Waranusast Rattapoom, Riyamongkol Panomkhawn, Weerakul Santi, Chaibhuddanugul Nattharut, Laoruengthana Artit, Mahatthanatrakul Akaworn
Department of Electrical and Computer Engineering, Faculty of Engineering, Naresuan University, Phitsanulok, Thailand.
Department of Orthopaedics,Faculty of Medicine, Naresuan University, Phitsanulok, Thailand.
Eur Spine J. 2025 Jul 21. doi: 10.1007/s00586-025-09167-3.
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.
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.
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.
We have successfully developed an algorithm for pedicle screw manufacturer identification, which demonstrated excellent accuracy and was comparable to experienced spine surgeons.
椎弓根螺钉制造商的识别对于翻修手术规划至关重要；然而，此信息有时无法获取。我们开发了一种基于深度学习的算法，用于从普通X光片中识别椎弓根螺钉的制造商。
我们收集了276例接受胸腰椎脊柱手术并使用来自三个国际制造商的椎弓根螺钉的患者的前后位（AP）和侧位X光片。样本被随机分配到训练集（178例）、验证集（40例）和测试集（58例）。该算法纳入了一个卷积神经网络（CNN）模型，将X光片分类为前后位和侧位，随后使用YOLO目标检测来定位椎弓根螺钉。然后，另一个CNN分类器模型在前后位和侧位视图中识别每个椎弓根螺钉的制造商。投票方案确定最终分类。作为比较，两名脊柱外科医生独立评估同一测试集，并比较准确性。
患者的平均年龄为59.5岁，共纳入1887枚椎弓根螺钉。该算法对前后位X光片的准确率达到了100%，对侧位X光片的准确率为98.9%，当同时考虑两种视图时准确率为100%。相比之下，脊柱外科医生的准确率为97.1%。统计分析显示该算法与外科医生之间的一致性近乎完美。
我们成功开发了一种用于识别椎弓根螺钉制造商的算法，该算法显示出优异的准确性，并且与经验丰富的脊柱外科医生相当。
