Congress:
ECR24
Poster Number:
C-24676
Type:
EPOS Radiologist (scientific)
Authorblock:
R. Sexauer1, F. Riehle2, N. Schmidt2; 1Liestal/CH, 2Basel/CH
Disclosures:
Raphael Sexauer:
Nothing to disclose
Friederike Riehle:
Nothing to disclose
Noemi Schmidt:
Nothing to disclose
Keywords:
Artificial Intelligence, Breast, Mammography, Technology assessment, Quality assurance
1) Waade GG, Danielsen AS, Holen ÅS, et al (2021) Assessment of breast positioning criteria in mammographic screening: Agreement between artificial intelligence software and radiographers. J Med Screen 28:448–455. https://doi.org/10.1177/0969141321998718
2) Hejduk, P., Sexauer, R., Ruppert, C. et al. (2023) Automatic and standardized quality assurance of digital mammography and tomosynthesis with deep convolutional neural networks. Insights Imaging 14, 90 (2023). https://doi.org/10.1186/s13244-023-01396-8
3) Eby PR, Martis LM, Paluch JT, et al (2023) Impact of Artificial Intelligence–driven Quality Improvement Software on Mammography Technical Repeat and Recall Rates. Radiol Artif Intell 5:e230038. https://doi.org/10.1148/ryai.230038