Arteriouvenous Fistula Inspecting and Evaluating Device

Currently, the prognosis of critically ill patients in the intensive care unit (ICU) is often assessed using the APACHE or SOFA scoring systems to predict mortality rates. However, these systems have been in use for over 20 years, and the data they are based on is outdated. Today, clinical data is more abundant and precise, and advancements in machine analysis capabilities have made traditional scoring systems insufficient for meeting the demands of accurate predictions and personalized medicine. To address this, our team has developed a survival prediction device for critically ill patients. Using an AI algorithm model, it utilizes clinical data such as physiological, laboratory, and examination results to predict the patient's survival rates for the next 30, 60, and 90 days, providing a more accurate reference for prognosis assessment and medical resource allocation.

The diagnosis of ADHD is based on the core symptoms appearing in DSM-IV or V criteria. In addition to the criteria, some physicians use checklists to assist the diagnosis of ADHD. However, either symptoms or checklists are subjective, which leads to diagnostic problem. In this product, data from the gyroscope and accelerometer in a smart watch were used to analyze the movements of children with ADHD. The children with ADHD and controls wore the watches on their hands simultaneously in class and compared the data from recorders for analysis. This method can help us to establish a objective tool for diagnosis and evaluation of ADHD.

The "Dosage Evaluation Method" applies AI technology to assess dosages for high-risk medications, using Vancomycin as an example. This method addresses limitations in clinical practice, where dosages are typically determined by physician experience or population pharmacokinetic parameters, which may not account for individual patient variability. Although several AI-based prediction models for dosage evaluation currently exist, most of these models directly suggest dosages, often without fully considering physician expertise. The innovation of this method lies in its approach to validating only the physician-selected dosage, providing reference feedback without influencing the physician’s final decision. This design not only respects the physician’s professional judgment but also enhances the safety of medication administration.

Our product combines acoustic technology with artificial intelligence to offer an innovative device focused on muscle quality assessment. Traditional muscle testing methods often rely on the subjective experience of physicians or expensive equipment such as CT scans and MRIs, which are not only costly but also cumbersome. Our device uses non-invasive acoustic signal analysis, combined with AI models, to provide fast and accurate diagnoses, significantly reducing medical costs and improving diagnostic efficiency. The product is lightweight and portable, allowing physicians to use it flexibly in clinics, communities, or home visits, making it suitable for diverse scenarios such as long-term chronic disease patients, athletes, and the elderly. With high-precision AI algorithms, the device can instantly provide analysis reports on muscle condition, such as strength, elasticity, and power, helping physicians make more scientifically-based clinical decisions.

Our team has built predictive models based on whole transcriptome data from patients with advanced metastatic prostate cancer, focusing on two different mechanisms of action: androgen receptor antagonists and androgen synthesis inhibitors. These two types of drugs essentially offer only one chance of selection, as resistance to one renders the other ineffective. Using weighted gene correlation network analysis, we identified several gene modules correlated to specific drug responses. Then, we combined several machine-learning algorithms and constructed classification models for different drug mechanisms. Our goal is to use detection services to determine the most effective drug for patients before administration.