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Test-retest reproducibility of a deep learning–based automatic detection algorithm for the chest radiograph

Abstract

Objectives

To perform test-retest reproducibility analyses for deep learning–based automatic detection algorithm (DLAD) using two stationary chest radiographs (CRs) with short-term intervals, to analyze influential factors on test-retest variations, and to investigate the robustness of DLAD to simulated post-processing and positional changes.

Methods

This retrospective study included patients with pulmonary nodules resected in 2017. Preoperative CRs without interval changes were used. Test-retest reproducibility was analyzed in terms of median differences of abnormality scores, intraclass correlation coefficients (ICC), and 95% limits of agreement (LoA). Factors associated with test-retest variation were investigated using univariable and multivariable analyses. Shifts in classification between the two CRs were analyzed using pre-determined cutoffs. Radiograph post-processing (blurring and sharpening) and positional changes (translations in x- and y-axes, rotation, and shearing) were simulated and agreement of abnormality scores between the original and simulated CRs was investigated.

Results

Our study analyzed 169 patients (median age, 65 years; 91 men). The median difference of abnormality scores was 1–2% and ICC ranged from 0.83 to 0.90. The 95% LoA was approximately ± 30%. Test-retest variation was negatively associated with solid portion size (β, − 0.50; p = 0.008) and good nodule conspicuity (β, − 0.94; p < 0.001). A small fraction (15/169) showed discordant classifications when the high-specificity cutoff (46%) was applied to the model outputs (p = 0.04). DLAD was robust to the simulated positional change (ICC, 0.984, 0.996), but relatively less robust to post-processing (ICC, 0.872, 0.968).

Conclusions

DLAD was robust to the test-retest variation. However, inconspicuous nodules may cause fluctuations of the model output and subsequent misclassifications.

Key Points

• The deep learning–based automatic detection algorithm was robust to the test-retest variation of the chest radiographs in general.

• The test-retest variation was negatively associated with solid portion size and good nodule conspicuity.

• High-specificity cutoff (46%) resulted in discordant classifications of 8.9% (15/169; p = 0.04) between the test-retest radiographs.

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Abbreviations

CI:

Confidence interval

CR:

Chest radiograph

Diffscore :

Difference of abnormality scores between the test-retest chest radiographs

DLAD:

Deep learning–based automatic detection algorithm

ICC:

Intraclass correlation coefficient

IQR:

Interquartile range

LoA:

Limits of agreement

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Funding

This study was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF), funded by the Ministry of Science, ICT & Future Planning (grant number 2017R1A2B4008517).

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Correspondence to Chang Min Park.

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Guarantor

The scientific guarantor of this publication is Chang Min Park.

Conflict of interest

The authors (H.K., C.M.P., and J.M.G.) received research grants from Lunit Inc. (Seoul, South Korea), which developed the deep learning–based detection algorithm (Lunit INISIGHT for Chest Radiography) used in this study. However, Lunit Inc. had no role in the study design; in the collection, analysis, and interpretation of the data; in the writing of the report; and in the decision to submit the article for publication.

Statistics and biometry

No complex statistical methods were necessary for this paper.

Informed consent

Written informed consent was waived by the Institutional Review Board.

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Institutional Review Board approval was obtained.

Methodology

• retrospective

• diagnostic or prognostic study

• performed at one institution

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Kim, H., Park, C.M. & Goo, J.M. Test-retest reproducibility of a deep learning–based automatic detection algorithm for the chest radiograph. Eur Radiol 30, 2346–2355 (2020). https://doi.org/10.1007/s00330-019-06589-8

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Keywords

  • Radiography
  • Computer-assisted radiographic image interpretation
  • Solitary pulmonary nodule
  • Reproducibility of results
  • Artificial intelligence