The Transformative Power of Digital Breast Tomosynthesis and Artificial Intelligence in Breast Cancer Diagnosis.

IF 2.9 3区 医学 Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Vivianne Freitas, Sandeep Ghai, Frederick Au, Derek Muradali, Supriya Kulkarni
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引用次数: 0

Abstract

The integration of Digital Breast Tomosynthesis (DBT) and Artificial Intelligence (AI) represents a significant advance in breast cancer screening. This combination aims to address several challenges inherent in traditional screening while promising an improvement in healthcare delivery across multiple dimensions. For patients, this technological synergy has the potential to lower the number of unnecessary recalls and associated procedures such as biopsies, thereby reducing patient anxiety and improving overall experience without compromising diagnostic accuracy. For radiologists, the use of combined AI and DBT could significantly decrease workload and reduce fatigue by effectively highlighting breast imaging abnormalities, which is especially beneficial in high-volume clinical settings. Health systems stand to gain from streamlined workflows and the facilitated deployment of DBT, which is particularly valuable in areas with a scarcity of specialized breast radiologists. However, despite these potential benefits, substantial challenges remain. Bridging the gap between the development of complex AI algorithms and implementation into clinical practice requires ongoing research and development. This is essential to optimize the reliability of these systems and ensure they are accessible to healthcare providers and patients, who are the ultimate beneficiaries of this technological advancement. This article reviews the benefits of combined AI-DBT imaging, particularly the ability of AI to enhance the benefits of DBT and reduce its existing limitations.

数字乳房断层合成和人工智能在乳腺癌诊断中的变革力量。
数字乳腺断层合成(DBT)和人工智能(AI)的结合代表了乳腺癌筛查的重大进展。这种组合旨在解决传统筛查中固有的几个挑战,同时有望在多个方面改善医疗保健服务。对于患者来说,这种技术协同作用有可能减少不必要的召回和相关程序(如活检)的数量,从而减少患者的焦虑,在不影响诊断准确性的情况下改善整体体验。对于放射科医生来说,结合使用人工智能和DBT可以通过有效地突出乳房成像异常来显着减少工作量和减轻疲劳,这在大容量临床环境中尤其有益。卫生系统将从简化的工作流程和促进DBT的部署中获益,这在缺乏专业乳腺放射科医生的地区尤其有价值。然而,尽管有这些潜在的好处,实质性的挑战仍然存在。弥合复杂人工智能算法的开发与临床实践之间的差距需要持续的研究和开发。这对于优化这些系统的可靠性并确保医疗保健提供者和患者可以访问这些系统至关重要,他们是这项技术进步的最终受益者。本文综述了人工智能-DBT联合成像的优点,特别是人工智能增强DBT优点并减少其现有局限性的能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
6.20
自引率
12.90%
发文量
98
审稿时长
6-12 weeks
期刊介绍: The Canadian Association of Radiologists Journal is a peer-reviewed, Medline-indexed publication that presents a broad scientific review of radiology in Canada. The Journal covers such topics as abdominal imaging, cardiovascular radiology, computed tomography, continuing professional development, education and training, gastrointestinal radiology, health policy and practice, magnetic resonance imaging, musculoskeletal radiology, neuroradiology, nuclear medicine, pediatric radiology, radiology history, radiology practice guidelines and advisories, thoracic and cardiac imaging, trauma and emergency room imaging, ultrasonography, and vascular and interventional radiology. Article types considered for publication include original research articles, critically appraised topics, review articles, guest editorials, pictorial essays, technical notes, and letter to the Editor.
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