Name: 
WANG Yixiu
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Research Assistant Professor
Department: 
Electronic Engineering
email: 
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王一休
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中大全球首創手術機械人多功能自動化具身智能平台 完成活體動物驗證

香港文匯報訊 香港中文大學工程學院與醫學院跨學科研究團隊取得重大突破,成功研發人工智能驅動的手術機械人自動化新技術,並全球首次完成了多功能手術自動化的活體動物驗證。研究團隊採用高度模擬臨床手術環境的活體動物模型,對該人工智能系統進行嚴格測試。

Date: 
Tuesday, August 5, 2025

AI機械臂完成活體動物測試 中大:手術「第三隻手」

中大工程學院與醫學院研發人工智能(AI)驅動的手術機械人自動化新技術,首度完成活體動物測試。中大指出,傳統手術自動化方法需依賴額外傳感器的輸入或基於人工預定的規則和模型,新技術能實時分析內窺鏡圖像,毋須額外傳感器,有望讓自動化機械臂成為外科醫生的「第三隻手」,輔助複雜手術,減輕醫生負擔並縮短手術時間。

 

Date: 
Wednesday, August 6, 2025
Media: 
Mingpao.com

CUHK’s newly-created embodied intelligence platform successfully completes the world’s first multi-task surgical automation tests on a live animal

Date: 
2025-08-06
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A multidisciplinary research team from The Chinese University of Hong Kong (CUHK)’s Faculty of Engineering and Faculty of Medicine (CU Medicine) has developed new artificial intelligence (AI)-powered surgical robot automation techniques, successfully completing the world’s first multi-task surgical automation tests on a live animal. The research has been published in the prestigious multidisciplinary research journal Science Robotics.

Embodied intelligence technology leads to breakthroughs in surgical robot automation

Surgical robots have performed millions of minimally invasive procedures worldwide. Autonomy is envisaged for next-generation surgical robots, enhancing operational efficiency and consistency, while alleviating pressure on medical resources.

Professor Dou Qi, Assistant Professor from CUHK’s Department of Computer Science and Engineering, who led the study, said: “Traditional surgical automation approaches often relied on additional sensors or predefined models, which limited their clinical applicability. We used innovative AI techniques to create a brand-new embodied intelligence framework for surgical robot automation, contributing a data-driven and purely vision-based solution that is the first of its kind globally.”

This surgical embodied intelligence framework can analyse endoscopic images in real time, without additional sensors. The framework integrates advanced visual foundation models, reinforcement learning and visual servoing techniques to achieve accurate, efficient and safe automation of various surgical tasks. Its foundation-model-based visual perception allows it to robustly perform surgical scene understanding and depth estimation in practice. The reinforcement learning-based control policy was trained using SurRoL, an embodied AI simulator that the team developed, and the simulation-trained policy can be directly deployed in real-world robots via zero-shot sim-to-real transfer. In this research, the developed AI system has been seamlessly integrated into the Sentire® Surgical System which has distinctive AI-readiness and AI-friendly characteristics. This data-driven paradigm eliminates task-specific engineering, providing a general-purpose solution for versatile surgical autonomy through embodied AI, accelerating the translation from concept to pre-clinical testing.

In vivo testing validates AI-powered multi-task autonomy and human-robot collaboration

The research team conducted in vivo testing of the AI system using a live animal model that replicated clinical surgical conditions. The system successfully performed multiple autonomous surgical tasks, including tissue retraction, gauze picking and blood vessel clipping – actions that surgeons regularly perform during operations.

Dr Yip Hon-chi, Assistant Professor from Department of Surgery at CU Medicine, who led the animal testing, said: “This represents a breakthrough in AI-powered surgical robot automation, validated across diverse tasks and environmental conditions. Our system demonstrates remarkable generalisability, maintaining stable performance despite environmental changes such as different tissue appearances and varying lighting conditions.”

The technology has the potential to enable the automated robotic arm to function as a surgeon’s third hand, providing assistance during complex procedures. By automating routine tasks with an AI assistant, the system can potentially significantly reduce surgeon workload, improve overall surgical efficiency and shorten procedure time for patients.

InnoHK Multi-Scale Medical Robotics Center (MRC) provides an international platform for high-impact research

The InnoHK Multi-Scale Medical Robotics Center (MRC) played a pivotal role in this groundbreaking research. SurRoL was developed through a strategic collaboration between CUHK and Johns Hopkins University (JHU) in the United States, fostered by the MRC’s international network. The research team open-sourced the surgical embodied AI software infrastructure to the global surgical robotics research community in 2021, and it has since been adopted by numerous prestigious research institutions worldwide.

Professor Samuel Au Kwok-wai, Co-director of MRC and Professor from CUHK’s Department of Mechanical and Automation Engineering, said: “This work exemplifies the exceptional innovations that can emerge from international collaborations cultivated by the MRC. The research has achieved pioneering advancements in AI-powered surgical robot automation,”

The live animal experiments were conducted in the MRC’s hybrid operating room, which provided professional support for pre-clinical evaluation. This environment allowed the surgeon to rigorously test the newly developed AI algorithms under conditions that closely resemble actual surgical settings. Professor Philip Chiu Wai-yan, Co-director of MRC and Dean of CU Medicine, said: “The MRC creates a unique synergy of engineering innovation and surgical expertise, significantly accelerating the journey from laboratory concepts to pre-clinical studies. This engineer-clinician collaborative research showcases the transformative potential of AI co-pilots in robotic surgery, positioning CUHK at the forefront of the global advancement of surgeon-AI-robot partnerships.”

The work was supported by the InnoHK initiative of the Hong Kong government’s Innovation and Technology Commission, the Hong Kong Research Grants Council, and the National Natural Science Foundation of China.

Video: In vivo testing
Video: Presentation and demonstration

 

Source: https://www.cpr.cuhk.edu.hk/en/press/cuhks-newly-created-embodied-intelligence-platform-successfully-completes-the-worlds-first-multi-task-surgical-automation-tests-on-a-live-animal/

A multidisciplinary research team from CUHK's Faculty of Engineering and CU Medicine has developed new AI-powered surgical robot automation techniques, successfully completing the world’s first multi-task surgical automation tests on a live animal.(from left) Professor Dou Qi, Dr Yip Hon-chi, Professor Samuel Au Kwok-wai and Professor Philip Chiu Wai-yan.

Professor Dou Qi.

 

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SHAO Baihao
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TIAN Yusheng
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Lecturer
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XU Mengya
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Research Assistant Professor
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YU Bei
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WAI Hoi To
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Assistant Dean
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韋凱滔

Three Engineering Professors named RGC Senior Research Fellows and Research Fellow

Date: 
2025-07-25
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Three scholars from Faculty of Engineering received awards from the 2025-26 Research Grants Council (RGC) Senior Research Fellow Scheme and RGC Research Fellow Scheme in recognition of their distinguished research achievements.

Professor Xing Guoliang, Professor, Department of Information Engineering, and Professor Chen Shih-Chi, Professor, Department of Mechanical and Automation Engineering were named in the RGC Senior Research Fellow Scheme (SRFS). Professor Xing’s research project is “Multi-modal Perception Fusion and Interaction for Infrastructure-assisted Driving Systems”, while Professor Chen’s is “Closed-loop High-throughput Super-resolution Two-photon Lithography”. Each SRFS awardee will be given the title “RGC Senior Research Fellow” and CUHK will receive a fellowship grant of about HK$8.2 million per award to cover salary costs for relief teachers and support for research projects over a period of 60 months.

Professor Zhou Renjie, Associate Professor, Department of Biomedical Engineering was named in the RGC Research Fellow Scheme (RFS). His research project is “High-sensitivity Morpho-molecular Microscopy for High-throughput Imaging Applications”. Each RFS awardee will be given the title “RGC Research Fellow” and CUHK will receive a fellowship grant of about HK$5.5 million per award to cover salary costs for relief teachers and support for research projects over a period of 60 months.

More details: Seven CUHK scholars named RGC Senior Research Fellows or Research Fellows | CUHK Communications and Public Relations Office


 

Professor Xing Guoliang, Professor in the Department of Information Engineering.

Professor Chen Shih-Chi, Professor in the Department of Mechanical and Automation Engineering.

Professor Zhou Renjie, Associate Professor in the Department of Biomedical Engineering.

 

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