Chronic kidney disease (CKD) represents a significant public health challenge, often referred to as a “silent disease” due to its asymptomatic progression during early stages (1–2). Consequently, most diagnoses occur during advanced stages (3 and beyond), where treatment options are more complex and outcomes are less favorable. Globally, CKD affects over 850 million individuals, with 434.3 million cases in Asia alone. Despite its prevalence, early-stage awareness remains alarmingly low, with only 5% of affected individuals aware of their condition. Existing screening methods are predominantly hospital-based, expensive, and time-intensive, limiting their accessibility, particularly in resource-constrained settings. This underscores an urgent need for more accessible and efficient diagnostic tools to enable early intervention. In response to this critical problem, we developed KidneyLifePlus+, an AI-powered platform that leverages advanced machine learning models, including U-net, ResNet-50, and YOLO v8, to analyze retinal images for early CKD detection. These models are integrated to ensure high screening accuracy by identifying subtle biomarkers indicative of CKD progression. Complementing the software, we designed proprietary hardware capable of capturing high-resolution retinal images, delivering performance comparable to hospital-grade equipment. By ensuring affordability and ease of use, the system extends screening capabilities beyond clinical environments, making it suitable for deployment in community healthcare settings. KidneyLifePlus+ addresses key limitations of traditional methods by offering a rapid, cost-effective, and highly accurate diagnostic solution. The platform’s potential to enhance early detection rates could significantly improve clinical outcomes and quality of life for CKD patients. Furthermore, this innovation contributes to global efforts to reduce the burden of CKD by promoting equitable access to diagnostic services, particularly in underserved regions.

In 2565 B.E., 泰國's elderly, comprising 18.3% of the population at 12,116,199, faced health challenges, with diabetes, cerebrovascular disease, arthritis, and lung cancer prevalent. Caregiving hurdles arose as many family members worked outside, impacting the care of elderly individuals with these conditions. To address this, the "SVMR Medication Reminder and Care Robot for the Elderly" was developed. Known as the Smart Versatile Medication Robot (SVMR) or "New Robot," it serves as a user-friendly solution for home-based elderly care. Recognizing the adverse effects of missed medication on health, the SVMR system, combining hardware (New Robot) and software (Application), aimed to alleviate caregiving burdens. The New Robot's hardware includes a customizable medication reminder system, a video call system, closed-circuit camera system, doctor's recommendation display system, and an SOS system for emergency assistance. The Application complements this with features like medication schedule setting, video call communication, activity tracking, daily schedule management, and live camera monitoring. During the SVMR prototype trial, one unit was tested, with developers' relatives trying the medication dispensing system. Positive results emerged, showcasing improved medication adherence among the elderly and affording caregivers more time for other responsibilities. Satisfaction levels, as assessed through interviews, were notably high. Elderly feedback suggested the need for additional compartments for different medications and enhanced notification methods, particularly when they were not in proximity to the medication cabinet. In essence, the SVMR system provides a comprehensive solution to the challenges faced by households with elderly members, ensuring better disease management, increased medication adherence, and support for caregivers, all within a concise and user-friendly framework.

Cardiac Arrhythmia is one of the conditions in the group of heart and blood vessel diseases that can lead to sudden cardiac arrest (sudden death) and other conditions if not diagnosed quickly and accurately. According to research, heart and blood vessel diseases are the most common diseases and have a mortality rate of one-half of all non-communicable diseases. According to WHO statistics in 2012, it was found that there were 7.4 million deaths from heart and blood vessel diseases, and in 2017, the number of deaths increased to 177 million people, or about 94,444 people per day. Diagnosis of heart and blood vessel diseases can be done by measuring the electrical activity of the heart, and after the examination, a specialized physician will read and analyze the graph to find abnormal patterns. Currently, the shortage of qualified heart specialists to read the graph and screen for heart disease is a medical position shortage, which requires transferring data to hospitals with specialists, resulting in delays in diagnosis and treatment and even death. The project "EIPCA: Electrocardiogram Interpretation Pattern for Cardiovascular Abnormalities prediction" is an application program that assists in screening for fatal diseases that arise from abnormal heart rhythm. It employs artificial intelligence to aid in the screening and analysis of the electrical waveforms generated by an ECG machine, thus reducing diagnosis time and addressing the shortage of cardiology experts. EIPCA is comprised of two systems: (1) a system for screening and analyzing ECG waveforms using artificial intelligence to solve the problem of a shortage of specialized cardiology physicians, and (2) a system for risk assessment of fatal diseases by analyzing the ECG waveform data. The target group of the project is Rural hospitals, as well as health-related agencies. The project team hopes that the development of this project will significantly improve the efficiency and speed of screening for heart-related diseases, ultimately reducing the mortality rate from these diseases in the future.