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Background: Recent advances in three-dimensional (3D) printing and augmented reality (AR) have expanded anatomical modeling possibilities for caregiver craniosynostosis education. The purpose of this study is to characterize caregiver preferences regarding these visual models and determine the impact of these models on caregiver understanding of craniosynostosis. Methods: The authors constructed 3D-printed and AR craniosynostosis models, which were randomly presented in a cross-sectional survey. Caregivers rated each model's utility in learning about craniosynostosis, learning about skull anatomy, viewing an abnormal head shape, easing anxiety, and increasing trust in the surgeon in comparison to a two-dimensional (2D) diagram. Furthermore, caregivers were asked to identify the fused suture on each model and indicate their preference for generic versus patient-specific models. Results: A total of 412 craniosynostosis caregivers completed the survey (mean age 33 years, 56% Caucasian, 51% male). Caregivers preferred interactive, patient-specific 3D-printed or AR models over 2D diagrams (mean score difference 3D-printed to 2D: 0.16, P < 0.05; mean score difference AR to 2D: 0.17, P < 0.01) for learning about craniosynostosis, with no significant difference in preference between 3D-printed and AR models. Caregiver detection accuracy of the fused suture on the sagittal model was 19% higher with the 3D-printed model than with the AR model (P < 0.05) and 17% higher with the 3D-printed model than with the 2D diagram (P < 0.05). Conclusions:Our findings indicate that craniosynostosis caregivers prefer 3D-printed or AR models over 2D diagrams in learning about craniosynostosis. Future craniosynostosis skull models with increased user interactivity and patient-specific components can better suit caregiver preferences.

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Background Neuroprosthetic devices controlled by persons with standard limb amputation often lack the dexterity of the physiological limb due to limitations of both the user’s ability to output accurate control signals and the control system’s ability to formulate dynamic trajectories from those signals. To restore full limb functionality to persons with amputation, it is necessary to first deduce and quantify the motor performance of the missing limbs, then meet these performance requirements through direct, volitional control of neuroprosthetic devices. Methods We develop a neuromuscular modeling and optimization paradigm for the agonist-antagonist myoneural interface, a novel tissue architecture and neural interface for the control of myoelectric prostheses, that enables it to generate virtual joint trajectories coordinated with an intact biological joint at full physiologically-relevant movement bandwidth. In this investigation, a baseline of performance is first established in a population of non-amputee control subjects ($$n = 8$$ n = 8 ). Then, a neuromuscular modeling and optimization technique is advanced that allows unilateral AMI amputation subjects ($$n = 5$$ n = 5 ) and standard amputation subjects ($$n = 4$$ n = 4 ) to generate virtual subtalar prosthetic joint kinematics using measured surface electromyography (sEMG) signals generated by musculature within the affected leg residuum. Results Using their optimized neuromuscular subtalar models under blindfolded conditions with only proprioceptive feedback, AMI amputation subjects demonstrate bilateral subtalar coordination accuracy not significantly different from that of the non-amputee control group (Kolmogorov-Smirnov test, $$P \ge 0.052$$ P ≥ 0.052 ) while standard amputation subjects demonstrate significantly poorer performance (Kolmogorov-Smirnov test, $$P < 0.001$$ P < 0.001 ). Conclusions These results suggest that the absence of an intact biological joint does not necessarily remove the ability to produce neurophysical signals with sufficient information to reconstruct physiological movements. Further, the seamless manner in which virtual and intact biological joints are shown to coordinate reinforces the theory that desired movement trajectories are mentally formulated in an abstract task space which does not depend on physical limb configurations.

Background Mechanical ventilators are essential to patients who become critically ill with acute respiratory distress syndrome (ARDS), and shortages have been reported due to the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Methods We utilized 3D printing (3DP) technology to rapidly prototype and test critical components for a novel ventilator multiplexer system, Vent-Lock, to split one ventilator or anesthesia gas machine between two patients. FloRest, a novel 3DP flow restrictor, provides clinicians control of tidal volumes and positive end expiratory pressure (PEEP), using the 3DP manometer adaptor to monitor pressures. We tested the ventilator splitter circuit in simulation centers between artificial lungs and used an anesthesia gas machine to successfully ventilate two swine. Results As one of the first studies to demonstrate splitting one anesthesia gas machine between two swine, we present proof-of-concept of a de novo, closed, multiplexing system, with flow restriction for potential individualized patient therapy. Conclusions While possible, due to the complexity, need for experienced operators, and associated risks, ventilator multiplexing should only be reserved for urgent situations with no other alternatives. Our report underscores the initial design and engineering considerations required for rapid medical device prototyping via 3D printing in limited resource environments, including considerations for design, material selection, production, and distribution. We note that optimization of engineering may minimize 3D printing production risks but may not address the inherent risks of the device or change its indications. Thus, our case report provides insights to inform future rapid prototyping of medical devices.

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