Automated quality assessment of chest radiographs based on deep learning and linear regression cascade algorithms.
Objectives: Develop and evaluate the performance of deep learning and linear regression cascade algorithms for automated assessment of the image layout and position of chest radiographs.Methods: This retrospective study used 10 quantitative indices to capture subjective perceptions of radiologists r...
| Publicado en: | European Radiology Vol. 32; no. 11; pp. 7680 - 7691 |
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| Autores principales: | , , , , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Springer Nature
Nov2022
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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=160256583&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 160256583 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09387994 NPH jtl: European Radiology issn: 09387994 maglogo: N pubinfo: dt: Nov2022 vid: 32 iid: 11 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 160256583 160256583 NLM35420306 160256583 10.1007/s00330-022-08771-x NLM35420306 160256583 ppf: 7680 ppct: 11 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Automated quality assessment of chest radiographs based on deep learning and linear regression cascade algorithms. aug: au: Meng, Yu Ruan, Jingru Yang, Bailin Gao, Yang Jin, Jianqiu Dong, Fangfang Ji, Hongli He, Linyang Cheng, Guohua Gong, Xiangyang affil: The Second Clinical Medical College, Zhejiang Chinese Medical University, 310053, Hangzhou, China sug: subj: Radiography, Thoracic Methods Retrospective Design Linear Regression Algorithms Adult Ferrans and Powers Quality of Life Index Funding Source Adult: 19-44 years ab: Objectives: Develop and evaluate the performance of deep learning and linear regression cascade algorithms for automated assessment of the image layout and position of chest radiographs.Methods: This retrospective study used 10 quantitative indices to capture subjective perceptions of radiologists regarding image layout and position of chest radiographs, including the chest edges, field of view (FOV), clavicles, rotation, scapulae, and symmetry. An automated assessment system was developed using a training dataset consisting of 1025 adult posterior-anterior chest radiographs. The evaluation steps included: (i) use of a CNN framework based on ResNet - 34 to obtain measurement parameters for quantitative indices and (ii) analysis of quantitative indices using a multiple linear regression model to obtain predicted scores for the layout and position of chest radiograph. In the testing dataset (n = 100), the performance of the automated system was evaluated using the intraclass correlation coefficient (ICC), Pearson correlation coefficient (r), mean absolute difference (MAD), and mean absolute percentage error (MAPE).Results: The stepwise regression showed a statistically significant relationship between the 10 quantitative indices and subjective scores (p < 0.05). The deep learning model showed high accuracy in predicting the quantitative indices (ICC = 0.82 to 0.99, r = 0.69 to 0.99, MAD = 0.01 to 2.75). The automatic system provided assessments similar to the mean opinion scores of radiologists regarding image layout (MAPE = 3.05%) and position (MAPE = 5.72%).Conclusions: Ten quantitative indices correlated well with the subjective perceptions of radiologists regarding the image layout and position of chest radiographs. The automated system provided high performance in measuring quantitative indices and assessing image quality.Key Points: • Objective and reliable assessment for image quality of chest radiographs is important for improving image quality and diagnostic accuracy. • Deep learning can be used for automated measurements of quantitative indices from chest radiographs. • Linear regression can be used for interpretation-based quality assessment of chest radiographs. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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