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Assessing diagnostic complexity: An image feature-based strategy to reduce annotation costs.

Jose R Zamacona1, Ronald Niehaus1, Alexander Rasin1

  • 1School of Computing, DePaul University, Chicago, USA.

Computers in Biology and Medicine
|February 26, 2015
PubMed
Summary
This summary is machine-generated.

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This study introduces a method to classify diagnostic complexity in CT scans, helping radiologists manage workload. By identifying easy versus hard cases, costs can be reduced with minimal impact on accuracy.

Area of Science:

  • Medical Imaging
  • Radiology
  • Artificial Intelligence in Medicine

Background:

  • Computer-aided diagnosis (CAD) systems aid radiologists by analyzing medical images.
  • Efficient management of radiologist time and diagnostic complexity is crucial for cost reduction and workload optimization.
  • Current CAD systems require further development to effectively manage radiologist time and diagnostic complexity.

Purpose of the Study:

  • To develop strategies for managing clinical radiologists' limited time using predictive model diagnosis.
  • To introduce a metric for discriminating diagnostic complexity in CT scans (easy vs. hard).
  • To demonstrate how classifying diagnostic complexity can optimize radiologist case allocation.

Main Methods:

  • A metric was developed to categorize diagnostic complexity of CT scans.
Keywords:
Computer-aided diagnosisImage classificationResource allocation

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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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Published on: October 11, 2018

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  • Diagnostic complexity was learned using a classification approach with low-level image features.
  • A lung nodule image dataset was used to evaluate the proposed method.

    • Main Results:

    • A simple division of cases into hard and easy to diagnose significantly lowered interpretation costs.
    • This approach resulted in limited loss in prediction accuracy.
    • A significant subset (66%) of lung nodule image data could be classified using only 18% of the original low-level image features.

    Conclusions:

    • Classifying diagnostic complexity is an effective strategy for managing radiologist workload and reducing costs.
    • Low-level image features can accurately predict diagnostic complexity for a substantial portion of medical image data.
    • This method offers a practical approach to optimize resource allocation in radiological diagnosis.