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【2021 Application Example】 Life-saving is as urgent as a spark AI critical illness system monitors and grasps the golden rescue period

60-year-old Mr. Huang was admitted to the hospital due to a stroke. After lying in the intensive care unit for two weeks, his condition suddenly took a turn for the worse. After rescue, he was lucky enough to survive. In fact, with the assistance of AI critical illness early warning technology, hospitals can detect signs and take timely and accurate medical measures 6-8 hours before a patient's heart stops, which can greatly reduce the chance of death in the hospital.

The deterioration of the condition is a process that evolves over time, and its subtle changes are by no means without context. Previous research reports show that about 60 to 70% of inpatients who experience unexpected in-hospital cardiac arrest had symptoms 6 to 8 hours before their cardiac arrest, but only a quarter of them were recognized by clinical staff. Detection and discovery, therefore, there is a need for a risk warning tool or system that can be used earlier and continuously to monitor the condition, alert medical staff to pay attention to subtle changes in the patient's condition at any time, and take timely and accurate intervention measures before the condition progresses to effectively reduce adverse events. or the risk of serious adverse events.

Unexpected deterioration cannot be detected early

Acute and severe patients often undergo unpredictable changes, and timely detection or prediction of potential acute and severe patients is an important issue. The currently commonly used clinical assessment method is Modified Early Warning Score (MEWS), which uses simple physiological parameter assessment (including heartbeat, respiratory rate, systolic blood pressure, body temperature, urine output and state of consciousness) to screen out high-risk patients, and has been proven to be predictive. Patient clinical prognosis.

MEWS is a scoring mechanism with a single time point and a standardized formula. However, the AI ​​​​crisis warning system developed by Boxin Medical Electronics - Hospital Emergency and Critical Care Early Warning Index System (EWS) is designed to predict patient status with immediate response. , collect the physiological data of patients over time for deep learning, find the best prediction model, and improve the overall accuracy.

Boxin Medical Electronics uses a big data analysis model to build an early warning system (EWS), IoT Internet of Things and 5G communication technology, allowing medical staff to remotely monitor the physiological status of patients through communication equipment, and monitor emergency and severe cases quickly The patient's condition changes and the golden rescue period of 6-8 hours before cardiac arrest can be grasped.

Boxin Medical Electronics imports AI vision After interpretation, unmanned operation can greatly reduce medical care manpower

▲After Boxin Medical Electronics introduces AI visual interpretation, unmanned operation can greatly reduce medical manpower

The AI ​​technology developed by Boxin Medical Electronics is the Gradient Boosting Ensemble Learning System (GBELS) to build an early warning system. It is a learning-based EWS prediction algorithm developed by the company, which is an integrated learning ( Ensemble Learning) and is classified as supervised learning, providing the following three functions:

1. Early warning risk notification is used to analyze representative data using GBELS to provide an early risk score so that medical staff can conduct immediate clinical assessment and provide appropriate medical treatment.

2. Reduce medical manpower: Collect continuous physiological monitoring data, such as heartbeat, respiration, blood pressure and blood oxygen concentration, etc., to reduce the time for medical staff to write cases.

3. Combine IOT logistics network and 5G communication technology to quickly transmit medical data such as monitoring parameters and imaging data, and assist medical staff to monitor changes in patients' condition remotely through communication equipment.

AI critical illness system monitoring to master the golden treatment period

Boxin Medical Electronics stated that assessing the severity of the disease in acute and severe patients is a complex task, and patients often experience unpredictable changes. Clinical medical staff often judge the condition based on their own clinical experience or intuition, which lacks science and objectivity, resulting in the inability to correctly identify and timely detect potentially acute and severe patients, resulting in or misdiagnosis leading to increased in-hospital mortality of patients. The introduction of an AI early critical illness warning system can assist emergency and critical care medical staff to correctly predict the patient's condition and allow patients to receive the care they need immediately. This can reduce the manpower arrangement of the emergency and critical care ward at the same time and reduce labor costs.

In addition, the easy-to-carry design will help the system be introduced into ambulances, home care and other places in the future, so that emergency patients can receive appropriate care earlier. Other departments within the hospital can also develop new applications around this system, which can effectively accelerate the development and promotion of smart medical technology. With the COVID-19 epidemic still raging in many countries around the world, this system can also help hospitals in various places to operate more effectively. Caring for and monitoring the condition of critically ill patients.

In addition to AI critical illness warning, Boxin Medical Electronics has also developed AI image interpretation - Medical Physiological Monitor Life Cycle Compliance Testing (AVS), which uses AI image interpretation technology to develop automated quality inspection of life support medical equipment. The instrument solves the time-consuming problem of medical instrument testing. It can reduce testing time by 70%, increase the number of tests by 3 times, and effectively reduce labor costs by 50%. At the same time, it is 100% compliant with regulatory requirements, and gradually solves the shortage of manpower and medical resources in the medical field. , medical work overload and other issues. It has now taken root in mainland China and is actively preparing for its launch in Europe. It will develop towards the Japanese and American markets in the future.

Boxin Medical Electronics develops AI image interpretation-medical Physiological monitor life cycle compliance testing (AVS) solves the problem of time-consuming testing of medical equipment and can reduce testing time by 70%

▲Boxin Medical Electronics develops AI image interpretation-medical physiological monitor life cycle compliance testing (AVS) to solve the time-consuming problem of medical instrument testing and can reduce testing by 70% time.

At this stage, Boxin Medical's smart medical technology has been introduced into medical hospitals including Hsinchu MacKay, Changkei, Dongyuan General Hospital, Kaohsiung University of Technology Affiliated Hospital, Zhenxin Hospital, Hsintai Hospital, Taipei Medical University Affiliated Hospital, etc. GE Healthcare.Inc, an internationally renowned medical materials manufacturer, and Mindray Medical, China's largest medical materials manufacturer, are both representative customers of Boxin Medical Electronics.

「Translated content is generated by ChatGPT and is for reference only. Translation date:2024-05-19」

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【解決方案】佐翼科技無人機導入高爾夫球場域 可節省一半人力
Droxo Tech Applies Drones in Golf Courses to Reduce Manpower by Half

For most golf courses, the operations and management is a headache "Golf courses are selling turf and need to be properly taken care of," a golf course manager bluntly pointed out Facing the market pain points of labor shortage, aging population and high cost, the use of AI drones for pesticide spraying and pest control will reduce labor costs by more than half and greatly improve the overall operational efficiency At noon in early summer, an AI drone is slowly taking off at the Taipei Golf Club in Taoyuan Its main task is to test AI drone fertilizing and pesticide spraying on the golf course In fact, drones of Droxo Tech, the company performing this task, are widely used for fertilization, pesticide spraying, and pest and disease control for rice, bananas, and tea trees For golf courses with turfs that often cover tens to hundreds of hectares, AI drones are needed to assist in turf maintenance Data collection, development of pesticide spraying AI models, and multispectral image 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internal and external resources Only then will we be able to gradually achieve the goal of making golf courses smarter and smoothly assist the industry with transformation Zuoyi Technology's CEO, Liu Junlin 「Translated content is generated by ChatGPT and is for reference only Translation date:2024-05-19」

這是一張圖片。 This is a picture.
AI Assists the Red Cross for Smarter Emergency Response

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【導入案例】赫銳特科技VCSEL封裝元件瑕疵導入AOI檢測 提升產能效率20
HRT Technology Improves Production Efficiency by 20% Through AOI Detection of Defects in VCSEL Packaging

In 2017, the launch of the iPhone X made 3D sensor technology used in Face ID highly popular, which drove the development of VCSEL, a core component in the 3D sensor module In the detection of defects in incoming packaged VCSEL, the use of AI inference models can solve the industry's issue with low yield and improve reliability to 95 VCSEL technology currently can be used in many applications and various end consumer markets, including robots, mobile devices, surveillance, drones, and ARVR VCSELs are a good solution in applications that require high-speed modulation capabilities, such as cameras and biometrics VCSEL technology has a wide range ofnbsp applications, including in drones Pictured Zoyi Technology's Agricultural Drone VCSEL technology has a wide range of applications, AI technology assists in defect detection HRT Technology stated that the packaged VCSEL market is also facing strong price competition from competitors, and needs to further reduce costs and enhance product 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rework due to failure, improving yield to 95 and above in the reliability verification This is expected to help companies reduce production costs by 10 and increase production efficiency by 20 and above The difference before and after implementing AI image detection HRT Technology pointed out that this technology is an AI application developed based on tiny images It uses deep learning algorithms to identify defects in the images The trained network automatically classifies image data to predetermined categories Defect categories can be determined through reference images, so cumbersome programming is not required In the industrial machine vision environment, deep learning is mainly used for classification tasks in applications, such as inspection of industrial products or identification of parts In the future, with the development of IoT wearable devices and the trend of energy saving, the size of optoelectronic components will continue to shrink This technology can be applied to the detection of defects in the appearance of other tiny optoelectronic components in the future