Universität zu Köln

Soh, Chien Lin ORCID: 0000-0002-9756-1394, Shah, Viraj, Arjomandi Rad, Arian ORCID: 0000-0002-4931-4049, Vardanyan, Robert ORCID: 0000-0002-8111-2084, Zubarevich, Alina ORCID: 0000-0002-2444-5747, Torabi, Saeed, Weymann, Alexander, Miller, George and Malawana, Johann (2022). Present and future of machine learning in breast surgery: systematic review. Br. J. Surg., 109 (11). S. 1053 - 1063. OXFORD: OXFORD UNIV PRESS. ISSN 1365-2168

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Abstract

A systematic review evaluating the applications of machine learning in the field of breast surgery. We evaluate the current evidence and areas of application in order to improve patient outcomes. We further examine the limitations of the field and provide recommendations for future research in the field of machine learning and breast surgery. 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.

Item Type:	Journal Article
Creators:	Creators Email ORCID ORCID Put Code Soh, Chien Lin UNSPECIFIED orcid.org/0000-0002-9756-1394 UNSPECIFIED Shah, Viraj UNSPECIFIED UNSPECIFIED UNSPECIFIED Arjomandi Rad, Arian UNSPECIFIED orcid.org/0000-0002-4931-4049 UNSPECIFIED Vardanyan, Robert UNSPECIFIED orcid.org/0000-0002-8111-2084 UNSPECIFIED Zubarevich, Alina UNSPECIFIED orcid.org/0000-0002-2444-5747 UNSPECIFIED Torabi, Saeed UNSPECIFIED UNSPECIFIED UNSPECIFIED Weymann, Alexander UNSPECIFIED UNSPECIFIED UNSPECIFIED Miller, George UNSPECIFIED UNSPECIFIED UNSPECIFIED Malawana, Johann UNSPECIFIED UNSPECIFIED UNSPECIFIED
URN:	urn:nbn:de:hbz:38-684478
DOI:	10.1093/bjs/znac224
Journal or Publication Title:	Br. J. Surg.
Volume:	109
Number:	11
Page Range:	S. 1053 - 1063
Date:	2022
Publisher:	OXFORD UNIV PRESS
Place of Publication:	OXFORD
ISSN:	1365-2168
Language:	English
Faculty:	Unspecified
Divisions:	Unspecified
Subjects:	no entry
Uncontrolled Keywords:	Keywords Language ARTIFICIAL-INTELLIGENCE; BIG DATA; PREDICTION Multiple languages Surgery Multiple languages
URI:	http://kups.ub.uni-koeln.de/id/eprint/68447