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Fully automated pedicle screw manufacturer identification in plain radiograph with deep learning methods
Rattapoom Waranusast1, Panomkhawn Riyamongkol1, Santi Weerakul2
1Department of Electrical and Computer Engineering, Faculty of Engineering, Naresuan University, Phitsanulok, Thailand.
Introduction:
Pedicle screw manufacturer identification is crucial for revision surgery planning; however, this information is occasionally unavailable. We developed a deep learning-based algorithm to identify the pedicle screw manufacturer from plain radiographs.
Methods:
We collected anteroposterior (AP) and lateral radiographs from 276 patients who had thoracolumbar spine surgery with pedicle screws from three international manufacturers. The samples were randomly assigned to training sets (178), validation sets (40), and test sets (58). The algorithm incorporated a convolutional neural network (CNN) model to classify the radiograph as AP and lateral, followed by YOLO object detection to locate the pedicle screw. Another CNN classifier model then identified the manufacturer of each pedicle screw in AP and lateral views. The voting scheme determined the final classification. For comparison, two spine surgeons independently evaluated the same test set, and the accuracy was compared.
Results:
The mean age of the patients was 59.5 years, with 1,887 pedicle screws included. The algorithm achieved a perfect accuracy of 100% for the AP radiograph, 98.9% for the lateral radiograph, and 100% when both views were considered. By comparison, the spine surgeons achieved 97.1% accuracy. Statistical analysis revealed near-perfect agreement between the algorithm and the surgeons.
Conclusion:
We have successfully developed an algorithm for pedicle screw manufacturer identification, which demonstrated excellent accuracy and was comparable to experienced spine surgeons.
