Impact of AI-Assisted Mammography Reading on Quality Indicators in the Czech Breast Cancer Screening Programme: A Retrospective Study

  1. Lucia Veverková
  2. Zuzana Doležalová
  3. Veronika Maráčková
  4. Eva Mathew
  5. Markéta Urbánková
  6. Monika Ambrožová
  7. Tomáš Piskovský
  8. Ondřej Ngo
  9. Ondřej Májek
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Abstract

Objectives

The aim of mammographic screening is the early detection of invasive cancers. In the era of artificial intelligence (AI), this tool may improve diagnosis of earlier stages. The purpose of this study was to assess the impact on selected quality indicators retrospectively.

Method

The data source was the Breast Cancer Screening Registry using data from one Screening Unit that currently uses AI routinely. The indicators of the cancer detection rate (CDR), further assessment rate (FAR), and recall rate (RR) in the year 2023, when AI was used, and the year 2022, without AI, in women aged 45-69 were compared. The statistical evaluation used the chi-square test and logistic regression adjusting for the effects of age, a woman’s risk level, and the screening round at a 5% significance level.

Results

In 2022, without AI, 4,034 women aged 45–69 were included, compared with 4,049 women in 2023 when AI was used. This study showed a non-significant increase in CDR from 5.0 breast cancers detected per 1,000 women (non-AI assessment) to 5.2 (AI-assisted assessment), p = 0.919; OR (95% CI): 1.034 (0.542–1.974), a significant decrease in the FAR from 5.2% to 3.9%, p < 0.001; OR (95% CI): 0.665 (0.529–0.836), and a decrease in RR from 2.4% to 1.9%, p = 0.083; OR (95% CI): 0.754 (0.548–1.037).

Conclusion

AI has the potential to be a useful tool in the early detection of breast cancer by improving quality through a decrease in FAR and RR, while probably maintaining CDR.

Key points and Clinical Relevance Statement

Question

What is the impact of AI-assissted reading of screening mammography on quality indicators in breast cancer screening programme?

Findings

Results of this study shows that using of AI-assisted reading of screening mammography in real clinical practice contributes to reducing further assessment rate and recall rate, while cancer detection rate remains most likely unchanged.

Clinical Relevance Statement

AI-assissted reading is an advanced option how to improve the sensitivity and specificity of screening mammography in real-world clinical practice.

Article activity feed

  1. Version published to 10.64898/2026.05.25.26353869 on medRxiv