Vivianne Freitas, Sandeep Ghai, Frederick Au, Derek Muradali, Supriya Kulkarni
{"title":"The Transformative Power of Digital Breast Tomosynthesis and Artificial Intelligence in Breast Cancer Diagnosis.","authors":"Vivianne Freitas, Sandeep Ghai, Frederick Au, Derek Muradali, Supriya Kulkarni","doi":"10.1177/08465371241301957","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p>","PeriodicalId":55290,"journal":{"name":"Canadian Association of Radiologists Journal-Journal De L Association Canadienne Des Radiologistes","volume":" ","pages":"8465371241301957"},"PeriodicalIF":2.9000,"publicationDate":"2024-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Canadian Association of Radiologists Journal-Journal De L Association Canadienne Des Radiologistes","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1177/08465371241301957","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING","Score":null,"Total":0}
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.
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.