Artificial Intelligence in Medicine Laboratory, Departament de Matemàtiques i Informàtica, Universitat de Barcelona, Spain; Computer Vision Center, Barcelona, Spain.
ScreenPoint Medical, Nijmegen, the Netherlands.
Eur J Radiol. 2024 Jun;175:111457. doi: 10.1016/j.ejrad.2024.111457. Epub 2024 Apr 16.
This review provides an overview of the current state of artificial intelligence (AI) technology for automated detection of breast cancer in digital mammography (DM) and digital breast tomosynthesis (DBT). It aims to discuss the technology, available AI systems, and the challenges faced by AI in breast cancer screening.
The review examines the development of AI technology in breast cancer detection, focusing on deep learning (DL) techniques and their differences from traditional computer-aided detection (CAD) systems. It discusses data pre-processing, learning paradigms, and the need for independent validation approaches.
DL-based AI systems have shown significant improvements in breast cancer detection. They have the potential to enhance screening outcomes, reduce false negatives and positives, and detect subtle abnormalities missed by human observers. However, challenges like the lack of standardised datasets, potential bias in training data, and regulatory approval hinder their widespread adoption.
AI technology has the potential to improve breast cancer screening by increasing accuracy and reducing radiologist workload. DL-based AI systems show promise in enhancing detection performance and eliminating variability among observers. Standardised guidelines and trustworthy AI practices are necessary to ensure fairness, traceability, and robustness. Further research and validation are needed to establish clinical trust in AI. Collaboration between researchers, clinicians, and regulatory bodies is crucial to address challenges and promote AI implementation in breast cancer screening.
本文综述了人工智能（AI）技术在数字乳腺摄影（DM）和数字乳腺断层合成（DBT）中自动检测乳腺癌的最新进展。旨在讨论 AI 技术在乳腺癌筛查中的应用、现有的 AI 系统以及 AI 面临的挑战。
本综述重点探讨了 AI 技术在乳腺癌检测中的发展，包括深度学习（DL）技术及其与传统计算机辅助检测（CAD）系统的区别。讨论了数据预处理、学习范式以及独立验证方法的必要性。
基于 DL 的 AI 系统在乳腺癌检测方面取得了显著的改进。它们有可能改善筛查结果，减少假阴性和假阳性，并检测到人类观察者可能错过的细微异常。然而，缺乏标准化数据集、训练数据中的潜在偏差以及监管审批等挑战，阻碍了其广泛应用。
AI 技术有可能通过提高准确性和减少放射科医生的工作量来改善乳腺癌筛查。基于 DL 的 AI 系统在提高检测性能和消除观察者之间的变异性方面显示出了潜力。需要制定标准化的指南和可信赖的 AI 实践，以确保公平性、可追溯性和稳健性。需要进一步的研究和验证来建立 AI 在乳腺癌筛查中的临床信任。研究人员、临床医生和监管机构之间的合作对于解决挑战和促进 AI 在乳腺癌筛查中的应用至关重要。
