Automated quality assessment of chest radiographs based on deep learning and linear regression cascade algorithms

• Published in: European radiology Vol. 32; no. 11; pp. 7680 - 7690
• Main Authors: Meng, Yu, Ruan, Jingru, Yang, Bailin, Gao, Yang, Jin, Jianqiu, Dong, Fangfang, Ji, Hongli, He, Linyang, Cheng, Guohua, Gong, Xiangyang
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.11.2022; Springer Nature B.V
• Subjects: Adult; Algorithms; Automation; Chest; Correlation coefficient; Correlation coefficients; Datasets; Deep Learning; Diagnostic Radiology; Humans; Image quality; Imaging; Internal Medicine; Interventional Radiology; Layouts; Linear Models; Machine learning; Medicine; Medicine & Public Health; Model accuracy; Neuroradiology; Performance evaluation; Position measurement; Quality assessment; Quality control; Radiographs; Radiography; Radiography, Thoracic - methods; Radiology; Regression analysis; Regression models; Retrospective Studies; Statistical analysis; Ultrasound; Deep learning; Chest X-ray; Linear models; Quality control
• ISSN: 1432-1084; 0938-7994; 1432-1084
• DOI: 10.1007/s00330-022-08771-x

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.