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In this study, the authors sought to investigate the feasibility of a deep learning-based detection (DLD) system for multiclass legions on chest radiograph. The study included almost 16,000 chest radiographs collected from two tertiary hospitals, which were then used to develop a DLD system. The authors found that the DLD system showed potential for detections of lesions and pattern classification.

Key points

  • The DLD system was feasible for detection with pattern classification of multiclass lesions on chest radiograph.
  • The DLD system had high performance of image-wise classification as normal or abnormal chest radiographs (AUROC, 0.985) and showed especially high specificity (99.0%).
  • In lesion-wise detection of multiclass lesions, the DLD system outperformed all 9 observers (FOM, 0.962 vs. 0.886; p < 0.001).

Article: Deep learning-based detection system for multiclass lesions on chest radiographs: comparison with observer readings

Authors: Sohee Park, Sang Min Lee, Kyung Hee Lee, Kyu-Hwan Jung, Woong Bae, Jooae Choe, Joon Beom Seo

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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.