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NUS Researchers Develop Smart Sutures for Monitoring Deep Surgical Wounds – OpenGov Asia

One of the important steps to prevent infection, wound separation and other complications is monitoring surgical wounds after an operation. However, when the surgical site is deep in the body, monitoring is normally limited to clinical observations or costly radiological investigations that often fail to detect complications before they become life-threatening. Hard bioelectronic sensors can be implanted in the body for continuous monitoring, but may not integrate well with sensitive wound tissue.
To detect wound complications as soon as they happen, a team of researchers from the NUS Electrical and Computer Engineering as well as the NUS Institute for Health Innovation & Technology has invented a smart suture that is battery-free and can wirelessly sense and transmit information from deep surgical sites. These smart sutures incorporate a small electronic sensor that can monitor wound integrity, gastric leakage and tissue micromotions while providing healing outcomes that are equivalent to medical-grade sutures.
Currently, postoperative complications are often not detected until the patient experiences systemic symptoms like pain, fever, or a high heart rate. These smart sutures can be used as an early alert tool to enable doctors to intervene before the complication becomes life-threatening, which can lead to lower rates of re-operation, faster recovery, and improved patient outcomes.
– John Ho, Assistant Professor, NUS
The NUS team’s invention has three key components: a medical-grade silk suture that is coated with a conductive polymer to allow it to respond to wireless signals; a battery-free electronic sensor; and a wireless reader used to operate the suture from outside the body.
One advantage of these smart sutures is that their use involves minimal modification of the standard surgical procedure. During the stitching of the wound, the insulating section of the suture is threaded through the electronic module and secured by applying medical silicone to the electrical contacts.
The entire surgical stitch then functions as a radio-frequency identification (RFID) tag and can be read by an external reader, which sends a signal to the smart suture and detects the reflected signal. A change in the frequency of the reflected signal indicates a possible surgical complication at the wound site.
The smart sutures can be read up to a depth of 50 mm, depending on the length of stitches involved, and the depth could potentially be further extended by increasing the conductivity of the suture or the sensitivity of the wireless reader. Similar to existing sutures, clips and staples, the smart sutures may be post-operatively removed by a minimally invasive surgical or endoscopic procedure when the risk of complications has passed.
In experiments, the team showed that wounds closed by the smart sutures and unmodified, medical-grade silk sutures both healed naturally without significant differences, with the former providing the added benefit of wireless sensing.
The team also tested the polymer-coated sutures and found their strength and biotoxicity to the body was indistinguishable from normal sutures, and also ensured that the power levels needed to operate the system were safe for the human body.
In future, the team is looking to develop a portable wireless reader to replace the setup currently used to wirelessly read out the smart sutures, enabling surveillance of complications even outside of clinical settings. This could enable patients to be discharged earlier from the hospital after surgery.
The team is now working with surgeons and medical device manufacturers to adapt the sutures for detecting wound bleeding and leakage after gastrointestinal surgery. They are also looking to increase the operating depth of the sutures, which will enable deeper organs and tissues to be monitored.
As reported by OpenGov Asia, a research team from the NUS’ Department of Biomedical Engineering and the Institute for Health Innovation and System (iHealthtech), as well as clinical collaborators from Singapore General Hospital developed a smart wearable sensor with a platform that combines an electronic chip, and a mobile app has been developed that could assess chronic wounds in real-time.
The Government Big Data Institute (GBDi) is set to depart from the Digital Economy Promotion Agency (Depa) to serve as a key national driving force for big data analytics following high demand, while the move is expected to help unlock the limit of its budget support. The departure plan was approved by the National Digital Economy and Society Committee in November but still requires cabinet approval, which is expected to take place in March 2022.
Demand for big data analytics is projected to sharping increase in the near future and GBDi’s operation under the Depa may no longer be suitable and the government agreed to split the GBDi from the Depa as its role has been expanding. The GBDi, which was established in 2019 to promote the analysis and management of big data for state agencies, will change its name to the National Big Data Institute (NBDi) and increase the number of staff from 70 to 200 by the end of 2022.
The President and Chief Executive of Depa stated that data analytics and cybersecurity are crucial for the overall economy and enterprises in the digital ecosystem. Big data analytics is vital for the development of the government’s digital policy and GBDi is the major organisation to deal with data analysis for government policymaking.
GBDi also works with other government agencies on big data projects and serves as one of the three core drivers for digital transformation development in the country. The other two drivers are the Internet of Things (IoT) Institute and the Startups Institute. The institute has played a role in various key digital projects by the government, including a study and design of IT architecture for the public health sector, development of applications and platforms for the tourism sector and design of big data architecture and comprehensive virtualisation dashboard for the agriculture sector.
It was noted that Depa set out various missions to be undertaken in 2022, including the dVenture programme aimed at accelerating growth of start-ups, smart city ambassadors, eatsHUB delivery platform, d-Station sales agent for startups’ products and the list of digital services for government procurement.
Referring to the eatsHUB platform, the Depa President said the project is developed in collaboration with SET-listed TV Direct, a home shopping operator. It is aimed at becoming a national food delivery platform that could promote the ecosystem of small and roadside eateries. The platform focuses on services for each community that involves eateries and drivers within 1-5 kilometres, with various features provided including a point of sales system, self-promotion campaigns, a messaging service and a call centre. The commission fee collected from eateries, also known as gross profit (GP) could be around 8-10% in the initial stage, he said.
Depa will make the list of digital services which comply with the Capability Maturity Model Integration (CMMI) standard and labelling scheme called dSure, which can guarantee the functionality, safety and security of digital products for government procurement. Private enterprises which purchase the digital products and services on the list will also be eligible for a 200% corporate income tax deduction. Some 500 products and services are targeted to be on the list in 2022.
For patients who need physical therapy after a stroke, painful injury or debilitating illness, getting them to stay motivated and committed to their rehabilitation programme can be a tough sell. By leveraging novel smart technologies like virtual reality (VR) and gamification, physiotherapists at SingHealth institutions, such as Sengkang General Hospital (SKH), have found new and efficient ways to help patients meet their rehabilitation goals while allowing therapists to monitor their progress and recovery more efficiently.
The latest high-technology addition to SKH’s outpatient rehabilitation gym is a brand of senior-friendly smart computerised exercise equipment designed for safe and effective strength and power training. The majority of patients seen at the outpatient rehabilitation clinic have conditions such as stroke, Parkinson’s disease, vestibular issues, as well as cardiac and pulmonary conditions.
Unlike most strengthening equipment that is bilaterally designed (involving two sides), the HUR system allows for unilateral (one-sided) limb strengthening to meet the needs of patient rehabilitation. The system also allows physiotherapists to pre-set customised resistance and repetitions for each patient. Patients can access the information on the machine independently to track their progress, reducing the need for assistance or guidance.
Therapists can focus on the patient and treatment in a more precise and targeted way. The rehabilitation intensity of the patient is also not limited to the physical constraints of the assisting therapists, which is a concern in traditional rehabilitation. The use of technology would also result in more consistent training.
– Ms Tay Ee Ling Senior Principal Physiotherapist, Sengkang General Hospital
There are also VR-enhanced treadmills. Using VR simulations and intelligent feedback, patients navigate obstacles and different terrains while improving their mobility, balance, and fitness levels. Technological equipment that incorporates VR and gaming make otherwise mundane rehabilitation activities like walking fun and interactive, thereby increasing patient interest and participation.
Importantly, smart technology is a good add-on to existing rehabilitation techniques, and is beneficial for both patients and therapists — patients are more actively involved and take ownership of their training, while the physical workload of therapists is reduced, enabling them to spend more time engaging with patients and better cater to each individual’s needs.
The SKH team is looking into the use of technology and remote monitoring to support patients at home. It is currently working on a collaborative research project with SingHealth institutions, including Singapore General Hospital (SGH) and SingHealth Community Hospitals (SCH), to prevent falls among the elderly.
Technology is becoming more prevalent. However, key components in a patient’s journey, such as human connection and touch, are still integral parts of physiotherapy, and physiotherapists will be there to journey with the patients through their recovery.
As reported by OpenGov Asia, SingHealth and SGInnovate announced a three-year partnership to build and scale up health science innovations today, with Artificial Intelligence (AI) in healthcare as the first area of focus. The partnership seeks to advance the development and adoption of AI as well as other emerging technologies to improve diagnostics and treatment, and enhance healthcare delivery and clinical outcomes for Singapore and beyond. Mr Ong Ye Kung, Minister for Health, witnessed the inking of the Memorandum of Understanding (MOU) by both parties today.
The partnership looks to tap into each organisation’s strengths – SingHealth’s extensive clinical and research capabilities and pool of clinicians and healthcare innovators, together with SGInnovate’s Deep Tech expertise and diverse community of corporates, startups and innovators – to maximise the potential of health science innovation. It will focus on three main areas: Advancing AI thought leadership in healthcare and innovation communities, supporting the growth of startups and their innovations in the fields of health and biomedical sciences, as well as building the health innovation talent pool in Singapore and beyond.
In daily life, there are many reliability and safety issues. Electronics degrade due to complex electronics ageing, latent software faults, and the interactions between the two. Also, electronic system failures are inevitable because of the current methods to assess reliability and safety. These issues are very likely to lead to serious consequences.
In view of this, two universities, The Hong Kong Polytechnic University (PolyU) and the University of Maryland – College Park (UMD), have jointly established a research and development laboratory, namely the – Centre for Advances in Reliability and Safety (CAiRS).
The Centre gathers top researchers from all over the world, uses the most advanced equipment and leverages innovative artificial intelligence technology to conduct various product reliability and system safety research to accurately predict the occurrence of failures and prevent them from occurring.
Harnessing its advanced equipment and, top-notch scientific research talents, CAiRS is dedicated to the research and development of breakthrough technologies. Their research solutions can be widely adopted by all industries in Hong Kong that value reliability and system safety, according to the Deputy President and Provost of PolyU.
CAiRS has been admitted as one of the research laboratories in the InnoHK Clusters, a major initiative of the HKSAR. CAiRS has carried out five research programmes to date. They are “Anomaly Detection and Syndromic Surveillances”, “Innovative Diagnostics for Health Management”, “Prognostics for Remaining Useful Life Assessment”, “Safety Assurance: Improve functional safety” and “Data Analytics Platform for Reliability” (Totally have 15 projects).
The range of applications of the research is extremely wide and includes robots, medical equipment, vehicles, telecommunications, consumer goods, public utilities, transportation, microelectronics, electrical installations, sensors, IoT products and other advanced manufacturing technology. Moreover, CAiRS has signed cooperation agreements with 28 well-known local companies to jointly conduct research and improve the reliability and safety of products and systems.
The Deputy President and Provost of PolyU also stated that the University has been actively cooperating with world-renowned universities and establishing close partnerships with industry to benefit society through cutting-edge research. I believe that CAiRS can effectively translate scientific research results to real-world solutions, creating a positive impact for various industries as well as society.
The Centre Director and Executive Director, Centre for Advances in Reliability and Safety (CAiRS) noted that CAiRS focuses on the use of artificial intelligence to develop new personalized management models. The application and results of the Centre’s research are very important to the development of smart cities.
The scientific research team of CAiRS and I am delighted to use their expertise to collaborate with partners in different industries. CAiRS will build an international brand for the products and systems in Hong Kong, and contribute to the development of smart cities and advanced manufacturing.
PolyU is committed to conducting state-of-the-art interdisciplinary research in response to the needs of industry and society. With over 20 specialists and scholars from the Faculty of Engineering of PolyU and UMD, UMD’s excellent research foundation in product reliability, and strong support from industry, CAiRS will bring benefits and contributions to smart city development and advanced manufacturing.
A lot of effort has been devoted by researchers to unlock more natural forms of communication without requiring contact between the user and the device. Voice commands are a prominent example that has found their way into modern smartphones and virtual assistants, letting us interact and control devices through speech.
Hand gestures constitute another important mode of human communication that could be adopted for human-computer interactions. Recent progress in camera systems, image analysis and machine learning have made optical-based gesture recognition a more attractive option in most contexts than approaches relying on wearable sensors or data gloves.
To tackle these issues, a team led by Zhiyi Yu of Sun Yat-sen University, China, recently developed a new hand gesture recognition algorithm that strikes a good balance between complexity, accuracy, and applicability. As detailed in their paper, which was published in the Journal of Electronic Imaging, the team adopted innovative strategies to overcome key challenges and realise an algorithm that can be easily applied to consumer-level devices.
One of the main features of the algorithm is adaptability to different hand types. The algorithm first tries to classify the hand type of the user as either slim, normal, or broad-based on three measurements accounting for relationships between palm width, palm length, and finger length. If this classification is successful, subsequent steps in the hand gesture recognition process only compare the input gesture with stored samples of the same hand type.
Traditional simple algorithms tend to suffer from low recognition rates because they cannot cope with different hand types. By first classifying the input gesture by hand type and then using sample libraries that match this type, we can improve the overall recognition rate with almost negligible resource consumption.
– Zhiyi Yu, Lead Author
Another key aspect of the team’s method is the use of a “shortcut feature” to perform a prerecognition step. While the recognition algorithm is capable of identifying an input gesture out of nine possible gestures, comparing all the features of the input gesture with those of the stored samples for all possible gestures would be very time-consuming. To solve this problem, the prerecognition step calculates a ratio of the area of the hand to select the three most likely gestures of the possible nine.
This simple feature is enough to narrow down the number of candidate gestures to three, out of which the final gesture is decided using a much more complex and high-precision feature extraction based on Hu invariant moments. The gesture prerecognition step not only reduces the number of calculations and hardware resources required but also improves recognition speed without compromising accuracy.
The team tested their algorithm both in a commercial PC processor and an FPGA platform using a USB camera. They had 40 volunteers make the nine hand gestures multiple times to build up the sample library, and another 40 volunteers to determine the accuracy of the system. Overall, the results showed that the proposed approach could recognise hand gestures in real-time with an accuracy exceeding 93%, even if the input gesture images were rotated, translated, or scaled. According to the researchers, future work will focus on improving the performance of the algorithm under poor lighting conditions and increasing the number of possible gestures. Gesture recognition has many promising fields of application and could pave the way to new ways of controlling electronic devices.
As reported by OpenGov Asia, China has made great achievements in scientific and technological innovation during the 13th Five-Year Plan period. As China embarks on a new journey to build a modern socialist country in all respects, sci-tech innovation will play a vital role in promoting the country’s overall development.
In the 2021-22 Mid-Year Economic and Fiscal Outlook (MYEFO), the Treasury detailed that the federal government would part with an additional AU$ 252.5 million over four years to implement further initiatives under the digital economy strategy.
The total amount will be divided across several projects; the largest share of AU$161 million will go towards the digital identity system; AU$27 million to the Office of the National Data Commissioner to improve the sharing and promote greater use of public sector data; and nearly AU$3 million for the Australian Bureau of Statistics to scope enhancements to the data.gov.au website to improve public access to government data.
AU$111 million will be used to support the commercialisation, adoption, and use of quantum technology, which includes AU$  70 million for the quantum commercialisation hub that was announced recently by the federal government under its new Blueprint for Critical Technologies.
A further AU$22.6 million will be put towards round two of the 5G innovation initiative to support private sector investment in 5G testbeds and trials, while AU$ 800,000 over two years will be used to identify interventions to meet Australia’s digital skills and inclusions needs in consultation with industry, education, and training sectors.
As a response to recommendations from the inquiry into future directions for the consumer data right, the government said it would also fund AU$1.8 million over two years to make Victorian energy reference data available through the CDR and provide an AU$ 6 million concessional loan to the Australian Energy Market Operator (AEMO) in 2021-22 to enable AEMO to build the necessary IT system to share data through the CDR regime.
MYEFO stated that the Digital Economy Strategy provides the foundations to grow the digital economy and focuses investment on the settings, infrastructure, and incentives to ensure businesses can lift productivity and be globally competitive.
Meanwhile, the Digital Transformation Agency (DTA), will receive an additional AU$ 59 million over four years to continue to provide enhanced digital and IT oversight and advice. In other areas, the Australian Space Agency will be given an additional AU$ 23 million over five years, as well as AU $2 million per annum ongoing to support the growth of the local space sector, including for the creation of Australia’s Mars rover.
Other winners of MYEFO include the National Archives of Australia, which will get AU$68 million over four years to preserve at-risk records, provide additional staffing and capability to improve digitisation on-demand services and invest in cybersecurity and future digital enhancements.
Separately, the National Collecting Institutions will be provided AU$50.5 million over four years, with which AU$ 8.5 million over two years will be used to support the National Library of Australia’s digital information resource.
The federal government also announced that it would expand its digital games tax offset from 1 July 2022 to include ongoing development work, known as “live ops”, on digital games following their public release. An extra AU$ 19.6 million will be used to back this expansion.
With regards to aged care, a further AU$ 154 million will be forked out for two years to replace the aged care IT system and begin work on an IT system to support a new in-home care program.
The MYEFO also detailed that Treasury would receive an additional AU$ 23.5 million that will partly be used to implement government reforms in relation to the payments system and crypto-assets.
On Thursday, the NSW government also released its 2021-22 half-year review, in which it indicated there would be new investments allocated from the Digital Restart Fund. These include AU$187 million over four years to create a whole-of-government ERP system for the six “clusters” Regional NSW, Stronger Communities, Premier and Cabinet, Treasury, Customer Service and Planning, and Industry and Environment; AU$122 million over three years to further modernise the licencing and compliance program; AU$32.5 million over three years to support the Department of Communities and Justice cybersecurity project; and another AU$23.5 million over three years to deliver the NSW police cybersecurity transformation program.
Taiwan has been known as the leader in the global industrial production line of semiconductor processing, where its mature supply chain in the semiconductor manufacture industry has already realised the different levels of relevant technologies and applications.
In the last decade, the invention of Si photonics has led to the advancement of photonics integration circuits by using semiconductor manufacture production line, allowing the advantages of multi-functional and cost-effective optical signal processing techniques, such as broadband, high resolution, low power consumption and low EM interference, to be built inside a small chip.
In the light of such technology trends, the Ministry of Science and Technology (MOST) has promoted the special topics project, to fulfil the future technology development in the future. Si photonic fibre gyroscope is one of the funded projects in the Department of Photonics, National Sun Yat-sen University. The team uses several new designs of optical and electrical circuits in silicon photonic integration chips, not only reducing the overall size of a chip but also setting up a new level of angular velocity sensing capability. The unmanned vehicle and aerial camera with such a light-sensing tool can be made with a stabiliser.
In addition, the Si photonics gyroscope has lots of potential for consumer purposes, such as bioengineering, autonomous cars, robot, and navigation, in comparison to other types of gyroscopes. Interferometric fibre optical gyroscope (IFOG) is one of the indispensable components for sensing angular velocity in medium- and high-end navigation systems, such as aerospace, military, underwater and unmanned vehicles.  Ring laser gyroscope (RLG) and hemispherical resonant gyroscope (HRG) are the most stable and highest resolution ones as per up records till now. However, the complicated structure needs delicate assembly engineering, resulting in high costs. On the other hand, a microelectromechanical system (MEMS) gyroscope fabricated in semiconductor foundry can be adapted to the mass production line, thus leading to the lowest cost per piece.
Nevertheless, the mechanical properties render it to be low precision from up-to-date technologies. In the viewpoint of gyroscope performance, IFOG could vary from high-end modules to medium-end ones, depending on the design and its related assembly technologies. With the promotion of Si photonic technologies, photonics integration offers an excellent solution for future gyroscope and related technologies.
The team of Si photonic gyroscope in the Department of Photonics at NSYSU has been funded by MOST for 4 years. Base on Si photonic technology, several industrial partners have been tied within the project, forming a collaboration link. In the past three years, excellent research work and Si photonic IFOG submodule have been attained, including the patents, top international conference and journal paper publication.
As reported by OpenGov Asia, MOST announced that 20 tech startup companies would showcase Taiwan’s Biotech capabilities to the world connect with the global ecosystem, resources and industries in the forum organised by Taiwan Tech Arena (TTA). There are 20 TTA startup teams are selected by industrial experts and focused on global bio-industrial market potential startups.
Taiwan has demonstrated how to democratically tackle the COVID-19 threatening and how to be a truly global partner by utilising technologies. Taiwan’s efforts and commitments have drawn international attention and the relationship between Taiwan and the U.S. has become stronger than ever in the past year. The U.S. is leading the trends of advanced science and technology development and has a vivid startup ecosystem, while Taiwan has renowned semiconductor and ICT industries and long supported technology startups.
By working together, Taiwan can speed up the transition from scientific findings into practical technology applications and create a win-win situation and achieve future possible collaborations in the US. The companies presented disruptive biotech innovations such as vocal implant systems, AI Video-based telemedicine solutions and detection of respiratory function with ultrasound technology.
With resolution 1,000 times greater than a light microscope, electron microscopes are exceptionally good at imaging materials and detailing their properties. But like all technologies, they have some limitations.
To overcome these limitations, scientists have traditionally focused on upgrading hardware, which is costly. But researchers at the U.S. Department of Energy’s (DOE) Argonne National Laboratory are showing that advanced software developments can push their performance further.
Our method is helping improve the resolution of existing instruments so people don’t need to upgrade to new expensive hardware so often.
– Tao Zhou, Aassistant Scientist, Argonne & Lad Author
Argonne researchers have recently uncovered a way to improve the resolution and sensitivity of an electron microscope by using an artificial intelligence (AI) framework uniquely. Their approach, published in npj Computational Materials, enables scientists to get even more detailed information about materials and the microscope itself, which can further expand its uses.
Electrons act like waves when they travel, and electron microscopes exploit this knowledge to create images. Images are formed when a material is exposed to a beam of electron waves. Passing through, these waves interact with the material, and this interaction is captured by a detector and measured. These measurements are used to construct a magnified image.
Along with creating magnified images, electron microscopes also capture information about material properties, such as magnetization and electrostatic potential, which is the energy needed to move a charge against an electric field. This information is stored in a property of the electron wave known as a phase. Phase describes the location or timing of a point within a wave cycle, such as the point where a wave reaches its peak.
When measurements are taken, information about the phase is seemingly lost. As a result, scientists cannot access information about magnetization or electrostatic potential from the images they acquire. Knowing these characteristics is critical to controlling and engineering desired properties in materials for batteries, electronics and other devices. That’s why retrieving phase information is important.
Retrieving phase information is a decades-old problem. It originated in X-ray imaging and is now shared by other fields, including electron microscopy. To resolve this problem, Argonne computational scientists propose leveraging tools built to train deep neural networks, a form of AI.
Neural networks are essentially a series of algorithms designed to mimic the human brain and nervous system. When given a series of inputs and output, these algorithms seek to map out the relationship between the two. But to do this accurately, neural networks have to be trained. That’s where training algorithms come into play.
Using these training algorithms, the research team demonstrated a way to recover phase information. But what makes their approach unique is that it also enables scientists to retrieve essential information about their electron microscope.
Their method also improves the resolution and sensitivity of existing equipment. This means that researchers will be able to recover tiny shifts in phase, and in turn, get information about small changes in magnetization and electrostatic potential, all without requiring costly hardware upgrades.
As reported by OpenGov Asia, DOE’s Argonne National Laboratory has received nearly $3 million in funding for two interdisciplinary projects that will further develop artificial intelligence (AI) and machine learning technology.
The two grants were presented by the DOE’s Office of Advanced Scientific Computing Research (ASCR). They will aid Argonne scientists and collaborators to seek AI and machine learning work in the development of approaches to handle enormous data sets or develop better outcomes where minimal data exists.
By integrating mathematics and scientific principles, they will construct strong and accurate surrogate models. These types of models can greatly reduce the time and cost of working complex simulations, such as those used to forecast the climate or weather.


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