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The purpose of this study was to evaluate the calibration of a deep learning (DL) model in a diagnostic cohort, as well as to improve the model’s calibration through recalibration procedures. The authors found that the calibration of the DL algorithm can be augmented through simple recalibration procedures, and improved calibration may enhance the interpretability and credibility of the model for users.

Key points

  • A deep learning model tended to overestimate the likelihood of the presence of abnormalities in chest radiographs.
  • Simple recalibration of the deep learning model using output scores could improve the calibration of model while maintaining discrimination.
  • Improved calibration of a deep learning model may enhance the interpretability and the credibility of the model for users.

Article: Automated identification of chest radiographs with referable abnormality with deep learning: need for recalibration

Authors: Eui Jin Hwang, Hyungjin Kim, Jong Hyuk Lee, Jin Mo Goo & Chang Min Park

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Welcome to the blog on Artificial Intelligence of the European Society of Radiology

This blog aims at bringing educational and critical perspectives on AI to readers. It should help imaging professionals to learn and keep up to date with the technologies being developed in this rapidly evolving field.