王任直

教授

教育背景

医学博士(日本北里大学)

研究领域
神经系统疾病智能化数据库(中台)的设计、建立、资料收集、数据分析和应用;多组学技术在神经系统疾病发病机制研究中的应用;脑肿瘤诊断和治疗靶点的研究;人工智能技术在神经系统疾病诊断和治疗中的作用;医学图像后处理技术在脑认知障碍疾病中的应用;脑肿瘤智能化辅助诊疗决策系统的建立;脑血管病智能化辅助诊疗决策系统的建立;透明脑+神经导航+机器人设备的研发和应用;智能化康复设备和机器人研发和应用
电子邮件
wangrenzhi@cuhk.edu.cn
个人简介

香港中文大学(深圳)医学院顾问,教授,博士生导师;北京协和医院神经外科教研室主任、主任医师,二级教授,博士生导师,北京协和医学院(八年制) 再生医学系副主任,国家七十周年庆典纪念章获得者。

曾任北京协和医院神经外科主任,外科学系副主任,外科学系党总支书记,北京协和医院党委委员。现任中国医师协会智慧医疗分会副主委,世界华人医师协会智慧医疗分会副主委,中国神经科学学会神经肿瘤分会副主委,中国罕见病联盟下丘脑-垂体学组组长,中国垂体腺瘤协作组、胶质瘤协作组、神经外科重症管理协作组顾问,国家干细胞临床委员会委员,卫生部中央保健局专家,国家自然科学基金委员会评委,科技部科技发展基金项目评委以及国家科学技术奖励评审委员会委员,教育部出国留学人员科研基金评审委员,国内、外多家医学专业杂志编委和论文评阅人。

1983年毕业于白求恩医科大学临床医疗系,此后一直在北京协和医院学习和工作。从事神经外科工作40年,善于处理神经外科各种疑难复杂问题。尤其擅长垂体腺瘤、脑胶质瘤、脑血管病、颅咽管瘤、颅底肿瘤、脑干肿瘤等疾病的诊断和治疗。作为首席科学家承担国家863课题三项;作为课题负责人,承担国家自然科学基金委中美合作重大课题一项,面上课题多项,国家科技部重大研究前期专项一项,科技部、卫生部、北京市科委及卫计委、国家教委博士点基金课题等多项。申请国家级各级政府科研课题24项,作为第一作者或通讯作者发表SCI医学论文128篇,获得国内外专利七项,正在申请中三十二项,主编或主译各类医学论著18部,参与编写各种医学类教科书十余部,组织编写各类疾病共识或指南二十余部。曾获国家科技进步一等奖一项(参加),国家发明二等奖一项,中华医学科技进步一等奖一项,三等奖一项,国家教委科技进步二等奖两项,北京市科技进步三等奖一项。

项目团队关注医疗大数据和人工智能技术十余年,通过与多家科研院所以及公司合作,结合我们已经积累的工作经验,完成以下工作:A、已经开发完成“鞍区肿瘤智能化辅助诊疗系统”的研发,目前已经在五百余家基层医院试用,取得很好的效果;B、建立了国内最大的两种疾病的数据库:高血压脑出血数据库,目前已经积累一万余例病人资料,五万余例次CT扫描数据;垂体腺瘤数据库,已有四万五千余例病人资料;C、倡导并协助建立了中国第一个专注于高血压脑出血外科治疗工作的“高血压脑出血外科治疗联盟”,目前联盟单位共有300余家医院。高血压脑出血智能化辅助诊疗决策模型初步完成,准备在联盟内部医院完成进一步临床验证;D、脑组织分割和重建模型已经建立,“透明脑”工作已经完成;E、具有完全自主知识产权,有别于国内、外核心技术的“神经导航”工作已经完成,而且这套系统具有简单、轻便、易操作和造价低等特点,而且系统误差小于0.5毫米;F、涉及部分研发成果,已经申请专利三十余项,著作权证书2项。

学术著作

作为第一作者或通讯作者发表SCI医学论文128篇,涉及人工智能、脑肿瘤、脑出血等各个领域,进3年内相关论文现摘录如下:

1.        Duo Y, Yang Y, Xu T, Ri Z, Wang R: Coordination Chemistry Reviews. Chem Soc Rev 2023, 482:215070. (IF 24.833,通讯作者)

2.        Ren Y, Bao X, Feng M, Xing B, Lian W, Yao Y, Wang R: D87 CAR-T cells potently inhibit invasive nonfunctioning pituitary adenomas. SCIENCE CHINA Life Sciences, 2023. (IF 10.384,通讯作者)

3.        Jiang S, Geng R, Wang R, Li X, Bao X: The potential of hydrogels as a niche for promoting neurogenesis and regulating neuroinflammation in ischemic stroke. MATER DESIGN, 2023, 229:111916. (IF 9.417,通讯作者)

4.        Fang Y, Wang H, Feng M, Chen H, Zhang W, Wei L, Pei Z, Wang R, Wang S: Application of Convolutional Neural Network in the Diagnosis of Cavernous Sinus Invasion in Pituitary Adenoma. Front Oncol 2022, 12:835047. (IF 6.244,通讯作者)

5.        Wang H, Zhang W, Li S, Fan Y, Feng M, Wang R: Development and Evaluation of Deep Learning-based Automated Segmentation of Pituitary Adenoma in Clinical Task. J Clin Endocrinol Metab 2021, 106(9):2535-2546. (IF 6.134,通讯作者)

6.        Fan Y, Li Y, Bao X, Zhu H, Lu L, Yao Y, Li Y, Li L, Su M, Feng F, Feng S,  Feng M, Wang R. Development of machine learning models for predicting postoperative delayed remission of patients with Cushing's disease. J Clin Endocrinol Metab. 2021,106(1):e217-e231. doi: 10.1210/clinem/dgaa698. (IF 5.958,通讯作者)

7.        Dai C, Fan Y, Liu X, Bao X, Yao Y, Wang R, Feng M. Predictors of Immediate Remission After Surgery in Cushing's Disease Patients: A Large Retrospective Study From a Single Center. Neuroendocrinology. 2021,106(1):e217-e231. doi: 10.1159/000509221.  (IF 4.91,通讯作者)

8.        Fan Y, Li Y, Bao X, Zhu H, Lu L, Yao Y, Li Y, Su M, Feng F, Feng S et al: Development of Machine Learning Models for Predicting Postoperative Delayed Remission in Patients With Cushing's Disease. J Clin Endocrinol Metab 2021, 106(1):e217-e231. (IF 6.495,通讯作者)

9.        Fang Y, Wang H, Feng M, Zhang W, Cao L, Ding C, Chen H, Wei L, Mu S, Pei Z et al: Machine-Learning Prediction of Postoperative Pituitary Hormonal Outcomes in Nonfunctioning Pituitary Adenomas: A Multicenter Study. Front Endocrinol (Lausanne) 2021, 12:748725. (IF 3.644,通讯作者)

10.    Zhang W, Sun M, Fan Y, Wang H, Feng M, Zhou S, Wang R: Machine Learning in Preoperative Prediction of Postoperative Immediate Remission of Histology-Positive Cushing's Disease. Front Endocrinol (Lausanne) 2021, 12:635795. (IF 3.644,通讯作者)

11.    Wang H, Zhang W, Li S, Fan Y,  Feng M, Wang R. Development and Evaluation of Deep Learning-based Automated Segmentation of Pituitary Adenoma in Clinical Task. J Clin Endocrinol Metab. 2021, 106(9):2535-2546. doi: 10.1210/clinem/dgab371. (IF 5.958,通讯作者)

12.    Yue Q, Ma X, Feng M, Yao Y, Bao X, Deng KWang R. Multiple myeloma complicated by skull plasmacytoma discovered after heda injury. J Integr Neurosci. 2021, 20(2):459-462. doi:10.31083/j.jin2002048.  (IF 2.117, 通讯作者)

13.    Zhang W, Sun M, Fan Y, Wang H, Feng M, Zhao S, Wang R. Machine Learning in Preoperative Prediction of Postoperative Immediate Remission of Histology-Positive Cushing's Disease. Front Endocrinol. 2021, 12:635795. doi: 10.3389/fendo.2021.635795. eCollection 2021.  (IF 5.555, 通讯作者)

14.    Sun X, Feng M, Lu L, Zhao Z, Bao X, Deng K, Yao Y, Zhu H, Wang R. Lipid Abnormalities in Patients With Cushing's Disease and Its Relationship With Impaired Glucose Metabolism. Front Endocrinol. 2021, 11:600623. doi: 10.3389/fendo.2020.600323. eCollection 2020.  (IF 5.555, 通讯作者)

15.    Fan Y, Chai Y, Li K, Fang H, Mou A, Feng S, Feng M, Wang R: Non-invasive and real-time proliferative activity estimation based on a quantitative radiomics approach for patients with acromegaly: a multicenter study. J Endocrinol Invest 2020, 43(6):755-765. (IF 5.467,通讯作者)

16.    Dai C, Fan Y, Li Y, Bao X, Li Y, Su M, Yao Y, Deng K, Xing B, Feng F et al: Development and Interpretation of Multiple Machine Learning Models for Predicting Postoperative Delayed Remission of Acromegaly Patients During Long-Term Follow-Up. Front Endocrinol (Lausanne) 2020, 11:643. (IF 3.644,通讯作者)

17.    Fan Y, Li D, Liu Y, Feng M, Chen Q, Wang R: Toward better prediction of recurrence for Cushing’s disease: a factorization-machine based neural approach. International Journal of Machine Learning and Cybernetics 2020, 12(3):625-633. (IF 1.11,通讯作者)

18.    Fan Y, Li Y, Li Y, Feng S, Bao X, Feng M, Wang R: Development and assessment of machine learning algorithms for predicting remission after transsphenoidal surgery among patients with acromegaly. Endocrine 2020, 67(2):412-422. (IF 3.925,通讯作者)

19.    Duo Y, Luo G, Zhang W, Wang R, Xiao GG, Li Z, Li X, Chen M, Yoon J, Tang BZ: Noncancerous disease-targeting AIEgens. Chem Soc Rev, 2023, 52:1024-1064. (IF 60.615)

20.    Zhang X, Zhang Z, Diao W, Zhou C, Song Y, Wang R, Luo X, Liu G. Early-diagnosis of major depressive disorder: From biomarkers to point-of-care testing. Trends Analyt Chem. 2023, 159: 116904. (IF 14.908)

21.    Zheng X, Wang H, Zhang W, Fegn S, Liu Y, Li S, Bao X, Lu L, Feng M, Zhao S, Wang R. Diagnosis, manifestations, laboratory investigations, and prognosis in pediatric and adult Cushing’s Disease in a large center in China.

22.    Fan Y, Jiang S, Hua M, Feng S, Feng M, Wang R: Machine Learning-Based Radiomics Predicts Radiotherapeutic Response in Patients With Acromegaly. Front Endocrinol (Lausanne) 2019, 10:588. (IF 3.644,通讯作者)

23.    Fan Y, Hua M, Mou A, Wu M, Liu X, Bao X, Wang R, Feng M: Preoperative Noninvasive Radiomics Approach Predicts Tumor Consistency in Patients With Acromegaly: Development and Multicenter Prospective Validation. Front Endocrinol (Lausanne) 2019, 10:403.  (IF 3.644,通讯作者)

24.    Fan Y, Liu Z, Hou B, Li L, Liu X, Liu Z, Wang R, Lin Y, Feng F, Tian J et al: Development and validation of an MRI-based radiomic signature for the preoperative prediction of treatment response in patients with invasive functional pituitary adenoma. Eur J Radiol 2019, 121:108647. (IF 4.531,通讯作者)