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AI lesion risk score at different exposure settings

Tingberg, Anders LU orcid ; Dahlblom, Victor LU orcid ; Bakic, Predrag LU ; Schurz, Haiko ; Strand, Fredrik ; Zackrisson, Sophia LU and Dustler, Magnus LU (2024) 17th International Workshop on Breast Imaging, IWBI 2024 In Proceedings of SPIE - The International Society for Optical Engineering 13174.
Abstract

Purpose: The purpose of this study was to investigate whether the lesion risk score provided by an AI system is influenced by the selection of exposure parameters. Methods: A breast phantom which contains a lesion, was imaged with digital mammography with different imaging conditions. The tube voltage, the dose level and the anode-filter combination were varied based on an exposure obtained with automatic exposure control. The organ dose for each image was extracted from the DICOM header. The images were analyzed with an AI system, which provided a lesion risk score (suspicion for malignancy) for each exposure condition. Correlations between the lesion risk score and the exposure conditions were investigated. Results: The results of the... (More)

Purpose: The purpose of this study was to investigate whether the lesion risk score provided by an AI system is influenced by the selection of exposure parameters. Methods: A breast phantom which contains a lesion, was imaged with digital mammography with different imaging conditions. The tube voltage, the dose level and the anode-filter combination were varied based on an exposure obtained with automatic exposure control. The organ dose for each image was extracted from the DICOM header. The images were analyzed with an AI system, which provided a lesion risk score (suspicion for malignancy) for each exposure condition. Correlations between the lesion risk score and the exposure conditions were investigated. Results: The results of the study showed that the organ dose had a strong impact on the lesion risk score. Reducing the organ dose to a low level resulted in that the AI system no longer detected the lesion. Conclusions: Images of suboptimal quality may result in inaccurate AI system performance. In our preliminary analysis, the breast phantom and the lesion were proven to be realistic enough for being analyzed by the AI system.

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author
; ; ; ; ; and
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
Artificial intelligence, Breast organ dose, Breast phantom, Digital mammography, Image quality, Suspicion for malignancy
host publication
17th International Workshop on Breast Imaging, IWBI 2024
series title
Proceedings of SPIE - The International Society for Optical Engineering
editor
Giger, Maryellen L. ; Whitney, Heather M. ; Drukker, Karen and Li, Hui
volume
13174
article number
131741G
publisher
SPIE
conference name
17th International Workshop on Breast Imaging, IWBI 2024
conference location
Chicago, United States
conference dates
2024-06-09 - 2024-06-12
external identifiers
  • scopus:85195413668
ISSN
0277-786X
1996-756X
ISBN
9781510680203
DOI
10.1117/12.3026976
language
English
LU publication?
yes
id
b8922b3c-ee64-4c0e-9bfc-7cfb3fa15922
date added to LUP
2024-09-30 10:13:50
date last changed
2024-09-30 10:43:39
@inproceedings{b8922b3c-ee64-4c0e-9bfc-7cfb3fa15922,
  abstract     = {{<p>Purpose: The purpose of this study was to investigate whether the lesion risk score provided by an AI system is influenced by the selection of exposure parameters. Methods: A breast phantom which contains a lesion, was imaged with digital mammography with different imaging conditions. The tube voltage, the dose level and the anode-filter combination were varied based on an exposure obtained with automatic exposure control. The organ dose for each image was extracted from the DICOM header. The images were analyzed with an AI system, which provided a lesion risk score (suspicion for malignancy) for each exposure condition. Correlations between the lesion risk score and the exposure conditions were investigated. Results: The results of the study showed that the organ dose had a strong impact on the lesion risk score. Reducing the organ dose to a low level resulted in that the AI system no longer detected the lesion. Conclusions: Images of suboptimal quality may result in inaccurate AI system performance. In our preliminary analysis, the breast phantom and the lesion were proven to be realistic enough for being analyzed by the AI system.</p>}},
  author       = {{Tingberg, Anders and Dahlblom, Victor and Bakic, Predrag and Schurz, Haiko and Strand, Fredrik and Zackrisson, Sophia and Dustler, Magnus}},
  booktitle    = {{17th International Workshop on Breast Imaging, IWBI 2024}},
  editor       = {{Giger, Maryellen L. and Whitney, Heather M. and Drukker, Karen and Li, Hui}},
  isbn         = {{9781510680203}},
  issn         = {{0277-786X}},
  keywords     = {{Artificial intelligence; Breast organ dose; Breast phantom; Digital mammography; Image quality; Suspicion for malignancy}},
  language     = {{eng}},
  publisher    = {{SPIE}},
  series       = {{Proceedings of SPIE - The International Society for Optical Engineering}},
  title        = {{AI lesion risk score at different exposure settings}},
  url          = {{http://dx.doi.org/10.1117/12.3026976}},
  doi          = {{10.1117/12.3026976}},
  volume       = {{13174}},
  year         = {{2024}},
}