Application and prospect of machine learning in orthopaedic trauma.
10.7507/1002-1892.202308064
- Author:
Chuwei TIAN
1
;
Xiangxu CHEN
2
;
Huanyi ZHU
1
;
Shengbo QIN
3
;
Liu SHI
1
;
Yunfeng RUI
1
Author Information
1. Department of Orthopedics, Zhongda Hospital Affiliated to Southeast University, Nanjing Jiangsu, 210009, P. R. China.
2. Trauma Center, Zhongda Hospital Affiliated to Southeast University, Nanjing Jiangsu, 210009, P. R. China.
3. School of Medicine, Southeast University, Nanjing Jiangsu, 210009, P. R. China.
- Publication Type:Journal Article
- Keywords:
Artificial intelligence;
imaging diagnosis;
machine learning;
medical decision;
orthopaedic trauma
- MeSH:
Artificial Intelligence;
Orthopedics;
Machine Learning;
Algorithms
- From:
Chinese Journal of Reparative and Reconstructive Surgery
2023;37(12):1562-1568
- CountryChina
- Language:Chinese
-
Abstract:
OBJECTIVE:To review the current applications of machine learning in orthopaedic trauma and anticipate its future role in clinical practice.
METHODS:A comprehensive literature review was conducted to assess the status of machine learning algorithms in orthopaedic trauma research, both nationally and internationally.
RESULTS:The rapid advancement of computer data processing and the growing convergence of medicine and industry have led to the widespread utilization of artificial intelligence in healthcare. Currently, machine learning plays a significant role in orthopaedic trauma, demonstrating high performance and accuracy in various areas including fracture image recognition, diagnosis stratification, clinical decision-making, evaluation, perioperative considerations, and prognostic risk prediction. Nevertheless, challenges persist in the development and clinical implementation of machine learning. These include limited database samples, model interpretation difficulties, and universality and individualisation variations.
CONCLUSION:The expansion of clinical sample sizes and enhancements in algorithm performance hold significant promise for the extensive application of machine learning in supporting orthopaedic trauma diagnosis, guiding decision-making, devising individualized medical strategies, and optimizing the allocation of clinical resources.
