, this requires elaborate bench procedures, which tend to be expensive, time consuming, prone to manual errors, cumbersome in use and most often require damaging or disposing of the current socket. In this study, we propose an original CAD/CAM workflow that allows replicating the desired socket-foot configuration for below-knee sprinting prostheses, as well as performing socket adaptations and introducing fine-tuning adjustments to the alignments. The workflow is exemplified with reference to two case studies involving elite Paralympic runners with transtibial and partial foot amputations, respectively.

The prosthetic socket is the most critical component of a lower limb prosthesis, requiring precise customization to the individual’s residual limb. This study presents proof-of-concept for a novel artificial intelligence (AI)-driven rectification template for transfemoral sockets tailored to a single prosthetist. Using a dataset of nine persons with transfemoral amputation, the study workflow required the manual casting and 3D scanning of unrectified and rectified plaster positives, anatomical landmark identification, and unsupervised training of an algorithm using Principal Component Analysis (PCA). The AI captured both explicit and implicit rectification strategies, generating an average rectification template and synergistic modes of variation. Validation was conducted via a leave-one-out approach, comparing AI-generated versus manually crafted rectified positives using clinically-relevant metrics: perimeter and volume differences. The first four PCA modes explained 78% of rectification variability, with key modifications observed in distal and medial regions. Volume differences between AI and manually rectified positives were within clinically acceptable limits for all participants, with 44% rated “good” and 56% “acceptable”. This proof-of-concept provides a new perspective on the application of AI to replicate prosthetist-specific rectification strategies for transfemoral sockets. It may potentially help streamline fabrication by capturing both explicit and implicit prosthetist knowledge. The approach may be useful in clinical training, documentation, and socket fabrication, particularly in resource-limited settings, and may contribute to more consistent and efficient prosthetics care.

Monitoring the activities of athletes with lower-limb amputations who use running-specific prostheses is essential for evaluating their training regimes, as well as the effectiveness and mechanical fatigue wear of their prostheses over time. Recent advancements in Inertial Measurement Units (IMUs) and activity detection algorithms offer new opportunities for objective assessment, but their application in Paralympic sports remains unexplored. The aims of this work were to design and implement an innovative protocol and analytical software for short-term and long-term activity detection of athletes with transtibial and transfemoral amputation and then test its validity on a sample of elite Paralympic runners and triathletes. Overall, the ability of the model to detect activities presented an accuracy of 98%, and the error in the stride counting for all activities fell within a 1% margin.