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Possible Bias in Supervised Deep Learning Algorithms for CT Lung Nodule Detection and Classification.

Nikos Sourlos1, Jingxuan Wang1, Yeshaswini Nagaraj2

  • 1Department of Radiology, University Medical Center of Groningen, 9713 GZ Groningen, The Netherlands.

Cancers
|August 26, 2022
PubMed
Summary

Artificial Intelligence (AI) algorithms for lung nodule detection in chest CT scans show promise but face implementation challenges due to biases. Addressing these biases is crucial for clinical integration, though complete mitigation remains difficult.

Keywords:
AIbiaschest CTclassificationdeep learningdetectionlung cancerpulmonary nodulesvalidation

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Area of Science:

  • Radiology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Artificial Intelligence (AI) algorithms are developed for lung nodule detection and classification in chest CT scans.
  • Despite numerous AI developments, clinical workflow implementation is limited.
  • False-positive findings and algorithmic bias are key barriers to adoption.

Purpose of the Study:

  • To review and discuss different types of biases present in AI algorithms for chest CT lung nodule detection and classification.
  • To present examples from existing literature illustrating these biases.
  • To explore potential mitigation strategies for identified biases.

Main Methods:

  • Literature review of AI algorithms for chest CT lung nodule detection and classification.
  • Categorization and discussion of various bias types.
  • Analysis of case studies demonstrating bias occurrence.
  • Identification of bias mitigation techniques.

Main Results:

  • Several types of biases can manifest in AI algorithms for lung nodule detection and classification.
  • Examples of bias in the literature are provided.
  • Methods for mitigating these biases are discussed.

Conclusions:

  • Bias is an inherent challenge in AI for chest CT lung nodule analysis.
  • Complete mitigation of these biases is extremely difficult, if not impossible.
  • Further research is needed to develop robust and unbiased AI tools for clinical use.