The next generation of healthcare ecosystem in the metaverse

The Metaverse has gained wide attention for being the application interface for the next generation of Internet. The potential of the Metaverse is growing, as Web 3·0 development and adoption continues to advance medicine and healthcare. We define the next generation of interoperable healthcare ecosystem in the Metaverse. We examine the existing literature regarding the Metaverse, explain the technology framework to deliver an immersive experience, along with a technical comparison of legacy and novel Metaverse platforms that are publicly released and in active use. The potential applications of different features of the Metaverse, including avatar-based meetings, immersive simulations, and social interactions are examined with different roles from patients to healthcare providers and healthcare organizations. Present challenges in the development of the Metaverse healthcare ecosystem are discussed, along with potential solutions including capabilities requiring technological innovation, use cases requiring regulatory supervision, and sound governance. This proposed concept and framework of the Metaverse could potentially redefine the traditional healthcare system and enhance digital transformation in healthcare. Similar to AI technology at the beginning of this decade, real-world development and implementation of these capabilities are relatively nascent. Further pragmatic research is needed for the development of an interoperable healthcare ecosystem in the Metaverse.

Modeling the excitation of nerve axons under transcutaneous stimulation

Computational models enable a safe and convenient way to study the excitation of nerve fibers under external stimulation. Contemporary models calculate the electric field distribution from transcutaneous stimulation and the resulting neuronal response separately. This study uses finite element methods to develop a multi-scale model that couples electric fields within macroscopic tissue layers and microscopic nerve fibers in a single-stage computational framework. The model included a triaxial myelinated nerve fiber bundle embedded within a volume conductor of tissue layers to represent the median nerve innervating the forearm muscles. The model captured the excitability of nerve fibers under transcutaneous stimulation and their nerve-tissue interactions to a transient external stimulus. The determinants of the strength-duration curve, rheobase, and chronaxie for the proposed model had close correlations with in-vivo experimentation on human participants. Additionally, the excitability indices for the triaxial myelinated nerve fiber implemented using the finite element method agreed well with experimental data from the literature. The validity of the proposed model encourages its use for applications involving transcutaneous stimulation. Capable of capturing field distribution across realistic morphologies, the model can serve as a testbed to improve stimulation protocols and electrode designs with subject-level specificity.

A translational perspective towards clinical AI fairness

Artificial intelligence (AI) has demonstrated the ability to extract insights from data, but the fairness of such data-driven insights remains a concern in high-stakes fields. Despite extensive developments, issues of AI fairness in clinical contexts have not been adequately addressed. A fair model is normally expected to perform equally across subgroups defined by sensitive variables (e.g., age, gender/sex, race/ethnicity, socio-economic status, etc.). Various fairness measurements have been developed to detect differences between subgroups as evidence of bias, and bias mitigation methods are designed to reduce the differences detected. This perspective of fairness, however, is misaligned with some key considerations in clinical contexts. The set of sensitive variables used in healthcare applications must be carefully examined for relevance and justified by clear clinical motivations. In addition, clinical AI fairness should closely investigate the ethical implications of fairness measurements (e.g., potential conflicts between group- and individual-level fairness) to select suitable and objective metrics. Generally defining AI fairness as "equality" is not necessarily reasonable in clinical settings, as differences may have clinical justifications and do not indicate biases. Instead, "equity" would be an appropriate objective of clinical AI fairness. Moreover, clinical feedback is essential to developing fair and well-performing AI models, and efforts should be made to actively involve clinicians in the process. The adaptation of AI fairness towards healthcare is not self-evident due to misalignments between technical developments and clinical considerations. Multidisciplinary collaboration between AI researchers, clinicians, and ethicists is necessary to bridge the gap and translate AI fairness into real-life benefits.

Artificial intelligence enabled smart digital eye wearables

PURPOSE OF REVIEW

Smart eyewear is a head-worn wearable device that is evolving as the next phase of ubiquitous wearables. Although their applications in healthcare are being explored, they have the potential to revolutionize teleophthalmology care. This review highlights their applications in ophthalmology care and discusses future scope.

RECENT FINDINGS

Smart eyewear equips advanced sensors, optical displays, and processing capabilities in a wearable form factor. Rapid technological developments and the integration of artificial intelligence are expanding their reach from consumer space to healthcare applications. This review systematically presents their applications in treating and managing eye-related conditions. This includes remote assessments, real-time monitoring, telehealth consultations, and the facilitation of personalized interventions. They also serve as low-vision assistive devices to help visually impaired, and can aid physicians with operational and surgical tasks.

SUMMARY

Wearables such as smart eyewear collects rich, continuous, objective, individual-specific data, which is difficult to obtain in a clinical setting. By leveraging sophisticated data processing and artificial intelligence based algorithms, these data can identify at-risk patients, recognize behavioral patterns, and make timely interventions. They promise cost-effective and personalized treatment for vision impairments in an effort to mitigate the global burden of eye-related conditions and aging.

Automatic Calibration of Electrode Arrays for Dexterous Neuroprostheses: a review

BACKGROUND

Electrode arrays can simplify the modulation of shape, size, and position for 
customized stimulation delivery. However, the intricacy in achieving the desired outcome 
stems from optimizing for the myriad of possible electrode combinations and stimulation 
parameters to account for varying physiology across users.

Objective: This study reviews automated calibration algorithms that perform such an 
optimization to realize hand function tasks. Comparing such algorithms for their calibration 
effort, functional outcome, and clinical acceptance can aid with the development of better 
algorithms and address technological challenges in their implementation.

Methods: A systematic search was conducted across major electronic databases to identify 
relevant articles. The search yielded 36 suitable articles; among them, 14 articles that met the 
inclusion criteria were considered for the review.

Results: Studies have demonstrated the realization of several hand function tasks and 
individual digit control using automatic calibration algorithms. These algorithms significantly 
improved calibration time and functional outcomes across healthy and people with neurological 
deficits. Also, electrode profiling performed via automated algorithms was very similar to a 
trained rehabilitation expert. Additionally, emphasis must be given to collecting subject-specific a priori data to improve the optimization routine and simplify calibration effort.

Conclusion: With significantly shorter calibration time, delivering personalized stimulation, 
and obviating the need for an expert, automated algorithms demonstrate the potential for home-based rehabilitation for improved user independence and acceptance.

Conformable Electrode Arrays for Wearable Neuroprostheses

Wearable electrode arrays can selectively stimulate muscle groups by modulating their shape, size, and position over a targeted region. They can potentially revolutionize personalized rehabilitation by being noninvasive and allowing easy donning and doffing. Nevertheless, users should feel comfortable using such arrays, as they are typically worn for an extended time period. Additionally, to deliver safe and selective stimulation, these arrays must be tailored to a user's physiology. Fabricating customizable electrode arrays needs a rapid and economical technique that accommodates scalability. By leveraging a multilayer screen-printing technique, this study aims to develop personalizable electrode arrays by embedding conductive materials into silicone-based elastomers. Accordingly, the conductivity of a silicone-based elastomer was altered by adding carbonaceous material. The 1:8 and 1:9 weight ratio percentages of carbon black (CB) to elastomer achieved conductivities between 0.0021-0.0030 S cm^-1 and were suitable for transcutaneous stimulation. Moreover, these ratios maintained their stimulation performance after several stretching cycles of up to 200%. Thus, a soft, conformable electrode array with a customizable design was demonstrated. Lastly, the efficacy of the proposed electrode arrays to stimulate hand function tasks was evaluated by in vivo experiments. The demonstration of such arrays encourages the realization of cost-effective, wearable stimulation systems for hand function restoration.

Augmentation of neural activity in peripheral nerve of sheep using 6 kHz subthreshold currents

OBJECTIVE

To determine how increased excitability from subthreshold currents would alter neural activity as it propagates through the subthreshold currents.

APPROACH

Experiments were performed on two Romney cross-breed sheep in vivo, by applying subthreshold currents either at the stimulus site or between the stimulus and recording sites. Neural recordings were obtained from nerve cuff implanted on the peroneal or sciatic nerve branches, while stimulus was applied to either the peroneal nerve or pins placed through the lower hindshank.

MAIN RESULTS

Showed that subthreshold currents applied to the same site as stimulus increased excitation of underlying nerve fibres (p < 0.005). With stimulus and subthreshold currents applied to different sites on the peroneal nerve, the primary compound action potential (CAP) in the sciatic displayed a temporal shift of -2.5 to -3 µs which agreed with changes observed in the CAP waveform (p > 0.05).

SIGNIFICANCE

These findings contribute to the understanding of mechanisms in myelinated fibres of subthreshold current neuromodulation therapies.

Enabling Free-Standing 3D Hydrogel Microstructures with Microreactive Inkjet Printing

Reactive inkjet printing holds great prospect as a multimaterial fabrication process because of its unique advantages involving customization, miniaturization, and precise control of droplets for patterning. For inkjet printing of hydrogel structures, a hydrogel precursor (or cross-linker) is printed onto a cross-linker (or precursor) bath or a substrate. However, the progress of patterning and design of intricate hydrogel structures using the inkjet printing technique is limited by the erratic interplay between gelation and motion control. Accordingly, microreactive inkjet printing (MRIJP) was applied to demonstrate a spontaneous 3D printing of hydrogel microstructures by using alginate as the model system. In addition, a printable window within the capillary number-Weber number for the MRIJP technique demonstrated the importance of velocity to realization of in-air binary droplet collision. Finally, systematic analysis shows that the structure and diffusion coefficient of hydrogels are important factors that affect the shape of printed hydrogels over time. Based on such a fundamental understanding of MRIJP of hydrogels, the fabrication process and the structure of hydrogels can be controlled and adapt for 2D/3D microstructure printing of any low-viscosity (<40 cP) reactive inks, with a representative tissue-mimicking structure of a ∼200 μm diameter hollow tube presented in this work.

Direct Patterning of Highly Conductive PEDOT:PSS/Ionic Liquid Hydrogel via Microreactive Inkjet Printing

The gelation of poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS) has gained popularity for its potential applications in three dimensions, while possessing tissue-like mechanical properties, high conductivity, and biocompatibility. However, the fabrication of arbitrary structures, especially via inkjet printing, is challenging because of the inherent gel formation. Here, microreactive inkjet printing (MRIJP) is utilized to pattern various 2D and 3D structures of PEDOT:PSS/IL hydrogel by in-air coalescence of PEDOT:PSS and ionic liquid (IL). By controlling the in-air position and Marangoni-driven encapsulation, single droplets of the PEDOT:PSS/IL hydrogel as small as a diameter of ≈260 μm are fabricated within ≈600 μs. Notably, this MRIJP-based PEDOT:PSS/IL has potential for freeform patterning while maintaining identical performance to those fabricated by the conventional spin-coating method. Through controlled deposition achieved via MRIJP, PEDOT:PSS/IL can be transformed into different 3D structures without the need for molding, potentially leading to substantial progress in next-generation bioelectronics devices.