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Performance changes due to differences among annotating radiologists for training data in computerized lesion

Yukihiro Nomura1,2, Shouhei Hanaoka3,4, Naoto Hayashi5

  • 1Center for Frontier Medical Engineering, Chiba University, 1-33 Yayoi-cho, Inage-ku, Chiba, 263-8522, Japan. ynomura@chiba-u.jp.

International Journal of Computer Assisted Radiology and Surgery
|April 16, 2024
PubMed
Summary
This summary is machine-generated.

Radiologist experience did not correlate with annotation variability affecting computer-aided detection (CAD) software performance. Retraining CAD with integrated annotations showed varied results depending on the software, impacting diagnostic accuracy.

Keywords:
AnnotationComputer-aided detection (CAD)Machine learningRetraining

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Area of Science:

  • Medical imaging analysis
  • Machine learning in healthcare
  • Radiology informatics

Background:

  • Annotation quality by radiologists significantly impacts machine learning-based computer-aided detection (CAD) software performance.
  • Radiologist experience in image interpretation is a potential source of annotation variability.

Purpose of the Study:

  • To investigate how varying radiologist experience influences annotation variability.
  • To assess the impact of retraining CAD software with annotations from radiologists of different experience levels on its performance.
  • To evaluate the effect of integrated annotations from multiple radiologists on CAD software performance.

Main Methods:

  • Utilized two CAD software types for lung nodule and cerebral aneurysm detection.
  • Twelve radiologists with diverse experience levels independently annotated lesions.
  • Investigated performance changes through repeated CAD software retraining with individual and integrated radiologist annotations.

Main Results:

  • CAD software performance varied significantly after retraining with different radiologists' annotations.
  • In some instances, retraining led to degraded software performance compared to the initial version.
  • Integrated annotations showed varied performance trends based on CAD software type, with decreased performance in cerebral aneurysm detection compared to single-annotator use.

Conclusions:

  • No direct correlation was found between radiologist experience and annotation variability affecting CAD performance.
  • The performance impact of retraining with integrated annotations differed based on the specific CAD software.
  • Annotation variability among radiologists can influence CAD software performance, necessitating careful consideration during retraining and development.