Test-retest reproducibility of a deep learning-based automatic detection algorithm for the chest radiograph.
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 simu...
| Publicado en: | European Radiology Vol. 30; no. 4; pp. 2346 - 2356 |
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| Autores principales: | , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Springer Nature
Apr2020
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=142141896&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 142141896 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09387994 NPH jtl: European Radiology issn: 09387994 maglogo: N pubinfo: dt: Apr2020 vid: 30 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 142141896 142141896 NLM31900698 142141896 10.1007/s00330-019-06589-8 NLM31900698 142141896 ppf: 2346 ppct: 10 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Test-retest reproducibility of a deep learning-based automatic detection algorithm for the chest radiograph. aug: au: Kim, Hyungjin Park, Chang Min Goo, Jin Mo affil: Department of Radiology, Seoul National University College of Medicine, 101, Daehak-ro, Jongno-gu, 03080, Seoul, South Korea sug: subj: Lung Neoplasms Diagnosis Radiographic Image Interpretation, Computer-Assisted Methods Radiography, Thoracic Methods Algorithms Aged Pneumonectomy Male Lung Neoplasms Surgery Retrospective Design Middle Age Female Reproducibility of Results Human Funding Source Aged: 65+ years Middle Aged: 45-64 years Male Female ab: 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. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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