School of Clinical Medicine, University of Cambridge, Cambridge, UK.
Department of Medicine, Faculty of Medicine, Imperial College London, London, UK.
Br J Surg. 2022 Oct 14;109(11):1053-1062. doi: 10.1093/bjs/znac224.
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.
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.
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.
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.
机器学习是一套模型和方法，能够自动检测大量数据中的模式，提取信息，并在不确定条件下使用这些信息进行决策。机器学习的潜力巨大，乳腺外科医生必须努力掌握最新的知识及其应用。
从 2021 年 12 月开始，对 Embase、MEDLINE、Cochrane 数据库和 Google Scholar 中的原始文章进行了系统的数据库搜索，这些文章探讨了机器学习和/或人工智能在乳腺外科中的应用。
搜索共产生了 477 篇文章，其中 14 项研究纳入了本综述，涉及 73847 名患者。确定了机器学习应用的四个主要领域：手术结果的预测模型；基于乳腺成像的背景；乳腺癌患者的筛查和分诊；以及网络检测的实用工具。机器学习在术前规划和提供癌症和美容背景下手术信息方面具有明显的价值。在预测死亡率、发病率和生活质量方面，机器学习在所有研究中均优于传统的统计建模。机器学习模式和关联可以支持规划、解剖可视化和手术导航。
机器学习为改善乳腺手术结果和以患者为中心的护理提供了有前途的应用。然而，在将人工智能应用于日常手术实践方面，仍然存在重要的局限性和伦理问题。
