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Introduction <p>This study aimed to develop a deep learning-based system to automatically assess patient positioning compliance in chest radiographs, focusing on cervical spine visibility, scapular overlap, and costophrenic angle completeness, to improve image quality and diagnostic accuracy.</p> Methods <p>A total of 3663 de-identified posterior-anterior chest X-rays were collected and annotated for cervical spine, scapulae, and costophrenic angles. Compliance criteria included visibility of at least three cervical vertebrae, non-overlapping scapulae, and fully visible costophrenic angles. A hybrid deep learning framework combining Faster R-CNN for region localization and ResNet-50 for compliance classification was employed. Progressive training and data augmentation addressed class imbalance, and model performance was evaluated using accuracy, sensitivity, specificity, Intersection over Union (IoU), and Y-axis IoU (Y-IoU).</p> Results <p>Cervical spine compliance was identified with 94.54% accuracy, 96.47% sensitivity, and 91.24% specificity. Costophrenic angle assessment achieved 98.06% accuracy, 98.28% sensitivity, and 95.24% specificity, with validation on additional datasets yielding 98.91% accuracy. Scapulae localization succeeded in 99.38% of images, and compliance classification reached 92.10% (left) and 92.38% (right) accuracy, with errors mainly due to patient positioning or slight scapula-lung overlap.</p> Conclusions <p>The proposed deep learning system provides objective, reproducible evaluation of chest X-ray positioning, enhancing image quality and reducing observer variability. Integration into clinical workflows could support radiographers in achieving standardized radiographs, improving diagnostic reliability and patient care.</p> Implications for practice <p>The AI system developed in this study helps standardize and improve the quality of chest X-ray imaging procedures, reduces errors from manual interpretation, enhances diagnostic accuracy, and serves as an important tool for clinical quality control and professional development of radiologic technologists. It also has the potential for future implementation in multi-center clinical practice.</p>

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Application of deep learning strategies in the standardization and diagnostic efficiency enhancement of chest X-ray imaging

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月18日

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  • Yung-Cheng Wang,
  • Wei-Chi Chen,
  • Kang-Ping Lin,
  • Sen-Ping Lin,
  • Wen-Chang Tseng

摘要

Introduction

This study aimed to develop a deep learning-based system to automatically assess patient positioning compliance in chest radiographs, focusing on cervical spine visibility, scapular overlap, and costophrenic angle completeness, to improve image quality and diagnostic accuracy.

Methods

A total of 3663 de-identified posterior-anterior chest X-rays were collected and annotated for cervical spine, scapulae, and costophrenic angles. Compliance criteria included visibility of at least three cervical vertebrae, non-overlapping scapulae, and fully visible costophrenic angles. A hybrid deep learning framework combining Faster R-CNN for region localization and ResNet-50 for compliance classification was employed. Progressive training and data augmentation addressed class imbalance, and model performance was evaluated using accuracy, sensitivity, specificity, Intersection over Union (IoU), and Y-axis IoU (Y-IoU).

Results

Cervical spine compliance was identified with 94.54% accuracy, 96.47% sensitivity, and 91.24% specificity. Costophrenic angle assessment achieved 98.06% accuracy, 98.28% sensitivity, and 95.24% specificity, with validation on additional datasets yielding 98.91% accuracy. Scapulae localization succeeded in 99.38% of images, and compliance classification reached 92.10% (left) and 92.38% (right) accuracy, with errors mainly due to patient positioning or slight scapula-lung overlap.

Conclusions

The proposed deep learning system provides objective, reproducible evaluation of chest X-ray positioning, enhancing image quality and reducing observer variability. Integration into clinical workflows could support radiographers in achieving standardized radiographs, improving diagnostic reliability and patient care.

Implications for practice

The AI system developed in this study helps standardize and improve the quality of chest X-ray imaging procedures, reduces errors from manual interpretation, enhances diagnostic accuracy, and serves as an important tool for clinical quality control and professional development of radiologic technologists. It also has the potential for future implementation in multi-center clinical practice.