Conclusion:

Notably, human inter-reader disagreement of PGMI assessment in screening mammography is substantially high. The results emphasize the necessity for further rethinking the assessment of both individual quality features and overall image quality.

AI software may reliably categorize such quality. This gives the potential for objective standardization, comprehensive long-term monitoring but also immediate feedback that will help radiographers achieve and maintain the required high level of quality in screening programs. Consideration and evaluation should be given to detailed functions, possible combinations with other radiological AI solutions, and practical implementation in given workflows.

Limitations:

No limitations were identified.

Funding for this study:

No funding was received for this study.

Conflict of interest:

Tina Santner, Stefano Gianolini, Johanne-Gro Stalheim, Stephanie Frei, Michaela Hondl, Vanessa Fröhlich, Solveig Hofvind, Gerlig Widmann – nothing to disclose. Carlotta Ruppert - employed at b-rayZ AG.

Ethics approval:

The study was approved by the ethics commission of the Medical University of Innsbruck (reference number 1321/2021).