AI-based image quality assessment of positioning in mammography: considerations and challenges.

ABSTRACT:

Objectives

Artificial intelligence (AI) could facilitate and objectify quality assessment in the daily routine. The purpose was to explore the extent to which an AI prototype algorithm is able to replicate the perfect-good-moderate-inadequate (PGMI) system (perfect, good, moderate, inadequate).

Materials and methods

From a multicentre case collection, 200 standard mammograms (800 images) were selected. A deep learning-based prototype software was used to rate the images in analogy to the PGMI system. The AI results were compared with a reference standard obtained through consensus reading by three expert radiographers and one expert radiologist, using quadratically weighted Cohen's kappa with confidence intervals (CI) and context-based interpretation. Frequency and reasons f

SUBMITTER: Santner T 

PROVIDER: S-EPMC12909628 | biostudies-literature | 2026 Feb

REPOSITORIES: biostudies-literature