Health and Well-Being

Histopathology is a cornerstone of modern medicine, providing crucial information that enables doctors to formulate optimal treatment strategies before, during, and after surgeries. However, current methods for obtaining histological images grapple with a compromise between speed and accuracy and suffer from organ-dependent inconsistencies. Addressing these challenges, our technology was developed as a versatile solution to cater to a wide array of clinical scenarios. It sets a new benchmark for medical standards with its rapid, precise, and label-free on-the-spot imaging capability. Computation High-throughput Autofluorescence Microscopy by Pattern Illumination is a one-of-a-kind patented solution n that can detect and provide instant information about cancer status before, during, and after surgeries. This technology lets surgeons place fresh tissue samples taken directly from the patient into the microscope and receive high-resolution and virtually stained histological images in just three minutes. The primary adopters of this technology are expected to be healthcare organizations, hospitals, and research institutions, or any entity involved in histopathology, cancer diagnosis, and surgery. This technology fills a crucial void in the market by providing swift, high-resolution, label-free imaging of thick tissue samples, an achievement previously unattainable. Consequently, this technology not only accelerates the diagnostic process but also enhances its precision, revolutionizing the field of histopathology

Along with the global aging population, the number of people with dementia is also increasing. Moreover, digitalization of society is isolating elderly people. Enjoyable learning using digital devices can be the solution. The product is in the form of a table with a touch screen on the surface. The embedded software is connected to the contents server, which provides various programs such as visual perception and cognitive tests, videos, trainings, and analysis results. They prevent brain aging by stimulating both the left and right brains. The product is beneficial to children as well. The rich educational contents are designed to develop the problem solving ability and the metacognitive ability. The training course includes solid figures, basic Korean, mathematics, and adages etc. Analysis results are also provided to compensate for a vulnerable subject. In addition, safety education and news from local education offices are provided in conjunction with the government.

Neurological diseases are the second leading cause of death. CT scans have been used as the primary modality to diagnose brain abnormalities such as Intracranial Haemorrhage (ICH) and neurodegeneration. Radiologists usually have to deal with an overwhelming scan backlog and writing radiology reports is a time consuming process. Manual segmentation of lesions is tedious and existing heuristics have been shown to overestimate lesion volumes. Clinicians are also wary of the ‘black box’ nature of deep learning models. Hence, an automated tool in the workflow could substantially improve clinical productivity and interpretability is crucial to build trust with clinical stakeholders. Our proposed technology is an AI solution that automates ICH detection and brain tissue segmentation on CT scans, producing accurate volumetric information to assist triaging. Our technology also comes with a set of tools to interact with the AI models and generate reports easily. Moreover, we strengthen our AI transparency with interpretable models. Our platform also focuses on model robustness tests to assure AI safety.  

Diabetes is associated with macrovascular and microvascular complications, including Diabetic Foot Ulcers (DFU). To identify and manage DFU risk, diabetic patients are recommended to go for a regular foot assessment. Patients who are at‐risk diabetic foot should undergo regular podiatry evaluation, however specialised diabetes centers are currently facing high rates of ulcer recurrence. Frequent visits to these centers can strain an already overwhelmed healthcare system. The technology developer has invented an Artificial Intelligence (AI) model that is able to detect pre-ulceration. By detecting feet at risk of developing DFU, the model is able to refer patients for timely intervention before it becomes a DFU. Users only need to submit photos of their feet from different angles and an anomaly score will be calculated.

Norovirus is a highly contagious non-enveloped virus responsible for causing >90% of viral gastroenteritis, and >50% of all gastroenteritis outbreaks worldwide. According to the WHO, norovirus causes an estimated 685 million cases of infection and 200,000 deaths per year. Its resilience poses challenges for eradication through altering pH, heat exposure, or common disinfectants. Notably, alcohol-based hand sanitisers are not as effective against this virus, according to the US CDC. To address this, a biotech company has successfully developed a novel virus-binding protein technology derived from jack beans or sword beans. This patented lectin protein exhibits antiviral properties and has demonstrated the ability to neutralise not only norovirus, but also coronavirus and Hepatitis A virus. It has also demonstrated activity against Escherichia coli bacteria.  By utilising this innovative technology, viral outbreaks can be prevented. This versatile lectin protein can be incorporated as an active ingredient into various product formulations. The technology owner is especially interested to work with companies from health service sectors, and personal care product manufacturers.

This invention is a portable electromyography (EMG) sensor for muscle activity detection.  Unlike conventional EMG devices, which are bulky and confined to clinic settings, the sensor is built to be compact and wearable. It enables real-time, reliable biofeedback regardless of user’s location, bridging the accessibility gap in EMG analysis outside the traditional medical environments. This portability is achieved by integrating reusable micro-structured electrodes and highly integrated sensing system onto a soft and flexible substrate. The design ensures accurate EMG detection while offering a comfortable experience for extended use. The technology consists of three main components: Use of nanofabrication to build the electrodes followed by electric signal detection, replacing conventional gel electrodes. A processing unit for amplification to digital signals. Software to visualize EMG signals. The EMG sensor performance and analysis capabilities allows for collection of signals at high frequency to monitor muscle fatigue conditions. The technology owner is seeking for collaborations in the sports and fitness industry in providing accurate muscle activity signals, enhancing tracking of individual’s physical health actively and for athletes to make optimal adjustments to their training and tailor their approach towards fitness goals. However, its applications extend beyond fitness, with potential uses in elderly health care, virtual reality, gaming, and human-robot interaction. This technology taps into the growing demand for advanced, portable health monitoring systems, offering a solution that bridges the gap between medical-grade equipment and consumer fitness products. The sensor is also currently being trial to aid in rehabilitation in the hospitals.

Neural probes are used for capturing electrical activities and for exploring functional connectivity in the brain. For neural probes to be effective and be able to capture the activities happening at the scale of neural cells in vivo, they need to be small, made of bio-compatible material, and ideally, be flexible. This ensures that they do not trigger an inflammatory response or have a risk of breakage.  The technology presented here covers the requirements stated above for an ideal neural probe. The probes are flexible and allow superior precise targeting even with movement. The technology employed also avoids breaking and micromotion during the in-vivo trials. The probe’s design is also customizable for different requirements and can support combination of single/dual side, linear/tetrode, recording/stimulating/mixed and single/multi shank configurations for differing use cases. The probes can support up to 32 channels and provide multiple connectivity options for integration. 

This technology is a patented synbiotics (combination of probiotics and prebiotics) cleaning solution that offers a safe and sustainable alternative to traditional cleaning products and disinfectants. When released onto the surface, the probiotics will digest and break down dirt, grime, and other unwanted substances while the prebiotics in the solution act as an additional source of nutrition for the probiotics. The resultant surface microbiome provides a continuous cleaning effect that is longer lasting than traditional cleaning chemicals and disinfectants. Often, the overuse of traditional chemicals and disinfectants results in antimicrobial resistance (AMR), allergenic reactions to the user, negative impact on the environment and short effective lifespan. With this synbiotics technology, users can overcome these limitations and achieve a long-term effective cleaning system and a natural microflora to the environment. When utilised in healthcare settings, the synbiotics cleaning solution demonstrated a higher reduction of pathogens (80% more), decreased AMR (up to 99.9%) and health-associated infections (52% lesser). The technology owner is interested in co-development projects and test-bedding opportunities with companies looking for a sustainable and long-lasting cleaning technology i.e., cleaning equipment and automation manufacturers/suppliers and cleaning service providers.

As the world continues to make strides in artificial intelligence (AI), the need for transparency in the field intensifies. Clear and understandable explanations for the predictions of AI models not only enhances user confidence but also enables effective decision-making. Such explanations are especially crucial in sectors like healthcare where predictions can have significant and sometimes life-changing consequences. A prime example is the diagnosis of cardiovascular diseases based on heart murmurs, where an incorrect or misunderstood diagnosis can have severe implications. The technology, DiagramNet, is designed to offer human-like intuitive explanations for diagnosing cardiovascular diseases from heart sounds. It leverages the human  reasoning processes of abduction and deduction to generate hypotheses of what diseases could have caused the specific heart sound, and to evaluate the hypotheses based on rules.