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【2020 Solutions】 AetherAI's Digital Pathology AI Solutions Enhance Healthcare Quality and Reduce Physician Workload

In the medical imaging AI industry, an increasing number of startups are emerging, among which AetherAI has recently attracted attention. At last year's MICCAI, an international medical imaging conference, AetherAI's digital pathology AI defeated Stanford University's team, aiming to utilize artificial intelligence to achieve precision medicine and alleviate the burden of time-consuming tasks on physicians.

Providing digital pathology solutions to meet artificial intelligence application needs

Led by Dr. Zhao-Yuan Yeh, AetherAI, though only a few years old, includes members skilled in both healthcare and technology, possessing strong interdisciplinary integration capabilities. They excel in medical research, data science, software development, systems engineering, and medical knowledge and information technology, committed to offering solutions for digital transformation in pathology and AI-assisted diagnostics. With digital pathology becoming a milestone in the development of whole-slide imaging technology, AetherAI has introduced the aetherSlide system solution. Besides developing digital slide management and viewing systems, it also integrates image annotation, deep neural network inference, and training functionalities, thereby fulfilling the needs for AI module applications and development.

Photo of Dr. Zhao-Yuan Yeh, Co-founder and CEO of AetherAI

▲ Although AetherAI has been formed only a few years ago, it has recently gained significant attention in the medical imaging AI industry. Just recently, Dr. Zhao-Yuan Yeh, Co-founder and CEO, was awarded as one of Taiwan's Top 100 MVP Managers. (Photo credit: AetherAI's Facebook page)

A key feature of the digital pathology system is the customizable digital slide status bar, which can sort cases by priority, urgency, etc., thus providing clear visibility and facilitating time management. The interface is also user-friendly, offering hotkeys combinations, one-click slide assembly, along with tools such as rulers, magnifiers, and seamless rotations that enhance consultation or discussion efficiency. Regarding AI services, it offers applications like cancer detection and quantification, immunostaining quantification, and blood cell classification counting. Cooperation between AI and physicians minimizes repetitive work, and the system supports extensive training annotation features with various selection modes, multi-category tagging, and freehand drawing. Annotations integrate seamlessly into deep learning training, structured format output, and AI training data generation in everyday processes.

AetherAI system technology demonstration image

▲ Last year, AetherAI developed an AI medical imaging development platform (aetherAI), notable for its diagnostic automation, providing an end-to-end digital pathology AI development process. (Photo credit: AetherAI's Facebook page)

It is worth mentioning that the digital pathology system supports robust management functions, especially capable of being integrated according to the hospital department's work assignment process, and even with existing hospital information systems, this saves man-power, enhances administrative efficiency through digitization. In terms of file formats, it supports multiple brands of slide scanner types and whole-slide image formats like svs, ndpi, scn, mrxs, bif, tif. Additionally, to reduce the burden of long-term storage in medical facilities, AetherAI's infrastructure supports significant expansion capabilities, offering scalable options based on user needs, and supporting local and data center options for long-term storage solutions.

Illustration of AetherAI's digital pathology system whole slide image formats

▲ AetherAI's digital pathology system boasts strong management features and supports various brands of slide scanners, enhancing workflow efficiency through digital operations.

AetherAI Digital Pathology AI Applications Reduce Doctor's Burden and Enhance Productivity and Consistency

At the recent AI HUB conference, AetherAI demonstrated its AI medical imaging development platform launched last year (aetherAI), its main feature being diagnostic automation. This allows departments within hospitals to integrate various types of DICOM files and medical knowledge, boasting a highly scalable AI model capability, and providing an end-to-end digital pathology AI development workflow. Currently, it offers digital pathology AI modules including automated bone marrow smear classification, nasopharyngeal carcinoma recognition, and glomerulus detection applications, involving nearly ten different types of datasets. So, what are the tangible benefits for doctors? Simply put, with a prior scan using AI, cancers can be confirmed without the lengthy manual review previously typical in bone marrow exams, thus greatly shortening repetitive tasks for doctors and enhancing efficiency in complex diagnoses. Currently, aetherAI has reached recognition levels comparable to a pathology doctor's visual assessment standards, achieving an identification rate as high as 97% for nasopharyngeal cancer.

▲ AetherAI's AI medical imaging development platform (aetherAI) can significantly reduce repetitive tasks for doctors, leading to more efficient and effective high-complexity diagnoses.

Currently, AetherAI's partners and customers mainly include large medical centers, with the University of Pittsburgh Medical Center leading internationally. In Taiwan, includes major medical institutions such as Taipei National University Hospital, Taipei Veterans General Hospital, Chang Gung Hospital, Cathay General Hospital, Tri-Service General Hospital, Chung Shan Medical University Hospital, Taipei Medical University Hospital, among others, aiming to use artificial intelligence for precise medical applications, enabling deep learning in clinical practice to reduce the workload for doctors and elevate the consistency of medical quality.

AetherAI official website

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

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Defect identification rate reaches 100%, Nairi Technology is favored by major panel manufacturers

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【解決方案】2秒鐘完成結帳動作 Viscovery AI影像辨識助攻智慧零售
Complete checkout in 1 second, Viscovery AI image recognition assists smart retail

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【解決方案】滴水不漏的智慧工安巡檢 鑫蘊林科Linker Vision的影像分析AI平台 創造巡檢時間從100分鐘降至3秒新紀錄
Watertight smart industrial safety inspection Linker Vision’s image analysis AI platform sets a new record of inspection time reduced from 100 minutes to 3 seconds

With the rise of smart manufacturing, there is a demand for industrial safety inspections in high-risk industries such as chemical, energy, and electrical industries Take pipeline inspection in the chemical industry as an example It relies heavily on manual regular inspection and monitoring, and lacks intelligent monitoring by a professional AI team This is not only time-consuming and labor-intensive, but may also cause accidental risks to employees in various industrial safety environments The image analysis AI platform developed by Xinyun Linke not only improves employee personal safety and reduces risk factors, but also significantly reduces the time for human visual inspection of pipeline abnormalities from an average of 100 minutes to 3 seconds Paul Shieh, founder and chairman of Linker Vision Co, Ltd Linker Vision, said, "The overall technological development and progress in the United States comes from entrepreneurship Linker Vision's original intention to start a business in Taiwan has been I hope that through my past experience in entrepreneurship in the United States, I can introduce the American entrepreneurial spirit and culture to Taiwan's budding entrepreneurial fertile ground and truly implement it "American entrepreneurial culture encourages employees to value ownership and emphasizes that employees regard themselves as owners of the company Be a part of the company, with a work attitude and spirit that would be better than mine The company's achievements are your own achievements, breaking the original employer-employee relationship The company will reward outstanding employees with stocks, share the glory together, and establish a partnership with employees Partnership On the other hand, Taiwan still has room for efforts in entrepreneurial culture and management, and it retains the traditional thinking of employers and employees It is expected that Xinyun Linke's establishment of American entrepreneurial culture and values in Taiwan will serve as 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technology of automated machine learning can help clients customize automatic AI model training or repeated training when the factory environment changes, providing more accurate AI models and allowing customers to operate autonomously Through the above-mentioned key features and advantages of the automated AI technology provided by Xinyunlinke, we believe that it can definitely meet the needs of customers for an automated end-to-end AI independent learning platform, and at the same time, it can significantly save customers the cost of AI team establishment In terms of technological competitiveness, in addition to providing the chemical industry with AI image analysis applications in smart industrial safety, Xie Yuanbao said that Xinyunlinke can also extend the process application of automatic annotation and automated machine learning to different industrial landing services, such as Various fields such as self-driving cars, smart warehousing self-propelled robots and self-driving buses in future smart cities are all in line with the spirit of automated mobility of Mobility as a Service We look forward to the role played by Xinyunlinke The process of image annotation in different industries accelerates the efficiency of developing image recognition services in different fields We believe that by providing client-to-end AI solutions and a complete set of automated AI image analysis pre-operation processes from Data Selection AI technology, Auto-Labeling AI technology, and automated machine learning AI technology, we can greatly satisfy our customers The demand for AI autonomous learning platform Image analysis AI platform sets a new record for smart industrial safety inspections from 100 minutes to 3 seconds Seeing the high demand for industrial safety supervision in high-risk industries such as the chemical industry in recent years, Xinyunlinke launched the "Vision AI Platform", which uses AI image recognition technology Its main functions include real-time AI 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counting, screen availability, smoke detection, pipeline corrosion and damage , illegal stacking for use in different industries, customers can build exclusive AI models without spending time writing programs in the continuous learning part, the Vision AI system can provide customers with the performance and accuracy of AI models, and have the ability to adapt as the environment changes Continuous learning ability Vision AI has a simple user interface and intuitive operation For cross-field industries, this platform has automated and flexible AI capabilities Customers can build self-defined AI models without spending time writing programs, and Vision AI gives AI models the ability to continuously learn and improve, allowing customers to save the labor cost of building an AI team In addition, the platform can significantly reduce the manpower allocation for routine inspections required for operational safety management, improve employee safety in the working environment, and reduce 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short-term layout, the company will actively cooperate with domestic players such as Hon Hai and TSMC to implement image analysis AI technology in fields such as self-driving cars, smart industrial safety, and smart warehousing robots In the medium to long term, Xinyunlinke will target the United States, Europe, Japan and other countries as its global market layout, establish investment and cooperation partnerships with major international companies such as Microsoft, and replicate its successful experience and promote it internationally Xinyunlinke official website Xie Yuanbao, founder and chairman of Xinyunlinke 「Translated content is generated by ChatGPT and is for reference only Translation date:2024-05-19」