To evaluate the calibration of a deep learning (DL) model in a diagnostic cohort and to improve model’s calibration through recalibration procedures.
Chest radiographs (CRs) from 1135 consecutive patients (M:F = 582:553; mean age, 52.6 years) who visited our emergency department were included. A commercialized DL model was utilized to identify abnormal CRs, with a continuous probability score for each CR. After evaluation of the model calibration, eight different methods were used to recalibrate the original model based on the probability score. The original model outputs were recalibrated using 681 randomly sampled CRs and validated using the remaining 454 CRs. The Brier score for overall performance, average and maximum calibration error, absolute Spiegelhalter’s Z for calibration, and area under the receiver operating characteristic curve (AUROC) for discrimination were evaluated in 1000-times repeated, randomly split datasets.
The original model tended to overestimate the likelihood for the presence of abnormalities, exhibiting average and maximum calibration error of 0.069 and 0.179, respectively; an absolute Spiegelhalter’s Z value of 2.349; and an AUROC of 0.949. After recalibration, significant improvements in the average (range, 0.015–0.036) and maximum (range, 0.057–0.172) calibration errors were observed in eight and five methods, respectively. Significant improvement in absolute Spiegelhalter’s Z (range, 0.809–4.439) was observed in only one method (the recalibration constant). Discriminations were preserved in six methods (AUROC, 0.909–0.949).
The calibration of DL algorithm can be augmented through simple recalibration procedures. Improved calibration may enhance the interpretability and credibility of the model for users.
• 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.
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Area under the receiver operating characteristic curve
Negative predictive value
Positive predictive value
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This study has received funding from the Basic Science Research Program through the National Research Foundation of Korea (NRF), funded by the Ministry of Science, ICT & Future Planning (grant number: 2019R1A2C1087960), the Seoul National University Hospital Research fund (grant number: 03-2019-0190), and the Seoul Research & Business Development Program (grant number: FI170002). The funding sources and Lunit Inc. did not have any role either in the design of the study; the acquisition, analyses, and interpretation of the data; and manuscript preparation.
The scientific guarantor of this publication is Chang Min Park.
Conflict of interest
The authors of this manuscript declare relationships with Lunit Inc.
Eui Jin Hwang, Hyungjin Kim, Jin Mo Goo, and Chang Min Park report grants from the Lunit Inc., outside the present study.
Statistics and biometry
No complex statistical methods were necessary for this paper.
Written informed consent was waived by the institutional review board.
Institutional review board approval was obtained.
Study subjects or cohorts overlap
The study cohort in the present study (1135/1135) have been reported in a previous study (Hwang EJ, et al Radiology 293(3):573–580); however, the purpose of the previous study was to evaluate the discrimination of a DL algorithm and its potential to improve physicians’ diagnostic performances, which was entirely different from that of the present study.
• diagnostic or prognostic study
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Hwang, E.J., Kim, H., Lee, J.H. et al. Automated identification of chest radiographs with referable abnormality with deep learning: need for recalibration. Eur Radiol 30, 6902–6912 (2020). https://doi.org/10.1007/s00330-020-07062-7
- Thoracic radiography
- Artificial intelligence
- Deep learning