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Workload reduction of digital breast tomosynthesis screening using artificial intelligence and synthetic mammography : a simulation study

Dahlblom, Victor LU orcid ; Dustler, Magnus LU orcid ; Zackrisson, Sophia LU and Tingberg, Anders LU orcid (2025) In Journal of Medical Imaging 12.
Abstract

Purpose: To achieve the high sensitivity of digital breast tomosynthesis (DBT), a time-consuming reading is necessary. However, synthetic mammography (SM) images, equivalent to digital mammography (DM), can be generated from DBT images. SM is faster to read and might be sufficient in many cases. We investigate using artificial intelligence (AI) to stratify examinations into reading of either SM or DBT to minimize workload and maximize accuracy. Approach: This is a retrospective study based on double-read paired DM and one-view DBT from the Malmö Breast Tomosynthesis Screening Trial. DBT examinations were analyzed with the cancer detection AI system ScreenPoint Transpara 1.7. For low-risk examinations, SM reading was simulated by... (More)

Purpose: To achieve the high sensitivity of digital breast tomosynthesis (DBT), a time-consuming reading is necessary. However, synthetic mammography (SM) images, equivalent to digital mammography (DM), can be generated from DBT images. SM is faster to read and might be sufficient in many cases. We investigate using artificial intelligence (AI) to stratify examinations into reading of either SM or DBT to minimize workload and maximize accuracy. Approach: This is a retrospective study based on double-read paired DM and one-view DBT from the Malmö Breast Tomosynthesis Screening Trial. DBT examinations were analyzed with the cancer detection AI system ScreenPoint Transpara 1.7. For low-risk examinations, SM reading was simulated by assuming equality with DM reading. For high-risk examinations, the DBT reading results were used. Different combinations of single and double reading were studied. Results: By double-reading the DBT of 30% (4452/14,772) of the cases with the highest risk, and single-reading SM for the rest, 122 cancers would be detected with the same reading workload as DM double reading. That is 28% (27/95) more cancers would be detected than with DM double reading, and in total, 96% (122/127) of the cancers detectable with full DBT double reading would be found. Conclusions: In a DBT-based screening program, AI could be used to select high-risk cases where the reading of DBT is valuable, whereas SM is sufficient for low-risk cases. Substantially, more cancers could be detected compared with DM only, with only a limited increase in reading workload. Prospective studies are necessary.

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author
; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
artificial intelligence, breast cancer screening, digital breast tomosynthesis, synthetic mammography
in
Journal of Medical Imaging
volume
12
article number
S22005
publisher
SPIE
external identifiers
  • scopus:105015694497
  • pmid:40313361
ISSN
2329-4302
DOI
10.1117/1.JMI.12.S2.S22005
language
English
LU publication?
yes
id
6b781690-924a-4505-954a-b759b436d46d
date added to LUP
2025-10-03 13:18:50
date last changed
2025-10-17 15:12:31
@article{6b781690-924a-4505-954a-b759b436d46d,
  abstract     = {{<p>Purpose: To achieve the high sensitivity of digital breast tomosynthesis (DBT), a time-consuming reading is necessary. However, synthetic mammography (SM) images, equivalent to digital mammography (DM), can be generated from DBT images. SM is faster to read and might be sufficient in many cases. We investigate using artificial intelligence (AI) to stratify examinations into reading of either SM or DBT to minimize workload and maximize accuracy. Approach: This is a retrospective study based on double-read paired DM and one-view DBT from the Malmö Breast Tomosynthesis Screening Trial. DBT examinations were analyzed with the cancer detection AI system ScreenPoint Transpara 1.7. For low-risk examinations, SM reading was simulated by assuming equality with DM reading. For high-risk examinations, the DBT reading results were used. Different combinations of single and double reading were studied. Results: By double-reading the DBT of 30% (4452/14,772) of the cases with the highest risk, and single-reading SM for the rest, 122 cancers would be detected with the same reading workload as DM double reading. That is 28% (27/95) more cancers would be detected than with DM double reading, and in total, 96% (122/127) of the cancers detectable with full DBT double reading would be found. Conclusions: In a DBT-based screening program, AI could be used to select high-risk cases where the reading of DBT is valuable, whereas SM is sufficient for low-risk cases. Substantially, more cancers could be detected compared with DM only, with only a limited increase in reading workload. Prospective studies are necessary.</p>}},
  author       = {{Dahlblom, Victor and Dustler, Magnus and Zackrisson, Sophia and Tingberg, Anders}},
  issn         = {{2329-4302}},
  keywords     = {{artificial intelligence; breast cancer screening; digital breast tomosynthesis; synthetic mammography}},
  language     = {{eng}},
  publisher    = {{SPIE}},
  series       = {{Journal of Medical Imaging}},
  title        = {{Workload reduction of digital breast tomosynthesis screening using artificial intelligence and synthetic mammography : a simulation study}},
  url          = {{http://dx.doi.org/10.1117/1.JMI.12.S2.S22005}},
  doi          = {{10.1117/1.JMI.12.S2.S22005}},
  volume       = {{12}},
  year         = {{2025}},
}