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Related Experiment Video

Updated: May 8, 2026

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

Deep Learning Algorithms Versus Radiologists in Digital Breast Tomosynthesis for Breast Cancer Detection: Systematic

Shewen Lyu1, Zepeng Wang1, Yujing Mu1

  • 1Beijing University of Chinese Medicine Third Affiliated Hospital, 51 Xiaoguan Street, Andingmenwai, Chaoyang District, Beijing, 100029, China, 86 13911683278.

Journal of Medical Internet Research
|May 6, 2026
PubMed
Summary

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This summary is machine-generated.

Deep learning (DL) algorithms for digital breast tomosynthesis (DBT) show strong diagnostic performance, exceeding junior radiologists' sensitivity. However, DL assistance did not improve overall radiologist performance, indicating its current role as a supplementary tool.

Area of Science:

  • Radiology
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Deep learning (DL) algorithms are increasingly used for digital breast tomosynthesis (DBT).
  • These algorithms show potential in improving lesion detection and classification in mammography.
  • The clinical utility of DL in DBT requires further investigation.

Purpose of the Study:

  • To compare the diagnostic performance of DL algorithms for DBT against radiologists with varying experience levels.
  • To assess the impact of DL assistance on radiologist diagnostic performance.
  • To evaluate the clinical utility of DL in DBT interpretation.

Main Methods:

  • Systematic literature search of major databases (PubMed, Embase, Web of Science, Cochrane Library) up to November 2025.
Keywords:
AIartificial intelligencebreast neoplasmsdeep learningdiagnostic accuracydigital breast tomosynthesismeta-analysis

Related Experiment Videos

Last Updated: May 8, 2026

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

  • Included studies compared stand-alone DL, radiologist interpretation, and DL-assisted diagnosis for DBT.
  • Study quality assessed using PROBAST+AI; performance metrics pooled using meta-analysis.

    • Main Results:

    • 13 studies with 38,565 patients analyzed. Stand-alone DL achieved pooled sensitivity 0.88, specificity 0.74, AUC 0.89.
    • DL performance was comparable to all and senior radiologists but showed significantly higher sensitivity than junior radiologists.
    • DL assistance did not significantly improve diagnostic metrics for radiologists at any experience level.

    Conclusions:

    • DL algorithms demonstrate strong diagnostic proficiency in DBT, particularly in enhancing sensitivity compared to junior radiologists.
    • DL tools may serve as valuable adjunctive aids, especially in less experienced settings, to reduce oversight.
    • Current DL models function primarily as supplementary aids; further prospective studies are needed for optimal clinical integration.