[1]刘 超,石艳丽,赵 怡.乳腺癌人工智能辅助诊断专利情报研究[J].医学信息,2023,36(05):46-51.[doi:10.3969/j.issn.1006-1959.2023.05.008]
　LIU Chao,SHI Yan-li,ZHAO Yi.Patent Analysis of AI Assisted Diagnosis of Breast Cancer[J].Journal of Medical Information,2023,36(05):46-51.[doi:10.3969/j.issn.1006-1959.2023.05.008]
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乳腺癌人工智能辅助诊断专利情报研究()
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医学信息[ISSN:1006-1959/CN:61-1278/R]

卷:
36卷
期数:
2023年05期
页码:
46-51
栏目:
医学数据科学
出版日期:
2023-03-01

文章信息/Info

Title:
Patent Analysis of AI Assisted Diagnosis of Breast Cancer
文章编号:
1006-1959（2023）05-0046-06
作者:
刘 超; 石艳丽; 赵 怡
（国家知识产权局专利局专利审查协作北京中心，北京 100083）
Author(s):
LIU Chao; SHI Yan-li; ZHAO Yi
(Patent Examination CooperationCenter of the Patent Office,CNIPA,Beijing 100083,China)
关键词:
乳腺癌; 人工智能; 辅助诊断; 专利申请人
Keywords:
Breast cancer; Artificial intelligence; Assisted diagnosis; Patent;  applicant
分类号:
R736.3
DOI:
10.3969/j.issn.1006-1959.2023.05.008
文献标志码:
A
摘要:
目的 通过对乳腺癌人工智能辅助诊断专利情报进行分析，为技术研发提供参考。方法 在incoPat数据库中检索乳腺癌人工智能辅助诊断全球专利，阅读专利文献并手动标引，分析全球和中国专利申请趋势、专利申请主要原创国家/地区、重要专利申请人。结果 全球和我国专利申请分别在2015年、2017年开始快速增加；美国申请量占全球总量的43.78%，我国占24.07%，其次为韩国、日本、荷兰、德国；重要申请人包括国外的飞利浦、西门子、Enlitic、IBM和我国的腾讯、联影等。结论 乳腺癌人工智能辅助诊断专利申请量趋势与人工智能算法的发展趋势一致，2015年后全球专利申请量快速增长，预期未来将持续发展。美国和我国技术优势明显。重点申请人包括医疗龙头企业、医学影像公司、计算机企业、新科创企业。重要申请人的成功因素包括在医学影像数据领域的数据优势、先进的人工智能算法、大数据领域的技术优势、与企业、高校、医院的合作以及公司间的并购。
Abstract:
Objective To provide guidance for technology research and development by analyzing patent information of artificial intelligence assisted diagnosis of breast cancer.Methods The global patents of breast cancer artificial intelligence-assisted diagnosis were searched in the incoPat database, and the patent literature was read and manually indexed to analyze the global and Chinese patent application trends, the main original countries / regions of patent applications, and important patent applicants.Results Global and Chinese patent applications began to increase rapidly in 2015 and 2017 respectively. The United States accounted for 43.78% of the global total, China accounted for 24.07%, followed by South Korea, Japan, the Netherlands and Germany. Important applicants includeD Philips, Siemens, Enlitic, IBM, Tencent and United Imaging.Conclusion The trend of number of patent applications is consistent with the development trend of artificial intelligence algorithms. Since 2015, the number of global patent applications has increased rapidly, and it is expected to continue developing in the future. The United States and China have great technological advantages. Important applicants include leading medical enterprises, medical imaging companies, computer enterprises, and new innovative enterprises. Key success factors of the applicants are advantages in the field of medical image data, advanced artificial intelligence algorithms, technical advantages in the field of big data, cooperation with enterprises, universitiesor hospitals, and mergers and acquisitions between companies.

参考文献/References:

[1]钟玉婷,钟坚.人工智能发展水平测度指标体系及其应用[J].社会科学动态,2022(6):54-59.[2]Johnson M,Albizri A,Simsek S.Artificial intelligence in healthcare operations to enhance treatment outcomes: a framework to predict lung cancer prognosis[J].Annals of Operations Research,2020,308(1-2):1-31.[3]王立石,宋洁,张洁.基于人工智能的智慧医疗对专利保护政策的挑战及应对[J].软件,2019,40(8):171-174.[4]王嘉良.基于Web of Science可视化分析人工智能在乳腺癌领域的研究热点[J].河南医学研究,2021,30(29):5391-5396.[5]Davuluri S,Kishore DR.Cancer Clumps Detection using Image Processing Based on Cell Counting and Artificial Neural Network Techniques[J].International Journal of Engineering and Advanced Technology,2019,9(2):5124-5126.[6]马梦伟,秦耿耿,徐维敏,等.基于X线及超声乳腺影像报告和数据系统构建机器学习模型预测乳腺癌分子分型[J].中国医学影像技术,2020,36(12):1814-1819.[7]张永梅,陈彤,马健喆,等.基于难样本挖掘和深度学习的乳腺癌检测方法[J].计算机工程与设计,2021,42(6):1727-1734.[8]Vijayakumar K,Kadam VJ,Sharma SK.Breast cancer diagnosis using multiple activation deep neural network[J].Concurrent Engineering,2021,29(3):275-284.[9]Khan S,Islam N,Jan Z, et al.A novel deep learning based framework for the detection and classification of breast cancer using transfer learning[J].Pattern Recognition Letters,2019,125:1-6.[10]左文思,金林原,李芬穗.超声造影对不同分子分型乳腺癌的诊断价值[J].分子影像学杂志,2019,42(4):423-429.[11]江泽飞,许凤锐.乳腺癌精准治疗：20年探索历程[J].中国实用外科杂志,2020,40(1):83-88.[12]Wang X,Guo Y,Wang Y,et al.Automatic breast tumor detection in ABVS images based on convolutional neural network and superpixel patterns[J].Neural Computing and Applications,2019,31(4):1069-1081.[13]Dharani NP,Bojja P,Kumari PR,et al.Detection of Breast Cancer by Thermal Based Sensors using Multilayered Neural Network Classifier[J]. International Journal of Engineering and Advanced Technology,2019,9(2):5615-5618.[14]任鸿昌,杨建武,李成林,等.联合应用彩超、钼靶、MRI对早期乳腺癌的诊断价值[J].武警医学,2021,32(3):201-204. [15]邵旭辉,张军胜,张华文,等.动态增强MRI、钼靶对致密型乳腺病变的诊断效能对比研究[J].中国CT和MRI杂志,2021,19(12):87-89.[16]Yala A,Lehman C,Schuster T,et al.A Deep Learning Mammography-based Model for Improved Breast Cancer Risk Prediction[J].Radiology,2019,292(1):60-66.[17]淦凤萍,肖雪花,胡美娟.人工智能超声对乳腺癌早期诊断及预后评估的价值分析[J].癌症进展,2022,20(14):1480-1486.[18]Frazer H,Qin AK,Pan H,et al.Evaluation of deep learning-based artificial intelligence techniques for breast cancer detection on mammograms: Results from a retrospective study using a BreastScreen Victoria dataset[J].Journal of Medical Imaging and Radiation Oncology,2021,65(5):529-537.[19]Pacilè S,Lopez J,Chone P,et al.Improving Breast Cancer Detection Accuracy of Mammography with the Concurrent Use of an Artificial Intelligence Tool[J].Radiology:Artificial Intelligence,2020,2(6):1-9.[20]Wu N,Phang J,Park J,et al.Deep Neural Networks Improve Radiologists’ Performance inBreast Cancer Screening.[J].IEEE Transactions on Medical Imaging,2020,39(4):1184-1194.

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更新日期/Last Update: 1900-01-01