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Variability: Analysis

Variability: Analysis

Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
The range is a simple measure of variability, indicating the difference between the highest and...

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Comparing percent breast density assessments of an AI-based method with expert reader estimates: inter-observer

Stepan Romanov¹, Sacha Howell², Elaine Harkness¹

¹University of Manchester, Manchester, United Kingdom.

Journal of Medical Imaging (Bellingham, Wash.)

|June 16, 2025

Summary

This summary is machine-generated.

Automated breast density assessment using artificial intelligence (AI) shows improved inter-observer agreement compared to human experts. This AI tool provides consistent results without affecting breast cancer risk prediction accuracy.

Keywords:

breast breast density deep learning inter-observer variability mammography reader bias

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Published on: August 30, 2013

Area of Science:

Radiology
Medical Imaging
Artificial Intelligence in Medicine

Background:

Breast density is a key factor in breast cancer risk assessment.
Manual assessment of mammographic density by experts shows significant inter-observer variability.
Automated methods are being developed to improve consistency and accuracy in breast density estimation.

Purpose of the Study:

To investigate the inter-reader variability between expert assessors and a deep learning approach for breast density estimation.
To compare the risk prediction capabilities of expert readers and an automated deep learning model.

Main Methods:

Utilized screening data from a cohort of 1328 women.
Compared two expert readers and a single reader against the Manchester artificial intelligence - visual analog scale (MAI-VAS) deep learning model.
Employed Bland-Altman analysis for variability assessment and matched concordance index for risk prediction.

Main Results:

The MAI-VAS deep learning model demonstrated substantially lower limits of agreement (SD ±21) compared to two expert readers (SD ±31).
Inter-observer agreement for the AI tool with a single expert was comparable to that between two experts.
Breast cancer risk discrimination by the deep learning method was similar to that of a single expert (concordance 0.628 vs. 0.624).

Conclusions:

The artificial intelligence breast density assessment tool MAI-VAS exhibits superior inter-observer agreement compared to the agreement between two human experts.
Deep learning-based methods for breast density assessment offer consistent scores without compromising breast cancer risk prediction.