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人工智能 x 基础科学系列论坛：当医学插上人工智能的翅膀
活动日期： 2023年6月29日
特邀主持人介绍
张忠凯，博士，中国科学院创新研究院人工智能与机器人创新中心副教授，于2023年2月全职归国加入中科院香港创新研究院并组建医学仿真团队。该团队致力于开发新一代实时仿真算法, 并将其应用于机器人控制与手术模拟训练。2018年毕业于法国国家信息与自动化研究院。博士期间解决了软体机器人建模，控制与力反馈的多个关键性难题。作为核心研发人员开发的基于SOFA 的软体机器人仿真软体已成为行业标准。之后在法国斯特拉斯堡大学以及法国国家科研中心从事医疗机器人与人工智能的博士后研究。在此期间，研发了世界首台具有OCT 扫描功能的内窥镜机器人的自动控制算法，并提出了解决人工智能约束问题的新方法。自2021年起担任法国农业科学院数学与计算机研究中心终身研究员，领导团队从事移动机器人与人工智能的研究工作。
特邀嘉宾与主题介绍
分享主题：智能手术器械的多模态信息感知、处理与应用
嘉宾简介：王广志，博士，清华大学医学院长聘教授、生物医学工程系执行系主任 。长期从事医学影像处理、影像引导手术的教学与研究。发表学术研究论文150多篇，获发明专利授权20多项。现任中国生物医学工程学会副理事长，中国医学影像技术研究会副会长。
分享背景：近年来基于深度学习等人工智能方法的辅助诊断取得很大进展，手术机器人等智能化医疗器械也日益受到业界关注，创新产品不断涌现，并不断拓展临床应用领域。
手术机器人等智能化器械的核心作用是有效地辅助医生更加精准、安全、高效地完成手术操作，规避手术风险，减少副作用。因此，多模态信息的智能化感知、识别、处理、决策和操作，已经成为新一代智能化手术器械发展必不可少的核心技术。如何更有效地将上述智能化要素与临床应用场景结合，给医生提供更有效的智能化装备，成为研究的热点，也提出了新的技术挑战，需要密切融合人工智能的最新发展，形成新的解决方案。
分享摘要：结合我们的工作体会，分析影像引导手术场景中机器人智能辅助作用形式，并以神经外科手术机器人为例，从多层次感知与决策角度，探讨在临床应用中如何借助多模态信息感知，提升手术机器人的准确性、便捷性及自主能力。
分享主题：多模态数据导航血管介入手术机器人
嘉宾简介：谢晓亮，中国科学院自动化研究所研究员，主要研究方向为手术机器人开发与临床应用、机器人智能控制。主持国家自然科学基金面上项目2项、青年基金1项、参加重点基金项目2项；主持国家重点研发计划智能机器人专项课题1项；获授权国家发明专利20余项、PCT专利3项；在IEEE Trans. on Neural Networks and Learning Systems、IROS等国际期刊和会议上发表论文30余篇。获机器人领域顶级会议IEEE ICRA最佳论文提名奖1次，IEEE RCAR最佳论文奖1次，获得北京协和医院科研成果奖一等奖。
分享背景：随着人口老龄化的不断加剧和生活水平的不断提高，心脑血管疾病、肿瘤等具有典型老龄化特征的患者人群迅猛增长。例如，我国各类心脑血管疾病患者总数超过2.9亿。庞大的患者人群给临床治疗与健康生活保障带来巨大压力，成为目前重大的国计民生问题。
血管介入手术是治疗心脑血管最有效的方式。但是纯人工手术存在诸多问题，需要临床医师具备丰富的临床经验、高超的手术技能、及良好的生理心理素质，因此，技术精湛的医生成为稀缺资源。借助机器人辅助医生实施介入手术，能有效降低手术难度、提高手术精度。鉴于此，立足于当前人工智能与先进机器人技术的发展，开展了智能化精准血管介入手术研究。包括：
开发了具有多器械协同递送功能的血管介入手术机器人系统，并对其进行精准控制。根据同时协同递送不少于2种器械的临床需求，通过梳理分析各类手术器械的输送时序与特点，结合医生操作需求与仿生学原理，设计了新一代血管介入手术机器人，并成功完成了多中心多例临床试验。
血管介入手术机器人多模影像导航。将术前的血管三维影像信息与术中影像叠加融合显示在一起，为医生提供了多视角的导航画面。结合高级医生力觉操控技能学习方法，研究了血管介入手术机器人拟人化操控策略，提升了机器人智能化层级，在扩展手术机器人的适应症的同时，有效降低介入手术难度。
分享摘要：复杂血管环境对血管介入手术机器人的器械安全精准操控提出了更大挑战，本报告将从机器人本体设计、术中导航方法、临床应用三个层面介绍最新研究进展。
相关项目：
[5] Yanjie Zhou, Xiaoliang Xie, Xiaohu Zhou, Shiqi Liu, Guibin Bian, and Zengguang Hou, “A real-time multi-functional framework for guidewire morphological and positional analysis in interventional X-ray fluoroscopy,”IEEE Transactions on Cognitive and Developmental Systems, vol. 13, no. 3, pp. 657-667, September, 2021.
分享主题：Computational Analysis of Non-small Cell Lung Cancer Drug Resistance
嘉宾简介：Hong Yan received his PhD degree from Yale University. He was Professor of Imaging Science at the University of Sydney and currently is Wong Chun Hong Professor of Data Engineering and Chair Professor of Computer Engineering at City University of Hong Kong. Professor Yan's research interests include image processing, pattern recognition, and bioinformatics. He has over 600 journal and conference publications in these areas. Professor Yan is an IEEE Fellow and IAPR Fellow. He received the 2016 Norbert Wiener Award from the IEEE SMC Society for contributions to image and biomolecular pattern recognition techniques. He is a foreign member of the European Academy of Sciences and Arts and a Fellow of the US National Academy of Inventors.
分享背景：Lung cancer has the highest incidence and mortality rates among all cancer types in the world. The mutation of a protein called the epidermal growth factor receptor (EGFR) is a major cause of non-small cell lung cancer (NSCLC). Anti-EGFR tyrosine kinase inhibitors (TKIs) can work effectively initially, which reduce tumor size and increase the patient survival time. However, almost all patients develop drug resistance after about a year of treatment due to one or more additional mutations of EGFR.
In collaboration with medical doctors, our research group has studied EGFR mutations at the molecular and atomic levels. We have collected EGFR mutations from the literature and from clinical cases at Queen Mary Hospital. Some of the mutations observed locally are rare and have never been reported in scientific journals before. Based on computational models, we analyzed how the 3D structure of EGFR will change due to a mutation. Then for each drug, we computed its binding strength with EGFR before and after the secondary mutation. The reduction in the binding strength reflects the degradation of the drug effectiveness. We have built and published a 3D structural database of EGFR mutants. We have analyzed the characteristics of all known EGFR mutations.
Biomolecular surface complementarity is important in protein-drug interactions. We can find EGFR surface atoms using the alpha-shape model. The solid angles at these atoms represent the surface curvature, which is used as a geometric property in our analysis. Concave shapes around the binding sites are more likely to offer opportunities for drug binding than convex shapes. If the drug binds to EGFR mutants very tightly, then it will strengthen the response. We have studied the EGFR mutation-induced drug resistance by analyzing the solid angles around the binding sites. We have introduced the concept of eigen-binding surface to investigate common properties of biomolecular surfaces that are significant in protein-drug complexes.
EGFR is a member of the ErbB family. A pair of this family members can form a dimer to perform various biological functions. We have investigated the EGFR (ErbB-1) and ErbB-3 heterodimerization, regarded as the origin of intracellular signaling pathways. We combined the molecular interaction in EGFR heterodimerization with that between the EGFR tyrosine kinase and its inhibitor. For 168 clinical subjects, we characterized their corresponding EGFR mutations using molecular interactions, with three potential dimerization partners (ErbB-2, IGF-1R and c-Met) of EGFR and two of its small molecule inhibitors (gefitinib and erlotinib). Based on molecular dynamics simulations and structural analysis, we modeled these mutant-partner or mutant-inhibitor interactions using the binding free energy and its components.
Our work leads to a deeper understanding of the mechanisms of cancer drug resistance. The knowledge gained can help the design of new and more effective drugs. The databases and computer algorithms developed provide a useful reference to medical doctors for assessment of drug resistance level for different EGFR mutants and for planning personalized treatment of lung cancer patients.
分享摘要：The mutation of a protein called the epidermal growth factor receptor (EGFR) is a major cause of non-small cell lung cancer (NSCLC). Almost all patients develop drug resistance after about a year of treatment due to one or more additional mutations of EGFR. In collaboration with medical doctors, we have built and published a 3D structural database of all known EGFR mutants. We have developed an alpha-shape based model to characterize the contact surfaces of EGFR-drug complexes and introduced the concept of eigen-binding surface to investigate common properties of biomolecular surfaces that are significant in protein-drug complexes. Our work leads to a deeper understanding of the mechanisms of cancer drug resistance. The knowledge gained can help the design of new and more effective drugs. The databases and computer algorithms developed provide a useful reference to medical doctors for assessment of drug resistance level for different EGFR mutants and for planning personalized treatment of lung cancer patients.
https://bcc.ee.cityu.edu.hk/med/