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Deep Learning-Based Interpretable AI for Prostate T2W MRI Quality Evaluation.

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This study developed an AI tool for consistent T2W prostate MRI quality assessment, achieving 84.7% accuracy in identifying suboptimal scans. This AI can help identify MRI sequences needing re-acquisition, improving diagnostic accuracy.

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Area of Science:

  • Radiology
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Prostate MRI quality assessment is subjective and varies between readers.
  • Quality degradation impacts diagnostic accuracy, necessitating objective evaluation methods.
  • Sequence-specific quality assessment can improve outcomes for prostate MRI.

Purpose of the Study:

  • To develop an AI tool for consistent evaluation of T2W prostate MRI quality.
  • To identify suboptimal MRI scans efficiently and minimize user bias.
  • To provide voxel-level quality heatmaps for enhanced interpretation.

Main Methods:

  • Retrospective study of 1046 patients with T2W MRIs.
  • AI classification model based on 3D DenseNet121 architecture using MONAI.
  • Multiclass (0, 1, 2) and binary (0/1 vs. 2) classification with expert reader scoring and radiologist reproducibility assessment.

Main Results:

  • AI achieved 73.9% multiclass and 84.7% binary classification accuracy on the test dataset.
  • AI showed substantial agreement (κ=0.704) with ground truth quality assessments.
  • AI identified obscuration of the rectoprostatic space as a critical non-diagnostic feature.

Conclusions:

  • A 3D AI model can assess T2W prostate MRI quality with moderate accuracy.
  • AI-generated quality heatmaps aid in interpreting image quality issues.
  • AI offers reproducible identification of MRI sequences requiring re-acquisition, improving downstream cancer detection.