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【2020 Application Example】 LEO National Computers AI Mobile Vision Smart Box - Fixed-Point Vision Testing for Mobility-Impaired Elders

When it comes to vision testing, most people think of visiting an ophthalmologist, but this can be inconvenient for those living in rural areas or for older elders. Mobile vision testing could easily solve this issue.

Image of AI Mobile Vision Smart Box
▲LEO National Computers has launched the 'AI Mobile Vision Smart Box', aiming to provide vision tests deep into rural and community areas to solve the medical disparity between urban and rural areas.

The 'AI Mobile Vision Smart Box' resolves urban-rural medical disparities

Taiwan has officially entered an aging society. According to health insurance data, the rate of cataract changes in individuals over 70 is as high as 90%. In the 29 districts of New Taipei City, up to 13 districts lack ophthalmology clinics. Some areas due to their remoteness and low population density have no doctors willing to provide services, highlighting the significant disparity in medical resources. LEO National Computers, founded by Dr. Jian Ming-Ren in 1985, aims to tackle the issue of insufficient medical staff by using AI technology, thus cooperating with the team from the Service System Technology Center of the Industrial Technology Research Institute.

Since 2014, the Industrial Technology Research Institute has been involved in the integration platform for fundus cameras, collecting millions of fundus photographs from teaching hospitals and clinics, from which they selected about several hundred thousand suitable data entries. Professional ophthalmologists review, annotate, and grade each photo into one of four different disease condition levels, which are then fed into artificial intelligence for training. Following this, new functionalities were gradually developed according to medical field needs, offering a fully automated self-service fundus photography service.

This case was facilitated by technology transfer coaching from the Industrial Technology Research Institute, with National Computers providing integrated service operation and customer service. The Industrial Technology Research Institute was responsible for system integration and platform maintenance. Additionally, the field service was provided by a university optical ophthalmology department offering testing locations and services, promoting to diabetes care networks, optometric centers, opticians, ophthalmology clinics, and community service points for fundus camera testing. The 'AI Mobile Vision Smart Box' was also officially showcased at the AI HUB conference, aiming to enhance the provision of vision tests in rural and community areas in the future, addressing the issue of insufficient medical resources in rural areas.

Image of AI Mobile Vision Smart Box
▲ The 'AI Mobile Vision Smart Box' integrates ophthalmic handheld instruments like slit lamps, tonometers, and fundus cameras with a mobile vision testing system into a single suitcase, capable of providing 2 to 5 vision tests.

'AI Mobile Vision Smart Box' instantly uploads data

Image of AI Mobile Vision Smart Box with ophthalmic handheld instruments
▲ The usage of the 'AI Mobile Vision Smart Box' is quite simple, with a built-in local area network allowing for the immediate uploading of scanned images and data.

The 'AI Mobile Vision Smart Box' combines ophthalmic handheld instruments such as slit lamps, tonometers, and fundus cameras with a mobile vision testing system into a single suitcase, offering 2 to 5 types of vision testing functions. The design is patient-centered, providing identity verification, test data retrieval, an automatic retina comparison system, and medical record file management, especially enabling individual patient file management. Additionally, with the built-in local wireless network and smart gateway, it facilitates the immediate upload of all testing data, including images and measurements.

Image of Wireless Data Upload by AI Mobile Vision Smart Box
▲Currently, the 'AI Mobile Vision Smart Box' has collaborated with major hospitals in Taipei and family medicine clinics in New Taipei City, with plans to expand further into rural areas.

'AI Mobile Vision Smart Box' apart from being used in fixed locations such as medical institutions and health check centers, its portability allows optometrists or nurses to carry it to ordinary homes or rural areas to perform eye examinations, enhancing the convenience and mobility of medical staff, and allowing vision testing to move out of hospitals into communities. Currently, the 'AI Mobile Vision Smart Box' is collaborating with major hospitals in Taipei and family medicine clinics in New Taipei City, hoping to bring eye examinations closer to mobility-impaired elders in rural and community areas, aiming to achieve early detection and treatment.

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

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【解決方案】優式AI智能割草機器人 搶攻高爾夫藍海市場
USRROBOT's AI Lawn Mowing Robot Enters the Blue Ocean of Golf Market

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CCTV Intelligent Video Search System

Search for a specific person, find someone with a suitcase entering the factory in Gao'an area Color features of the person and the object confirmed, person in blue and black top, suitcase in black color, throughCCTV the intelligent video search system, by setting object and color retrieval conditions, it can successfully locate three video clips containing the target subject This greatly aids operational staff in finding the target items, and through this system, search speed can far surpass manual effort6fold Pain Points The CSE-Kaohsiung Plant is densely equippedCCTVto monitor every corner of the plant area, but when an incidenthappens, it's impossible within a limited time throughCCTVvideo playback to find the incident, the implications and risks behind this are self-evident Many areas that are usually unmanned can easily become security blind spots Thus, how to monitor a vast plant area more intelligently and effectively is one of the crucial aspects of building a smart plant for 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searching Problem Scenario Object Detection The data source for object detection comprises two parts Open-source datasetsOIDv4and AES Kaohsiung PlantCCTVImage files For these files, search for usable data, specificallyOIDv4image files For these files, extract the defined nine major categories of objects for training data among them, two object categories, knives and gasoline barrels, were not found inOIDv4found usable data for knives and gasoline barrels, while the remaining seven categories of objects are available fromOIDv4useful training data found for the remaining seven categories of objects, all marked Regarding the Kaohsiung PlantCCTVimage files, select some frames Frame of the footage, and manually annotate the objects to be_detected for training and testing data Nine Major Objects Color Recognition The data source for color recognition is divided into two partsInternet image screenshots, and Kaohsiung PlantCCTVimage files Currently, no publicly available open-source datasets 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confusion Next, use the extracted training data from the Kaohsiung Plant toFine-Tuneenhance the detection rate of the object in specific designated areas Finally, select the model that computes the lowest loss value in the test set during the training process as the main object_detection model Dynamic Ignoring AIHelp You View CCTV The intelligent video search system primarily serves as an assistive system for searching surveillance footage, capable of speeding up the process of finding target events by setting search conditions for objects By simply defining the search conditions, you can quickly produce thumbnails of critical objects and playback for review, shortening the time required for manual case retrieval of the past The search time is quickly6doubled, allowing the front-end security unit to use this platform to strengthen the first line of risk management supervision and take timely preventive measures 「Translated content is generated by ChatGPT and is for reference only Translation date:2024-12-12」