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Background: <p>Gadolinium-based contrast agents used for brain magnetic resonance imaging improve lesion detection but are associated with acute side effects, concerns about long-term tissue deposition, and economic and environmental costs. Regulatory bodies and radiological societies therefore recommend minimizing their use where possible. This study aims to develop and evaluate a real-time artificial intelligence algorithm, Apollo SmartGAD, that optimizes imaging workflows by recommending contrast-enhanced, reduced-dose contrast-enhanced, or non-enhanced imaging protocols during acquisition.</p> Methods: <p>This retrospective, multicenter study evaluated 1251 adult magnetic resonance imaging examinations (mean age 52.1 ± 20.4 years; 577 females). Apollo SmartGAD analyzes images acquired during scanning and recommends non-enhanced, reduced-dose contrast-enhanced, or standard-dose contrast-enhanced imaging protocols. Performance was assessed in two distinct cohorts: patients undergoing first-time brain assessment and patients undergoing glioma monitoring.</p> Results: <p>Here we show that, in the first-time brain assessment cohort, Apollo SmartGAD identifies examinations not requiring contrast administration with a sensitivity of 73%, a specificity of 79%, and a negative predictive value of 86%. In the glioma monitoring cohort, the algorithm differentiate between non-enhanced, reduced-dose contrast-enhanced, and standard-dose contrast-enhanced protocols with an overall sensitivity of 77% and a specificity of 78%.</p> Conclusions: <p>This study demonstrates that workflow-integrated, real-time artificial intelligence can support contrast administration decisions during brain magnetic resonance imaging. Such decision support may reduce unnecessary contrast use while preserving diagnostic performance, with potential benefits for patient safety, clinical efficiency, and sustainability.</p>

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Leveraging AI for selective use of gadolinium contrast in brain MR

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月30日

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  • Silvia Ingala,
  • Marko Bauer,
  • Ivar I. J. H. G. Wamelink,
  • Mathias Perslev,
  • Kaining Sheng,
  • Mads Birch Sørensen,
  • Sebastian Nørgaard Llambias,
  • Arpit Kothari,
  • Era Dwivedi,
  • Martin Lillholm,
  • Frederik Barkhof,
  • Michael Bachmann Nielsen,
  • Mads Nielsen,
  • Bas M. M. S. Jasperse,
  • Vera C. W. Keil,
  • Akshay Pai

摘要

Background:

Gadolinium-based contrast agents used for brain magnetic resonance imaging improve lesion detection but are associated with acute side effects, concerns about long-term tissue deposition, and economic and environmental costs. Regulatory bodies and radiological societies therefore recommend minimizing their use where possible. This study aims to develop and evaluate a real-time artificial intelligence algorithm, Apollo SmartGAD, that optimizes imaging workflows by recommending contrast-enhanced, reduced-dose contrast-enhanced, or non-enhanced imaging protocols during acquisition.

Methods:

This retrospective, multicenter study evaluated 1251 adult magnetic resonance imaging examinations (mean age 52.1 ± 20.4 years; 577 females). Apollo SmartGAD analyzes images acquired during scanning and recommends non-enhanced, reduced-dose contrast-enhanced, or standard-dose contrast-enhanced imaging protocols. Performance was assessed in two distinct cohorts: patients undergoing first-time brain assessment and patients undergoing glioma monitoring.

Results:

Here we show that, in the first-time brain assessment cohort, Apollo SmartGAD identifies examinations not requiring contrast administration with a sensitivity of 73%, a specificity of 79%, and a negative predictive value of 86%. In the glioma monitoring cohort, the algorithm differentiate between non-enhanced, reduced-dose contrast-enhanced, and standard-dose contrast-enhanced protocols with an overall sensitivity of 77% and a specificity of 78%.

Conclusions:

This study demonstrates that workflow-integrated, real-time artificial intelligence can support contrast administration decisions during brain magnetic resonance imaging. Such decision support may reduce unnecessary contrast use while preserving diagnostic performance, with potential benefits for patient safety, clinical efficiency, and sustainability.