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Congress: ECR24

Poster Number: C-19422

Type: EPOS Radiologist (scientific)

Authorblock: T. Santner¹, C. Ruppert², S. Gianolini³, J-G. Stalheim⁴, S. Frei⁵, M. Hondl Adametz⁶, V. Fröhlich⁷, S. Hofvind⁸, G. Widmann¹; ¹Innsbruck/AT, ²Zürich/CH, ³Glattpark/CH, ⁴Bergen/NO, ⁵Lausanne/CH, ⁶Vienna/AT, ⁷Wiener Neustadt/AT, ⁸Oslo/NO

Disclosures:

Tina Santner: Nothing to disclose

Carlotta Ruppert: Employee: b-rayZ AG

Stefano Gianolini: Nothing to disclose

Johanne-Gro Stalheim: Nothing to disclose

Stephanie Frei: Nothing to disclose

Michaela Hondl Adametz: Nothing to disclose

Vanessa Fröhlich: Nothing to disclose

Solveig Hofvind: Nothing to disclose

Gerlig Widmann: Nothing to disclose

Keywords: Artificial Intelligence, Breast, Mammography, Screening, Quality assurance

References

[1]

National Health Service Breast Screening Programme (1989): Quality Assurance Guidelines for Mammography, Pritchard Report, Oxford: NHS Breast Screening Programme.

Cited by:

Klabunde, Carrie/Bouchard, Francoise/Taplin, Stephen/Scharpantgen, Astrid/Ballard-Barbash, Rachel (2001): Quality assurance for screening mammography: an international comparison, in: Journal of Epidemiology and Community Health, 55, 204-212.

[2]

Taplin, Stephen H./Rutter, Carolyn M./Finder, Charles/Mandelson, Margaret T./Houn, Florence/White, Emily (2002): Screening Mammography: Clinical Image Quality and the Risk of Interval Breast Cancer, in: American Journal of Radiology, 178, 797-803.

[3]

Waade, Gunvor G/ Danielsen, Anders Skyrud/Holen Åsne S/Larsen, Marthe/Hanestad, Berit/Hopland, Nina-Merete/Kalcheva, Vanya/Hofvind, Solveig (2021): Assessment of breast positioning criteria in mammographic screening: Agreement between artificial intelligence software and radiographers, in: Journal of Medical Screening, 28(4),448-455.

[4]

Hill, Cathy/Robinson, Leslie (2015): Mammography image assessment: validity and reliability of current scheme, in: Radiography, 21, 304-307.

[5]

Boyce, Michelle/Gullien, Randi/Parashar, Deepak/Taylor, Kathryn (2015): Comparing the use and interpretation of PGMI scoring to assess the technical quality of screening mammograms in the UK and Norway, in: Radiography, 21, 342-347.

[6]

Schönenberger, Claudio/Hejduk, Patryk/Ciritsis, Alexander/Marcon, Magda/Rossi, Cristina/Boss, Andreas (2021): Classification of mammographic breast microcalcifications using a deep convolutional neural network: a BI-RADS-based approach, in: Investigative Radiology, 56(4), 224–231.

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