Application of machine learning in key properties of medical metal materials
10.12307/2024.406
- VernacularTitle:机器学习在医用金属材料特性研究中的应用
- Author:
Liu SHI
1
;
Pengchen LIANG
;
Qing CHANG
;
Erhong SONG
Author Information
1. 上海大学微电子学院,上海市 201800
- Keywords:
medical metal material;
machine learning;
material characteristics;
corrosion performance;
mechanical property;
stainless steel co-base alloy;
cobalt-chromium alloy;
titanium alloy;
magnesium alloy;
characteristic parameter
- From:
Chinese Journal of Tissue Engineering Research
2024;28(17):2766-2773
- CountryChina
- Language:Chinese
-
Abstract:
BACKGROUND:The combination of machine learning and medical metal materials can make up for the inefficiency and high cost of traditional experiments and computational simulations,and quickly and accurately predict the characteristics of metal materials by analyzing large amounts of data,optimize material design and performance,and improve the safety and efficiency of medical applications. OBJECTIVE:To summarize the research progress and shortcomings of machine learning in the characteristics of medical materials. METHODS:The first author searched CNKI,PubMed,X-MOL,and Web of Science databases by computer to search all relevant articles from January 2013 to April 2023.The Chinese search terms were"machine learning of medical metal materials,medical titanium alloy,medical magnesium alloy,medical metal material properties".The English search terms were"machine learning medical metal materials,medical stainless steel alloy,medical cobalt-chromium alloy,medical titanium alloy,medical magnesium alloy".Finally,70 relevant articles were included for a summary. RESULTS AND CONCLUSION:(1)The introduction of machine learning as a material design methodology has opened up new paradigms for material science research as the accessibility of large amounts of data generated by traditional experimental and computational simulation methods increases.(2)The machine learning workflow is divided into four main parts:data collection and preprocessing,feature engineering,model selection and training,and model evaluation,each of which is indispensable.(3)Medical metal materials are categorized into:stainless steel co-base alloys,cobalt-chromium alloys,titanium alloys,and magnesium alloys.For stainless steel co-base alloy,machine learning predicts its mechanical properties,to improve the generalization ability of machine learning.For cobalt-chromium alloy,machine learning predicts its mechanical properties,and it can conclude that cobalt-chromium alloy is the optimal material for hip implants.For titanium alloy,machine learning predicts its mechanical properties,and it can select the implant with the best mechanical properties.For magnesium alloy,machine learning predicts its corrosion resistance and mechanical properties;the ensemble model can accurately predict the mechanical properties of magnesium alloys,and the random forest model can predict the optimal elemental contents of magnesium alloys as vascular stents.(4)Machine learning has deficiencies in the field of medical materials.For example,the model is relatively lagging;the data failed to be standardized,and the generalization is low.To solve such problems,we should make full use of deep learning and segmentation algorithm technology,use unified standard data,and improve the model to increase the generalization ability.
