Artificial retinas have revolutionized the lives of many blind people by enabling their ability to perceive vision via an implanted chip. Despite significant advancements, there are some limitations that cannot be ignored. Presenting all objects captured in a scene makes their identification difficult. Addressing this limitation is necessary because the artificial retina can utilize a very limited number of pixels to represent vision information. This problem in a multi-object scenario can be mitigated by enhancing images such that only the major objects are considered to be shown in vision. Although simple techniques like edge detection are used, they fall short in representing identifiable objects in complex scenarios, suggesting the idea of integrating primary object edges. To support this idea, the proposed classification model aims at identifying the primary objects based on a suggested set of selective features. The proposed classification model can then be equipped into the artificial retina system for filtering multiple primary objects to enhance vision. The suitability of handling multi-objects enables the system to cope with real-world complex scenarios. The proposed classification model is based on a multi-label deep neural network, specifically designed to leverage from the selective feature set. Initially, the enhanced images proposed in this research are compared with the ones that utilize an edge detection technique for single, dual, and multi-object images. These enhancements are also verified through an intensity profile analysis. Subsequently, the proposed classification model's performance is evaluated to show the significance of utilizing the suggested features. This includes evaluating the model's ability to correctly classify the top five, four, three, two, and one object(s), with respective accuracies of up to 84.8%, 85.2%, 86.8%, 91.8%, and 96.4%. Several comparisons such as training/validation loss and accuracies, precision, recall, specificity, and area under a curve indicate reliable results. Based on the overall evaluation of this study, it is concluded that using the suggested set of selective features not only improves the classification model's performance, but aligns with the specific problem to address the challenge of correctly identifying objects in multi-object scenarios. Therefore, the proposed classification model designed on the basis of selective features is considered to be a very useful tool in supporting the idea of optimizing image enhancement.
Artificial Intelligence , Neural Networks, Computer , Retina , Retina/diagnostic imaging , Humans , Image Enhancement/methods , Algorithms , Image Processing, Computer-Assisted/methods , Visual Prosthesis
Dynamic consent management allows a data subject to dynamically govern her consent to access her data. Clearly, security and privacy guarantees are vital for the adoption of dynamic consent management systems. In particular, specific data protection guarantees can be required to comply with rules and laws (e.g., the General Data Protection Regulation (GDPR)). Since the primary instantiation of the dynamic consent management systems in the existing literature is towards developing sustainable e-healthcare services, in this paper, we study data protection issues in dynamic consent management systems, identifying crucial security and privacy properties and discussing severe limitations of systems described in the state of the art. We have presented the precise definitions of security and privacy properties that are essential to confirm the robustness of the dynamic consent management systems against diverse adversaries. Finally, under those precise formal definitions of security and privacy, we have proposed the implications of state-of-the-art tools and technologies such as differential privacy, blockchain technologies, zero-knowledge proofs, and cryptographic procedures that can be used to build dynamic consent management systems that are secure and private by design.
Data provenance means recording data origins and the history of data generation and processing. In healthcare, data provenance is one of the essential processes that make it possible to track the sources and reasons behind any problem with a user's data. With the emergence of the General Data Protection Regulation (GDPR), data provenance in healthcare systems should be implemented to give users more control over data. This SLR studies the impacts of data provenance in healthcare and GDPR-compliance-based data provenance through a systematic review of peer-reviewed articles. The SLR discusses the technologies used to achieve data provenance and various methodologies to achieve data provenance. We then explore different technologies that are applied in the healthcare domain and how they achieve data provenance. In the end, we have identified key research gaps followed by future research directions.
Biomedical Research , Delivery of Health Care/methods
This paper presents a 1600-pixel integrated neural stimulator with a correlated double-sampling readout (DSR) circuit for a subretinal prosthesis. The retinal stimulation chip inserted beneath the photoreceptor layer comprises an array of an active pixel sensor (APS) and biphasic pulse shaper. The DSR circuit achieves a high signal-to-noise ratio (SNR) of the APS with a short integration time to simultaneously improve the temporal and spatial resolutions of restored vision. This DSR circuit is adopted along with a 5 × 5-pixel tile, which reduces pixel size and improves the SNR by increasing the area occupied by storage capacitors. Moreover, a low-mismatch reference generator enables a low standard deviation between individual pulse shapers. The 1600-pixel retinal chip, fabricated using the 0.18 µm 1P6M CMOS process, occupies a total area of 4.3 mm × 3.3 mm and dissipates an average power of 3.4 mW; this was demonstrated by determining the stimulus current patterns corresponding to the illuminations of an LCD projector. Experimental results show that the proposed high-density stimulation array chip can achieve a high temporal resolution owing to its short integration time.
Artificial Limbs , Retina , Retina/diagnostic imaging , Prosthesis Implantation
Viruses infect millions of people worldwide each year, and some can lead to cancer or increase the risk of cancer. As viruses have highly mutable genomes, new viruses may emerge in the future, such as COVID-19 and influenza. Traditional virology relies on predefined rules to identify viruses, but new viruses may be completely or partially divergent from the reference genome, rendering statistical methods and similarity calculations insufficient for all genome sequences. Identifying DNA/RNA-based viral sequences is a crucial step in differentiating different types of lethal pathogens, including their variants and strains. While various tools in bioinformatics can align them, expert biologists are required to interpret the results. Computational virology is a scientific field that studies viruses, their origins, and drug discovery, where machine learning plays a crucial role in extracting domain- and task-specific features to tackle this challenge. This paper proposes a genome analysis system that uses advanced deep learning to identify dozens of viruses. The system uses nucleotide sequences from the NCBI GenBank database and a BERT tokenizer to extract features from the sequences by breaking them down into tokens. We also generated synthetic data for viruses with small sample sizes. The proposed system has two components: a scratch BERT architecture specifically designed for DNA analysis, which is used to learn the next codons unsupervised, and a classifier that identifies important features and understands the relationship between genotype and phenotype. Our system achieved an accuracy of 97.69% in identifying viral sequences.
This study examined the psychometric properties of the Barriers Self-Efficacy Scale-Physical Activity for Korean-speaking adults with osteoarthritis at risk for metabolic syndrome (N = 150). Factor analysis identified three dimensions of the Korean Barriers scale, explaining 65.9 % of the total variance. Confirmatory factor analysis indicated that the structural validity adequately fits the data. Construct validity confirmed significant associations between the amount of physical activity and psychological variables. The test-retest reliability was 0.87; the alpha was 0.90. The standardized response mean (0.497) indicated responsiveness to medium-magnitude change. The Korean Barriers scale can assess self-efficacy to engage in regular physical activity in clinical settings.
Exercise , Self Efficacy , Adult , Humans , Psychometrics , Reproducibility of Results , Republic of Korea , Surveys and Questionnaires
The present study aimed to evaluate the performance of automated skeletal maturation assessment system for Fishman's skeletal maturity indicators (SMI) for the use in dental fields. Skeletal maturity is particularly important in orthodontics for the determination of treatment timing and method. SMI is widely used for this purpose, as it is less time-consuming and practical in clinical use compared to other methods. Thus, the existing automated skeletal age assessment system based on Greulich and Pyle and Tanner-Whitehouse3 methods was further developed to include SMI using artificial intelligence. This hybrid SMI-modified system consists of three major steps: (1) automated detection of region of interest; (2) automated evaluation of skeletal maturity of each region; and (3) SMI stage mapping. The primary validation was carried out using a dataset of 2593 hand-wrist radiographs, and the SMI mapping algorithm was adjusted accordingly. The performance of the final system was evaluated on a test dataset of 711 hand-wrist radiographs from a different institution. The system achieved a prediction accuracy of 0.772 and mean absolute error and root mean square error of 0.27 and 0.604, respectively, indicating a clinically reliable performance. Thus, it can be used to improve clinical efficiency and reproducibility of SMI prediction.
Age Determination by Skeleton , Artificial Intelligence , Humans , Age Determination by Skeleton/methods , Reproducibility of Results , Hand/diagnostic imaging , Wrist/diagnostic imaging
Introduction: The strut iliac bone graft has been widely used to achieve fusion in various anterior cervical spinal surgeries but some complications often remain, such as pain and gross deformity. Considering these, we designed a new technique to restore the iliac ridge, using the outmost part of the iliac crest. We aim to assess the efficacy of our new restoration technique of the iliac ridge after harvesting strut bone graft for anterior cervical fusion. The clinical and radiological outcomes of our hinged roof method were evaluated. Technical Note: A retrospective review was conducted of 29 patients who underwent hinged roof reconstruction of the iliac ridge after harvesting a bicortical strut bone graft for anterior cervical fusion using a cervical plate system. The clinical outcome for pain and gross appearance and radiological results were evaluated. Three months after the surgery, pain at the donor site became minimal or absent in all cases. At 1 year follow-up, no patient had reported pain and palpable discomfort, such as step-off on the donor site. Final X-ray and follow-up computed tomography revealed a bony union of the reconstructed iliac ridge to both margins. Conclusions: By showing good clinical and radiological outcomes, the authors' hinged roof reconstruction of the iliac crest after harvesting strut bone graft seemed to be a simple and effective technique that can reduce complications, such as pain and deformity on the donor iliac crest.
Sensor technologies (including electrodes) have been widely utilized in many applications, especially in fields such as smart factories, automation, clinics, laboratories, and more [...].
Technology , Electrodes , Automation , Equipment Design
In this study, a pulse frequency modulation (PFM)-based stimulator is proposed for use in biomedical implantable devices. Conventionally, functional electrical stimulation (FES) techniques have been used to reinforce damaged nerves, such as retina tissue and brain tissue, by injecting a certain amount of charge into tissues. Although several design methods are present for implementing FES devices, an FES stimulator for retinal implants is difficult to realize because of the chip area, which needs to be inserted in a fovea, sized 5 mm x 5 mm, and power limitations to prevent the heat generation that causes tissue damage. In this work, we propose a novel stimulation structure to reduce the compliance voltage during stimulation, which can result in high-speed and low-voltage operation. A new stimulator that is composed of a modified high-speed PFM, a 4-bit counter, a serializer, a digital controller, and a current driver is designed and verified using a DB HiTek standard 0.18 µm process. This proposed stimulator can generate a charge up to 130 nC, consumes an average power of 375 µW during a stimulation period, and occupies a total area of 700 µm × 68 µm.
Electric Stimulation Therapy , Visual Prosthesis , Electrodes, Implanted , Retina , Fovea Centralis , Electric Stimulation , Equipment Design
COVID-19 has had a disproportionate impact on the elderly, who are over-represented among those who suffered severe illness or death. The obvious implication is that the share of the elderly in the population significantly affects the impact of COVID-19 on the overall health of a country. More generally, the elderly share has far-reaching economic and social ramifications. In this paper, we perform empirical analysis of cross-country data from 1970 to 2018 to identify the determinants of the share of the elderly-i.e., those aged 65 and over-in a country's population. We find that the quality of health care, life expectancy, and female labor participation increases the elderly share while higher fertility and female education attainment lower the elderly share. In addition, we find that the share is higher for high income countries and countries in Europe and Central Asia.
Voice-activated artificial intelligence (AI) technology has advanced rapidly and is being adopted in various devices such as smart speakers and display products, which enable users to multitask without touching the devices. However, most devices equipped with cameras and displays lack mobility; therefore, users cannot avoid touching them for face-to-face interactions, which contradicts the voice-activated AI philosophy. In this paper, we propose a deep neural network-based real-time sound source localization (SSL) model for low-power internet of things (IoT) devices based on microphone arrays and present a prototype implemented on actual IoT devices. The proposed SSL model delivers multi-channel acoustic data to parallel convolutional neural network layers in the form of multiple streams to capture the unique delay patterns for the low-, mid-, and high-frequency ranges, and estimates the fine and coarse location of voices. The model adapted in this study achieved an accuracy of 91.41% on fine location estimation and a direction of arrival error of 7.43° on noisy data. It achieved a processing time of 7.811 ms per 40 ms samples on the Raspberry Pi 4B. The proposed model can be applied to a camera-based humanoid robot that mimics the manner in which humans react to trigger voices in crowded environments.
Internet of Things , Sound Localization , Algorithms , Artificial Intelligence , Humans , Neural Networks, Computer
Significant progress has been made in the field of micro/nano-retinal implant technologies. However, the high pixel range, power leakage, reliability, and lifespan of retinal implants are still questionable. Active implantable devices are safe, cost-effective, and reliable. Although a device that can meet basic safety requirements set by the Food and Drug Administration and the European Union is reliable for long-term use and provides control on current and voltage parameters, it will be expensive and cannot be commercially successful. This study proposes an economical, fully controllable, and configurable wireless communication system based on field-programmable gated arrays (FPGAs) that were designed with the ability to cope with the issues that arise in retinal implantation. This system incorporates hexagonal biphasic stimulation pulses generated by a digital controller that can be fully controlled using an external transmitter. The integration of two separate domain analog systems and a digital controller based on FPGAs is proposed in this study. The system was also implemented on a microchip and verified using in vitro results.
Prostheses and Implants , Retina , Equipment Design , Reproducibility of Results , Telemetry/methods , Wireless Technology
Power-efficient digital controllers are proposed for wireless retinal prosthetic systems. Power management plays an important role in reducing the power consumption and avoiding malfunctions in implantable medical devices. In the case of implantable devices with only one-way communication, the received power level is uncertain because there is no feedback on the power status. Accordingly, system breakdown due to inefficient power management should be avoided to prevent harm to patients. In this study, digital power controllers were developed for achieving two-way communication. Three controllers-a forward and back telemetry control unit, a power control unit, and a preamble control unit-operated simultaneously to control the class-E amplifier input power, provided command data to stimulators, monitored the power levels of the implanted devices, and generated back telemetry data. For performance verification, we implemented a digital power control system using a field-programmable gate array and then demonstrated it by employing a wireless telemetry system.
Visual Prosthesis , Wireless Technology , Amplifiers, Electronic , Communication , Equipment Design , Humans , Telemetry
In this study, we propose a low-area multi-channel controlled dielectric breakdown (CDB) system that simultaneously produces several nanopore sensors. Conventionally, solid-state nanopores are prepared by etching or drilling openings in a silicon nitride (SiNx) substrate, which is expensive and requires a long processing time. To address these challenges, a CDB technique was introduced and used to fabricate nanopore channels in SiNx membranes. However, the nanopore sensors produced by the CDB result in a severe pore-to-pore diameter variation as a result of different fabrication conditions and processing times. Accordingly, it is indispensable to simultaneously fabricate nanopore sensors in the same environment to reduce the deleterious effects of pore-to-pore variation. In this study, we propose a four-channel CDB system that comprises an amplifier that boosts the command voltage, a 1-to-4 multiplexer, a level shifter, a low-noise transimpedance amplifier and a data acquisition device. To prove our design concept, we used the CDB system to fabricate four nanopore sensors with diameters of <10 nm, and its in vitro performance was verified using λ-DNA samples.
Nanopores , Nanotechnology , DNA , Nanotechnology/methods , Point-of-Care Systems
This study examined the psychometric properties of the Korean version of the Patient Knowledge Questionnaire-Osteoarthritis (PKQ-OA-K). A cross-sectional survey was conducted with 157 adults with osteoarthritis from the outpatient clinic at a university hospital in Korea. The overall correct answer rate for the PKQ-OA-K was 60.4%; notably, the drug therapy subscale had the lowest median score percentage (42.9%). For structural validity, exploratory factor analysis identified the PKQ-OA-K as two-dimensional, explaining 52.4% of the total variance. Confirmatory factor analysis showed that the two-factor model adequately fit the data. The PKQ-OA-K was positively correlated with education level (r = 0.24) and osteoarthritis outcomes (r = 0.17), thus verifying the hypotheses of construct validity. The intraclass correlation coefficient for test-retest reliability was 0.52; alpha was 0.44. The PKQ-OA-K has excellent validity but imperfect reliability for adults with osteoarthritis. This study recommends cautious use of the PKQ-OA-K to assess Korean patients' knowledge of osteoarthritis.
Osteoarthritis , Adult , Cross-Sectional Studies , Humans , Psychometrics , Reproducibility of Results , Republic of Korea , Surveys and Questionnaires
The COVID-19 pandemic has given rise to a spike in financial market volatility. In this paper, we attempt to assess the effects of financial & news-driven uncertainty shocks in growing Asian economies, using country-specific bond volatility shocks as a measure of local interest rate uncertainty. Also, we contrast the effects of local uncertainty with global stock market uncertainty. Using bond market data from nine Asian markets, we uncover a transmission mechanism of uncertainty shocks via the bond market. The mechanism works as a crowding-out effect due to government-led excessive market borrowing with supply-side consequences for the private sector, as opposed to economic policy or global stock market uncertainty which works more like a demand shock as in the literature. We conclude that countries with growing fiscal deficits that entail a larger government bond market or higher current account deficits, tend to experience an increase in the cost of borrowing due to this bond market volatility or interest rate uncertainty shocks.
This retrospective observational study aimed to examine the correlation and correspondence between skeletal maturation indicators (SMI), cervical vertebral maturation indicators (CVMI), and radius-ulna-short bones (RUS) skeletal maturity scores in Korean adolescents, and to determine whether easily obtainable SMI or CVMI can replace the RUS skeletal maturity score. A total of 1017 participants were included with both hand-wrist radiograph and lateral cephalogram acquired concurrently. From the lateral cephalogram, CVMI was determined; through the hand-wrist radiograph, SMI was categorized, and the RUS skeletal maturity score was evaluated as well. Associations were examined using the Mann-Whitney U test, Spearman's rank-order correlation analysis, and multiple correspondence analysis. There was no statistically significant difference in chronological age between males and females; however, the SMI, CVMI, and RUS skeletal maturity scores were significantly higher in females. The SMI, CVMI, and RUS skeletal maturity scores showed a statistically significant strong degree of both positive correlation and correspondence. However, a precisely corresponding RUS skeletal maturity score was difficult to obtain for a specific CVMI and SMI stage, implying the absence of a quantitative correlation. In conclusion, detailed evaluation should be conducted using the RUS skeletal maturity score, preferably in cases that require bone age determination or residual growth estimation.
As technology evolves, more components are integrated into printed circuit boards (PCBs) and the PCB layout increases. Because small defects on signal trace can cause significant damage to the system, PCB surface inspection is one of the most important quality control processes. Owing to the limitations of manual inspection, significant efforts have been made to automate the inspection by utilizing high resolution CCD or CMOS sensors. Despite the advanced sensor technology, setting the pass/fail criteria based on small failure samples has always been challenging in traditional machine vision approaches. To overcome these problems, we propose an advanced PCB inspection system based on a skip-connected convolutional autoencoder. The deep autoencoder model was trained to decode the original non-defect images from the defect images. The decoded images were then compared with the input image to identify the defect location. To overcome the small and imbalanced dataset in the early manufacturing stage, we applied appropriate image augmentation to improve the model training performance. The experimental results reveal that a simple unsupervised autoencoder model delivers promising performance, with a detection rate of up to 98% and a false pass rate below 1.7% for the test data, containing 3900 defect and non-defect images.
This paper introduces an ambient light rejection (ALR) circuit for the autonomous adaptation of a subretinal implant system. The sub-retinal implants, located beneath a bipolar cell layer, are known to have a significant advantage in spatial resolution by integrating more than a thousand pixels, compared to epi-retinal implants. However, challenges remain regarding current dispersion in high-density retinal implants, and ambient light induces pixel saturation. Thus, the technical issues of ambient light associated with a conventional image processing technique, which lead to high power consumption and area occupation, are still unresolved. Thus, it is necessary to develop a novel image-processing unit to handle ambient light, considering constraints related to power and area. In this paper, we present an ALR circuit as an image-processing unit for sub-retinal implants. We first introduced an ALR algorithm to reduce the ambient light in conventional retinal implants; next, we implemented the ALR algorithm as an application-specific integrated chip (ASIC). The ALR circuit was fabricated using a standard 0.35-µm CMOS process along with an image-sensor-based stimulator, a sensor pixel, and digital blocks. As experimental results, the ALR circuit occupies an area of 190 µm2, consumes a power of 3.2 mW and shows a maximum response time of 1.6 s at a light intensity of 20,000 lux. The proposed ALR circuit also has a pixel loss rate of 0.3%. The experimental results show that the ALR circuit leads to a sensor pixel (SP) being autonomously adjusted, depending on the light intensity.
Prostheses and Implants , Retina , Algorithms , Image Processing, Computer-Assisted , Light
