Method Based on Deep Learning for Evaluating Clarity of Chest X-ray Images
10.3969/j.issn.1005-5185.2024.06.018
- VernacularTitle:基于深度学习的胸部X线图像清晰度评价方法
- Author:
Liangliang SONG
1
;
Qian WANG
;
Xiao HAN
;
Chuanfu LI
;
Xiaohu LI
;
Yongqiang YU
Author Information
1. 安徽医科大学第一附属医院影像科,安徽 合肥 230022
- Keywords:
Deep learning;
Quality control;
Radiography,thoracic;
Decision making,computer-assisted
- From:
Chinese Journal of Medical Imaging
2024;32(6):616-621
- CountryChina
- Language:Chinese
-
Abstract:
Purpose Develop deep learning models to assess the clarity of chest X-ray images and validate the model's effectiveness by comparing it with the subjective evaluations of radiologists.Materials and Methods A retrospective collection of 9 135 chest X-ray images from 590 hospitals in Anhui Province,spanning from June 2015 to August 2022,was organized involving multiple radiologists who repeatedly evaluated the clarity of the images using a five-level scoring system.Individual assessments were designated as A and B,whereas the collective result of multiple assessments was designated as C.By constructing a deep learning model based on ResNet-50,image clarity evaluations of chest X-ray images were performed,we used the result C as the training and testing data for the model.The model's evaluation results were denoted as D.A radiology quality control expert conducted an audit assessment of the model's evaluation results and the multi-person assessments of physicians,serving as the reference standard for image clarity.Their assessment results were labeled as E.Statistical analysis,including Spearman's rank correlation coefficient,root mean square error(RMSE)and accuracy was employed to evaluate the effectiveness of the model.Results Compared with the reference standard E,D achieved an average accuracy of 0.85,exceeding the accuracy of C,which stood at 0.84.The ρ values for A,B,C,D and E were 0.58(0.54,0.62),0.59(0.55,0.63),0.74(0.71,0.77)and 0.80(0.78,0.82),respectively.The model exhibited the highest correlation with E.The ρ between A and B was 0.45(0.41,0.49),indicating a lower correlation between two individual subjective assessments of image clarity.The RMSE values for A,B,C,D and E were 0.99,0.94,0.72,and 0.71,respectively.The model's RMSE was lower than that of manual assessments.Conclusion This research model is capable of accurately assessing the clarity of chest X-ray images,and reducing the subjective interference of manual evaluation through deep learning methods,thereby providing an effective and objective evaluation tool for the assessment of clarity in clinical radiographic images.
