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Automatic Localization and Brand Detection of Cervical Spine Hardware on Radiographs Using Weakly Supervised Machine
Raman Dutt1, Dylan Mendonca1, Huai Ming Phen1
1Department of Computer Science, Shiv Nadar University, Greater Noida, Uttar Pradesh, India (R.D.); Department of Chemical Engineering and Applied Chemistry (D.M.) and Department of Computer Science (M.G.), University of Toronto, Toronto, Canada; and Departments of Radiology (H.M.P., S.B., J.G., T.Y., H.T.) and Biomedical Informatics (I.B.), Emory University, Atlanta, Ga.
This study developed an AI pipeline to accurately identify cervical spine hardware brands from radiographs using deep learning algorithms. The system achieved high accuracy in localizing and classifying both anterior and posterior implants.
Area of Science:
- Radiology
- Medical Imaging
- Artificial Intelligence
Background:
- Cervical spine hardware identification from radiographs is crucial for patient care and research.
- Manual identification of implant brands can be time-consuming and prone to error.
Purpose of the Study:
- To create an automated, end-to-end pipeline for localizing and identifying cervical spine hardware brands on routine radiographs.
- To leverage deep learning algorithms for enhanced accuracy and efficiency in implant analysis.
Main Methods:
- A retrospective study involving 984 patients with cervical spine implants (2014-2018).
- Development of object detection and image classification models using convolutional neural networks (CNNs).
- Weakly supervised learning framework for training and validation on annotated radiographic images.
Main Results:
- The hardware localization model achieved an intersection over union of 86.8% and an F1 score of 94.9%.
- Brand classification demonstrated high performance: F1 scores of 98.7% for anterior and 93.5% for posterior hardware.
- High sensitivity and specificity were achieved for both anterior and posterior hardware classification.
Conclusions:
- The developed pipeline accurately localizes and classifies cervical spine hardware brands.
- Weakly supervised learning enables efficient and precise identification of implant prostheses.
- This AI-driven approach offers a promising tool for clinical and research applications in spine surgery.
