Objective: The objective of our study was to compare acquisition times and interpretation times of screening examinations using screen-film mammography and soft-copy digital mammography.
Materials And Methods: Technologist study acquisition time from examination initiation to release of the screenee was measured for both screen-film and digital mammography (100 cases each) in routine clinical practice. The total interpretation time for screening mammography was also measured for 183 hard-copy screen-film cases and 181 soft-copy digital cases interpreted by a total of seven breast imaging radiologists, four experienced breast imagers, and three breast imaging fellows.
Results: Screening mammography acquisition time averaged 21.6 minutes for screen-film and 14.1 minutes for digital, a highly significant 35% shorter time for digital than screen-film (p < 10(-17)). The average number of images per case acquired with digital mammography was higher than that for screen-film mammography (4.23 for screen-film, 4.50 for digital; p = 0.047). The total interpretation time averaged 1.4 minutes for screen-film mammography and 2.3 minutes for digital mammography, a highly significant 57% longer interpretation time for digital (p < 10(-11)). In addition, technical problems delaying interpretation were encountered in none of the 183 screen-film cases but occurred in nine (5%) of the 181 digital cases.
Conclusion: Compared with screen-film mammography, the use of digital mammography for screening examinations significantly shortened acquisition time but significantly increased interpretation time. In addition, more technical problems were encountered that delayed the interpretation of digital cases.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.2214/AJR.05.1397 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Diversity, inclusivity and traceability of mammography datasets used in development of Artificial Intelligence technologies: a systematic review.
Clin Imaging
February 2025
University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK; Institute of Inflammation and Ageing, University of Birmingham, Birmingham, UK; National Institute for Health and Care Research Birmingham Biomedical Research Centre, University of Birmingham, Birmingham, UK; Birmingham Health Partners Centre for Regulatory Science and Innovation, University of Birmingham, Birmingham, UK. Electronic address:
Article Synopsis
- This review analyzes various mammography datasets used for AI development in breast cancer screening, focusing on their transparency, content, and accessibility.
- A search identified 254 datasets, with only 28 being accessible; most datasets came from Europe, East Asia, and North America, raising concerns over poor demographic representation.
- The findings highlight significant gaps in diversity within these datasets, underscoring the need for better documentation and inclusivity to enhance the effectiveness of AI technologies in breast cancer research.
Microcalcifications in benign breast biopsies: association with lesion type and risk.
Breast Cancer Res Treat
December 2024
Quantitative Health Sciences, Mayo Clinic, Jacksonville, FL, USA.
Article Synopsis
- The study aimed to understand the link between microcalcifications in benign breast disease (BBD) and the risk of developing ductal carcinoma in situ (DCIS) or invasive breast cancer (IBC).
- Researchers analyzed data from nearly 5,000 BBD biopsies and used statistical models to assess the impact of microcalcifications on cancer risk.
- Findings showed that while microcalcifications were more common in older patients, they did not significantly increase the risk of developing DCIS or IBC compared to those without calcifications.
Revolutionizing Breast Cancer Detection With Artificial Intelligence (AI) in Radiology and Radiation Oncology: A Systematic Review.
Cureus
April 2024
Trauma Surgery, Order of St. Francis (OSF) St Francis Medical Centre, University of Illinois Chicago, Peoria, USA.
The number one cause of cancer in women worldwide is breast cancer. Over the last three decades, the use of traditional screen-film mammography has increased, but in recent years, digital mammography and 3D tomosynthesis have become standard procedures for breast cancer screening. With the advancement of technology, the interpretation of images using automated algorithms has become a subject of interest.
View Article and Find Full Text PDFDeep learning-based breast region segmentation in raw and processed digital mammograms: generalization across views and vendors.
J Med Imaging (Bellingham)
January 2024
Radboud University Medical Center, Department of Medical Imaging, Nijmegen, The Netherlands.
Purpose: We developed a segmentation method suited for both raw (for processing) and processed (for presentation) digital mammograms (DMs) that is designed to generalize across images acquired with systems from different vendors and across the two standard screening views.
Approach: A U-Net was trained to segment mammograms into background, breast, and pectoral muscle. Eight different datasets, including two previously published public sets and six sets of DMs from as many different vendors, were used, totaling 322 screen film mammograms (SFMs) and 4251 DMs (2821 raw/processed pairs and 1430 only processed) from 1077 different women.
Relationship between Race and Access to Newer Mammographic Technology in Women with Medicare Insurance.
Radiology
February 2023
From the Neiman Health Policy Institute, 1891 Preston White Dr, Reston, VA 20191 (E.W.C., M.W., E.Y.R.); Health Services Management, University of Minnesota, St Paul, Minn (E.W.C.); Department of Radiology, SUNY Downstate Health Sciences University, Brooklyn, NY (J.S.); Department of Radiology, Mayo Clinic Arizona, Phoenix, Ariz (B.K.P.); and Department of Radiology, Albert Einstein College of Medicine, Montefiore Medical Center, Bronx, NY (J.A.B.).
Background Racial disparities in breast cancer mortality have been reported. Mammographic technology has undergone two major technology transitions since 2000: first, the transition from screen-film mammography (SFM) to full-field digital mammography (FFDM) and second, the transition to digital breast tomosynthesis (DBT). Purpose To examine the relationship between use of newer mammographic technology and race in women receiving mammography services.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!