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YY/T 1907-2023: Artificial intelligence medical device - Coronary CT image processing software - Algorithm performance test methods
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Artificial intelligence medical device - Coronary CT image processing software - Algorithm performance test methods
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YY/T 1907-2023
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Basic data | Standard ID | YY/T 1907-2023 (YY/T1907-2023) | | Description (Translated English) | Artificial intelligence medical device - Coronary CT image processing software - Algorithm performance test methods | | Sector / Industry | Medical Device & Pharmaceutical Industry Standard (Recommended) | | Classification of Chinese Standard | C30 | | Classification of International Standard | 11.040.99 | | Word Count Estimation | 20,264 | | Date of Issue | 2023-09-05 | | Date of Implementation | 2024-09-15 | | Issuing agency(ies) | State Drug Administration | | Summary | This standard specifies the algorithm performance testing method for coronary CT image processing software using artificial intelligence technology. This standard applies to software products that use artificial intelligence technology to post-process coronary CT images. This document is not suitable for image pre-processing and process optimization. | YY/T 1907-2023: Artificial intelligence medical device - Coronary CT image processing software - Algorithm performance test methods
---This is a DRAFT version for illustration, not a final translation. Full copy of true-PDF in English version (including equations, symbols, images, flow-chart, tables, and figures etc.) will be manually/carefully translated upon your order.
ICS 11.040.99
CCSC30
Pharmaceutical Industry Standards of the People's Republic of China
Artificial intelligence medical device coronary artery CT imaging
Processing software algorithm performance testing methods
Published on 2023-09-05
Implemented on 2024-09-15
Released by the State Drug Administration
Foreword
This document complies with the provisions of GB/T 1.1-2020 "Standardization Work Guidelines Part 1.Structure and Drafting Rules of Standardization Documents"
Drafting.
Please note that some content in this document may be subject to patents. The publisher of this document assumes no responsibility for identifying patents.
This document is proposed by the National Medical Products Administration.
This document is under the jurisdiction of the responsible unit for artificial intelligence medical device standardization technology.
This document was drafted by. China Institute of Food and Drug Control, Shanghai Changzheng Hospital, Fuwai Hospital of the Chinese Academy of Medical Sciences, Capital Medical University
Chaoyang Hospital Affiliated to the University, General Hospital of the Eastern Theater Command of the Chinese People's Liberation Army, Medical Device Technical Review Center of the State Food and Drug Administration, China
Suzhou Institute of Advanced Studies of University of Science and Technology, Tsinghua University, Liaoning Provincial Medical Device Inspection and Testing Institute, Chinese People's Liberation Army General Hospital, Shanghai United
Ying Intelligent Medical Technology Co., Ltd., Shanghai Siemens Medical Equipment Co., Ltd., Keya Medical Technology Co., Ltd., Shenzhen Ruixin Intelligent
Medical Technology Co., Ltd., Shukun (Beijing) Network Technology Co., Ltd., Infer Medical Technology Co., Ltd., Hangzhou Shenrui Bolianke
Technology Co., Ltd., Philips (China) Investment Co., Ltd.
The main drafters of this document. Li Jiage, Liu Shiyuan, Lu Bin, Xiao Yi, Yang Qi, Zhang Longjiang, Peng Liang, Wang Chenxi, Wang Jing, Wang Hao, Hao Ye, Meng Xiangfeng,
Zhou Shaohua, Feng Jianjiang, Liu Kai, Zhang Longda, He Kunlun, Li Shu, Wu Dijia, Fei Zhenyu, Li Guang, Ma Jun, Zheng Chao, Wang Shaokang, Qiao Xin, Ge Xin,
Liu Pan.
Artificial intelligence medical device coronary artery CT imaging
Processing software algorithm performance testing methods
1 Scope
This document describes the algorithm performance testing method of coronary CT image processing software using artificial intelligence technology.
This document applies to software products that use artificial intelligence technology to post-process coronary CT images.
This document is not suitable for image pre-processing and process optimization.
CCTA) and CT plain imaging.
2 Normative reference documents
The contents of the following documents constitute essential provisions of this document through normative references in the text. Among them, the dated quotations
For undated referenced documents, only the version corresponding to that date applies to this document; for undated referenced documents, the latest version (including all amendments) applies to
this document.
YY/T 1833.1 Quality requirements and evaluation of artificial intelligence medical devices Part 1.Terminology
YY/T 1833.2 Quality requirements and evaluation of artificial intelligence medical devices Part 2.General requirements for data sets
YY/T 1833.3 Quality requirements and evaluation of artificial intelligence medical devices Part 3.General requirements for data annotation
3 Terms and definitions
The terms and definitions defined in YY/T 1833.1, YY/T 1833.2, YY/T 1833.3 and the following apply to this document.
3.1
coronary centerlinecoronarycenterline
A curve formed along the axis of the blood vessel and connecting the lumen center points on each cross-sectional image of the coronary artery. The starting point of the curve is the crown
The opening of the coronary artery, the end point is the clearly identifiable farthest end of each coronary artery.
Note. The coronary artery centerline is generally presented on curved images.
3.2
Fractional flow reserve calculated from coronary CT vascular images.
Note. Appendix A provides additional explanation of fractional flow reserve.
3.3
A quantitative index to calculate the degree of coronary artery calcification, usually using ECG gating before coronary CT angiography.
CT plain scan (axial scan), combined with mathematical formulas to quantify coronary artery calcification.
Note 1.Currently commonly used calculation methods include Agatston integral, volume integral and mass integral.
Note 2.Agatston score is a commonly used indicator of coronary artery calcium score. Appendix B provides a supplementary explanation of its calculation method.
Note 3.Coronary artery calcification is characterized based on the lesion size exceeding a certain area or volume threshold and the density exceeding the prescribed CT value threshold. Different calculation methods
Not all the same, the guide recommends using Agatston points.
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