Towards Automated Quality Assurance: Integrating Deep Learning and Classical ML into the Digital Radiography Pipeline.
Authors
Affiliations (5)
Affiliations (5)
- Department of Orthopedic Surgery, College of Medicine, National Taiwan University, No.7, Chung-Shan South Road, Zhong-Zheng District, Taipei 100, Taiwan.
- Department of Orthopedic Surgery, Mayo Clinic, Rochester 55905, MN, USA.
- Graduate Institute of Networking and Multimedia, National Taiwan University, Taipei 106, Taiwan.
- Department of Computer Science and Information Engineering, National Taiwan University, Taipei 106, Taiwan.
- Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester 55905, MN, USA.
Abstract
<b>Background/Objectives:</b> To develop and evaluate a deep learning-based quality control system for Lumbar Spinal Digital Radiographs (LSDR), designed to automate and improve their evaluation and reduce reliance on manual reviews. <b>Methods:</b> This retrospective study utilized a deep learning workflow comprising image segmentation, feature extraction, and a classification model. The dataset, including anteroposterior (AP) and lateral (LAT) X-ray images, was expanded through data augmentation techniques. Four U-Net-based models were assessed: standard U-Net, Swin-UNet, Attention U-Net, and Attention U-Net with the weight map, with the latter selected for its superior performance. Extracted features, such as brightness, contrast, and anatomical positioning, were used in an XGBoost classifier, which was evaluated using mean intersection over union (mIoU), accuracy, sensitivity, specificity, and AUC. <b>Results:</b> The Attention U-Net with weighted attention outperformed the other models, achieving high mIoU scores in both AP and LAT views. The XGBoost classifier achieved the best performance in classifying images as "qualified" or "unqualified," with an AUC of approximately 0.9, high accuracy, and balanced sensitivity and specificity. This approach effectively addressed class imbalances and improved model accuracy compared to traditional machine learning models such as MLP and SVM. <b>Conclusions:</b> The developed automated quality control system demonstrated potential for enhancing image quality, enhancing diagnostic reliability, and optimizing clinical workflow efficiency.