Digital Health

In healthcare, accurately assessing pain is critical, yet it remains a significant challenge, particularly for patients unable to effectively communicate their discomfort. This includes individuals with cognitive impairments, critical care patients, and pre-verbal children. Traditional pain assessment methods rely heavily on verbal communication or subjective observer-based pain assessment, which can lead to missed or poorly managed pain, resulting in either under-treatment or over-analgesia treatment. To address this issue, this advanced AI-based solution automates the process of estimating pain intensity by analyzing facial expressions captured in video data. The technology is a deep learning system consisting of facial landmarks (specific points on a person's face, such as the corners of the eyes, nose, mouth, and other prominent facial features) extraction, 3D normalization and Spatial-Temporal Attention Long Short-Term Memory (STA-LSTM) model. These facial landmarks provide critical information about facial expressions, allowing the system to accurately assess pain levels. By integrating this technology into clinical settings, healthcare providers can significantly enhance patient care, reduce the burden on staff, and improve overall pain management practices. This solution not only ensures timely and appropriate intervention but also protects patient privacy by using non-identifiable facial landmarks, making it a powerful tool for modern healthcare environments.

Cancer is one of the most deadly diseases worldwide, requiring pathological testing to confirm the diagnosis. With recent advancements in digital pathology, there is a growing demand for AI-based digital pathology diagnostic products to manage and shorten diagnosis time for accurate and targeted treatments. Thyroid cancer is a relatively common type of cancer. The malignancy of thyroid nodules is determined through the pathological examination of cells obtained via Fine Needle Aspiration (FNA) or Core Needle Biopsy (CNB). However, due to the complexities of thyroid nodule pathology, over 30% of patients' biopsy samples are classified as indeterminate, meaning they cannot be definitively diagnosed as benign or malignant. Additional testing, such as Next-Generation Sequencing (NGS) molecular tests are required to provide further clarification. However, the high cost of testing limits patient accessibility. This technology is an AI-powered solution that analyze pathology images obtained through Core Needle Biopsy (CNB), providing diagnostic results for benign or malignant nodules. It offers interpretation and visualization of AI analysis results. This allows for accurate diagnosis of not only the benign(C2) and malignant(C6) cases traditionally identified by pathologists but also indeterminate(C3,C4,C5) cases that were previously challenging to diagnose. This approach ultimately reduces unnecessary surgeries, thereby improving the quality of life for patients. It offers a cost-effective alternative to expensive and less accessible NGS testing, delivering similar diagnostic accuracy without the need for additional tests. The technology owner is seeking partnerships with medical institutions specializing in oncology, including major hospitals and clinical laboratories, pharmceutical and biotechnology companies. 

The field of medical aesthetics has witnessed remarkable growth, driven by the increasing demand for non-surgical cosmetic treatments. Patients are increasingly seeking minimally invasive procedures that provide effective results with minimal discomfort, reduced downtime, and fewer side effects. This has spurred the development of advanced technologies such as dermal fillers, botulinum toxin injections, and various energy-based devices to address common concerns like wrinkles, skin laxity, and volume loss. Despite these advancements, traditional needle-based injection methods still present challenges. They can cause discomfort, bruising, and anxiety, which often deter patients from seeking treatment or lead to suboptimal results due to patient hesitation. Additionally, needle-based procedures carry inherent risks, such as infection, uneven distribution of the injected substance, and potential damage to surrounding tissues. To overcome these issues, a laser-guided, needleless drug injection device has been developed. This technology utilizes a 2940nm Er:YAG laser that interacts with water to create internal explosions, generating strong pressure. This pressure converts the drug, sprayed from a nozzle, into a jet form, allowing it to be delivered precisely and evenly into the desired skin layer. The technology enables the rapid injection of large quantities of medication, offering a pain-free, controlled, and efficient alternative to traditional needle-based injections. The technology owner is seeking collaboration with partners in the aesthetics, pharmaceutical, medical laser device manufacturers, and professional medical institutions. The goal is to expand the application of this technology beyond aesthetics, into areas such as drug delivery for vaccines and other therapeutic uses.

In modern healthcare, remote patient monitoring is increasingly important, offering significant advantages in patient care and accessibility. Non-contact monitoring reduces the need for physical interaction, crucial during infectious disease outbreaks or in intensive care units. It also allows for discomfort-free monitoring, making it ideal for vulnerable populations like the elderly, infants, and critically ill patients. This technology is particularly valuable in settings where traditional contact-based methods are impractical, such as in telemedicine and home care. Additionally, it reduces the need for multiple devices to measure various bio-signals, streamlining processes and cutting costs. The technology utilises remote photoplethysmography (rPPG) to measure bio-signals without attaching any sensors. rPPG technology works by analyzing subtle changes in the light reflected from a person's skin, which are correlated with underlying physiological signals. By utilizing standard cameras and sophisticated signal processing algorithms, rPPG can accurately capture and interpret these bio-signals from a distance, offering a convenient and reliable alternative to conventional methods. This technology can measure pulse, oxygen saturation, and blood pressure as raw data and analyze it to measure stress index, drowsiness index. The technology owner is seeking collaborators in: Transportation Companies or Automotive Manufacturers: To enhance mobility services by integrating driver bio-signal and drowsiness monitoring, providing a safer and more innovative driving experience. Healthcare and Telemedicine Providers: To improve the quality of remote healthcare services through continuous biometric signal monitoring and non-face-to-face consultations, particularly for home care and telemedicine. Insurance Providers, Daycare Centers, and Health & Wellness/Fitness Apps: To expand current health and wellness offerings by incorporating bio-signal measurement technology into fitness apps, smart appliances.

Stroke is the second leading cause of death worldwide and in Asia. 85% of stroke patients suffer from cerebral infarction, where blood clots accumulate in brain blood vessels and prevent blood from flowing smoothly. Accurate diagnosis is crucial, and while MRI (magnetic resonance imaging) and MRA (magnetic resonance angiography) scans are essential tools, they fall short by not providing vital blood flow data. This limitation can lead to challenges in both diagnosis and treatment planning. The cerebral blood flow assessment solution provides detailed information about blood vessels and quantitative data on CBF (Cerebral Blood Flow) in a non-invasive way. It allows a precise and straightforward evaluation of cerebrovascular conditions, reducing the need for physicians to rely on invasive procedures and helping them optimize personalized treatment strategies for patients. As the global population ages and the incidence of stroke increases, the demand for more healthcare solutions is also growing. The cerebral blood flow assessment solution provides quantitative flow data, allowing faster and more precise decision making to complement coventional methods of making treatment decisions based on basic medical imaging. Ideal collaboration partners in the value chain include medical institutions, which provide imaging data and clinical context, and with medical device manufacturers that specialize in MR and CT imaging devices.

The AI-based software for gastric cancer prevention and management is a pioneering medical device that leverages real-time AI diagnostics to improve patient outcomes. This innovative solution aims to enhance the survival rates and quality of life for high-risk gastric cancer patients, reduce healthcare costs by preventing the disease from progressing to more severe stages, and is currently undergoing clinical trials in major hospitals in Korea with related patents secured. Among the world's seven major cancers, the one with the lowest 5-year relative survival rate of 20-30% demonstrates that regular surveillance diagnostics of the high risk patients is the most well-proven preventive measure. The core technology integrates comprehensive AI diagnostics from pre-cancerous stages to cancer and a multi-modal AI personalized surveillance system for optimal preventive management and patient care. Developed in Korea, where millions of endoscopic examinations are conducted annually, this software is designed to maximize the diagnostic and consultative capabilities of physicians and enhance the preventive management functions for patients. The user interface is tailored to meet the needs of doctors, high-risk patients, and their caregivers, with AI algorithms and interfaces continuously improving.  

Gum disease, a widespread and severe condition, often remains undetected until it progresses to advanced stages, leading to significant oral health issues and costly treatments. This AI-driven gum disease detection and management system is designed specifically for home use and caregiving environments, such as care homes. It enables non-specialists to monitor oral health effectively, providing an early and accurate gum disease screening. This technology empowers caregivers, family members, and individuals to take proactive steps in maintaining oral health, especially for care home residents and people with low mobility who may have limited access to regular dental care. Using advanced machine learning algorithms, the system analyses oral images provided by the users to detect early signs of gum disease with high accuracy. Early detection allows timely intervention, reducing the risk of severe gum issues and enhancing overall health. The AI system is user-friendly and requires minimal training to operate, making it ideal for non-professional settings. It addresses a significant market need by providing accessible, reliable, and cost-effective oral health monitoring tools, ensuring that quality oral care is available to those who need it most. This technology improves individual health outcomes and reduces the burden on professional dental services by enabling early disease management.

Dementia, particularly Alzheimer's disease, is a significant socioeconomic burden in aging societies. Traditional blood tests are unable to  detect Alzheimer's biomarkers despite that the biomarkers appear 10-20 years before the appearance of cognitive decline symptoms. A groundbreaking ultrasensitive blood biomarker detection technology has been developed to overcome this limitation. This patented technology detects disease biomarkers at femtogram (fg/mL) levels, achieving sensitivity over 10,000 times higher than conventional methods. In addition to advanced biomarker detection, a comprehensive lifelog data collection technology has been integrated. Using smart rings and other wearable devices, extensive lifelog data is gathered, including sleep patterns, activities, and urine test results. This multimodal data collection supports the creation of the world’s largest Asian Senior Cohort, targeting over 30,000 individuals for the next 5 years. A key component of this innovative approach is the AI-based data analysis for the diagnosis and prognosis of dementia and cognitive dysfunction. AI algorithms analyze the comprehensive datasets, providing precise diagnostic insights and predicting disease progression. This AI-driven analysis, combined with ultrasensitive biomarker detection and detailed lifelog data, forms a robust data platform for early diagnosis, continuous monitoring, and personalized healthcare solutions. By focusing on these cutting-edge technologies, this platform offers a holistic and precise approach to managing cognitive health in super-aging societies. This innovative solution supports precision medicine and provides a comprehensive method for the early detection and ongoing management of dementia and other age-related diseases.

Arm and hand impairment is the major problem usually found in stroke patients, which extremely affects their quality of life. Moreover, motor rehabilitation using conventional physical therapy is ineffective in stroke patients who have severe to moderate arm and hand impairment due to inability to perform voluntary movement. In order to help these patients regain arm and hand function, we have researched and developed brain-computer interface system for motor rehabilitation in stroke patients. This system can help stroke patients learn how to do voluntary arm and hand movement by using only their thoughts or brain signals; additionally, it can also enhance neural plasticity that is the key concept in stroke recovery. Finally, we aim to distribute this technology to the department of rehabilitation in hospitals, rehabilitation clinics, and home use.