Automatic and standardized quality assurance of digital mammography and tomosynthesis with deep convolutional neural networks

Patryk Hejduk; Raphael Sexauer; Carlotta Ruppert; Karol Borkowski; Jan Unkelbach; Noemi Schmidt

doi:10.1186/s13244-023-01396-8

Automatic and standardized quality assurance of digital mammography and tomosynthesis with deep convolutional neural networks

Insights Imaging. 2023 May 18;14(1):90. doi: 10.1186/s13244-023-01396-8.

Authors

Patryk Hejduk¹, Raphael Sexauer², Carlotta Ruppert³, Karol Borkowski³, Jan Unkelbach⁴, Noemi Schmidt²

Affiliations

¹ Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Rämistr. 100, 8091, Zurich, Switzerland. patrykhejduk@gmail.com.
² Breast Imaging, Radiology and Nuclear Medicine, University Hospital Basel, Basel, Switzerland.
³ Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Rämistr. 100, 8091, Zurich, Switzerland.
⁴ Department of Radiation Oncology, University Hospital Zurich, Zurich, Switzerland.

Abstract

Objectives: The aim of this study was to develop and validate a commercially available AI platform for the automatic determination of image quality in mammography and tomosynthesis considering a standardized set of features.

Materials and methods: In this retrospective study, 11,733 mammograms and synthetic 2D reconstructions from tomosynthesis of 4200 patients from two institutions were analyzed by assessing the presence of seven features which impact image quality in regard to breast positioning. Deep learning was applied to train five dCNN models on features detecting the presence of anatomical landmarks and three dCNN models for localization features. The validity of models was assessed by the calculation of the mean squared error in a test dataset and was compared to the reading by experienced radiologists.

Results: Accuracies of the dCNN models ranged between 93.0% for the nipple visualization and 98.5% for the depiction of the pectoralis muscle in the CC view. Calculations based on regression models allow for precise measurements of distances and angles of breast positioning on mammograms and synthetic 2D reconstructions from tomosynthesis. All models showed almost perfect agreement compared to human reading with Cohen's kappa scores above 0.9.

Conclusions: An AI-based quality assessment system using a dCNN allows for precise, consistent and observer-independent rating of digital mammography and synthetic 2D reconstructions from tomosynthesis. Automation and standardization of quality assessment enable real-time feedback to technicians and radiologists that shall reduce a number of inadequate examinations according to PGMI (Perfect, Good, Moderate, Inadequate) criteria, reduce a number of recalls and provide a dependable training platform for inexperienced technicians.

Keywords: Artificial intelligence; Machine learning; Mammography; Quality control; Tomosynthesis.