AI lesion risk score at different exposure settings

Anders Tingberg; Victor Dahlblom; Predrag Bakic; Haiko Schurz; Fredrik Strand; Sophia Zackrisson; Magnus Dustler

doi:10.1117/12.3026976

29 May 2024 AI lesion risk score at different exposure settings

Anders Tingberg, Victor Dahlblom, Predrag Bakic, Haiko Schurz, Fredrik Strand, Sophia Zackrisson, Magnus Dustler

Proceedings Volume 13174, 17th International Workshop on Breast Imaging (IWBI 2024); 131741G (2024) https://doi.org/10.1117/12.3026976
Event: 17th International Workshop on Breast Imaging (IWBI 2024), 2024, Chicago, IL, United States

Abstract

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. 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. 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. 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.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Anders Tingberg, Victor Dahlblom, Predrag Bakic, Haiko Schurz, Fredrik Strand, Sophia Zackrisson, and Magnus Dustler "AI lesion risk score at different exposure settings", Proc. SPIE 13174, 17th International Workshop on Breast Imaging (IWBI 2024), 131741G (29 May 2024); https://doi.org/10.1117/12.3026976

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KEYWORDS

Artificial intelligence

Breast

Image quality

Image analysis

Imaging systems

Mammography

Medicine

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