TY - JOUR
T1 - Present and future of machine learning in breast surgery
T2 - systematic review
AU - Soh, Chien Lin
AU - Shah, Viraj
AU - Arjomandi Rad, Arian
AU - Vardanyan, Robert
AU - Zubarevich, Alina
AU - Torabi, Saeed
AU - Weymann, Alexander
AU - Miller, George
AU - Malawana, Johann
N1 - © The Author(s) 2022. Published by Oxford University Press on behalf of BJS Society Ltd.
PY - 2022/10/14
Y1 - 2022/10/14
N2 - BACKGROUND: Machine learning is a set of models and methods that can automatically detect patterns in vast amounts of data, extract information, and use it to perform decision-making under uncertain conditions. The potential of machine learning is significant, and breast surgeons must strive to be informed with up-to-date knowledge and its applications.METHODS: A systematic database search of Embase, MEDLINE, the Cochrane database, and Google Scholar, from inception to December 2021, was conducted of original articles that explored the use of machine learning and/or artificial intelligence in breast surgery in EMBASE, MEDLINE, Cochrane database and Google Scholar.RESULTS: The search yielded 477 articles, of which 14 studies were included in this review, featuring 73 847 patients. Four main areas of machine learning application were identified: predictive modelling of surgical outcomes; breast imaging-based context; screening and triaging of patients with breast cancer; and as network utility for detection. There is evident value of machine learning in preoperative planning and in providing information for surgery both in a cancer and an aesthetic context. Machine learning outperformed traditional statistical modelling in all studies for predicting mortality, morbidity, and quality of life outcomes. Machine learning patterns and associations could support planning, anatomical visualization, and surgical navigation.CONCLUSION: Machine learning demonstrated promising applications for improving breast surgery outcomes and patient-centred care. Neveretheless, there remain important limitations and ethical concerns relating to implementing artificial intelligence into everyday surgical practices.
AB - BACKGROUND: Machine learning is a set of models and methods that can automatically detect patterns in vast amounts of data, extract information, and use it to perform decision-making under uncertain conditions. The potential of machine learning is significant, and breast surgeons must strive to be informed with up-to-date knowledge and its applications.METHODS: A systematic database search of Embase, MEDLINE, the Cochrane database, and Google Scholar, from inception to December 2021, was conducted of original articles that explored the use of machine learning and/or artificial intelligence in breast surgery in EMBASE, MEDLINE, Cochrane database and Google Scholar.RESULTS: The search yielded 477 articles, of which 14 studies were included in this review, featuring 73 847 patients. Four main areas of machine learning application were identified: predictive modelling of surgical outcomes; breast imaging-based context; screening and triaging of patients with breast cancer; and as network utility for detection. There is evident value of machine learning in preoperative planning and in providing information for surgery both in a cancer and an aesthetic context. Machine learning outperformed traditional statistical modelling in all studies for predicting mortality, morbidity, and quality of life outcomes. Machine learning patterns and associations could support planning, anatomical visualization, and surgical navigation.CONCLUSION: Machine learning demonstrated promising applications for improving breast surgery outcomes and patient-centred care. Neveretheless, there remain important limitations and ethical concerns relating to implementing artificial intelligence into everyday surgical practices.
KW - Artificial Intelligence
KW - Breast Neoplasms/surgery
KW - Databases, Factual
KW - Female
KW - Humans
KW - Machine Learning
KW - Quality of Life
U2 - 10.1093/bjs/znac224
DO - 10.1093/bjs/znac224
M3 - Article
C2 - 35945894
SN - 0007-1323
VL - 109
SP - 1053
EP - 1062
JO - British Journal of Surgery
JF - British Journal of Surgery
IS - 11
ER -