Explainable machine learning for breast cancer diagnosis from mammography and ultrasound images: a systematic review

IF 4.1 Q1 HEALTH CARE SCIENCES & SERVICES BMJ Health & Care Informatics Pub Date : 2024-02-01 DOI:10.1136/bmjhci-2023-100954
Daraje kaba Gurmessa, Worku Jimma
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Abstract

Background Breast cancer is the most common disease in women. Recently, explainable artificial intelligence (XAI) approaches have been dedicated to investigate breast cancer. An overwhelming study has been done on XAI for breast cancer. Therefore, this study aims to review an XAI for breast cancer diagnosis from mammography and ultrasound (US) images. We investigated how XAI methods for breast cancer diagnosis have been evaluated, the existing ethical challenges, research gaps, the XAI used and the relation between the accuracy and explainability of algorithms. Methods In this work, Preferred Reporting Items for Systematic Reviews and Meta-Analyses checklist and diagram were used. Peer-reviewed articles and conference proceedings from PubMed, IEEE Explore, ScienceDirect, Scopus and Google Scholar databases were searched. There is no stated date limit to filter the papers. The papers were searched on 19 September 2023, using various combinations of the search terms ‘breast cancer’, ‘explainable’, ‘interpretable’, ‘machine learning’, ‘artificial intelligence’ and ‘XAI’. Rayyan online platform detected duplicates, inclusion and exclusion of papers. Results This study identified 14 primary studies employing XAI for breast cancer diagnosis from mammography and US images. Out of the selected 14 studies, only 1 research evaluated humans’ confidence in using the XAI system—additionally, 92.86% of identified papers identified dataset and dataset-related issues as research gaps and future direction. The result showed that further research and evaluation are needed to determine the most effective XAI method for breast cancer. Conclusion XAI is not conceded to increase users’ and doctors’ trust in the system. For the real-world application, effective and systematic evaluation of its trustworthiness in this scenario is lacking. PROSPERO registration number CRD42023458665. Data are available upon reasonable request.
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从乳房 X 射线照相术和超声波图像诊断乳腺癌的可解释机器学习:系统综述
背景 乳腺癌是女性最常见的疾病。最近,可解释人工智能(XAI)方法被用于研究乳腺癌。目前,针对乳腺癌的 XAI 研究还很少。因此,本研究旨在回顾一种用于从乳房 X 射线照相术和超声波(US)图像诊断乳腺癌的 XAI。我们调查了用于乳腺癌诊断的 XAI 方法是如何被评估的、现有的伦理挑战、研究差距、所使用的 XAI 以及算法的准确性和可解释性之间的关系。方法 在这项工作中,使用了《系统综述和元分析首选报告项目》清单和图表。从 PubMed、IEEE Explore、ScienceDirect、Scopus 和 Google Scholar 数据库中搜索了同行评审文章和会议论文集。论文筛选没有明确的日期限制。论文搜索日期为 2023 年 9 月 19 日,使用了 "乳腺癌"、"可解释"、"可解释"、"机器学习"、"人工智能 "和 "XAI "等搜索词的不同组合。Rayyan 在线平台检测了重复、纳入和排除的论文。结果 本研究共发现了 14 项利用 XAI 从乳房 X 射线照相术和 US 图像诊断乳腺癌的主要研究。在所选的 14 项研究中,只有 1 项研究对人类使用 XAI 系统的信心进行了评估,此外,92.86% 的已识别论文将数据集和数据集相关问题确定为研究差距和未来方向。结果表明,要确定最有效的乳腺癌 XAI 方法,还需要进一步的研究和评估。结论 XAI 并不能增加用户和医生对系统的信任。在实际应用中,还缺乏对其可信度的有效和系统评估。PROSPERO 注册号为 CRD42023458665。如有合理要求,可提供相关数据。
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来源期刊
CiteScore
6.10
自引率
4.90%
发文量
40
审稿时长
18 weeks
期刊最新文献
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