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Brain MRI Deep Learning and Bayesian Inference System Augments Radiology Resident Performance.

Jeffrey D Rudie1,2, Jeffrey Duda3, Michael Tran Duong3

  • 1Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, PA, Philadelphia, USA. Jeff.Rudie@gmail.com.

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Artificial intelligence (AI) systems for brain MRI diagnosis can enhance radiologist performance. This AI-powered clinical decision support tool significantly improved diagnostic accuracy for radiology residents, especially for rare diseases.

Keywords:
Artificial intelligenceAugmented performanceBayesian inferenceBrain MRIClinical decision supportConvolutional neural networksDeep learningNeuroradiologyU-Net

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Area of Science:

  • Medical imaging analysis
  • Artificial intelligence in radiology
  • Neuroradiology

Background:

  • Automated quantitative and probabilistic medical image analysis offers potential improvements in radiology workflow accuracy and efficiency.
  • Artificial intelligence (AI) systems combining deep learning and Bayesian networks can distinguish between 50 diagnostic entities on brain MRIs.

Purpose of the Study:

  • To evaluate if AI systems can serve as a clinical decision support tool to augment radiologist performance in brain MRI diagnosis.
  • To assess the impact of an AI-assisted tool on diagnostic accuracy for various levels of radiology expertise.

Main Methods:

  • Utilized AI systems integrating convolutional neural networks and Bayesian networks for brain MRI analysis.
  • Tested AI system performance using the Adaptive Radiology Interpretation and Education System (ARIES) in 194 cases.
  • Compared diagnostic accuracy (Top Diagnosis and Top Three Differential Diagnosis) of radiologists with and without AI assistance.

Main Results:

  • Radiology residents showed significant improvement with ARIES: Top Diagnosis (55% vs. 30%) and Top Three Differential Diagnosis (79% vs. 52%).
  • AI assistance led to the largest improvement in diagnostic accuracy for rare diseases (39% increase in T3DDx for residents).
  • Attending neuroradiologists' performance showed no significant difference with or without ARIES assistance.

Conclusions:

  • A hybrid AI system (deep learning and Bayesian inference) can augment the diagnostic accuracy of non-specialists in brain MRI interpretation.
  • AI-assisted decision support has the potential to elevate non-specialist diagnostic performance to near subspecialist levels for a wide range of conditions.
  • The study highlights the potential of AI as a valuable tool for enhancing diagnostic capabilities in radiology, particularly for less experienced practitioners.